Patent Publication Number: US-2022215944-A1

Title: Medical monitoring system

Description:
INCORPORATION BY REFERENCE TO ANY PRIORITY APPLICATIONS 
     Any and all applications for which a foreign or domestic priority claim is identified in the Application Data Sheet as filed with the present application are hereby incorporated by reference under 37 CFR 1.57. 
     This application is a continuation of U.S. patent application Ser. 16/016,386, filed Jun. 22, 2018, and entitled “MEDICAL MONITORING SYSTEM,” which is a continuation of U.S. patent application Ser. No. 14/032,132, filed Sep. 19, 2013, and entitled “MEDICAL MONITORING SYSTEM”; 
     which claims a priority benefit to U.S. Provisional Application No. 61/703,730, filed Sep. 20, 2012, and entitled “MEDICAL MONITORING SYSTEM”; 
     and which is a continuation-in-part of U.S. application Ser. No. 12/904,377, filed Oct. 14, 2010, and entitled “MEDICAL MONITORING SYSTEM,” which is a continuation-in-part of U.S. application Ser. No. 12/717,081, filed Mar. 3, 2010, and entitled “MEDICAL MONITORING SYSTEM,” which claims a priority benefit to U.S. Provisional Application No. 61/209,147, filed Mar. 4, 2009, and entitled “PROXIMITY DISPLAY MONITOR,” and to U.S. Provisional Application No. 61/296,439, filed Jan. 19, 2010, and entitled “MEDICAL MONITORING SYSTEM”; 
     and which is a continuation-in-part of U.S. application Ser. No. 13/589,010, filed Aug. 17, 2012, and entitled “HEALTH CARE SANITATION MONITORING SYSTEM,” which claims priority to U.S. Provisional Application No. 61/525,692, filed Aug. 19, 2011, and entitled “HEALTH CARE SANITATION MONITORING SYSTEM.” 
     The entire contents of all of the foregoing applications are hereby incorporated by reference herein. 
    
    
     BACKGROUND 
     Field 
     This disclosure relates to systems, devices, and methods with applications in, for example, hospitals and other patient care facilities. For example, the systems, devices, and methods described herein can be used for acquiring physiological information from patients, analyzing the physiological information, and communicating the physiological information to clinicians and other systems or devices. In addition, the systems, devices, and methods described herein can be used to encourage and/or monitor the usage of sanitation devices by healthcare clinicians. 
     Description of the Related Art 
     Hospitals, nursing homes, and other patient care facilities typically include patient monitoring devices at one or more bedsides in the facility. Patient monitoring devices generally include sensors, processing equipment, and displays for obtaining and analyzing a medical patient&#39;s physiological parameters. Physiological parameters include, for example, respiratory rate, SpO 2  level, pulse, and blood pressure, among others. Clinicians, including doctors, nurses, and certain other medical personnel use the physiological parameters obtained from the medical patient to diagnose illnesses and to prescribe treatments. Clinicians also use the physiological parameters to monitor a patient during various clinical situations to determine whether to increase the level of medical care given to the patient. 
     Patient monitors capable of measuring pulse oximetry parameters, such as SpO 2  and pulse rate in addition to advanced parameters, such as HbCO, HbMet and total hemoglobin (Hbt) and corresponding multiple wavelength optical sensors are described in at least U.S. patent applicaton Ser. No. 11/367,013, filed Mar. 1, 2006 and entitled Multiple Wavelength Sensor Emitters and U.S. patent applicaton Ser. No. 11/366,208, filed Mar. 1, 2006 and entitled Noninvasive Multi-Parameter Patient Monitor, both assigned to Masimo Laboratories, Irvine, CA (Masimo Labs) and both incorporated by reference herein. Further, noninvasive blood parameter monitors and corresponding multiple wavelength optical sensors, such as Rainbow™ adhesive and reusable sensors and RAD57™ and Radical-7™ monitors for measuring SpO 2 , pulse rate, perfusion index, signal quality, HbCO and HbMet among other parameters are also available from Masimo Corporation, Irvine, Calif. (Masimo). 
     Advanced physiological monitoring systems may incorporate pulse oximetry in addition to advanced features for the calculation and display of other blood parameters, such as carboxyhemoglobin (HbCO), methemoglobin (HbMet) and total hemoglobin (Hbt), as a few examples. Advanced physiological monitors and corresponding multiple wavelength optical sensors capable of measuring parameters in addition to SpO 2 , such as HbCO, HbMet and Hbt are described in at least U.S. patent applicaton Ser. No. 11/367,013, filed Mar. 1, 2006, titled Multiple Wavelength Sensor Emitters and U.S. patent application Ser. No. 11/366,208, filed Mar. 1, 2006, titled Noninvasive Multi-Parameter Patient Monitor, assigned to Masimo Labs and incorporated by reference herein. Further, noninvasive blood parameter monitors and corresponding multiple wavelength optical sensors, such as Rainbow™ adhesive and reusable sensors and RAD 57 ™ and Radical7™ monitors for measuring SpO 2 , pulse rate, perfusion index (PI), signal quality (SiQ), pulse variability index (PVI), HbCO and HbMet among other parameters are also available from Masimo. 
     Sanitation is also an important concern in hospitals and other patient care facilities since good sanitation practices may help limit the spread of germs and disease. Clinicians, such as doctors and nurses, who are exposed to multiple patients in these facilities may inadvertently contribute to the spread of germs if they are lax in their sanitation practices. Therefore, systems, devices, and methods which are capable of encouraging and/or monitoring the sanitation practices of clinicians would be advantageous. 
     SUMMARY 
     Various medical devices, systems, and methods are described herein. In some embodiments, a medical patient monitoring device for monitoring physiological information comprises: an interface configured to receive physiological information associated with at least one patient; and a detector for detecting the physical presence of a clinician token within a detection area in the vicinity of the medical patient monitoring device, wherein the medical patient monitoring device further comprises a processor that is configured to take a first predetermined action in response to detection of the clinician token in the detection area, the first predetermined action being associated with at least one attribute of the circumstances surrounding detection of the clinician token. 
     In some embodiments, a medical patient monitoring method comprises: receiving physiological information associated with at least one patient; and detecting the physical presence of a clinician token within a detection area in the vicinity of a medical patient monitoring device, using a processor, taking a first predetermined medical monitoring action in response to detection of the clinician token in the detection area, the first predetermined action being associated with at least one attribute of the circumstances surrounding detection of the clinician token. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various embodiments will be described hereinafter with reference to the accompanying drawings. These embodiments are illustrated and described by example only, and are not intended to limit the scope of the disclosure. 
         FIG. 1  is an exemplary block diagram showing a physiological monitoring system according to an embodiment of the present invention; 
         FIG. 2  is an exemplary block diagram showing another embodiment of a physiological monitoring system; 
         FIG. 3  is an exemplary block diagram showing a network interface module according to an embodiment of the present invention; 
         FIG. 4  is an exemplary flowchart diagram showing a process for context-based communication of physiological information according to an embodiment of the present invention; and 
         FIG. 5  is an exemplary block diagram showing an alarm notification system according to an embodiment of the present invention. 
         FIG. 6  is a block diagram illustrating an embodiment of a clinical network environment; 
         FIG. 7  is a block diagram illustrating a more detailed embodiment of the clinical network environment of  FIG. 6 ; 
         FIG. 8A  is a flow chart illustrating an embodiment of a process for journaling medical events in a journal database; 
         FIG. 8B  is a flow chart illustrating an embodiment of a process for correlating data from the journal database and the round-robin database; 
         FIG. 9  is a screen shot of an example user interface for monitoring patients in the clinical network environment of  FIG. 6 ; 
         FIG. 10  is a perspective view of an advanced patient-monitoring system; 
         FIG. 11  illustrates a proximity display in a multi-user environment; 
         FIG. 12  is a general block diagram of a proximity display monitor; 
         FIG. 13  illustrates a user display preference screen; 
         FIG. 14A  is a schematic diagram of a patient monitoring device that is capable of automatically detecting the presence of a clinician token; 
         FIG. 14B  is an exploded perspective view of an embodiment of a clinician token whose presence can be detected by a patient monitoring device; 
         FIG. 15  is a flowchart illustrating detection method for detecting the presence of a clinician token within the detection region of a patient monitoring device; 
         FIG. 16  illustrates an example graphical user interface of nurses&#39; station or a central patient monitoring station; 
         FIG. 17  is a flowchart illustrating a method for determining when to disable a clinician-specific action that had been previously enabled by a patient monitoring device based upon the detected presence of the clinician; 
         FIG. 18  is a schematic diagram of a system for enabling a patient monitoring device to automatically detect the presence of a clinician token; 
         FIG. 19  is a schematic illustration of a patient monitoring device network having a clinician proximity awareness feature; 
         FIG. 20  is a schematic drawing of a hospital floor with distributed WiFi access points that can be used to estimate the physical locations of medical devices, patients, and clinicians; 
         FIGS. 21A-F ,  22 A-E, and  23 A-C illustrate proximity display embodiments that advantageously provide user proximity feedback; 
         FIGS. 21A-F  illustrate a proximity display embodiment utilizing a virtual rotating triangular solid for proximity feedback; 
         FIGS. 22A-E  illustrate a proximity display embodiment utilizing a virtual rotating cube for proximity feedback; 
         FIGS. 23A-C  illustrate proximity display embodiment utilizing a virtual rotating planar solid for proximity feedback; 
         FIG. 24A  illustrates a first medical device and a second medical device that communicate with one another via a translation module; 
         FIG. 24B  illustrates a first medical device and a second medical device that communicate with one another via a translation module and a communication bus; 
         FIG. 25A  illustrates an example input message received by the translation module; 
         FIG. 25B  illustrates a message header segment of the input message of  FIG. 19A  that has been parsed into fields; 
         FIG. 25C  illustrates an encoded version of the parsed message header segment of  FIG. 25B ; 
         FIG. 25D  illustrates an example output message of the translation module based on the input message of  FIG. 25A ; 
         FIG. 26  illustrates a translation process for generating an output message based on an input message and a comparison with translation rules associated with the translation module; 
         FIG. 27A  illustrates a translation process in which the translation module facilitates communication of an HL7 message from a Hospital Information System (“HIS”) having a first HL7 format to an intended recipient medical device having a second HL7 format; 
         FIG. 27B  illustrates a translation process in which the translation module facilitates communication of an HL7 message from a medical device having a first HL7 format to a HIS having a second HL7 format; 
         FIG. 28  illustrates an example screenshot from a messaging implementation software tool for manually configuring translation rules to be used by the translation module; 
         FIGS. 29A and 29B  illustrate automatic rule configuration processes performed by the translation module; 
         FIGS. 29C and 29D  illustrate automatic rule configuration processes performed by the translation module for messages utilizing the HL7 protocol; 
         FIG. 30  is an example graph of the distribution of alarm events for a given physiological parameter as a function of alarm limit values; 
         FIG. 31  is a flow chart that illustrates a method for determining the variation in identified alarm conditions resulting from varying alarm criteria; 
         FIG. 32  illustrates an example report with a table showing how simulated alarm criteria affect alarm detection events; 
         FIG. 33  is a flow chart that illustrates another method for determining the variation in identified alarm conditions that occur as a result of varying alarm criteria; 
         FIG. 34  illustrates an example report with a table showing how simulated alarm criteria affect the number of alarm detection events as well as how the simulated alarm criteria affect, for example, false negatives and false positives; 
         FIG. 35  is a flow chart that illustrates a method for determining the variation in alarm notification events that occurs as a result of varying alarm notification delay times; 
         FIGS. 36A-B ,  37 A-F,  38 A-B,  39 A-B,  40 A-B,  41 ,  42 , and  43 A-B illustrate proximity displays that provide advantageous features in multi-user patient-monitoring environment; 
         FIGS. 36A-B  illustrate displays having layout zones; 
         FIGS. 37A-F  illustrate displays that vary layouts and font sizes according to the number of installed parameters; 
         FIGS. 38A-B  illustrate displays having parameter wells; 
         FIGS. 39A-B  illustrate displays that enlarge alarming parameters; 
         FIGS. 40A-B  illustrates displays of trend graphs having colored alarm zones; 
         FIG. 41  illustrate a display that inverts arrow keys to match the cursor; 
         FIG. 42  illustrates a display having user-selectable jump-screens; and 
         FIGS. 43A-B  illustrate trend graph displays. 
         FIG. 44  is a schematic diagram of a medical sanitation device that is capable of automatically detecting the presence of a clinician token. 
         FIG. 45  is a schematic illustration of a patient monitoring and clinician sanitation device network having clinician proximity awareness features. 
     
    
    
     DETAILED DESCRIPTION 
     In various embodiments, physiological monitoring systems are systems that monitor physiological signals generated by a medical patient and process the signals to determine any of a variety of physiological parameters of the patient. For example, in some cases, a physiological monitoring system can determine any of a variety of physiological parameters of a patient, including respiratory rate, inspiratory time, expiratory time, i:e ratio (e.g., inspiration-to-expiration ratio), inspiratory flow, expiratory flow, tidal volume, minute volume, apnea duration, breath sounds, rales, rhonchi, stridor, and changes in breath sounds such as decreased volume or change in airflow. In addition, in some cases the physiological monitoring system monitors other physiological sounds, such as heart rate to help with probe-off detection, heart sounds (e.g., S 1 , S 2 , S 3 , S 4 , and murmurs), and changes in heart sounds such as normal to murmur or split heart sounds indicating fluid overload. Moreover, the physiological monitoring system may use a second probe over the chest for better heart sound detection, keep the user inputs to a minimum (for example, only input height), and use a Health Level 7 (HL7) interface to automatically input demography. 
     A physiological monitoring system of certain embodiments includes one or more patient monitoring devices connected to a shared network using open architecture communications standards. The patient monitoring devices of certain embodiments include a physiological monitor coupled with a network interface module. The physiological monitor includes one or more sensors and a sensor processing module for processing signals from the sensors. The network interface module receives physiological information from the sensor processing module and transmits this information over the shared network. The network interface module may connect to a variety of physiological monitors. In addition, the network interface module of various implementations is a portable bedside device assigned exclusively to one medical patient. 
     In certain embodiments, the network interface module facilitates establishing a network connection directly with end users over the shared network. These end users, including doctors, nurses, and other hospital staff, may receive physiological information, alarms, and alerts from the network interface module on an electronic device, such as a pager, PDA, laptop, computer, computer on wheels (COW), or the like. 
     Referring to  FIG. 1 , certain embodiments of a physiological monitoring system  100  (e.g., alarm notification system) include an open network architecture using “off-the-shelf” hardware and communication protocols. This architecture in various implementations is a shared, or open, network includes multiple patient monitoring devices  110 , a network bus  120  (e.g., an Ethernet backbone), and a hospital WLAN  126 . In addition, the shared network may further include a connection  122  to the Internet  150 , to end user devices  152  over the Internet  150 , and to end user devices  128  over the hospital WLAN  126 . The physiological monitoring system  100  of certain embodiments is therefore an enterprise system that achieves a cost-effective replacement for currently available patient monitoring systems. 
     The physiological monitoring system  100  includes a plurality of bedside devices, e.g., patient monitoring devices  110 . The patient monitoring devices  110  of various embodiments include sensors  102 , one or more sensor processing modules  104 , and a communications module, e.g., network interface module  106 . In the depicted embodiment, two patient monitoring devices  110  are shown. One patient monitoring device includes one set of sensors  102 , one sensor processing module  104 , and one network interface module  106 . The other patient monitoring device  110  includes two sets of sensors  102 , two sensor processing modules  104 , and one network interface module  106 . 
     In certain embodiments, each patient monitoring device  110  is used by one medical patient. The patient monitoring devices  110  form a network of patient monitoring devices  110 , each of which can communicate with clinicians and other end users over a shared network, including a hospital network  126  and network interfaces to the Internet  150 . 
     One or more sensors  102  of the patient monitoring device  110  are attached to a medical patient. These sensors  102  may include ECG sensors, acoustic sensors, pulse oximeters, and other types of sensors. The sensors  102  obtain physiological information from a medical patient and transmit this information to the sensor processing module  104  through cables  103  or through a wireless connection (not shown). In certain embodiments, the physiological information includes one or more physiological parameters or values and waveforms corresponding to the physiological parameters. 
     The sensor processing module  104  receives physiological information from the sensors  102 . The sensor processing module  104  of certain embodiments includes a circuit having a processor, input ports for receiving the physiological information, software for processing the physiological information in the processor, an optional display, and optionally an input device (e.g., a keyboard). In addition, the sensor processing module  104  contains one or more output ports, such as serial ports. For example, an RS232, RS423, or autobaud RS232 (serial interface standard) port or a universal serial bus (USB) port may be included in the sensor processing module  104 . 
     In certain embodiments, the sensor processing module  104  generates waveforms from signals received from the sensors  102 . The sensor processing module  104  may also analyze single or multiparameter trends to provide early warning alerts to clinicians prior to an alarm event. In addition, the sensor processing module  104  in certain embodiments generates alarms in response to physiological parameters exceeding certain safe thresholds. 
     Example alerts include no communication with pulse oximeter, alarm silenced on pulse oximeter, instrument low battery (pulse oximeter), and transmitter low battery. Example alarms include SpO 2  levels and alarms, high and low SpO 2 , high and low PR, HbCO level and alarms, HbMET level and alarms, pulse rate and alarms, no sensor, sensor off patient, sensor error, low perfusion index, low signal quality, HbCO, HbMET, PI trend alarm, and desat index alarm. 
     The network interface module  106  in the depicted embodiment is connected to one or more sensor processing modules  104  through one or more connectors  108 , which may be serial connectors corresponding to the serial ports in the sensor processing modules  104 . Dashed lines on the connector  108  indicate that the network interface module  106  of certain embodiments is not permanently attached to the sensor processing modules  104 . In alternative embodiments (not shown), however, the network interface module  106  is contained within a sensor processing module  104 . 
     The network interface module  106  in various implementations includes a processor, an input port (such as a standard RS232 serial port), a network output port such as an Ethernet port, and software which enables the network interface module  106  to act as a network-communications enabled device. In addition, the network interface module  106  includes a storage device  114 , which may be included within the network interface module  106  or attached separately to the network interface module  106 . 
     The network interface module  106  manages the connectivity overhead for initiating and maintain connectivity with end user devices over the shared network. In certain embodiments, the network interface module  106  manages connectivity by acting as a microserver or web server. In such instances, the network interface module  106  is a network connection enabled device. As a web server, the network interface module  106  establishes direct connections to the Internet  150 , such that an end user may access web pages stored on the storage device  114  of the network interface module  106 . In one embodiment, the network interface module  106  therefore does not require a separate server for connecting to the Internet  150 . In one embodiment, the network interface module  106  connects to the Internet  150  directly through a modem, such that the connection  122  includes a modem. In managing connectivity over the shared network, the network interface module  106  may also perform security management functions, such as user authentication. 
     In certain embodiments, the network interface module  106  sends data over the shared network through an access point  124  or other wireless or wired transmitter. Alternatively, the network interface module  106  may communicate physiological information directly to end users over the Internet  150 . End users such as clinicians carrying notifier devices, e.g., end user devices  128 ,  152  connected to the hospital WLAN  126  may receive real-time viewing of physiological patient parameters and waveforms on demand or in the event of an alarm or alert. Real-time or slightly delayed transmission of physiological information in certain embodiments comports with standards for alarm latency in compliance with Joint Commission on Accreditation of Healthcare Organizations (JCAHO) standards for effective alarm response. The network interface module  106  of certain embodiments therefore adds functionality equivalent to a central nurses&#39; station. 
     In certain embodiments, the network interface module  106  performs context management. In one embodiment, context management includes associating context information with physiological information to form a contextual data package. Context information may include several categories of information, including the categories of context information related to the network interface module  106 , context information related to the medical patient, context information related to usage of the network interface module  106 , and context information related to a network connection. Within one or more of these context categories, context information might include a patient name, a patients&#39; unique hospital identification number, patient location, an identification number for a network interface module  106 , time stamps for events occurring in the physiological monitoring system  100 , environmental conditions such as changes to the state of the network and usage statistics of the network interface module  106 , and identification information corresponding to the network link (e.g., whether the network connection is WiFi or Ethernet). In one embodiment, the context information in the contextual data package may include all of or any subset of context information from one or more of the context categories. 
     The network interface module  106  receives context information, for example, by a nurse entering the information in the network interface module  106  or from a server  136 . In one embodiment, by receiving this information (including, e.g., patient identification number and location), the network interface module  106  becomes exclusively assigned to the medical patient. The network interface module  106  transmits or communicates the contextual data package to clinicians during an alarm or alert, upon clinician request, or on a scheduled basis. In addition, the network interface module  106  may transmit a continuous stream of physiological information to clinicians. 
     By optionally connecting to multiple sensor processing modules  104  in certain embodiments, the network interface module  106  is able to associate patient context information and other context information with multiple sensor processing modules  104 . Consequently, context can be created for one or more sensor processing modules  104  in addition to context being created for the network interface module  106 . 
     In addition to transmitting the contextual data package, the network interface module  106  in one embodiment stores the contextual data package in the storage device  114 . The storage device  114  may be a flash memory, a hard disk drive, or other form of non-volatile or volatile memory. In certain embodiments the storage device  114  acts as a flow control buffer. The network interface module  106  uses the storage device  114  acting as a flow control buffer to perform flow control during communications, as explained more fully below in connection with  FIG. 3 . 
     In some implementations, a server  136  may optionally be included in the physiological monitoring system  100 . The server  136  in these implementations is generally a computing device such as a blade server or the like. In certain embodiments, the server  136  is an appliance server housed in a data closet. In other embodiments, the server  136  is a server located at a central nurses&#39; station, such as a workstation server. 
     The server  136  receives contextual data packages from a plurality of network interface modules  106  and stores the contextual data package in a storage device  138 . In certain embodiments, this storage device  138  therefore archives long-term patient data. This patient data may be maintained even after the patient is discharged. In storing patient data, the server  136  may act as an interface between the shared network and an external electronic medical record (EMR) system. 
     The server  136  may also store data concerning user interactions with the system and system performance metrics. Integrated into the server  136  of certain embodiments is a journal database that stores every alert and alarm or a subset of the alerts and alarms as well as human interaction in much the same way as an aviation “black box” records cockpit activity. The journal is not normally accessible to the clinical end user and, without technical authorization, cannot be tampered with. In addition, the server  136  may perform internal journaling of system performance metrics such as overall system uptime. 
     In one embodiment, the journaling function of the server  136  constitutes a transaction-based architecture. Certain transactions of the physiological monitoring system  100  are journaled such that a timeline of recorded events may later be re-constructed to evaluate the quality of healthcare given. These transactions include state changes relating to physiological information from the patient monitoring devices  100 , to the patient monitoring devices  110 , to the hospital WLAN  126  connection, to user operation, and to system behavior. Journaling related to the physiological information received from a physiological monitor in one embodiment includes recording the physiological information itself, recording changes in the physiological information, or both. 
     The server  136  in certain embodiments provides logic and management tools to maintain connectivity between network interface modules  106 , clinician notification devices such as PDAs and pagers, and external systems such as EMRs. The server  136  of certain embodiments also provides a web based interface to allow installation (provisioning) of software rated to the physiological monitoring system  100 , adding new devices to the system, assigning notifiers (e.g., PDAs, pagers, and the like) to individual clinicians for alarm notification at beginning and end of shift, escalation algorithms in cases where a primary caregiver does not respond to an alarm, interfaces to provide management reporting on the alarm occurrence and response time, location management, and internal journaling of system performance metrics such as overall system uptime (see, e.g.,  FIG. 5  and accompanying description). 
     The server  136  in certain embodiments also provides a platform for advanced rules engines and signal processing algorithms that provide early alerts in anticipation of a clinical alarm. The operating system on the server  136  in one embodiment is Linux-based for cost reasons, though a Microsoft-based or other operating system may also be used. Moreover, the server  136  is expandable to include data storage devices and system redundancy capabilities such as RAID (random array of independent disks) and High Availability options. 
     In another embodiment (not shown), end user devices  128 ,  152  include one way POCSAG Pagers having a  2  line display with audible and vibrate mode, of suitable size and durability for severe mechanical environments typical of hospital general floor settings. In yet another embodiment, the end user devices  128 ,  152  include two way paging systems, such as Motorola Flex and WLAN pagers. One advantage of two-way paging is the ability to confirm message receipt and the ability to remotely silence alarms. Wireless PDAs may also be used by end users based on ruggedness and acceptable form factors as determined by an end user. An example of such a device is the Symbol Technology MC 50  PDA/Barcode Scanner. 
       FIG. 2  depicts another embodiment of the physiological monitoring system  200  of the present invention. The physiological monitoring system  200  includes network communications enabled devices  210 . The network communications enabled devices  210  are connected directly to a hospital network  220  through a wireless connection. In certain embodiments, the network communications enabled devices  210  include sensors and sensor processing modules, similar to the sensors  102  and sensor processing modules  104  of  FIG. 1 . Certain of these network communications enabled devices  210  are bedside devices, and others are handheld or otherwise patient-worn devices that may be used by an ambulatory (mobile) patient. 
     The hospital network  220  transmits physiological information and context information to clinician notifier devices, including pagers  240 , PDAs  230 , and the like. In certain embodiments, the hospital network  220  utilizes a server  250  to transmit contextual data packages to a page transmitter  242 , which further transmits the data to one-way wireless pagers  240 . An external interface  280  may be coupled with the server  250 . The external interface  280  could include one or more of the following: enterprise paging, nurse call systems, wide area paging systems, enterprise clinical and patient information systems, and third party monitoring and surveillance systems. 
     Certain other devices  260 , such as some patient monitoring equipment, are not network communications enabled devices. That is, these other devices  260  are unable to connect to a network unaided. In the depicted physiological monitoring system  200 , example devices  260  that are not network communications enabled are connected to a network interface module  270 . The network interface module  270  is connected to the non-network communication enabled other devices  260  through RS232 cables  264 . Such a connection is a standardized serial connection found on many devices. Because the network interface module  270  has an RS232 port, the network interface module  270  can allow non-network communication enabled patient monitoring devices to connect directly to the hospital network  220  and also to the Internet. 
     Moreover, by connecting to one or more other devices  260  in some embodiments, the network interface module  270  is able to associate patient context information and other context information with one or more other devices  260 . Consequently, context can be created for one or more other devices  260  in addition to context being created for the network interface module  270 . 
       FIG. 3  depicts a network interface module  300  in accordance with certain embodiments of the present invention. The network interface module  300  in the depicted embodiment includes an input port  302 , which in certain embodiments is a serial port for facilitating a connection to a sensor processing module. The network interface module  300  also includes a network interface  304 , which may be a wired interface (e.g., Ethernet) or a wireless interface such as WiFi, Bluetooth, or the like. Alternatively, the network interface module  104  may communicate through a cable TV interface or other type of interface. Such a CTV interface provides a subcarrier bi-directional communications capability that would simultaneously co-exist with video formats. 
     The network interface module  300  also communicates with a storage device  350 . While in the depicted embodiment the storage device  350  is shown as separate from the network interface module  300 , in some implementations the storage device  350  is part of the network interface module  300 . In addition, though not shown, the network interface module  300  may include a processor for implementing communications program code. Similarly, though not shown, the network interface module  300  may include an input device for a nurse to input context information and a display for receiving output from the network interface module  300 . 
     The network interface module  300  can be integrated into handheld, portable or stationary patient monitoring platforms or instruments or contained in an accessory package with an RS  232  input for general interface to such devices. In another embodiment, (not shown) active RFID tag capabilities are included with the network interface module  106 , with the clinician devices (e.g., notifier devices), or with both so that either a patient or a clinician can be located when an event occurs or on request. When operating on a shared network, the network interface module  106  is also compliant with to the open architecture communications standards of IEEE 802.1X (security and authorization), IEEE 802.3 (Ethernet), and WiFi (IEEE 802.11 a, b, g, e, i wireless protocols). 
     A context management module  310  in the network interface module  300  manages context data. In one embodiment, the context management module  310  receives context information, such as the context information described in connection with  FIG. 1  above. In one embodiment, a nurse or other clinician enters context information, such as patient name, identification number, and location, into the network interface module  300  via a keyboard or other input device (not shown) when the patient is admitted to the hospital or assigned a particular bed in the hospital. In other embodiments, the context management module  310  receives the context information from a server, such as the server  136  of  FIG. 1 . 
     The context management module  310  associates the context information with physiological information received from a sensor processing module. In certain embodiments, the context management module  310  performs this association when an alarm condition occurs. In such instances, the context management module  310  may create a contextual data package including a snapshot of historical physiological information together with the context information. In other embodiments, the context management module  310  performs an association continuously, and the network interface module  300  sends continuous or scheduled contextual data packages to end users. In addition, the context management module  310  or other modules in the network interface module  300  store the contextual data package in the storage device  350 . 
     The communications module  320  uses the network interface  304  to communicate with a network. In certain embodiments, the communications module  320  possesses the functionality of a web server. As a web server, the communications module  320  enables the network interface module  300  to communicate with a hospital network and the Internet directly, without using a server. Consequently, other devices such as physiological monitoring devices that are not network connection enabled may connect with the network interface module and thereby become network enabled. The network interface module  300  manages the connectivity overhead for initiating and maintaining connectivity, manages context information (e.g., any of the context information described above in connection with  FIG. 1 ), and provides a web server for displaying patient information on web-enabled devices. In one embodiment, a communications protocol based on XML technologies allows bedside devices to interface to a multitude of target end user platforms including PDAs, computer on wheels (COW), Tablet PCs, IP cell phones (smartphones), and fixed PCs. 
     In certain embodiments, the communications module  320  uses standard communications protocols to communicate with a network. Some examples of standard communications protocols include Ethernet, WiFi (WLAN), Bluetooth, and the like. By using standard communications protocols, the communications module  320  is able to send and receive data over a shared network or open network architecture. However, the communications module  320  may also be used on a proprietary network using proprietary protocols. 
     In embodiments where the network interface module  300  communicates over a shared network rather than a proprietary network, the network interface module  300  shares network resources with other devices on the network. In some cases, high-volume network traffic affects the reliability of network communications. Consequently, certain implementations of the network interface module  300  include a flow control module  330 . The flow control module  330  verifies that transmitted data was received by an end user. In the event that the end user did not receive the data, the flow control module  330  resends the data stored in the storage device  350 . In certain embodiments, the storage device  350  therefore acts as a flow control buffer. 
     A security module  340  manages user access to the network interface device  300  and to data stored in the storage device  350 . In certain embodiments, the security module  340  determines whether a user attempting to connect to the network interface module  300  is authorized to do so. In one implementation, the security module  340  uses the standard IEEE.802.1X network access control protocol to manage authentication. The network interface module  106  in certain embodiments provides security and encryption to meet the Health Insurance Portability and Accountability Act (HIPAA) requirements. 
     In certain embodiments, the network interface module  300  incorporates all or a portion of the functionality specified by the IEEE 1073 standard and the most recent update to the IEEE 1073 standard, namely the IEEE 11703 standard, both of which are hereby incorporated by reference. In certain embodiments, the context management module  310 , the communications module  320 , the flow control module  330 , and the security module  340  also incorporate functionality specified in the IEEE 1073 and 11703 standards. By using standard protocols, the network interface module  300  may be used to enable network communication for a wide variety of physiological monitoring devices. 
       FIG. 4  depicts a process  400  for context-based communication of physiological information according to an embodiment of the present invention. In certain embodiments, the process  400  is performed by any of the network interface modules described above in connection with  FIGS. 1-3 . In addition, the process  400  in certain embodiments may be performed by any of the physiological monitoring systems described in connection with  FIGS. 1, 2, and 5 . 
     The process  400  begins by receiving context information at  402 . In one embodiment, a device such as a network interface module receives the context information once, such as in an initialization step. The process  400  then receives physiological information at  404 . In certain embodiments, the process  400  continues to receive physiological information throughout the remaining steps of the process  400 . Alternatively, the process  400  may receive physiological information  400  for a portion of the process  400 . 
     At  405 , the process  400  determines whether an alarm condition or alert has occurred. If an alarm condition or alert has occurred, the process  400  proceeds to  406 . However, if an alarm condition or alert has not occurred, the process  400  loops back to  404 . In one embodiment, the looping back of the process  400  to  404  represents that a network interface module continually receives physiological information until an alarm condition or alert occurs. In certain embodiments (not shown), the process  400  may continue to receive physiological information even when an alarm condition or alert occurs. 
     At  406  the process  400  prepares a contextual data package. The contextual data package may include context information and a snapshot of physiological information. In one embodiment, the snapshot of physiological information includes the physiological information that gave rise to an alarm or alert. In one embodiment, the snapshot of physiological information includes information both before and after the occurrence of an alarm or alert. The contextual data package is stored in a flow control buffer at  408 . 
     At  410 , the process  400  establishes a network connection. In one embodiment, establishing a network connection at  410  includes connecting a network interface module to an end user device, such as a notifier device assigned to a nurse during his or her work shift. The process  400  then determines at  412  whether the user of the device (e.g., the nurse) has been authenticated. If the user has not been authenticated, the process  400  proceeds to  420 . On the other hand, if the user has been authenticated, the process  400  proceeds to  414 . 
     The process  400  at  414  communicates the contextual data package to the user. At  416 , the process  400  determines whether the contextual data package was received. If the contextual data package was received, the process  400  proceeds to  420 . Otherwise, the process  400  proceeds to  418 , where the process  400  accesses data stored in the flow control buffer. In one embodiment, the data accessed by the process  400  is equivalent to or substantially equivalent to the contextual data package communicated to the user at  414 . 
     The process  400  then loops back to  414 , where the process  400  communicates (e.g., resends) the contextual data package to the user, and then at  416  re-verifies that the package was received. The process  400  in some implementations continues to loop between steps  414 ,  416 , and  418  until the contextual data package was received. Thus, steps  414 ,  416 , and  418  in certain embodiments constitute flow control performed by the process  400 . These flow control steps allow the process  400  to overcome network transmission errors which may occur in shared networks. 
     If the contextual data package was received, the process  400  evaluates whether to continue the monitoring of physiological information at  420 . If the process  400  determines to continue monitoring, the process loops back to  404 , where the process  400  continues to receive physiological information. If, however, the process  400  determines not to continue monitoring, the process  400  ends. 
     In various embodiments of the process  400 , the contextual data package or the physiological information alone is transmitted to the user even in the absence of an alarm condition. In still other embodiments, fewer than all of the steps are performed, or the steps are performed in different order. For instance, the process  400  may only perform the steps of receiving physiological information at  404 , preparing a contextual data package at  406 , establishing a network connection at  410 , and communicating the contextual data package to the user at  414 . 
       FIG. 5  depicts an alarm notification system  500  in accordance with certain embodiments of the present invention. A clinical subsystem  510  defines the major software components of alarm notification system  500  including a clinical assignment module  512 , a bedside device initialization module  514 , a notification and viewing module  516 , an escalation rules module  518 , a clinical report module  520 , and a clinical data stores module  522 . An authentication feature is built into mobile computing devices in compliance with HIPAA and hospital IT policies. 
     The clinical assignment module  512  has an assignment function. A nursing supervisor assigns individual nurses to specific patients at the start of each shift and upon admission of new patients. Shift assignments take place at change of shift during a “report” transition exercise where individual nurses and nursing supervisor from previous shift “hand off” patients to the next shift. The report can be either formal where all nurses attend or informal dependent on hospital nursing service policies and procedures. The clinical assignment module  512  provides an intuitive interface that allows a listing of available nurses to be assigned individual patients. The major user of this module is the unit clerk as assigned by the nursing supervisor. A nurse can be assigned one or more patients or all patients. An alternative work flow is self assignment where individual nurses assign patients themselves in which case they perform functions of the unit clerk. In the self assignment model, a default is implemented where any unassigned patient is either assigned to all nurses or the nursing supervisor. 
     The bedside device initialization module  514  has bedside devices, such as the network interface modules described above, that are sometimes set up by an aide to the nurse. In the case where the nurse performs this task, she or he performs the functions of the nursing aide. Work flow includes delivering a device to bedside, applying sensors, initializing the device, and setting patient context, such as name, ID and location. 
     The notification and viewing module  516  assigns a wireless notification device, such as a one-way pager, PDA, IP telephone, COW, or Tablet to individual nurses. The device becomes associated with her or him. Alarms are routed to the notification device based on the clinical assignment module  512 . Non-dedicated notifier solutions such as hospital owned paging systems issued to nurses have unknown latency characteristics. A general purpose interface is available at the server with a latency of less than  1  second upon receipt from the bedside device and is time stamped upon presentation to the server external interface and stored in a journaling system within the server. An additional interface for mobile computing platforms such as PDA, COWS, and Tablets allows viewing of current and trend data for an individual patient. 
     The escalation rules module  518  has a rules engine that actuates an escalation policy defined by the hospital. The escalation rules module  518  provides alternative routing of alarms to alternative and additional clinical users in the event an alarm is not responded to or persists for a predefined (e.g., by a policy) period of time. The escalation rules module  518  in certain embodiments routes alarms to an emergency response team. 
     The clinical report module  520  provides predefined formatted reports on the clinical data from which to determine physiologic condition and/or progress. More than one report may be dependent on end user needs. Reports are not time critical views of individual patients and may be remotely viewed by clinicians who have alarm notification system  500  privileges and have been authenticated by the alarm notification system  500 . These reports are web browser views that allow clinicians to set viewing parameters such as time and parameter scales and alarm review. 
     The clinical data stores module  522  provides data storage and database resources to store information as known to those skilled in the art. 
     Further shown in  FIG. 5 , a technical support subsystem  530  is isolated from the clinical subsystem  510  in compliance with HIPAA and as such does not allow viewing or access to any patient information with the exception of the risk report module  538 . The technical support subsystem  530  includes a provisioning module  532 , an administration module, a service module  536 , a risk report module  538 , and a technical data store module  540 . 
     The provisioning module  532  provides provisioning, which is the initial installation of the system and first customer use. The primary user of the provisioning module  532  is the field installer. The provisioning module  532  contains all the start up scripts and system configurations to bring the system from shipping boxes to full alarm notification system  500  functionality. Provisioning includes steps to configure individual devices, notifiers such as pagers, PDA, COW, Tables and IP telephone at the customer site, preferably by wireless means (e.g., Bluetooth). 
     The administrative module  534  provides a system interface for the application administrator to set up users, set policies for various actor privileges such as a nurses aide being able to set or change alarms, set up allowed device connection identifications, and other general systems administrative duties typical of IT systems. 
     The service module  536  provides interfaces for various technical support actors including remote service, IT Service, and Biomed Service. Each of these actors may perform each others&#39; functions. Interfaces allow the service actors to access system performance data to access performance, for example, data traffic, device assets connected, software version management, CPU loading, network loading, etc. and execute remote technical service procedures, for example, resetting a printer queue, repartition of disk, uploading software patches, etc. The service module  536  includes a full journaling function that stores every user interaction or a portion of user actions that can be captured by the system, especially changes in default values or alarm settings. 
     The risk report module  538  provides summary reports on alarm occurrences, duration of alarm, clinical response time to alarms and other statistical data to determine overall effectiveness of clinical response to alarms in compliance with JCAHO, other regulatory bodies, and internal quality assurance committees. 
     The technical data stores module  540  has the same characteristics as the clinical data stores module  522  except that the technical data stores module  540  is used for technical data. The technical data stores module  540  may or may not share the same physical and logical entity as the clinical data stores module  522 . 
     Additionally shown in  FIG. 5 , an external interface subsystem  550  provides interfaces to bedside devices and external systems such as electronic medical records, admit discharge, transfer systems, POCSAG pager systems, middleware engines such as Emergin, and Web/XML enabled devices such as wireless PDAs, COWs and Tablet PCs. The external interface subsystem  550  has an HL7 interface  552 , a pager interface  554 , an XML/Web interface  556 , and a device interface  558 . 
     The HL7 interface  552  provides a bi-directional interface to electronic medical records (EMR) and supports both push and pull models. The push model is when a bedside nurse initiates data transfer. The pull model is when an EMR system polls the alarm notification system  500  server. The pager interface  554  provides output to external paging system. Message latency is identified to an end user for any user-owned paging solution. This same output can be used for middleware alarm notification systems such as Emergin. The XML/Web interface  556  provides bi-directional interface with mobile computing platforms such as wireless PDA, COWs, Tables, and Web-enabled IP phones. Mobile computing platforms support Web Browser XML applications. The device interface  558  provides a bi-directional interface to bedside devices as well as to other devices enabled by the communications module or accessory. Application Programmer Interface (API) capability is an option for interfacing to other bedside devices. 
     The major end users of the alarm notification system  500  system (not shown or described for simplicity) include hospital electronic medical records, admit discharge transfer, pharmacy, clinical information, patient flow tracking and others. Actors, e.g., users of the alarm notification system  500 , including clinical actors and technical support actors. The clinical actors include nursing supervisors, unit clerks, nursing aides, nurses, rapid response teams and respiratory therapists. 
     A nursing supervisor assigns individual nurses to specific patients at the beginning of each shift. Shift can vary according to hospital staffing policies. A unit clerk takes direction from the nursing supervisor, typically inputs assignments into system and monitors overall system. A unit clerk may not be available for all shifts. A nursing aide takes assignments from nurse or nursing supervisor, typically applies bedside device sensor, initializes the bedside device and sets alarms to default values. A nurse has primary responsibility for individual patient care and primary response to alarms. The nurse is assigned by nursing supervisor to more than one patient dependent on her/his skills and patient needs and is not always assigned the same patient. Nursing aides are not found in all hospitals. 
     A rapid response team responds to clinical emergencies initiated by either a bedside nurse or a nursing supervisor. The team supports more than one care unit and has one or more members depending on shift. Rapid Response Teams may not be implemented in all hospitals. A respiratory therapist has responsibilities for management of respiratory care for more than one patient and usually more than one care unit. Respiratory therapists are not found in some international settings. 
     Clinical actor performance substitution allows a high capability actor to assume the roles of other actors. Alarm notification system  500  allows mechanisms for such performance. For example, a nursing supervisor may perform functions of a unit clerk nursing aide, a nurse and a rapid response team. A nurse may perform functions of a unit clerk, a nursing aide and a rapid response team. In some international markets a nurse may perform the functions of a respiratory therapist. 
     The technical support actors include field installers, application administrators, remote services, IT engineers, biomedical engineers and risk managers. A field installer provisions the system for initial installation, installs components, and validates that the installation and configuration meet a purchasing contract. An application administrator sets up and maintains user accounts and systems defaults. A remote service provides remote diagnostics and system maintenance over a remote link, such as dial up and VPN. An IT engineer provides network support services if the system is integrated with the hospital IT network. A biomedical engineer provides bedside and system primary service. A risk manager reviews reports for quality and risk mitigation purposes. Technical support actors may also fill in for other actors. For example, an IT engineer, a biomedical engineer, or a remote service can perform the functions of an application administrator. An IT engineer or a biomedical engineer can perform each other&#39;s functions. 
     In certain embodiments, systems and methods are provided for rapidly storing and acquiring physiological trend data. For instance, physiological information obtained from a medical patient can be stored in a round-robin database. The round-robin database can store the physiological information in a series of records equally spaced in time. 
     Parameter descriptors may be used to identify parameter values in the records. The parameter values can be dynamically updated by changing the parameter descriptors to provide for a flexible database. In addition, the size of files used in the database can be dynamically adjusted to account for patient condition. 
     Additionally, in certain embodiments, medical data obtained from a clinical network of physiological monitors can be stored or journaled in a journal database. The medical data can include device events that occurred in response to clinician interactions with one or more medical devices. The medical event data may also include device-initiated events, such as alarms and the like. The medical data stored in the journal database can be analyzed to derive statistics or metrics, which may be used to improve clinician and/or hospital performance. 
     As used herein the terms “round-robin database” and “RRDB,” in addition to having their ordinary meaning, can also describe improved database structures having unique characteristics and features disclosed herein. Sometimes these structures are referred to herein as dynamic RRDB s or adaptive RRDB s. 
       FIG. 6  illustrates an embodiment of a clinical network environment  600 . The clinical network environment  600  includes a multi-patient monitoring system (MMS)  620  in communication with one or more patient monitors  640 , nurses&#39; station systems  630 , and clinician devices  650  over a network  610 . In certain embodiments, the MMS  620  provides physiological data obtained from the patient monitors  640  to the nurses&#39; station systems  630  and/or the clinician devices  650 . Additionally, in certain embodiments, the MMS  620  stores physiological information and medical event information for later analysis. 
     The network  610  of the clinical network environment  600  can be a LAN or WAN, wireless LAN (“WLAN”), or other type of network used in any hospital, nursing home, patient care center, or other clinical location. For ease of illustration, the remainder of this specification will describe clinical environments in the context of hospitals; however, it should be understood that the features described herein may also be employed in other clinical locations or settings. In some implementations, the network  610  can interconnect devices from multiple hospitals or clinical locations, which may be remote from one another, through the Internet, a leased line, or the like. Likewise, the various devices  620 ,  630 ,  640 , and  650  of the clinical network environment  100  may be geographically distributed (e.g., among multiple hospitals) or co-located (e.g., in a single hospital). 
     The patient monitors  640  may be point-of-care (POC) instruments or the like that monitor physiological signals detected by sensors coupled with medical patients. The patient monitors  640  may process the signals to determine any of a variety of physiological parameters. One example of a physiological parameter is blood oxygen saturation (SpO 2 ). Other examples of physiological parameters are described below with respect to  FIG. 7 . 
     The patient monitors  640  can provide the physiological information to the MMS  620 . The patient monitors  640  can also provide information on medical events, such as alarms, to the MMS  620 . Alarms can be triggered, for example, in response to a physiological parameter falling outside of a normal range. Alarms can also include alerts regarding equipment failures, such as a probe-off condition where a sensor has fallen off of a patient. Other examples of medical events are described below with respect to  FIG. 7 . 
     In various embodiments, the patient monitors  640  provide the physiological information and medical events to the MMS  620 . The MMS  620  is described in greater detail below. In some implementations, the patient monitors  640  may provide at least some of this information directly to the nurses&#39; station systems  630  and clinician devices  650 . 
     The nurses&#39; station systems  630  can be desktop computers, laptops, work stations, or the like that are located at a nurses&#39; station. One or more nurses&#39; station computers  630  can be located at a single nurses&#39; station. The nurses&#39; station computers  630  can receive and display physiological information and alarm data received from the MMS  620  (or monitors  640 ). In certain embodiments, the nurses&#39; station computers  630  use a graphical user interface (GUI) that provides a streamlined, at-a-glance view of physiological and medical information. An example of this GUI is described below with respect to  FIG. 9 . 
     The clinician devices  650  can include any of a variety of devices used by clinicians, such as pagers, cell phones, smart phones, personal digital assistants (PDA), laptops, tablet PCs, personal computers, and the like. The clinician devices  650  are able to receive, in some embodiments, physiological information and alarms from the MMS  620  (or monitors  640 ). Physiological and alarm data can be provided to a particular clinician device  650 , for example, in response to an alarm. The clinician devices  650  can, in some instances, receive values and waveforms of physiological parameters. 
     The MMS  620  in certain embodiments includes one or more physical computing devices, such as servers, having hardware and/or software for managing network traffic in the network  610 . This hardware and/or software may be logically and/or physically divided into different servers  620  for different functions, such as communications servers, web servers, database servers, application servers, file servers, proxy servers, and the like. 
     The MMS  620  can use standardized protocols (such as TCP/IP) or proprietary protocols to communicate with the patient monitors  640 , the nurses&#39; station computers  630 , and the clinician devices  650 . In one embodiment, when a patient monitor  640  wishes to connect to the MMS  620 , the MMS  620  can authenticate the patient monitor  640  and provide the monitor  640  with context information of a patient coupled to the monitor  640 . Context information can include patient demography, patient alarm settings, and clinician assignments to the patient, among other things. Examples of context information are described herein. The MMS  620  may obtain this context information from the nurses&#39; station systems  630  or other hospital computer systems, where patient admitting information is provided. 
     Upon connecting to a patient monitor  640 , the MMS  620  may receive physiological information and medical events from the patient monitors  640 . The MMS  620  may provide at least a portion of the physiological information and events to the nurses&#39; station systems  630  and/or clinician devices  650 . For example, the MMS  620  may provide physiological data and alarms for a plurality of patient monitors  640  to a nurses&#39; station system  630 , where nurses can evaluate the data and/or alarms to determine how to treat patients. Similarly, the MMS  620  may send wireless pages, emails, instant messages, or the like to clinician devices  650  to provide clinicians with physiological data and alarms. 
     Advantageously, in certain embodiments, the MMS  620  can store physiological information obtained from the patient monitors  640  in a round-robin database (RRDB)  624 . The RRDB  622  of various embodiments includes a streamlined database structure that facilitates rapidly storing and retrieving patient data. The RRDB  622  can therefore be used in certain embodiments to rapidly provide physiological trend data to the nurses&#39; stations  630  and to the clinician devices  650 . Thus, for example, if a clinician desires to see a patient&#39;s physiological trends over a certain time period, such as the past hour, the clinician can use a nurses&#39; station computer  630  or clinical device  650  to query the MMS  620 . The MMS  620  may then obtain physiological information corresponding to the desired time period from the RRDB  622 . Advantageously, the RRDB  622  can enable faster acquisition of trend data then is possible with relational databases currently used by hospital monitoring systems. Additional uses and optimizations of the RRDB  622  are described below. 
     In certain embodiments, the MMS  620  also archives or stores information about medical events in a journal database  624 . The medical events can include events recorded by devices such as the patient monitors  640 , nurses&#39; station systems  630 , and clinician devices  650 . In particular, the medical events can include device events that occur in response to a clinician&#39;s interaction with a device, such as a clinician-initiated deactivation of an alarm. The medical events can also include device events that occur without a clinician&#39;s interaction with the device, such as the alarm itself. Additional examples of medical events are described below with respect to  FIG. 7 . 
     The MMS  620  may analyze the medical event information stored in the journal database  624  to derive statistics about the medical events. For example, the MMS  620  can analyze alarm events and alarm deactivation events to determine clinician response times to alarms. Using these statistics, the MMS  620  may generate reports about clinician and and/or hospital performance. Advantageously, in certain embodiments, these statistics and reports may be used to improve the performance of clinicians and hospitals. 
     For instance, in certain situations, the reports might help hospitals discover the cause of issues with patient monitors  640 . The following example scenario can illustrate potential benefits of such a report. SpO 2  alarm levels tend to be different for adults and neonates. However, some clinicians may not know this and may modify neonate SpO 2  monitors to include adult alarm levels. These changes can result in many false alarms, which may cause clinicians to become frustrated and avoid using the patient monitors  640 . By journaling medical events such as clinician alarm changes, it can be determined by an analysis of the journaled data that clinicians were inappropriately adjusting alarm settings on neonate monitors. A hospital could then use this information to take corrective action, such as by fixing the alarm limits and training the clinicians. 
     Although not shown, administrative devices may be provided in the clinical network environment  600 . The administrative devices can include computing devices operated by hospital administrators, IT staff, or the like. Using the administrative devices, IT staff may, for example, promulgate changes to a plurality of patient monitors  640 , nurses&#39; station systems  630 , and the MMS  620 . The administrative devices may also allow IT staff to interface third-party systems with the MMS  620 , such as electronic medical record (EMR) systems. The third party systems may be used, for instance, to change alarm settings on a plurality of monitors from an administrative device. Actions performed by administrators, IT staff, and administrative devices in general may also be journaled in the journal database  624 . 
       FIG. 7  illustrates a more detailed embodiment of a clinical network environment  700 . The clinical network environment  700  includes a network  710 , a patient monitor  740 , a nurses&#39; station system  730 , an MMS  720 , an RRDB  722 , and a journal database  724 . These components may include all the functionality described above with respect to  FIG. 6 . One monitor  740  and nurses&#39; station system  730  are shown for ease of illustration. In addition, although not shown, the clinician devices  750  described above may also be included in the clinical network environment  700 . 
     The depicted embodiment of the patient monitor  740  includes a monitoring module  742 , an RRDB module  744 , and a journal module  746 . Each of these modules may include hardware and/or software. Other components, such as a communications module, are not shown but may be included in the patient monitor  740  in various implementations. 
     The monitoring module  742  can monitor physiological signals generated by one or more sensors coupled with a patient. The monitoring module  742  may process the signals to determine any of a variety of physiological parameters. For example, the monitoring module  742  can determine physiological parameters such as pulse rate, plethysmograph waveform data, perfusion index, and values of blood constituents in body tissue, including for example, arterial carbon monoxide saturation (“HbCO”), methemoglobin saturation (“HbMet”), total hemoglobin (“HbT” or “SpHb”), arterial oxygen saturation (“SpO 2 ”), fractional arterial oxygen saturation (“SpaO 2 ”), oxygen content (“CaO 2 ”), or the like. 
     In addition, the monitoring module  742  may obtain physiological information from acoustic sensors in order to determine respiratory rate, inspiratory time, expiratory time, inspiration-to-expiration ratio, inspiratory flow, expiratory flow, tidal volume, minute volume, apnea duration, breath sounds, rales, rhonchi, stridor, and changes in breath sounds such as decreased volume or change in airflow. In addition, in some cases the monitoring module  742  monitors other physiological sounds, such as heart rate (e.g., to help with probe-off detection), heart sounds (e.g., S 1 , S 2 , S 3 , S 4 , and murmurs), and changes in heart sounds such as normal to murmur or split heart sounds indicating fluid overload. Moreover, the monitoring module  742  may monitor a patient&#39;s electrical heart activity via electrocardiography (ECG) and numerous other physiological parameters. 
     In some implementations, the patient monitors  740  may also determine various measures of data confidence, such as the data confidence indicators described in U.S. Pat. No. 7,024,233 entitled “Pulse oximetry data confidence indicator,” the disclosure of which is hereby incorporated by reference in its entirety. The patient monitors  740  may also determine a perfusion index, such as the perfusion index described in U.S. Pat. No. 7,292,883 entitled “Physiological assessment system,” the disclosure of which is hereby incorporated by reference in its entirety. Moreover, the patient monitors  740  may determine a plethysmograph variability index (PVI), such as the PVI described in U.S. Publication No. 2008/0188760 entitled “Plethysmograph variability processor,” the disclosure of which is hereby incorporated by reference in its entirety. The parameters described herein are merely examples, and many other parameters may be used in certain embodiments. 
     In certain embodiments, the RRDB module  744  receives physiological information from the monitoring module  742  and transmits the physiological information over the network  710  to the MMS  720 . In response, the MMS  220  may store the physiological information in the RRDB  722 . Advantageously, in certain embodiments, the RRDB module  744  associates the physiological information with parameter descriptors prior to transmittal to the MMS  720 . The parameter descriptors may be identifiers that the RRDB module  744  associates with each measured physiological parameter value. The MMS  720  may use these parameter descriptors to identify the types of measured parameters received from the RRDB module  744 . 
     The parameter descriptors may be descriptors generated according to a markup language specification, such as an extensible markup language (XML) specification. As such, the parameter descriptors may include tags that enclose measured physiological values. These tags may be machine readable or human readable. For instance, the tags may include numerical identifiers (e.g., “0017”) or descriptive identifiers, such as “SPO2” or “SPHB.” A simplified example stream of physiological information from an SpO 2  sensor and an SpHb sensor associated with parameter descriptors might be as follows: &lt;SPO2&gt;96&lt;/SPO2&gt; &lt;SPHB&gt;14.1&lt;/SPHB&gt; &lt;SPO2&gt;97&lt;/SPO2&gt; &lt;SPHB&gt;14.0&lt;/SPHB&gt;, and so on. 
     In one embodiment, the RRDB module  744  may have stored (e.g., in a data file) a set of predefined parameter descriptors available for the patient monitor  740 . These parameter descriptors may correspond to possible parameters that may be measured by the patient monitor  740 . The parameter descriptors transmitted by the RRDB module  744  may depend on the particular subset of parameters measured by the patient monitor  740 . 
     If an additional (or different) parameter is subsequently measured by the patient monitor  740 , the RRDB module  740  may dynamically update the parameter descriptors that are sent to the MMS  720 . Likewise, if the patient monitor  740  ceases to measure one of the parameters, the RRDB module  744  may cease to transmit the corresponding parameter descriptor to the MMS  720 . 
     The patient monitor  740  also includes a journal module  746  in the depicted embodiment. The journal module  740  may record medical events related to the patient monitor  740 . These medical events can include clinician-initiated events, such as changes to alarm settings (e.g., maximum and minimum permitted parameter values), types of parameters monitored/sensors connected to the patient monitor  740 , and the like. The journal module  746  may record these events by, for example, acting as a key logger or the like to record button presses of a clinician. The journal module  746  may also include current-sense circuitry to detect when sensors or cables are connected to the monitor  740 , and so forth. The medical events may also include non-clinician initiated events, such as alarms and alerts. The medical events can also include events from administrative devices (not shown), such as EMR updates to alarm settings across the network  710 . 
     The journal module  746  may log these events locally at the patient monitor  740 . In addition, or instead of logging the events locally, the journal module  746  may transmit information about the events to the MMS  720 . In turn, the MMS  720  can store the event information in the journal database  724 . 
     The nurses&#39; station system  730  is shown in the depicted embodiment having a patient monitoring client  732 . The patient monitoring client  732  can enable the nurses&#39; station system  730  to receive and display physiological information and alarm information. The patient monitoring client  732  includes a user interface module  734 . The user interface module  734  may include, for example, software for displaying physiological information, patient information, and medical event information for a plurality of patient monitors  740 . The user interface module  734  may also allow clinicians to admit and discharge patients, remotely modify device alarm limits, and the like. An example user interface that may be generated by the user interface module  734  is described below with respect to  FIG. 9 . 
     The patient monitoring client  732  further includes a journal module  736 . The journal module  736  may include software for recording medical events related to the patient monitoring client  732 . For example, the journal module  736  may record which clinicians login to and logoff of the patient monitoring client  732  and when these events occur; admit and discharge events; and other clinician keystrokes, mouse clicks, and interactions with the patient monitoring client  732 . The journal module  736  may log this event information locally at the nurse&#39;s station system  730  and/or transmit the event information to the MMS  720 . 
     As shown, the MMS  720  may include a network management module  721 , an RRDB management module  723 , and a journal management module  725 , each of which may include one or more software components. In one embodiment, the network management module  721  receives messages containing physiological information and medical event data from the patient monitor  740 . The network management module  721  can provide at least a portion of this data to the nurses&#39; station system  730  and clinician devices  650  of  FIG. 6 . The network management module  721  can also provide the physiological information to the RRDB management module  723  and provide the medical event data to the journal management module  725 . 
     In certain embodiments, the RRDB management module  723  stores the physiological information received from the patient monitor  740  in the RRDB  722 . When the patient monitor  740  initially connects to the MMS  720 , or at another time, the RRDB management module  723  can create one or more RRDB files in the RRDB  722  corresponding to the patient monitor  740 . The contents of this file or files may depend on the type of patient monitor  740 , which may be defined by the patient monitor&#39;s  740  serial number, model number, vendor identifier, combinations of the same, or the like. Specific examples of the structure and contents of RRDB files are described in US Patent Publication 2009/0119330, the entire contents of which are hereby incorporated by reference herein. 
     The RRDB management module  723  can also provide physiological trend data stored in the RRDB to the network management module  721  for transmittal to monitors  740 , nurses&#39; station systems  730 , and/or clinician devices. The RRDB management module  723  may also provide physiological data from the RRDB  722  to the journal management module  725  for purposes described below with respect to  FIG. 8B . 
     The journal management module  725 , in certain implementations, receives medical event data from the monitor  740  and the nurses&#39; station system  730  and stores this data in the journal database  724 . In an embodiment, the journal database  724  is a relational database; however, other structures may be used. Each entry of event data may have a corresponding time stamp that indicates when an event occurred. This time stamp may be provided by the journal modules  746  or  736  or by the journal management module  725 . The journal management module  725  may also store event counters in the journal database  724  that reflect a number of times medical events occurred. For example, counters could be stored that count how many alarms occurred within a period of time or how many times a clinician logged on or logged off of a network device. 
     Advantageously, the journal management module  725  may, in certain embodiments, analyze the medical data in the journal database  724  to determine statistics or metrics of clinician and/or hospital performance. The journal management module  725  may provide an interface to users of the nurses&#39; station system  730  or another computing device to access these statistics. In one example embodiment, journal management module  725  can analyze alarm events and alarm deactivation events to determine clinician response times to alarms. The journal management module  725  may further determine the clinician response times in nurses&#39; day and night shifts. The journal management module  725  may generate reports of these statistics so that hospital administrators, for example, may determine which shifts perform better than others. 
     More generally, the journal management module  725  may generate reports about clinician and and/or hospital performance by analyzing various statistics derived from data in the journal database  724 . One example of a report is a monitoring report card, which grades a given hospital against other hospitals (or nurses&#39; station against nurses&#39; station, and the like) based at least partly on the derived statistics. Advantageously, hospital administrators, clinicians, and the like may use these statistics and reports to improve the clinician and hospital performance. 
     Some or all of the features of the clinical network environment  700  may be adapted in certain embodiments. For instance, either or both of the journal modules  746  or  736  may perform some or all of the functions of the journal management module  725 . Likewise, one or more journal databases  724  may be stored at the patient monitor  740  and/or nurses&#39; work station  730 . Similarly, the RRDB module  724  may perform some or all of the functions of the RRDB management module  723 , and an RRDB  722  may be stored at the patient monitor  740 . In addition, in some implementations, the clinician devices  650  of  FIG. 6  may have RRDB and/or journal modules as well. Many other adaptations, configurations, and combinations may be made in other embodiments. Additional information regarding embodiments of the RRDM can be found in US Patent Publication  2009 / 0119330 . 
       FIG. 8A  illustrates an embodiment of a process  800 A for journaling medical events in a journal database. In one embodiment, the process  800 A may be implemented by any of the MMS&#39;s described above (e.g., the MMS  620  or  720 ). In particular, the process  800 A may be implemented by the journal management module  725 . Alternatively, at least some of the blocks may be implemented by the journal modules  736 ,  746 . Advantageously, in certain embodiments, the process  800 A facilitates the generation of reports based on the journaled data. 
     At block  802 , medical events are journaled in a journal database. In response to requests for report from a user (e.g., a clinician), at block  804  statistics about the medical events are obtained from the journal database. The statistics may include the type, frequency, and duration of medical events, the identity of clinicians or patients associated with the events, alarm response times, combinations of the same, and the like. 
     A report is generated at block  806  regarding the medical event statistics. At block  808 , the report is used to identify potential areas of improvement in hospital operations. For example, the report can be a “monitoring report card” that assigns scores to the hospital or clinicians of the hospital based on their performance. 
       FIG. 8B  illustrates an embodiment of a process  800 B for correlating data from a journal database and an RRDB. In one embodiment, the process  800 B may be implemented by any of the MMS&#39;s described above (e.g., the MMS  620  or  720 ). In particular, the process  800 B may be implemented by the RRDB module  723  and journal management module  725 . Alternatively, at least some of the blocks may be implemented by the RRDB module  744  and journal modules  736 ,  746 . Advantageously, in certain embodiments, the process  800 B enables physiological information from the RRDB and medical events to be correlated in time. Such a reconstruction of events and physiological data can be akin to aviation “black box” technology, allowing the user to replay clinical actions leading up to medical incidents. 
     At block  812 , the request is received from a user to review journaled and physiological data corresponding to a period of time. The user may be a clinician, hospital administrator, or the like, who wishes to determine the cause of a problem in the healthcare of a patient. For instance, the user may wish to determine why clinicians failed to respond when a patient&#39;s SpO 2  dropped below safe levels. 
     At block  814 , journaled data is retrieved for the specified period of time from a journal database. This block may be performed by the journal management module  725 . At block  816 , physiological data for the specified period of time is retrieved from an RRDB. This block may be performed by the RRDB management module  723 . The journal data is correlated with the physiological data with respect to time at block  818 . This correlation may include reconstructing a timeline of medical events, with values of physiological parameters (optionally including waveforms) provided in the correct time sequence on the timeline. In some embodiments, to facilitate this coordination between the RRDB management module  723  and the journal management module  725 , timestamps in each database  722 ,  724  may be synchronized when the data is stored. 
     The correlated data is output for presentation to the user at block  820 . The output may include, for example, a graphical view of medical events superimposed on physiological information (e.g., a waveform), or the like. Many display formats may be used for the correlated data. 
       FIG. 9  illustrates an example graphical user interface (GUI)  900  for monitoring patients. The GUI  900  can be provided on a nurses&#39; station system or the like. The GUI  900  can also be displayed on a clinician device. 
     The GUI  900  includes several display areas. In the depicted embodiment, the GUI  900  includes a patient status display area  910 . The patient status display area  910  shows the status of multiple patients in a hospital or other clinical location. In an embodiment, patient status display area  910  depicts patient status for patients in a hospital department. Advantageously, in certain embodiments, the patient status display area  910  provides an “at-a-glance” view of multiple patients&#39; health status. 
     The patient status display area  910  includes a plurality of patient status modules  912 . Each patient status module  912  can correspond to a patient monitor that can be coupled to a medical patient. Each patient status module  912  can display a graphical status indicator  914 . An example graphical status indicator  914  is shown in the screens  900  as a miniature patient monitor icon. The graphical status indicator  914  can selectively indicate one of several states of a patient monitor. In one embodiment, four possible patient monitor states can be depicted by the graphical status indicator  914 . These include an alarm condition, a no alarm condition, patient context information status, and connection status. 
     In various implementations, the graphical status indicator  914  changes color, shape, or the like to indicate one of the different patient monitor states. For example, if an alarm condition is present, the graphical status indicator  914  could turn red to signify the alarm. If there is no context information available for the patient (see  FIG. 1 ), then the graphical status indicator  914  could turn yellow. If the device is not connected to the patient or the network, then the graphical status indicator  914  could turn gray. And if there is no alarm condition, if there is context information, and if the patient monitor is connected to the patient and the network, then the graphical status indicator  914  could turn green. Many other colors, symbols, and/or shapes could be used in place of or in combination with the above-described embodiments. 
     Advantageously, the graphical status indicator  914  shows at a glance the status of a patient monitor. Thus, in the patient status display area  910 , several graphical status indicators  914  corresponding to several patients show an at-a-glance view for the patient monitors corresponding to these patients. A clinician can therefore readily see the needs that a patient might have with regards to alarms, connection status, and context information. 
     Currently available graphical user interfaces for nurses&#39; station computers tend to show a plurality of wave forms or changing physiological parameter numbers for each patient. This method of displaying patient information can be cluttered, confusing, and even hypnotic in some situations. Nurses working on a night shift, for instance, may find it difficult to concentrate on an alarm when several other patients&#39; indicators on the display have changing numbers, changing waveforms, or the like. In contrast, in the graphical interface herein described, when the graphical status indicator  914  indicates an alarm condition, this alarm condition can stand out and be immediately recognized by the clinician. 
     Moreover, the graphical status indicator  914  simplifies the first level of analysis that nurses tend to perform. In currently available devices, nurses often have to analyze waveforms at the nurses&#39; station to determine the health status of a patient. However, using the screens  900 , a nurse need not interpret any waveforms or changing parameters of the patient, but instead can rely on the graphical status indicator  914  that indicates the presence of an alarm. 
     In certain embodiments, the patient status modules  912  can be selected by a single mouse click or the like. Selecting a patient status module  912  in one embodiment can bring up a patient monitor view area  920 . The patient monitor view area  920  shows a view of a patient monitor corresponding to a selected patient status module  912 . In certain implementations, the patient monitor view area  920  can show a view of the screen from the actual patient monitor device at the bedside of the patient. Thus, a clinician can readily recognize the physiological parameters of the patient in a format that the clinician is likely familiar with. The patient monitor view area  920  is currently receiving physiological information from a patient. 
     A history view area  930  in certain implementations can show medical event data corresponding to a selected patient monitor status module  912 . This medical event data can be obtained from a journal database for inclusion in the GUI  900 . The historical view  930  can show, for example, when a sensor was connected or disconnected from a patient, when alarms were active, and when a patient was admitted to the hospital or department. Although not shown, the history view area  930  can also be configured to show trend data obtained from an RRDB instead of, or in addition to, the journaled data. 
     Other features are described in U.S. Patent Application ##/###,### (Attorney Docket MASIMO.609P1), entitled “SYSTEMS AND METHODS FOR STORING, ANALYZING AND RETRIEVING MEDICAL DATA,” filed Oct. 14, 2010, the entire contents of which are hereby incorporated by reference herein. Transmission of Patient Information to Remote Devices 
     In some embodiments, the patient monitoring devices described herein are capable of transmitting patient information to one or more remote devices for review by a clinician. For example, such remote devices can include remote computers, smart phones, PDAs, etc. This is useful because it enhances the ability of a clinician to monitor a patient&#39;s condition remotely. For example, the clinician need not be at the patient&#39;s bedside or even at a hospital or other patient care facility in order to effectively monitor the patient&#39;s condition. 
     In some embodiments, any of the information collected by a patient monitoring device (e.g., the patient monitoring devices described herein) can be transmitted to a remote device. Such information can include, for example, values, trend data, etc. for a medical parameter (e.g., blood oxygen saturation, pulse rate, respiration rate, etc.). It can also include video of the patient and/or audio from the patient and/or the patient&#39;s room. For example, video cameras and/or microphones can be provided in the patient&#39;s room. In some embodiments, a video camera and/or microphone is incorporated with, for example, a medical monitoring device, such as those described herein. The video camera can image the patient using visible light when the ambient light in the patient&#39;s room is of sufficient intensity. The video camera can also be capable of detecting, for example, infrared light when the patient&#39;s room is too dark to provide video of acceptable quality using visible light. The video camera can also include an infrared illumination source to illuminate the patient and/or his or her surroundings. In some embodiments, the video camera includes an ambient light sensor that can be used to automatically switch the video camera into infrared mode when the ambient light falls below some threshold. The light sensor can also be used for switching on an infrared illumination source if one is included. 
     In some embodiments, the video camera can be mounted on a support structure (e.g., attached to the patient&#39;s bed or a monitoring device). The video camera can be mounted so as to provide a specified view of the patient (e.g., full body, torso, head, etc.). In some embodiments, the video camera can include a zoom function, which may be remotely controllable, so as to provide a desired view of the patient. For example, the video camera can be configured so as to provide a view of the patient&#39;s torso so as to allow a clinician to determine if the patient is breathing by noting the rise and fall of the patient&#39;s chest. The video camera can also be configured to provide other views which may be useful for other medical diagnostic purposes. 
     The transmission of patient information (e.g., medical parameter data, video/audio of the patient, etc.) can be made using, for example, one or more communication networks (e.g., computer networks such as LANs, WLANs, the Internet, etc., telephone networks, etc.). In some embodiments, one or more communication networks that are entirely or partially physically located in a hospital or other patient care center can be used. In some embodiments, external communication networks can be used to reach remote devices throughout the world. Thus, clinicians can remotely obtain a vast amount of information regarding the condition of their patients regardless of the clinician&#39;s location. In some embodiments, the clinician may also have the capability to directly communicate with the patient. For example, a patient monitoring device could include a speaker for broadcasting audio from the clinician&#39;s remote device to the patient. Similarly, a patient monitoring device could include a display for showing video from the clinician&#39;s remote device (e.g., video teleconferencing). In this way, the exchange of information can be bidirectional to allow the clinician to directly interact with the patient. 
     Hospital Systems with Location Awareness of Devices and Clinicians 
     Advanced monitoring systems are capable of displaying many different physiological parameters in many different formats. One possible drawback to this substantial performance capability and display flexibility is that excessive information may be presented to the caregivers that use these systems. These caregivers may include physicians, respiratory therapists, registered nurses, and other clinicians whose uses of the monitoring systems may vary from the taking of routine vital signs to the diagnosis and treatment of complex physiological conditions to clinical research and data collection. 
     Patient monitoring devices, such as those described herein, may include a keyboard, touchscreen, or other input device to allow a clinician to interact with the device. Such user interface devices can be used to allow a clinician to input login information, such as, for example a username and password. In some cases, a monitoring device may require a clinician to login to the device, for example, before permitting access to one or more of the functions offered by the device, and/or before permitting access to certain information available at the device. The nurses&#39; station, or central monitoring station, as described herein, is an example of one such monitoring device that may require a clinician to login in order to use it. Bedside patient monitors may require a clinician to login before initializing monitoring of a new patient. Even where a clinician is not required to login to a patient monitoring device before using it, the device may still require some type of interaction with an input device in order to cause it to take a particular action from amongst a set of available actions offered by the patient monitoring device. 
     For example, user input may be required in order to configure a patient monitoring device in a desired manner. In some embodiments, a clinician may use an input device to change the content offered on the display of the patient monitor device, or the formatting of the content, to suit his or her preferences. In some instances, a nurse may use the input device to manually configure the central monitoring station to display only monitoring information for those patients that are assigned to that particular nurse rather than displaying, for example, all the patients on the entire floor. A clinician may also use an input device to alter patient monitoring settings such as, for example, options for calculating physiological parameter values from raw data, alarm types, physiological parameter alarm limits (e.g., alarm thresholds), etc. 
     Given the time demands placed on clinicians in busy hospitals, this process of manually interacting with a patient monitoring device by, for example, physically manipulating an input device can be burdensome, especially when it may need to be repeated over and over throughout the day. In some cases, the time required to manually interact with a patient monitor device in order to access a particular function or configure the device can even jeopardize a patient&#39;s well-being in particularly urgent circumstances. For at least the foregoing reasons, it would be advantageous for hospital equipment, such as bedside patient monitors, central monitoring stations, and other devices, to have the capability to automatically detect the presence of a clinician, and to, for example, take some predetermined action based on the identity of the clinician whose presence is detected. 
     In some embodiments, a proximity display monitor advantageously adapts an advanced monitoring system to various user needs and preferences by adapting the display to the current observer according to, for example, preference, priority, or user acknowledgement. Accordingly, displayed parameters and formats may be chosen by default according to a predefined user class or customized for particular individuals or groups of individuals. One method of identifying persons in the vicinity of a proximity display is by an ID tag or other token. The ID tag may communicate the user to the proximity display monitor via radio-frequency identification (RFID) or wireless radio transmission as examples. Other types and/or standards of wireless communication can also be used, including, for example, ultrasound, Bluetooth, Near Field Communication (NFC), etc. If multiple users are in range of a proximity display monitor, a priority scheme or a user acknowledgment may be used to determine which users are accommodated. 
     In some embodiments, a proximity display monitor has a monitor and an interconnected sensor, the sensor transmits optical radiation into a tissue site and generates a sensor signal responsive to the optical radiation after attenuation by pulsatile blood flow within the tissue site. The monitor may compute physiological parameters responsive to the sensor signal and utilize a proximity display to show the physiological parameters on screen according to a display preference associated with a user in proximity to the monitor. A display can be incorporated with the monitor so as to present the physiological parameters for viewing by a caregiver. A transceiver can be incorporated with the monitor and may be responsive to an identification signal. The identification signal can correspond to a caregiver. A transmitter carried by the caregiver can send the identification signal over a range, for example, approximating the distance from the monitor that a person can reasonably view the display. A preferred screen can present the physiological parameters on the display according to the display preference associated with the caregiver as indicated by the identification signal. 
     In some embodiments, a proximity display monitor comprises a monitor having a display and a wireless transceiver. The wireless transceiver can be responsive to identification signals which indicate the proximity to the monitor of any users, who have corresponding display preferences. Preferred screens may present the physiological parameters on the display according to the display preferences. 
     In some embodiments, a proximity display monitor has an optical sensor attached to a fleshy tissue site. A sensor signal may be responsive to optical radiation transmitted by the sensor and detected by the sensor after absorption by pulsatile blood flow within the tissue site. The sensor signal can be communicated to a monitor, which processes the sensor signal so as to derive physiological parameters responsive to constituents of the pulsatile blood flow. The identity of a user in proximity to the monitor can be wirelessly signaled to the monitor. A screen preference, for example, can be determined from the user identity and used to display the physiological parameters on a monitor display. 
     In some embodiments, a proximity display monitor comprises a processor and a display. The processor can be responsive to a sensor signal generated from optical radiation transmitted into a fleshy tissue site and detected after attenuation by pulsatile blood flow within the tissue site. The processor can be configured to calculate a plurality of physiological parameters indicative of constituents of the pulsatile blood flow. The display may provide a visual representation of the physiological parameters values for viewing by proximate users. A wireless communications means can determine the identities of proximate users. Screen preference means may present the physiological parameters on the display. A lookup table means can relate the user identities to the screen preferences. 
       FIG. 10  illustrates a physiological measurement system having a noninvasive sensor  1010  attached to a tissue site  1000 , a patient monitor  1020 , and an interface cable  1030  interconnecting the monitor  1020  and the sensor  1010 . The physiological monitoring system may incorporate pulse oximetry in addition to advanced features, such as a multiple wavelength sensor and advanced processes for determining physiological parameters other than or in addition to those of pulse oximetry, such as carboxyhemoglobin, methemoglobin and total hemoglobin, as a few examples. The patient monitor  1020  has a proximity display  1021  that presents measurements of selected physiological parameters and that also provides visual and audible alarm mechanisms that alert a caregiver when these parameters are outside of predetermined limits. The patient monitor  1020  also has keys  1022  for controlling display and alarms functions, among other items. The proximity display  1021  and keys  1022  provide a user interface that organizes many parameters so that a caregiver can readily ascertain patient status using, for example, a portable, handheld device. 
       FIG. 11  illustrates various screens  1150  for a proximity display  1021  ( FIG. 10 ) advantageously configured to respond to the presence of a particular user  1130  and to that user&#39;s display preference. Users may be any of various caregivers such as treating physicians or attending nurses. In an embodiment, the proximity display  1021  ( FIG. 10 ) may also respond to any of a particular group of users. 
     As described with respect to  FIG. 13 , below, the presence or proximity of a particular user or group of users to the monitor  1020  ( FIG. 10 ) may be determined by a user wearing an RFID (radio frequency identification) tag or other wireless communications. Then, a particular screen or screens can be presented on the display according to a predetermined display preference associated with the user. In this manner, a proximity display  1021  ( FIG. 10 ) is tailored to the preferences of monitor users. An “RFID tag” or simply “tag” can include any wireless communication device and/or communication standard (e.g., RF, NFC, Bluetooth, ultrasound, infrared, and the like) that can remotely identify a proximate user to a monitor. Tags include, but are not limited to, devices in the form of badges, tags, clip-ons, bracelets or pens that house an RFID chip or other wireless communication components. Tags also encompass smart phones, PDAs, pocket PCs and other mobile computing devices having wireless communications capability. 
     As shown in  FIG. 11 , by example, an anesthesiologist  1131  proximate the monitor is identified and the display is changed to a screen  1110  showing pulse rate trend. When a nurse  1132  is proximate the monitor, the display is changed to a screen  1120  showing pulse oximetry parameters, a plethysmograph and alarm limits. When a respiratory therapist  1133  is proximate the monitor, the display is changed to a screen  1140  showing pulse oximetry, abnormal hemoglobin and perfusion indices. 
     In some embodiments, a proximity display monitor responds to the departure of all proximate users by automatically dimming the display to a reduced brightness setting. This feature advantageously avoids disturbance of a patient who is sleeping or attempting to sleep. In some embodiments, a proximity display monitor responds in a similar manner by automatically silencing pulse “beeps” and other non-critical sounds when there are no proximate users. 
       FIG. 12  illustrates a proximity display monitor  1200  that responds to a nearby user  1280  so as to display calculated parameters according to a user display preference. As shown in  FIG. 12 , the proximity display monitor  1200 , in some embodiments, has a front-end  1210  that interfaces with an optical sensor (not shown). The optical sensor generates a sensor signal responsive to pulsatile blood flow with a patient tissue site. An optical sensor is described in U.S. patent applicaton Ser. No. 11/367,013 titled Multiple Wavelength Sensor Emitters, cited above. The front-end  1210  conditions and digitizes the sensor signal  1212 , which is input to a digital signal processor (DSP)  1220 . The DSP  1220  derives physiological parameters  1222  according to the sensor signal  1212 . The calculated parameter values are communicated to a display driver  1230 , which presents the parameters on the display  1270  according to a predetermined format. A monitor having a front-end and DSP is described in U.S. patent applicaton Ser. No. 11/366,208 titled Noninvasive Multi-Parameter Patient Monitor, cited above. 
     Also shown in  FIG. 12 , the proximity display monitor  1200  has a transceiver or receiver  1240 , a lookup table  1250  and display preferences  1258 . The proximity display monitor  1200  may also include a communication module for communicatively coupling the proximity display monitor  1200  to other patient monitoring devices, such as, for example, other bedside patient monitors, a central patient monitoring station, etc. The user  1280  has an ID tag  1260  that identifies the user  1280  to the transceiver  1240 . When the user  1280  is in the vicinity of the proximity display monitor  1200 , the ID tag  1260  is able to communicate with the transceiver  1240  so as to identify the user  1280 . In an embodiment, the transceiver  1240  is an RFID reader and the ID tag  1260  has an embedded RFID chip containing a user code  1252 . In another embodiment, the transceiver  1240  complies with one or more short-range wireless communications standards, such as Bluetooth®. The user  1280  can initiate communications with the proximity display monitor  1200  by, for example, pressing a button or similar initiator on the ID tag  1260 , and a user code  1252  is transmitted to the transceiver  1240 . The transceiver  1240  communicates the user code  1252  to the DSP  1220 . The DSP can access the lookup table  1250  so as to derive a display preference  1258  from the received user code  1252 . The lookup table  1250  may be stored locally in the proximity display monitor&#39;s memory, or the lookup table may be stored remotely, for example, at a central patient monitoring station, which is communicatively coupled to the bedside proximity display monitor. The display preference  1258  indicates the display parameters  1222  and screen format  1224 , which are communicated to the display driver  1230 . 
     Further shown in  FIG. 12 , in some embodiments, the lookup table  1250  relates the user code  1252  to a caregiver ID  1256  and a priority  1254 . When multiple users are in the vicinity of the proximity display monitor  1200 , the priority  1254  determines which display preference  1258  is used to configure the display  1270 . 
       FIG. 13  illustrates a display preference screen  1300 , which provides information for a particular row of the look-up table  1250  ( FIG. 12 ). A setup or registration procedure allows users to specify one or more profiles including, for example, a display preference and various options for calculating parameters and triggering alarms. 
       FIG. 14A  is a schematic diagram of a patient monitoring device  1400  that is capable of automatically detecting the presence of a clinician token  1410 . In some embodiments, the clinician token  1410  is a portable item meant to be, for example, worn or carried by a clinician throughout the day. The patient monitoring device  1400  is able to recognize the presence of the clinician based upon the presence of that clinician&#39;s token. 
     The patient monitoring device  1400  includes a detector such as, for example, a communication module  1402 . The patient monitoring device  1400  also includes a display  1404 , and a processor  1406 . The processor  1406  can be used, for example, for carrying out clinically-useful tasks on the basis of physiological information collected from one or more patients (e.g., calculating physiological parameter values, determining alarm conditions, outputting physiological information via a clinician user interface, notifying a clinician of an alarm condition, etc.) The patient monitoring device  1400  can also include other modules to assist in the monitoring of patients, as described herein (e.g., an interface for receiving physiological information from a medical sensor or computer network, a user interface for facilitating interaction with a clinician, etc.). In some embodiments, the communication module  1402  is a transmitter, a receiver, or a transceiver. Other types of communication modules can also be used. In some embodiments, the communication module  1402  is a short-range transceiver. The short range transceiver can be, for example, a Bluetooth-enabled transceiver. Bluetooth is a wireless protocol for exchanging data between devices over relatively short distances. The communication module  1402  can also be an infrared transceiver, an RFID tag, or any other means of communication (e.g., short-range communication). 
     The communication module  1402  is capable, in some embodiments, of detecting signals from a remote device within a detection area  1420 . The size of the detection area of  1420  can be determined by, for example, the power levels of communication signals from the communication module  1402 . The size of the detection area  1420  may also be affected by the surroundings of the patient monitoring device  1400 . In some embodiments, the detection area  1420  is configured to have a radius of 30 feet or less. In some embodiments, the radius of the detection area  1420  is 20 feet or less. In some embodiments, the radius is 10 feet or less, while in some embodiments, the radius is 5 feet or less, or 3 feet or less. In some embodiments, the patient monitoring device  1400  has multiple detection areas. Such detection areas could be, for example, different distance ranges from the patient monitoring device  1400 . The patient monitoring device  1400  can be configured to perform different actions in response to detection of a clinician token in each of the different detection areas. 
     The clinician token  1410  can likewise include a communication module  1412 , which can be, for example, a transmitter, a receiver, or a transceiver, though other types of communication modules may also be used. As is the case with the patient monitoring device  1400 , the communication module  1412  included with the clinician token  1410  may be a short range transceiver, such as, for example, a Bluetooth transceiver. The patient monitoring device  1400  is capable of detecting the presence of a clinician based on, for example, recognition of one or more communication signals from a clinician token  1410 . A communication signal from the clinician token  1410  may come, for example, in response to a communication initiated by the patient monitoring device  1400 , or the communication signal from the clinician token  1410  may be initiated by the clinician token itself. Many different methods can be used for initiating, for example, wireless communication between remote devices. 
     The clinician token  1410  may also carry information, for example, in a memory. The memory may be, for example, volatile or nonvolatile memory. The information may be hardwired into the clinician token  1410  or programmable. In some embodiments, the clinician token  1410  includes a clinician ID  1414  that is unique to the clinician to whom the clinician token  1410  is assigned. The clinician token  1410  may also include other information such as, for example, a clinician&#39;s login information (e.g., user name and password), a code or other indicator for initiating a predetermined action to be performed by the patient monitoring device  1400  upon recognition of the clinician&#39;s presence (logging in the clinician, setting configuration preferences of the patient monitoring device  1400 , enabling a function, etc.). 
     The clinician token  1410  may also include an input module  1416  that allows the clinician to cause the communication module  1412  to remotely communicate with, for example, the patient monitoring device  1400 , or some other device that forms a part of the hospital&#39;s patient monitoring network. For example, the input module  1416  may include one or more buttons, or other input devices, that allow the clinician to initiate a communication with the patient monitoring device  1400  for the purpose of having that device recognize the clinician&#39;s presence. In addition, the clinician may use the input module  1416  to, for example, call in an emergency response team if the clinician discovers that a particular patient is in need of emergency attention, or to silence a monitoring alarm. The input module  1416  can also be used for other purposes, depending upon the application. 
     In some embodiments, the clinician token  1410  is a cell phone, notebook computer, PDA device, headset, etc., any one of which may be, for example, Bluetooth-enabled. In some embodiments, the clinician token  1410  is the pager, or other notification device, used to notify clinicians of physiological parameter alarm conditions, as described herein. In some embodiments, the clinician token  1410  is an active or passive RFID tag. An active RFID tag may be WiFi-enabled, for example. In some embodiments, the clinician token  1410  is a barcode (e.g., two-dimensional or three-dimensional). In some embodiments, the clinician token  1410  is a part of the clinician&#39;s body. For example, the clinician token  1410  may be a fingerprint, a retina, the clinician&#39;s face, etc. In such embodiments, the clinician ID  1414  is actually a unique biometric signature of the clinician. The communication module  1402  may be selected based upon the type of clinician token  1410  with which it is to communicate. For example, the communication module  1402  in the patient monitoring device  1400  may be an RFID interrogator, a barcode scanner, a fingerprint scanner, a retina scanner, a facial recognition device, etc. 
     In some embodiments, the clinician token  1410  is advantageously a consumer device that can be registered with the patient monitoring device  1400  but that has no prior connection or relationship with, for example, the patient monitoring device  1400 , a patient monitoring system, the hospital, etc. For example, the clinician token  1410  can be a consumer device that is not designed specifically for the purpose of communicating with the patient monitoring device  1400 , or any other device configured to be able to detect the presence of the clinician token. Many clinicians will already own, for example, a cell phone which is carried on the clinician&#39;s person throughout the day for the clinician&#39;s personal use. In some embodiments, the clinician&#39;s personal electronic device can function as the clinician token  1414 , for example, after a registration process that will be described herein. This can be advantageous because it does not require investment on the part of the hospital or other caregiver facility to provide each clinician with a special-purpose clinician token  1410 . Nevertheless, in some embodiments, the clinician token  1410  is a special-purpose device provided to the clinician for the primary purpose of operating with, for example, patient monitoring devices (e.g.,  1400 ) having presence detection functionality. 
       FIG. 14B  is an exploded perspective view of an embodiment of a clinician token  1410  whose presence can be detected by a patient monitoring device  1400 . In some embodiments, the clinician token includes an enclosure bottom  1420  and an enclosure top  1421  that are configured to be mated together to form a housing. The clinician token  1410  can also include a clip  1423  for attaching the token to the clothing of a clinician. The clinician token  1410  may also include a certification label  1424 . A presence detection board assembly  1425  can be provided inside the housing. The presence detection board assembly  1425  can include a processor, memory, etc. The clinician token  1410  can also include a battery  1426  and the wireless communication module  1412 . 
     In some embodiments, the clinician token  1410  is capable of responding to, for example, interrogation from a patient monitoring device only with a fixed response signal (e.g., a clinician ID  1414 ). In some embodiments, however, the clinician token  1410  is capable of transmitting multiple, and/or variable, signals and information to the patient monitoring device  1400 . The clinician token  1410  may include a processor capable of executing, for example, software applications that allow the clinician token  1410  the capability of a variety of intelligent communications with the patient monitoring device  1400 . 
     In some embodiments, a registration process is completed before the clinician token  1410  is used with the patient monitoring device  1400  to implement presence detection functionality. For example, during a registration process, the clinician token  1410  may be endowed with a unique clinician ID  1414  assigned to a particular clinician. This clinician ID may be stored in a database that is, for example, accessible by the patient monitoring device  1400  such that the patient monitoring device  1400  can determine the identity of the clinician based upon the clinician ID  1414  stored in the clinician token  1410 . The clinician ID  1414  can also be associated in the database with, for example, the clinician&#39;s assigned login information for accessing the patient monitoring device  1400 . 
     The database can also store an indication of the action, or actions, that the clinician desires a particular patient monitoring device to take upon detection of the clinician&#39;s presence. The database can store the clinician&#39;s configuration preferences for the patient monitoring device. For example, the particular physiological parameters and other monitoring information that are shown on the display  1404  of the patient monitoring device  1400  may be configurable. In the case of bedside patient monitors, for example, the display  1404  may be capable of showing numerical indicators of a particular physiological parameter, graphical indicators of the physiological parameter, visual alarms, multiple physiological parameters simultaneously, signal quality of physiological parameter signals from a patient sensor, etc. The clinician&#39;s configuration preferences can indicate to the monitoring device  1400  what type of information to display and how to format the displayed information. The clinician&#39;s configuration preferences for the patient monitoring device  1400  can also include patient monitoring settings such as, for example, physiological parameter alarm limits. 
     In the case of, for example, a central monitoring station, such as the type described herein, the clinician&#39;s configuration preferences may likewise include the type and display format of a physiological parameter, or other monitoring information, that is shown for each of the patients being monitored at the central monitoring station. In addition, the clinician&#39;s configuration preferences can include a fixed or dynamic list of patient rooms, or patient names, to be displayed at the central monitoring station. These rooms, or patients, can be those currently assigned to that particular clinician, for example. In general, however, the clinician&#39;s configuration preferences that are associated with the clinician ID  1414  can include any configurable feature, aspect, or function of the patient monitoring device  1400 . 
     In some embodiments, the database can be configured to receive a variety of input information to define, for example, different actions to be performed by a monitoring device  1400  upon detection of the clinician&#39;s token. Inputs to the database can include the clinician ID, the strength of the signal from the clinician&#39;s token, the estimated distance of the detected clinician token from the monitoring device, the length of time of detected presence of the token, a clinician priority level, the time of arrival, time of departure, the room or hospital ward associated with the monitoring device that has detected the clinician&#39;s presence, the clinician&#39;s job description/responsibilities, the number of previous visits by the same clinician within a particular predetermined time period, the clinician&#39;s arrival order (if multiple clinicians are detected), etc. Based upon this input, the database can output a set of actions to be performed by the patient monitoring device upon detection of the clinician. Alternatively, or in addition, such actions and preferences can be determined using logical rules applied to the input information. For example, the patient monitoring device can be configured to perform a first action if the clinician is a doctor, a second action if the clinician is a nurse, a third action if the clinician arrives during waking hours, a fourth action if the clinician arrives during sleeping hours, a fifth action if the presence of the clinician has been detected at least once before in the previous hour, a sixth action if the clinician stays for longer than some set period of time, etc. Different actions can be associated with the detected presence of a clinician based on any attribute of the clinician, any attribute of the circumstances surrounding the detection event (e.g., time, duration, order of arrival, etc.), and/or combinations of the same. In some embodiments, the patient monitoring device may be configured to perform different actions upon the detection of the same clinician, depending upon attributes of the circumstances surrounding the clinician&#39;s visit (e.g., perform a first action when the clinician first arrives, perform a second action if the clinician stays for more than a predetermined period of time, perform a third action if the clinician visits more than once in the same day, etc.). The logical rules can be applied to a particular set of attribute inputs, and then associated actions stored in the database. Alternatively, the logical rules can be evaluated in real time by a processor as attribute inputs are received. In some embodiments, the logical rules can be dynamically adjustable. 
     The database and/or logical rules can be stored locally by the patient monitoring device  1400 . Alternatively, or in addition, the database and/or logical rules can be stored remotely by a device that is communicatively coupled to the patient monitoring device  1400 . For example, in some embodiments, a bedside patient monitor is communicatively coupled to a central monitoring station, as described herein. In some embodiments, this communication link is via a wireless network. In such embodiments, when the bedside patient monitor detects the presence of a clinician, it can receive a clinician ID and/or other information from a clinician token. The bedside patient monitor can then communicate this information to the remote database maintained by the central monitoring station. The bedside patient monitor can also transmit to the central monitoring station other input information, as identified above (e.g., the estimated distance of the clinician token from the bedside patient monitor, the length of time the clinician token has been present in the detection area, and/or other information collected from, or using, the clinician token). 
     The central monitoring station can then query the database and/or logical rules using this information to determine any actions associated with the detection of the clinician&#39;s token under the circumstances indicated by the input information. Once an associated action has been determined from the database and/or logical rules, the central monitoring station can then command the bedside patient monitor as to the action it should take in response to detection of the clinician&#39;s presence. 
     In some embodiments, the database and/or logical rules is/are remotely accessible such that actions or preferences can be conveniently stored, updated, and accessed by users. For example, the database and/or logical rules can be remotely accessible via a web server. In some embodiments, the database and/or logical rules is/are stored locally by a patient monitoring device (e.g., a bedside patient monitor) instead of remotely. In such embodiments, however, the locally-stored database and/or logical rules may nevertheless be periodically remotely updated by, for example, the central monitoring station. 
     The database can associate with the clinician ID  1414  a particular action that the clinician may wish to initiate upon entering the detection area  1420  of the patient monitoring device  1400 . Examples of such actions that can be initiated automatically upon detection of the clinician&#39;s presence are described herein. In addition, in some embodiments, the database can also associate with the clinician ID  1414  a priority level. The priority level can indicate which clinician should be given priority access to a medical monitoring device  1400 , for example, when multiple clinicians are detected in the detection area  1420  simultaneously. 
     In some embodiments, the clinician&#39;s assigned login information, monitoring device configuration preferences, list of actions to automatically initiate upon recognition of the clinician&#39;s presence, priority level, and/or other information can be stored by the clinician token  1410  itself. In such embodiments, this information may be transmitted directly to the patient monitoring device  1400  by the clinician token  1410  as opposed to the patient monitoring device  1400  obtaining the information from a database using the clinician ID  1414  stored on the token  1410 . Other methods can also be used in order to associate, for example, a clinician ID  1414  with a predetermined action (e.g., logging in, configuration change, etc.) that the clinician wishes the patient monitoring device  1400  to take or assume when the clinician is in the detection area  1420  of the device  1400 . 
     In some embodiments, once a registration process is complete, the patient monitoring device  1400  is capable of detecting the presence of a particular clinician based upon the clinician&#39;s token  1410 , and of taking, for example, a clinician-specific and/or detection event-specific action based upon recognition of the clinician&#39;s presence. In some embodiments, the processor  1406  of the patient monitoring device  1400  is configured to execute detection logic  1408  for determining when a clinician token  1410  is or is not present in the detection area  1420  of the monitoring device  1400 . In some embodiments, the detection logic  1408  is a set of rules or other criteria that must be satisfied before a clinician token  1410  is determined to be present in the detection region  1420 , or before some clinician-specific action is performed. 
       FIG. 15  is a flowchart illustrating detection method  1500  for detecting the presence of a clinician token (e.g.,  1410 ) within the detection region of a patient monitoring device. The detection method  1500  can begin, for example, at a waiting state  1502  where the patient monitoring device  1400  has not detected the presence of a clinician. In the waiting state  1502 , the patient monitoring device  1400  can allow manual access, for example, to the features and information that can be provided by the device  1400 . In the waiting state  1502 , the patient monitoring device can also allow manual configuration of the device, or interaction with the device, by a clinician using an input device such as a keyboard, mouse, or touchscreen. Thus, the waiting state  1502  advantageously allows clinicians who may not have an assigned clinician token  1410  to nevertheless use and interact with the patient monitoring device  1400 . 
     At decision block  1504 , the processor  1406  executes the detection logic  1408  to determine whether a signal is detected from a clinician token  1410 . For example, in some embodiments, the communication module  1402  of the patient monitoring device  1400  may, for example, continuously, or periodically, transmit a clinician token discovery signal. At decision block  1504 , the processor  1406  can determine whether a response has been received from a clinician token  1410  to the patient monitoring device&#39;s discovery signal. Alternatively, or additionally, the clinician token  1410  can be configured to, for example, continuously, or periodically, transmit a discovery signal which the patient monitoring device  1400  can detect. Response signals from the clinician token  1410  can include, for example, the clinician ID  1414  or other information. If no signal is detected from a clinician token  1410 , then the detection method  1500  returns to the waiting state  1502 . If, however, a signal from a clinician token  1410  is detected, then the detection method  1500  can proceed to the next decision block  1506 . 
     At decision block  1506 , the processor  1406  executes the detection logic  1408  to determine whether the detected signal from the clinician token  1410  exceeds a signal strength threshold value. This test can be useful, for example, as an estimate of the physical distance between the clinician token  1410  and the patient monitoring device  1400 . For example, the patient monitoring device  1400  may be configured such that whether or not a detection event occurs, and/or the particular predetermined action it takes upon detection of a clinician, is dependent upon the estimate of the physical distance between the clinician and the patient monitoring device  1400 . This may be useful, for example, in the case of a central monitoring station that is near a high traffic area where many clinicians regularly pass by. In such situations it may be advantageous to set the signal strength threshold used in decision block  1506  at a relatively high level so as to limit the clinician detection events to situations where a clinician is a relatively small distance away from the central monitoring station. 
     Thus, the signal strength threshold can be configurable based, for example, upon a desired physical distance from a clinician token  1410  before recognizing a clinician presence detection event. If the signal strength of the signal detected from a clinician token  1410  is below the signal strength threshold used by the decision block  1506 , then the detection method  1500  returns to the waiting state  1502 . If, however, the signal strength exceeds the threshold, then the detection method  1500  can proceed to the next decision block  1508 . 
     As discussed above, signal strength from a clinician token  1410  can be used to determine when to recognize a detection event. For example, the signal strength from the clinician token can be used to determine an estimate of the distance between the clinician token  1410  and the patient monitoring device  1400 . However, some variation may exist in the signal strength detected from two different clinician tokens  1410  even if the two clinician tokens are located at substantially the same distance from the patient monitoring device. Such signal strength variations can result from, for example, the two clinician tokens being different makes or models, from the two tokens being worn differently (e.g., one of the tokens being worn inside a clinician&#39;s clothing while the other is worn outside a clinician&#39;s clothing), etc. In some embodiments, a signal strength correction value may be associated with each clinician token. This can be done, for example, by associating a signal strength correction value with the clinician ID from the token in the database which stores actions and preferences associated with the clinician token. 
     The signal strength correction value can be used to adjust, for example, the estimated distance between a given clinician token and the patient monitoring device. For example, a clinician token that is known to transmit a relatively strong signal at a given distance (e.g., compared to other clinician tokens at the given distance) can be associated with a signal strength correction value that increases the distance estimate for that clinician token. Similarly, a clinician token that is known to transmit a relatively weak signal at a given distance (e.g., compared to other clinician tokens at the given distance) can be associated with a signal strength correction value that decreases the distance estimate for that clinician token. In some embodiments, the signal strength correction value for a clinician token can be determined based upon factors that may include, but are not limited to, the make and model of the clinician token, the user&#39;s preference in wearing the token, the operating environment of the token, etc. 
     At decision block  1508 , the processor  1406  executes the detection logic  1408  to determine whether the signal strength of the signal from the clinician token  1410  has exceeded the signal strength threshold for a proximity time that is greater than a time threshold. This test can be useful to avoid recognizing a clinician presence detection event in cases where a clinician passes nearby the patient monitoring device  1400  but does so only transiently, not remaining within the detection region  1420  for a long enough period of time to merit a clinician presence detection event. This test can likewise help eliminate false clinician presence detection events in high-traffic areas around a patient monitoring device  1400  where many different clinicians routinely and regularly pass by. The proximity time threshold used by the decision block  1508  can be configurable. In some embodiments, the proximity time threshold may be set at, for example, 1 second, 2 seconds, or 5 seconds. Other proximity times can also be used, however. If the proximity time for a detected clinician token  1410  does not exceed the proximity time threshold used by decision block  1508 , then the detection method  1500  returns to, for example, the waiting state  1502 . If, however, the proximity time of the clinician token  1410  exceeds the proximity time threshold, then the detection method  1500  can proceed to block  1510 . 
     At block  1510 , a clinician presence detection event is recognized. At such time, the patient monitoring device  1400  can enable or initiate, for example, some predetermined action based upon the clinician identity associated with the recognized clinician token  1410 . For example, the patient monitoring device  1400  can login the clinician, change a configuration setting, authorize some action or feature that is typically restricted absent the presence of a clinician, etc. In the detection method  1500  illustrated in  FIG. 15 , whether or not a clinician presence detection event occurs is dependent upon the signal strength of a signal from the clinician token  1410  as well as the length of time that the signal from the clinician token  1410  exceeds a signal strength threshold. In some embodiments, however, a clinician presence detection event can be recognized based only on signal strength from the clinician token  1410 , or based only on the length of time that a signal is detected from a clinician token  1410 . 
     In some embodiments, other factors can be included in the detection logic  1408 , whether alone or in combination with signal strength from the clinician token  1410  and proximity time. For example, the recognition of a clinician presence detection event can be based, at least in part, on the identity of the clinician (some patient monitoring devices  1400  may only be accessible to certain clinicians). In addition, the recognition of a clinician presence detection event can be based upon the assigned priority of the clinician. For example, a nurse supervisor could be assigned a higher priority than other nurses on the shift such that the presence of the nurse supervisor will be recognized by a patient monitoring device  1400  even when the presence of another nurse has already been recognized by the device. The converse situation, however, may not result in a new clinician presence detection event; the detection of a lower priority clinician may not result in a detection event if the presence of a higher priority clinician has already been recognized by the patient monitoring device  1400 . The priority level is one example of a tiebreaker criteria that can be used by the detection logic  1408  in the event that multiple clinician tokens meet the other requirements to initiate a clinician detection event at the same time. Other criteria can also be used in this tiebreaker role. 
     It should be appreciated that a wide variety of factors can be included in the detection logic  1408  depending upon the hospital, the type of medical equipment involved (e.g., patient monitoring equipment or some other type of medical device). In addition, such factors can be accounted for in the detection logic  1408  in a variety of ways. For example, the detection logic  1408  can determine when thresholds are exceeded, when a Boolean expression is true or false, when a fuzzy logic expression is true or false, when a mathematical equation is satisfied or not, when a compound rule is satisfied or not, etc. 
     In some embodiments, a detection event can be recognized when the clinician token  1410  enters each of a plurality of detection areas. The detection areas can be overlapping or non-overlapping. For example, in some embodiments, the patient monitoring device  1400  may be configured to recognize the presence of clinician tokens  1410  within each of several distance ranges. Different actions and preferences can be associated with a detection event for each distance range. 
     As an example, the patient monitoring device  1400  can be configured to detect the presence of a clinician within a first distance range of 0-5 feet, within a second distance range of 5-15 feet, and within a third distance range of 15-30 feet. It should be appreciated, however, that different distance ranges can be used, whether overlapping or not, and any number of distance ranges can be used. As discussed herein, when the patient monitoring device  1400  detects the clinician token  1410  within one of these detection areas, a detection event is recognized. The patient monitoring device  1400  can be configured to perform a particular set of actions upon the occurrence of such a detection event. As discussed herein, the set of actions can be registered to the clinician token in a database that is communicatively coupled to the patient monitoring device  1400 . The set of actions that corresponds to each detection area may each be unique, or may share one or more common actions. 
     In the foregoing example, the patient monitoring device  1400  can be configured to display a clinician&#39;s preferred set of measurements in text or graphical indicators with a large size when the clinician is detected to have entered the third distance range so as to enable satisfactory viewing of the display from a distance. When the clinician enters the second distance range, the size of the text or graphical indicators can be reduced to a medium size. Similarly, once the clinician enters the first distance range, the text or graphical indicators can be reduced to a still smaller size. As another example, the audible volume of an alarm can be adjusted as the clinician moves from one distance range to another. For example, the audible volume of an alarm can be louder when the clinician is in the third distance range, while it can be made softer or turned off when the clinician is in the first distance range. It should be appreciated, however, that any action or preference of that patient monitoring device  1400  can be configurably associated with any detection area. It should also be appreciated that the distance ranges can be made arbitrarily small so as to provide, for example, relatively continuous changes in the size of display features, the volume of an alarm, etc. 
     Similarly, different detection events can be generated depending upon the length of time that a clinician token has been recognized within a given detection area. For example, multiple detection events can be generated while a clinician token is within a particular detection area in accordance with multiple time ranges. As an example, a first detection event may be generated when the clinician token is present within a given detection area for  0 - 3  seconds. A second detection event may be generated when the clinician token is present within the detection area for  3 - 10  seconds. It should be understood, however, that different time ranges can be used, and any number of time ranges can be used. In addition, a set of patient monitoring device actions and preferences can be associated with detection events resulting from each time range. 
     When a clinician detection event has been realized according to, for example, the detection method  1500 , the patient monitoring device  1400  can respond in a number of different ways, as indicated by the database and/or logical rules discussed herein. For example, the patient monitoring device  1400  can initiate a predetermined action based upon the identity of the clinician whose token has been detected in proximity to the monitoring device. In some embodiments, the predetermined action is that the patient monitoring device  1400  automatically logs the clinician in without requiring the clinician to, for example, physically interact with an input device. This process saves the clinician time and, in some cases, can also save patient lives. As described herein, the clinician&#39;s login information can be transmitted to the patient monitoring device  1400  from the clinician token  1410 , or it can be retrieved from a database using the clinician ID  1414  from the token  1410 . 
     In some embodiments, the patient monitoring device enables or disables a particular feature based upon detection of the clinician token  1410 . For example, the patient monitoring device may enable/disable menus and buttons (e.g., alarm limit menu, alarm silence, all mute, etc.) based upon the credentials of the detected clinician. In some embodiments, the patient monitoring device  1400  begins transmission of patient monitoring information to a remote device upon detecting the presence of a clinician. For example, a bedside patient monitor capable of capturing breathing sounds from a patient could automatically begin transmission of those breathing sounds to the clinician&#39;s Bluetooth headset, which, incidentally, can serve as the clinician token  1410  as well. In other embodiments, the patient monitoring device  1400  could begin transmission of any type of monitoring information to a remote device via, for example, the Internet upon detecting the presence of a particular clinician. For example, the patient monitoring device  1400  can transmit the patient&#39;s oxygen saturation trend data to the clinician&#39;s computer for later analysis and diagnosis. The patient monitoring device  1400  can also transmit any other type of patient information (e.g., medical parameter values and/or trend data, video and/or audio from the patient&#39;s room, etc.) to, for example, the clinician&#39;s computer, or some other device, in response to detection of the presence of some particular clinician in proximity to the patient monitoring device  1400 . 
     In some embodiments, the patient monitoring device automatically updates its configuration based upon configuration preferences of a detected clinician. For example, the patient monitoring device  1400  could alter the content of the information it displays or the format of the information that it displays. These configuration changes can be made, for example, based upon settings that the clinician indicates during the registration process for the clinician token  1410 . An example of such an embodiment is illustrated in  FIG. 16 . In some embodiments, a patient monitoring device changes the layout of a display screen (e.g., the number and types of parameters shown, the waveforms shown, trends, and other screen controls). Display layouts can be selected from predefined layouts, or a clinician can make a custom layout. The same is true of other configuration settings. Configuration settings can be associated with clinicians at an individual user or group level. A hierarchy of layouts modes can be established for layout conflicts. 
     The patient monitoring device  1400  can also update other configuration settings based upon registered preferences of the clinician. These can include physiological parameter alarm limits, alarm silence, all mute, averaging time, algorithm mode, etc., for example. In addition, the patient monitoring device  1400  could automatically create some type of report, such as a report of all alarm conditions that have been registered by that monitor over a predetermined period of time. 
     In addition, alarm annunciation and behavior can be altered in response to a clinician proximity detection event. For example, if the clinician is approaching a bedside patient monitoring device  1400  that is currently registering an alarm condition, the alarm can automatically be silenced in recognition that the clinician has entered within a certain radius of the monitoring device  1400 . In some embodiments, the way that the patient monitoring device  1400  notifies of an alarm condition can be dependent upon the physical location of a clinician. For example, if the patient monitoring device  1400  detects an alarm condition while the clinician is already in proximity to the monitoring device, then it may emit no audible alarm or a lower-volume audible alarm. Alarm volume can also be adjusted in other ways based upon detected clinician presence. Similarly, in such a scenario, the patient monitoring device  1400  may be configured not to transmit an alarm to the central monitoring station. In some embodiments, a medical monitoring device does not notify or page other clinicians in case of an alarm if a clinician is already present. Alarm notification behavior of the medical monitoring device can be altered in a variety of ways based upon detected presence of a clinician. A medical monitoring device with clinician proximity awareness can allow a detected clinician to acknowledge his or her presence. As long as clinician presence is detected, the length of expiry of alarms can be changed (e.g., made longer). 
     In some embodiments, the patient monitoring device  1400  is communicatively coupled to a patient&#39;s electronic medical record (EMR), as described herein. The detection of clinician presence can be used to determine what data is transmitted to the EMR, and/or when that data is transmitted to the EMR. For example, the patient monitoring device  1400  may measure and store data regarding a physiological parameter. When a clinician is detected in proximity to the patient monitoring device, the clinician can be automatically prompted whether to transmit certain physiological parameter measurements, or other data, to the patient&#39;s EMR. The clinician can review, for example, current or past measurements, and determine whether such measurements should be recorded in the EMR. 
     While in some embodiments, a clinician is prompted whether to log physiological parameter measurement values in the EMR, or elsewhere, when the clinician&#39;s presence is detected, in other embodiments such data could automatically be logged based upon detection of the clinician&#39;s presence. In either case, the patient monitoring device  1400  may be capable of determining the quality of the physiological signals upon which a particular measurement value is based using signal processing algorithms or other methods. If the patient monitoring device  1400  determines that signal quality, and the corresponding degree of confidence in the measurement values derived therefrom, is low, then the patient monitoring device may reduce the frequency with which measurement values are transmitted to the EMR. The patient monitoring device may also reduce the amount of data that is transmitted to the EMR at a time. This variation in the frequency and/or the amount of physiological parameter data that is stored to the EMR based on the quality of the physiological parameter signals being measured can be practiced with or without detecting the presence of a clinician nearby. 
     In some embodiments, the patient monitoring device  1400  responds to detection of a clinician&#39;s presence by changing the language in which textual information is displayed by the monitoring device in accordance with language preferences of the clinician. 
     In some embodiments, the patient monitoring device identifies and executes on-device confirmations that may be required for risk management based upon the detected clinician(s) in proximity to the monitoring device. In some embodiments, the patient monitoring device logs the number of clinician visits to a patient&#39;s bedside, the time of presence of each visit, the length of each clinician visit, the response time of clinicians to alarms, etc. A clinician may be permitted to chart parameters measured by the monitoring device to, for example, an electronic medical record with credentials based upon detection of clinician identity. Many other types of actions and/or configuration changes, or combinations of those described herein, can also be caused to automatically be initiated based upon the fact that a clinician has been detected in proximity to the patient monitoring device  1400 . 
       FIG. 16  illustrates an example graphical user interface  1600  of nurses&#39; station or central patient monitoring station. The graphical user interface  1600  includes features similar to those described with respect to  FIG. 9 . For example, the graphical user interface  1600  includes a patient status display area  1610 . The patient status display area  1610  includes a plurality of patient status modules  1612 , each having a graphical status indicator  1614 . The graphical user interface  1600  also includes a patient monitor view area  1620  and a history view area  1630 . 
     As illustrated in  FIG. 9 , the central patient monitoring station includes several patient status display areas, each showing monitoring information from a different patient. Unlike  FIG. 9 , however, which shows the status of a number of patients larger than a single nurse could possibly attend to individually,  FIG. 16  shows only those patients assigned to a particular clinician. The display of the central patient monitoring station can be automatically updated from that of  FIG. 9 , for example, to that of  FIG. 16  in recognition of the presence of a clinician. In this way, the clinician can quickly and conveniently check the status of each of his or her assigned patients at a glance by simply approaching the central patient monitoring station without having to actually physically interact with a central patient monitoring station. In addition, the proximity detection features described herein can be used to facilitate assignments of clinicians to patients at the nurses&#39; station. For example, patients can be added to the view of  FIG. 16  automatically if the clinician has been detected in proximity to the patient&#39;s bedside monitor within some predetermined period of time. 
       FIG. 17  is a flowchart illustrating a method  1700  for determining when to disable a clinician-specific action that had been previously enabled by a patient monitoring device  1400  based upon the detected presence of the clinician. The method  1700  begins at block  1702  where some clinician-specific action has been previously enabled, as described herein. The method  1700  then proceeds to decision block  1704  and decision block  1708 . For example, the method  1700  may involve detecting whether a previously-detected clinician remains in proximity to a patient monitoring device while simultaneously detecting whether a higher priority clinician arrives in proximity to the patient monitoring device. For example, the process illustrated by decision block  1708  can generate an interrupt signal if the presence of a higher priority clinician is detected. 
     At decision block  1704 , the processor  1406  executes the detection logic  1408  to determine whether the strength of a signal from the clinician token  1410  has fallen below a signal threshold. This threshold can be the same threshold as used by the decision block  1506  in  FIG. 15 . Alternatively, these two thresholds can be different to provide a degree of hysteresis in the detection system to guard against the situation where a clinician token  1410  could be recognized as switching between the present and absent states repeatedly in quick succession if the strength of the signal from the clinician token  1410  happens to be approximately equal to the selected threshold value. If the strength of the signal from the clinician token  1410  has not fallen below the signal threshold, then the method  1700  returns to block  1702  where the clinician-specific action remains enabled. If, however, the strength of the signal from the clinician token  1410  falls below the threshold used in decision block  1704 , then the method  1700  proceeds to decision block  1706 . 
     At decision block  1706 , the processor  1406  executes the detection logic  1408  to determine whether the strength of the signal from the clinician token  1410  has fallen below the signal threshold for an absence time that is greater than a time threshold. Thus, the combination of decision blocks  1704  and  1706  determine whether the clinician token has been outside of a particular range for a particular amount of time. In some embodiments, this time threshold can be variable depending upon, for example, the content of information displayed by the medical monitoring device  1400 . For example, if the monitoring device  1400  is displaying sensitive personal information, then the time threshold can be relatively short in order to protect the patient&#39;s confidentiality. 
     If the absence time does not exceed the time threshold used by the decision block  1706 , then the method  1700  returns to block  1702  where the clinician-specific action remains enabled. If, however, the absence time exceeds the time threshold, then the method  1700  proceeds to block  1710 . At block  1710 , the clinician is recognized as no longer being in proximity to the patient monitoring device  1400 . Therefore, the previously-enabled clinician-specific action is disabled. At such time, the patient monitoring device  1400  can return to a state similar to the waiting state  1502  described with respect to  FIG. 15 . In some embodiments, the action performed by the patient monitoring device  1400  at block  1710  can substantially reverse any action taken by the monitoring device at block  1510  in  FIG. 15 . For example, if the clinician was automatically logged in to the patient monitoring device  1400  when his or her presence was initially detected, then at block  1710 , that clinician can be logged out. Similarly, if the configuration of the monitoring device  1400  was changed based upon the detected clinician&#39;s preferences, then, at block  1710 , those configuration changes can be restored to, for example, a default state. 
     With reference now to the decision block  1708 , the processor  1406  executes the detection logic  1408  to determine whether the presence of a higher priority clinician has been detected. The detection of such a clinician can proceed, for example according to the detection method  1500  described with respect to  FIG. 15 . As described herein, each clinician can be assigned a priority value that can act as a tiebreaker criteria to determine the presence of which clinician to recognize when more than one clinician is detected. If no higher priority clinician is detected at decision block  1708 , then the method  1700  returns to block  1702 . If, however, a higher priority clinician is detected at decision block  1708 , then the method  1700  may proceed to block  1710  where the recognition of the presence of the previously-detected clinician is revoked, and the presence of the newly detected higher-priority clinician is recognized. 
       FIG. 18  is a schematic diagram of a system for enabling a patient monitoring device  1800  to automatically detect the presence of a clinician token  1810 . The patient monitoring device  1800  and the clinician token  1810  can be similar, for example, to the patient monitoring device  1400  and clinician token  1410  described herein with respect to  FIG. 14A  except as otherwise indicated. In the embodiment illustrated in  FIG. 18 , the patient monitoring device  1800  detects the presence of the clinician token  1810  with the assistance of, for example, one or more WiFi access points  1830 - 1832 . The WiFi access points  1830 - 1832  can be advantageously distributed throughout the patient care environment where patient monitoring is occurring. The WiFi access points  1830 - 1832  can operate based on IEEE 802.11 standards, for example. 
     The communication module  1802  of the patient monitoring device  1800  can be, for example, a WiFi-enabled radio for communicating with the WiFi access points  1830 - 1832 . In some embodiments, the clinician token  1810  is a WiFi-enabled RFID tag. By communicating with the WiFi access points  1830 - 1832 , the patient monitoring device  1800  can triangulate its position relative to that WiFi access points. Likewise, the position of the clinician token  1810  can be triangulated. Thus, the distributed WiFi access points  1830 - 1832  can be used by, for example, the patient monitoring device  1800  in order to determine the approximate position of the clinician token  1810  with respect to the monitoring device  1800 . In some embodiments, the patient monitoring device  1800  may also communicating directly with the clinician token  1810  in order to, for example, enhance the position approximation determined using the distributed WiFi access points  1830 - 1832 . 
       FIG. 19  is a schematic illustration of a patient monitoring device network  1900  having a clinician proximity awareness feature. The patient monitoring device network  1900  can be similar to those shown, for example, in  FIGS. 1, 2, 6, and 7 . The patient monitoring device network  1900  includes multiple bedside patient monitors  1902 ,  1912 ,  1922  for monitoring multiple patients  1906 ,  1916 ,  1926 . In some embodiments, each of the bedside patient monitors  1902 ,  1912 ,  1922  is similar to those shown in, for example,  FIG. 14A  ( 1400 ) and  FIG. 18  ( 1800 ). The bedside patient monitors  1902 ,  1912 ,  1922  are capable of detecting the presence of a clinician based upon the clinician tokens  1904 ,  1914 ,  1924 . The clinician tokens  1904 ,  1914 ,  1924  can be similar, for example, to those shown in  FIG. 14A  ( 1410 ) and  FIG. 18  ( 1810 ). 
     The patient monitoring device network  1900  also includes a nurses&#39; station  1932  for remotely monitoring each of the patients  1906 ,  1916 ,  1926 . The nurses&#39; station, or central monitoring station,  1932  can be similar to those described herein. The patient monitoring device network  1900  may also include a registration database  1942 . As described herein, the registration database  1942  can associate unique clinician IDs (e.g.,  1414 ,  1814 ) carried by the clinician tokens  1904 ,  1914 ,  1924 ,  1934  with information for controlling the patient monitoring devices  1902 ,  1912 ,  1922 ,  1932  when the tokens are in the presence of those devices. For example, the registration database  1942  can associate each unique clinician ID with login information, configuration preferences, and predetermined actions for the monitoring devices to perform after recognizing the presence of a clinician. 
     In the illustrated patient monitoring device network  1900 , each of the patient monitoring devices  1902 ,  1912 ,  1922 ,  1932  can communicate with one another via the network  1950 . In some embodiments, the network  1950  uses open source communications standards in order to facilitate communication between various medical devices. Though not illustrated, the patient monitoring device network  1900  can also include WiFi access points, page transmitters, pagers, and other devices described herein. 
       FIG. 20  is a schematic drawing of a hospital floor  2000  with distributed WiFi access points  2030 - 2034  that can be used to estimate the physical locations of medical devices  2002 ,  2004 , patients  2010 ,  2012 , and clinicians  2014 ,  2016 . The WiFi access points  2030 - 2034 , or other detectors, can be distributed throughout the hospital floor, or other physical region, in order to provide WiFi coverage throughout the patient care area. In some embodiments, the WiFi access points  2030 - 2034  have respective coverage areas  2040 - 2044  that the overlap one another. In some embodiments, the WiFi access points  2030 - 2034  are populated densely enough so that at least three coverage areas  2040 - 2044  of the WiFi access points  2030 - 2034  overlap in substantially every portion of the hospital floor in which it is desired to track the positions of medical devices  2002 ,  2004 , patients  2010 ,  2012 , and clinicians  2014 ,  2016 . The access points  2030 - 2034  can be mounted, for example, on or in walls, on or in ceilings, etc. 
     The medical devices  2002 ,  2004  can be similar to others described herein. For example, in some embodiments, the medical devices  2002 ,  2004  are patient monitoring devices. In some embodiments, the medical devices  2002 ,  2004  are fitted with tracking tags or tokens  2006 ,  2008 . The tracking tags  2006 ,  2008  can be similar to the clinician tokens described herein. In some embodiments, the tracking tags  2006 ,  2008  are WiFi-enabled RFID tags, though other types of tracking tags may also be suitable. Each tracking tag  2006 ,  2008  can include an equipment ID. 
     As already discussed herein, the clinicians  2014 ,  2016  may carry clinician tokens  2022 ,  2024 . The clinician tokens  2022 ,  2024  can be similar to those described herein. 
     For example, in some embodiments, the clinician tokens  2014 ,  2016  are WiFi-enabled RFID tags. In some embodiments, each patient  2010 ,  2012  may also be fitted with a patient token  2018 ,  2020 . The patient tokens  2018 ,  2020  can be similar to the clinician tokens described herein. In some embodiments, the patient tokens  2018 ,  2020  are WiFi-enabled RFID tags. These may be worn as bracelets, or otherwise suitably affixed to the patients. Each patient token  2018 ,  2020  can include a patient ID. 
     The distributed network of WiFi access points  2030 - 2034  can be used to communicate with the medical device tracking tags  2006 ,  2008 , the clinician tokens  2022 ,  2024 , and the patient tokens  2018 ,  2020  for the purpose of estimating the physical position of each of these tags and tokens in the hospital  2000 . For example, the WiFi access points  2030 - 2034  can be used to triangulate the position of each tag or token. 
     While  FIG. 20  illustrates a distributed network of WiFi access points  2030 - 2034  that can be used for detecting the positions of the tracking tags  2006 ,  2008 , the clinician tokens  2022 ,  2024 , and the patient tokens  2018 ,  2020 , other devices can also be used for similar purposes. For example, in some embodiments, the WiFi access points  2030 - 2034  are eliminated and medical devices  2002 ,  2004  with short range transceivers, or other detectors, are used in their place to create an ad hoc network. Each medical device  2002 ,  2004  can serve as a node in the ad hoc network, and each node can share information about, for example, the patients  2010 ,  2012  and the clinicians  2014 ,  2016  around it. In some embodiments, the medical devices  2002 ,  2004  are Bluetooth-enabled, though other short range wireless communications standards can also be used. 
     If the hospital floor  2000  contains a number of medical devices that are arranged densely enough, then the distributed medical devices  2002 ,  2004  can serve as a network for tracking the location of, for example, Bluetooth-enabled medical device tracking tags  2006 ,  2008 , patient tokens  2018 ,  2020 , and clinician tokens  2022 ,  2024 . In such an embodiment, the physical location of each tracking tag or token may only be identifiable if it is located within the range of a Bluetooth-enabled medical device. In addition, in some embodiments, the physical location of each tracking tag or token may not be able to be precisely identified, as each Bluetooth-enabled medical device may only be able to determine that the tracking tag or token is located somewhere within the medical device&#39;s detection area. Nevertheless, this level of tracking resolution may be sufficient in many cases. 
     In the embodiment illustrated in  FIG. 20 , a location monitoring server may be communicatively coupled to the WiFi access points  2030 ,  2034 . The location monitoring server may be configured to track the estimated position of each medical device  2002 ,  2004 , each patient  2010 ,  2012 , and each clinician  2014 ,  2016 . The location monitoring server may include a display to show this location information. In addition, the location monitoring server, or some other device, may execute logic that can be useful in enhancing features offered by the patient monitoring systems described herein. The location monitoring server may also be communicatively coupled to the medical devices  2002 ,  2004 . 
     The system illustrated in  FIG. 20  can be used, for example, to enhance the patient monitoring systems described herein. As already discussed, the patient monitoring systems described herein are capable of providing notifications to clinicians when, for example, a monitored patient&#39;s physiological parameter (e.g., SpO 2 , respiratory rate, etc.) triggers an alarm. In some embodiments, the clinician assigned to monitor the patient is notified first by, for example, a page, e-mail, text message, etc. If the first-notifying clinician does not respond within a set period of time, the patient monitoring system may be configured to execute an escalation algorithm whereby one or more additional clinicians are notified of the patient&#39;s alarm condition. In some embodiments, the clinician notifications that are sent out when an alarm condition exists can be controlled, at least in part, using location-based rules. For example, location-based rules can be used to determine which clinician is notified of an alarm condition initially, and which clinician, or clinicians, are notified if escalation becomes necessary. The location-based rules can receive as inputs information from the system illustrated in  FIG. 20  regarding the physical locations of, for example, patients  2010 ,  2012  and/or clinicians  2014 ,  2016 . 
     The location-based rules can be dependent upon, for example, the absolute or relative locations of the patient&#39;s  2010 ,  2012  and/or the clinicians  2014 ,  2016 . For example, if the patient  2010  undergoes an alarm condition, that patient&#39;s previously assigned clinician can first be notified so long as he or she is present on the same floor of the hospital (or some other domain). In some embodiments, the clinician located the closest to the patient who is experiencing the alarm condition can be notified regardless of whether the clinician was previously assigned to the patient. In some embodiments, the closest clinician to the patient experiencing the alarm condition can be notified only after the regularly-assigned clinician fails to respond within a certain amount of time. In some embodiments, a nearby clinician is notified of the alarm condition if the alarm condition is particularly urgent and requires immediate attention. Many other location-based rules can also be implemented. 
     Location-based rules can also be used for controlling whether a clinician is permitted to deactivate an alarm. As disclosed herein, the clinician tokens  2022 ,  2024  may include an input module (e.g.,  1416 ). One use for this input module is to remotely disable an alarm once the clinician has received notification of the alarm and is en route to the patient. However, in some embodiments, a location-based rule can be put into place that may prevent a clinician from remotely disabling an alarm if the clinician is, for example, more than some threshold distance away from the patient. 
     The location information provided by the system illustrated in  FIG. 20  can also be used to provide alerts to clinicians when a patient  2010 ,  2012  strays more than some threshold distance from the monitoring device assigned to the patient. While some examples of location-based rules have been discussed in the context of patient monitoring systems, the information provided by the system illustrated in  FIG. 20  can be used to implement a variety of location-based rules for many different kinds of medical devices. Such location-based rules can include, for example, any rule for determining an action to be performed where the selected action is dependent in whole, or in part, upon the estimated physical location of a device, clinician, and/or patient. 
     In some embodiments, location-based rules can also be provided for configuring the medical devices  2002 ,  2004  (e.g., to configure patient monitoring settings). For example, patient monitoring devices of the type described herein are sometimes configured with different physiological parameter alarm limits depending upon the patient ward that they are located in. For example, alarm limits for the pulse rates of neonates should generally be set differently than for the pulse rates of adults. Therefore, it may be desirable to provide a notification to a clinician if an attempt is made to monitor a patient located outside of the nursery using a monitoring device whose alarm limits have been set for neonates. This can be done since the location of the medical device can be detected by the system illustrated in  FIG. 20 . Other monitoring device configuration settings can also be recommended to clinicians, or automatically set, based upon the physical location of the monitoring device. In some embodiments, the configuration settings and techniques disclosed in US Patent Publication 2009/0275844, the entire contents of which are hereby incorporated by reference herein, can be controlled using the location-based rules described herein. 
       FIGS. 21A-F ,  22 A-D, and  23 A-C illustrate proximity displays  2100 ,  2200 ,  2300  that feature a multi-sided animation that appears to rotate from a first screen to a preferred screen in response to user proximity. This feature advantageously provides feedback to the user that the monitor has received an identification signal from the user, as described above, and has recognized the user&#39;s presence. As examples, the multi-sided presentation may be any of a triangular-shaped, a cubic-shaped or a planar solid having multiple facets and a different screen preference on two or more of the facets. One of ordinary skill will recognize that many other rotating geometric shapes can provide similar user feedback, including un-faceted shapes such as a sphere or cylinder. These multi-sided presentations are described in further detail below. 
       FIGS. 21A-F  illustrate a proximity display  2100  embodiment that utilizes a rotating triangular solid  2105  to depict transitions between multiple screens that correspond to different display preferences of monitor users that enter or exit proximity to the monitor. In particular, the triangular solid  2105  has a first side  2101 , a second side  2102  and a third side  2103  configured to display different user preferences of patient monitoring information in response to user proximity to the display. Further, the triangular solid  2105  is shown to rotate during a transition between the sides  2101 ,  2102 ,  2103  so as to provide feedback to a proximate user. 
     As shown in  FIG. 21A , the first side  2101  relating to a first user is shown in the display  2110 . As shown in  FIG. 21B , when a second user is proximate the display, the monitor identifies the second user, as described with respect to  FIG. 7  below, and virtually rotates the triangular solid  2105  from the first side  2101  to the second side  2102 . As shown in  FIG. 21C , the display  2110  then shows the second side  2102 , corresponding to the second user&#39;s display preference. As shown in  FIG. 21D , when a third user enters proximity to the monitor, the monitor identifies the third user and virtually rotates the triangular solid  2105  from the second side  2102  to a third side  2103 . As shown in  FIG. 21E , the display  2110  then shows the third side  2103 , corresponding to the third user&#39;s display preference. As shown in  FIG. 21F , when the first user is again identified, the display  2110  virtually rotates the triangular solid back to the first side  2101 . In this manner, the sides  2101 ,  2102 ,  2102  of the triangular solid  2105  are alternatively shown on the display  2110  according to different user preferences and based upon user proximity to the monitor. As described with respect to  FIG. 13  if several users are in proximity to the monitor at once, then priority or acknowledgement schemes are utilized to determine which screen to display. 
       FIGS. 22A-E  illustrate a proximity display  2200  embodiment that utilizes a rotating cube  2205  to depict transitions between multiple screens that correspond to different display preferences of monitor users that enter or exit proximity to the monitor. In particular, the cube  2205  has a first side  2201 , a second side  2202  and a third side  2203  configured to display different user preferences of patient monitoring information in response to user proximity to the display. Further, the cube  2205  is shown to rotate during a transition between the sides  2201 ,  2202 ,  2203  so as to provide feedback to a proximate user, in a manner similar to that described in detail with respect to  FIGS. 21A-F , above. 
       FIGS. 23A-C  illustrate a proximity display  2300  embodiment that utilizes a rotating planar solid  2305  to depict transitions between multiple screens that correspond to different display preferences of monitor users that enter or exit proximity to the monitor. In particular, the planar solid  2305  has a first side  2301  and a second side  2302  configured to display different user preferences of patient monitoring information in response to user proximity to the display. Further, the planar solid  2305  is shown to rotate during a transition between the sides  2301 ,  2302  so as to provide feedback to a proximate user, in a manner similar to that described in detail with respect to  FIGS. 21A-F  and  FIGS. 22A-E , above. 
     Although some features are described herein with respect to a bedside monitor, a proximity display is applicable to any monitoring device, medical or non-medical and at any location, such as at bedside or at central monitoring, such as a nurse&#39;s station. Further, a proximity display is applicable during physiological data collection or other monitor uses, such as historical data review, setting and verification of alarm limits and installation of software updates by medical personnel or equipment maintenance staff, to name a few. 
       FIG. 44  is a schematic diagram of a medical sanitation device  4490  that is capable of automatically detecting the presence of a clinician token  4410 . The clinician token  4410  may be, in some embodiments, similar to the clinician tokens described elsewhere herein (e.g., clinician token  1410 ). For example, the clinician token  4410  may include a communication module  4412 , such as a short range transceiver. The clinician token  4410  may also include a clinician ID that is uniquely assigned to a particular clinician. As discussed herein, the clinician token  4410  may be, for example, an RFID tag or a Bluetooth-enabled device. 
     The medical sanitation device  4490  includes a sanitation module  4492 . The sanitation module can be, for example, a dispenser for soap or some other sanitizing agent. The sanitation module  4492  can also be, however, any of a variety of other disinfecting devices. These may include any device used by hospital personnel to disinfect or otherwise reduce the possibility of transmission of germs, bacteria, disease, etc. The sanitation module  4492  may include a sensor for determining when the sanitation module  4492  is activated or in use by a clinician. In the cases where the sanitation module  4492  uses or dispenses a consumable (e.g. soap), the sanitation module  4492  may also include a sensor for detecting the remaining amount of the consumable. 
     The medical sanitation device  4490  also includes a detector such as, for example, a communication module  4494  and processor  4496 . The communication module  4494  can be similar to other communication modules described elsewhere herein (e.g., communication module  1412 ). For example, the communication module  4494  can be a transmitter, a receiver, or a transceiver capable of performing short range communication. The communication module  4494  can be used, for example, to obtain the clinician ID  4414  from the clinician token  4410 , as discussed herein. In some embodiments, the communication module is Bluetooth-enabled. In other embodiments, the communication module may be an RFID tag reader. Ultrasound, infrared, NFC, etc. can also be used. In still other embodiments, the communication module may detect clinician proximity based on signals from one or more wireless network access points, as discussed herein. The communication module  4494  is capable of detecting signals from a remote device (e.g., the clinician token  4410 ) within a detection area  4420 . The size of the detection area  4420  can be appropriately determined by, for example, the power levels of communication signals from the communication module  4494 . In some embodiments, the detection area  4420  is configured to approximately encompass the surrounding area in which a clinician could reasonably be located while using the medical sanitation device  4490 , though other sizes are also possible. 
     The medical sanitation device  4490  may also include a processor  4496  for performing tasks such as communication with a medical patient monitoring device  4400 , as discussed herein. The processor  4496  may also include detection logic  4498  for determining when the clinician token  4410  is located in physical proximity to the medical sanitation device  4490 . The detection logic  4498  can, for example, be similar to other detection logic discussed herein (e.g., detection logic  1408 ). 
     In some embodiments, the medical sanitation device  4490  is configured to automatically detect when a clinician token  4410  is located in physical proximity to the medical sanitation device  4490 . In some embodiments, the medical sanitation device  4490  can detect physical proximity of the clinician token  4410  without physical contact between the clinician token and the medical sanitation device. The medical sanitation device  4490  may detect the presence of a clinician, by virtue of his or her clinician token  4410 , when the clinician enters the detection area  4420  even if the clinician has not otherwise interacted with the sanitation device. Alternatively, and/or additionally, the medical sanitation device  4490  may be configured to detect the presence of a clinician in response to a notification from the sanitation module  4492  that the sanitation module is in use or has just been used, or in response to some other interaction of the clinician with the sanitation device. Thus, the medical sanitation device  4490  can detect whether a clinician is in vicinity of the device and whether the clinician actually sanitizes using the device. In either case, the medical sanitation device  4490  may recognize a detection event and then notify a remote device of the detection event. For example, in some embodiments, the medical sanitation device  4490  is communicatively coupled, either directly or indirectly, to a medical patient monitoring device  4400 . The medical patient monitoring device  4400  may be similar to any of the medical patient monitoring devices discussed herein. In some embodiments, the medical sanitation device  4490  notifies the medical patient monitoring device  4400  whenever a detection event occurs. 
     In some embodiments, the medical sanitation device  4490  notifies the remote device (e.g., the medical patient monitoring device  4400 ) of the detection event by transmitting the clinician ID  4414  from the detected clinician token  4410  to the remote device. The medical sanitation device  4490  may also notify the remote device of the time that the detection event occurred, the identity of the clinician, the amount of time that the sanitation module  4492  was used, the amount of time that the clinician token  4410  was located within the detection area  4420 , etc. The medical sanitation device  4490  may transmit information directly to the remote device, such as the medical patient monitoring device  4400 , via a direct link with the remote device (e.g., wired or wireless data link) or via a network. Alternatively, the medical sanitation device  4490  may be used to control the medical patient monitoring device  4400  indirectly by notifying some other device of the detection event, as discussed herein. 
     In some embodiments, the medical sanitation device  4490  is communicatively coupled to, for example, a registration database (e.g., registration database  1942 ). The registration database may be used to match the clinician ID  4414  detected by the sanitation device  4490  to the identity of a particular clinician. In addition, the registration database may be used to store a predetermined action that is to be carried out in response to the clinician detection event identified by the medical sanitation device  4490 . Such predetermined actions can be set during a registration process and/or using logical rules, as discussed herein. For example, the predetermined action may be one of several medical patient monitoring actions. The clinician ID, predetermined action, etc. may then be forwarded from the registration database to any device which may be responsible for tracking the usage of the medical sanitation device  4490  or for carrying out the predetermined action in response to the detection event at the medical sanitation device  4490 . 
     When the medical sanitation device  4490  identifies a clinician detection event, it may be configured to send a signal which causes the medical patient monitoring device  4400  to perform, for example, any of the following predetermined actions: display information indicative of one or more patients under the care of the clinician, log the clinician into the medical patient monitoring device, enable a function offered by the medical patient monitoring device, alter the substance of information displayed by the medical patient monitoring device, alter the formatting of information displayed by the medical patient monitoring device, transmit physiological information to a remote device, or set a patient monitoring option. The medical patient monitoring device  4400  may also be configured to perform other actions such as, for example, discussed herein in response to the clinician detection event at the medical sanitation device. 
     In some embodiments, the predetermined action is to make the medical patient monitoring device  4400  accessible to the clinician whose presence was detected at the medical sanitation device  4490  if his or her presence is later detected in proximity to the medical patient monitoring device  4400 , for example, within a predetermined period of time after having been detected at the sanitation device  4490  and/or a within a predetermined distance from the sanitation device. Then, when the clinician is subsequently detected in proximity to the medical patient monitoring device  4400 , using, for example, the techniques discussed herein, the medical patient monitoring device  4400  may be configured to perform any of the foregoing actions in response to this subsequent detection event at the patient monitoring device  4400 . In some embodiments, the medical patient monitoring device  4400  may be configured to trigger an alarm (e.g. audible or visual) if a clinician attempts to access the monitoring device  4400  without first having been detected at the medical sanitation device  4490  (e.g., within a predetermined period of time prior to attempting to access the patient monitoring device). 
     A reporting device may be used to log events such as clinician detection events at the medical sanitation device  4490 , clinician detection events at the medical patient monitoring device  4400 , elapsed time between detection events at the sanitation device  4490  and the monitoring device  4400 , attempted access events at the monitoring device  4400  without prior sanitation device usage  4490 , frequency of sanitation for each clinician, the number of times each clinician sanitized over a predetermined period of time, etc. These events can be stored in a storage module (e.g., locally at the medical sanitation device  4490 , locally at the patient monitoring device  4400 , or at a network device communicatively coupled to one or both of the sanitation device and the monitoring device) and then used to generate reports for hospital administrators. 
     In these ways, a clinician&#39;s access to the medical patient monitoring device  4400 , or some other device (e.g., any hospital device used for delivering care to a patient), can be made at least partially dependent upon whether the clinician has used, or been detected in proximity to, a medical sanitation device  4490  prior to attempting to access the monitoring device  4400  (e.g., within a predetermined period of time). Therefore, hospital sanitation procedures can be better enforced and/or monitored. 
       FIG. 45  is a schematic illustration of a patient monitoring and clinician sanitation device network  4500  having clinician proximity awareness features. The patient monitoring and clinician sanitation device network  4500  can be similar to those shown, for example, in  FIGS. 1, 2, 6, 7, and 19 . The patient monitoring and clinician sanitation device network  4500  includes multiple bedside patient monitors  4502 ,  4512 ,  4522  and a central nurses&#39; station  4532  for monitoring multiple patients  4506 ,  4516 ,  4526 , as discussed herein. Each of the bedside patient monitors  4502 ,  4512 ,  4522  is capable of detecting the presence of a clinician based upon the clinician tokens  4504 ,  4514 ,  4524 ,  4534 , as discussed herein. In addition, the network of devices  4500  includes medical sanitation devices  4560 ,  4562 , which are similarly capable of detecting the presence of a clinician based upon the clinician tokens, as described herein. The respective detection areas of the medical sanitation devices and the medical patient monitoring devices may or may not overlap. Finally, the network of devices  4500  also includes a registration database  4542  which can associate unique clinician IDs from the clinician tokens  4502 ,  4514 ,  4524 ,  4534  with information for controlling, for example, the patient monitoring devices when the tokens are detected in the presence of the sanitation devices  4560 ,  4562 . In the illustrated patient monitoring and clinician sanitation network  4500 , the patient monitoring devices  4502 ,  4512 ,  4522 ,  4534 , the sanitation devices  4560 ,  4562 , and the registration database  4542  can communicate with one another via the network  4550 . 
     As discussed herein, in some embodiments, when a clinician token is detected in proximity to a sanitation device  4560 ,  4524 , the sanitation device may directly or indirectly cause one or more of the patient monitoring devices  4502 ,  4512 ,  4522 ,  4532  to perform a predetermined action. The sanitation devices  4560 ,  4562  may, for example, communicate directly with a patient monitor (as illustrated with respect to sanitation device  4560  and bedside patient monitor  4502 ). Alternatively, a sanitation device  4560 ,  4562  may communicate with a patient monitor via a network  4550  and/or registration database  4542 . In some embodiments, the sanitation devices may simply log each clinician detection event in a database that is accessible by the patient monitoring devices. Then, when a clinician attempts to access one of the patient monitoring devices, that device may query the database to determine, for example, the times, locations, etc. of that clinician&#39;s previous interactions with the sanitation devices. The patient monitoring devices may include logic for determining whether or not to grant access to the clinician based upon the logged interactions of that clinician with the sanitation devices. In still other embodiments, the sanitation devices  4560 ,  4562  may log each detection event in the detected clinician token itself. In this way, patient monitoring devices  4502 ,  4512 ,  4522 ,  4532  could access the sanitation detection events directly from the clinician token when the same clinician token is later detected in proximity to a patient monitoring device. 
     In some embodiments, when a sanitation device identifies a clinician detection event, it may transmit the clinician ID associated with the detected clinician token to, for example, the registration database  4542 . The registration database  4542  may correlate the clinician ID with a predetermined action that is to be performed by one or more of the patient monitoring devices. This predetermined action can be communicated to the appropriate patient monitoring device(s) via the network  4550 . As discussed herein, the predetermined action could be to immediately log the clinician in to a patient monitoring device (e.g., the nearest patient monitoring device or one that is otherwise associated with the sanitation device  4560 ,  4562  that has detected the clinician token), to change the settings of the monitoring device, or perform any of the other predetermined actions discussed herein. Alternatively, the predetermined action caused by the detection event at the sanitation device could be to cause one or more of the patient monitoring devices  4502 ,  4512 ,  4522 ,  4532  to take one of the predetermined actions discussed herein in response to subsequent detection of the clinician&#39;s presence near one of those monitors (e.g., within some predetermined period of time). 
     In the case of a system  4500  such as the one illustrated in  FIG. 45 , which includes multiple sanitation devices  4560 ,  4562  and multiple patient monitoring devices  4502 ,  4512 ,  4522 ,  4532 , the system could be configured such that access to a particular monitoring device is only granted to a clinician after he or she has been detected at a sanitation device  4560 ,  4562  and only if the clinician&#39;s presence has not been detected at an intervening patient monitoring device. For example, the system can be configured so as to require a clinician to check in at a sanitation device not only within a predetermined period of time before accessing a patient monitoring device but also before each time the clinician accesses a patient monitoring device. 
     A reporting module could also be communicatively coupled to the network of devices  4500  so as to track each interaction of a clinician with a sanitation device  4560 ,  4562  or a patient monitoring device  4502 ,  4512 ,  4522 ,  4532  and to provide reports of such interactions upon demand. Such reports could provide information relating to the times and locations of each interaction with a sanitation device and a patient monitoring device, he elapsed times between interactions, the frequency of interactions, the number of alarms generated by attempting to access a monitoring device without previously sanitizing, etc. In addition, the sanitation devices  4560 ,  4562  could be configured so as to report when they are in need of replenishing soap or some other consumable sanitizing agent to another device on the network, such as the central nurses&#39; station  4532 . 
     In some embodiments, the bedside patient monitoring devices  4502 ,  4512 ,  4522  may include a microphone and a voice conversion module. Thus, the voice conversion module could be used to transcribe, for example, bedside conversations between a clinician and a patient. For example, such bedside transcription could be activated when a clinician is detected in proximity to a sanitation device  4560 ,  4562  and/or a patient monitoring device  4502 ,  4512 ,  4522 . The transcription could be automatically e-mailed to the patient&#39;s doctor, sent to a nurse via pager, e-mail, text message, etc., filed in the patient&#39;s medical record, etc. Alternatively and/or additionally, a recording of such conversations could be stored or transmitted to a remote device. In addition, the predetermined action to be taken when a clinician is detected in proximity to a sanitation or patient monitoring device could be to provide a reminder for a patient to take medicine according to a prescribed schedule. Translation of Medical Communication Protocols to Facilitate Communication between Devices and Systems 
     Healthcare costs have been increasing and the demand for reasonably-priced, high-quality patient care is also on the rise. Health care costs can be reduced by increasing the effectiveness of hospital information systems. One factor which may affect the efficacy of a health institution is the extent to which the various clinical computer systems employed at the health institution can interact with one another to exchange information. 
     Hospitals, patient care facilities, and healthcare provider organizations typically include a wide variety of different clinical computer systems for the management of electronic healthcare information. Each of the clinical computer systems of the overall IT or management infrastructure can help fulfill a particular category or aspect of the patient care process. For example, a hospital can include patient monitoring systems, medical documentation and/or imaging systems, patient administration systems, electronic medical record systems, electronic practice management systems, business and financial systems (such as pharmacy and billing), and/or communications systems, etc. 
     The quality of care in a hospital or other patient care facility could be improved if each of the different clinical computer systems across the IT infrastructure were able to effectively communicate with each other. This could allow for the exchange of patient data that is collected by one clinical computer system with another clinical computer system that could benefit from such patient data. For example, this may allow decisions relating to patient care to be made, and actions to be taken, based on a complete analysis of all the available information. 
     In current practice, individual clinical computer systems can be, and often are, provided by different vendors. As a result, individual clinical computer systems may be implemented using a proprietary network or communication infrastructure, proprietary communication protocols, etc.; the various clinical computer systems used in the hospital cannot always effectively communicate with each other. 
     Medical device and medical system vendors sometimes develop proprietary systems that cannot communicate effectively with medical devices and systems of other vendors in order to increase their market share and to upsell additional products, systems, and/or upgrades to the healthcare provider. Thus, healthcare providers are forced to make enterprise or system-wide purchase decisions, rather than selecting the best technology available for each type of individual clinical computer system in use. 
     One example where this occurs is in the area of life-saving technology available for patient monitoring. For example, many different bedside devices for monitoring various physiological parameters are available from different vendors or providers. One such provider may offer a best-in-class device for monitoring a particular physiological parameter, while another such provider may offer the best-in-class device for another physiological parameter. Accordingly, it may be desirable in some circumstances for a hospital to have the freedom to use monitoring devices from more than one manufacturer, but this may not be possible if devices from different manufacturers are incapable of interfacing and exchanging patient information. Accordingly, the ability to provide reasonably-priced, high-quality patient care can be compromised. In addition, since each hospital or patient care facility may also implement its own proprietary communication protocols for its clinical computer network environment, the exchange of information can be further hindered. 
     The Health Level Seven (“HL7”) protocol has been developed to provide a messaging framework for the communication of clinical messages between medical computer systems and devices. The HL7 communication protocol specifies a number of standards, guidelines, and methodologies which various HL7-compliant clinical computer systems can use to communicate with each other. 
     The HL7 communication protocol has been adopted by many medical device manufacturers. However, the HL7 standard is quite flexible, and merely provides a framework of guidelines (e.g., the high-level logical structure of the messages); consequently, each medical device or medical system manufacturer or vendor may implement the HL7 protocol somewhat differently while still remaining HL7-compliant. For example, the format of the HL7 messages can be different from implementation to implementation, as described more fully herein. In some cases, the HL7 messages of one implementation can also include information content that is not included in messages according to another HL7 implementation. Accordingly, medical devices or clinical computer systems that are all HL7-compliant still may be unable to communicate with each other. 
     Consequently, what is needed is a module that can improve the communication of medical messages between medical devices or systems that use different allowed implementations of an established communication protocol (e.g., HL7), thereby increasing the quality of patient care through the integration of multiple clinical computer systems. 
       FIG. 24A  illustrates a first medical device  2405  and a second medical device  2410  that communicate with one another via a translation module  2415 . The first medical device  2405  is configured to transmit and receive messages according to a first allowed format or implementation of an accepted electronic medical communication protocol, while the second medical device  2410  is configured to transmit and receive messages according to a second allowed format or implementation of the electronic medical communication protocol. In some embodiments, the first and second protocol formats are different implementations of the HL7 communication protocol. Other electronic medical communication protocols besides HL7 can also be used. 
     The translation module  2415  receives input messages having the first protocol format from the first medical device  2405  and generates output messages to the second medical device  2410  having the second protocol format. The translation module  2415  also receives input messages having the second protocol format from the second medical device  2410  and generates output messages to the first medical device  2405  having the first protocol format. Thus, the translation module  2415  enables the first and second medical devices  2405 ,  2410  to effectively and seamlessly communicate with one another without necessarily requiring modification to the communication equipment or protocol implemented by each device. 
     In certain embodiments, the translation module  2415  determines the protocol format expected by an intended recipient of the input message based on, for example, the information in the input message or by referencing a database that stores the protocol format used by various devices, and then generates the output message based on the protocol format used by the intended recipient device or system. The output message can be generated based upon a comparison with, and application of, a set of translation rules  2420  that are accessible by the translation module  2415 . 
     The translation rules  2420  can include rules that govern how to handle possible variations between formatting implementations within a common protocol. Examples of variations in formatting implementation of an electronic medical communication protocol include, for example, the delimiter or separator characters that are used to separate data fields, whether a particular field is required or optional, the repeatability of portions of the message (e.g., segments, fields, components, sub-components), the sequence of portions of the message (e.g., the order of fields or components), whether a particular portion of a message is included, the length of the message or portions of the message, and the data type used for the various portions of the message. 
     In certain embodiments, the translation rules  2420  define additions, deletions, swappings, and/or modifications that should be performed in order to “translate” an input message that adheres to a first HL7 implementation into an output message that adheres to a second HL7 implementation. The output message can have, for example, different formatting than the input message, while maintaining all, or a portion of, the substance or content of the input message. 
     In addition to translating between different implementations of a common electronic medical communication protocol (e.g., different formatting of HL7 messages), the translation module  2415  can also be configured to translate between input and output messages adhering to different communication protocols. In some embodiments, the translation module  2415  is capable of responding to and translating messages from, for example, one medical communication protocol to a separate medical communication protocol. For example, the translation module  2415  can facilitate communication between messages sent according to the HL7 protocol, the ISO  11073  protocol, other open protocols, and/or proprietary protocols. Accordingly, an input message sent according to the HL7 protocol can be translated to an output message according to a different protocol, or vice-versa. 
     The operation of the translation module  2415  and the translation rules  2420  will be described in more detail below. Various embodiments of system architectures including the translation module  2415  will now be described. 
     In certain embodiments, the first medical device  2405 , the second medical device  2410 , and the translation module  2415  are communicatively coupled via connection to a common communications network. In some embodiments, the translation module  2415  can be communicatively coupled between the first medical device  2405  and the second medical device  2410  (with or without a communications network) such that all messages between the first and second medical devices  2405 ,  2410  are routed through the translation module  2415 . Other architectures are also possible. 
     The first and second medical devices  2405 ,  2410  and the translation module  2415  can be included in, for example, a portion of the physiological monitoring system  200  of  FIG. 2  or the clinical network environment  600  of  FIG. 6  described above. In certain embodiments, the portion of the physiological monitoring system  200  comprises a portion of a messaging sub-system of the physiological monitoring system  200  for supporting the exchange of data between the various clinical computer systems used in the hospital. 
     In certain embodiments, the translation module  2415  can facilitate communication across multiple networks within a hospital environment. In other embodiments, the translation module  2415  can facilitate communication of messages across one or more networks extending outside of the hospital or clinical network environment. For example, the translation module  2415  can provide a communications interface with banking institutions, insurance providers, government institutions, outside pharmacies, other hospitals, nursing homes, or patient care facilities, doctors&#39; offices, and the like. 
     In some embodiments, the translation module  2415  of  FIG. 24  can be a component of, for example, the patient monitoring system  200  described herein. For example, the translation module  2415  can be communicatively coupled with the hospital network  220  illustrated in  FIG. 2 . In such embodiments, the translation module  2415  can facilitate the exchange of patient monitoring information, including, for example, physiological parameter measurements, physiological parameter trend information, and physiological parameter alarm conditions between bedside medical monitor devices, nurses&#39; monitoring stations, a Hospital or Clinical Information System (which may store Electronic Medical Records), and/or many other medical devices and systems. The translation module  2415  can enable seamless communication between different medical devices and systems, each of which may use a different implementation of an electronic medical communication protocol such as, for example, the HL7 communication protocol, within a clinical or hospital network environment. 
     In certain embodiments, the translation module  2415  can also facilitate communication between a first medical device that is part of the patient monitoring sub-system and a second medical device that is not part of, or is external to, the patient monitoring system  200 . As such, the translation module  2415  can be capable of responding to externally-generated medical messages (such as patient information update messages, status query messages, and the like from an HIS or CIS) and generating external reporting messages (such as event reporting messages, alarm notification messages, and the like from patient monitors or nurses&#39; monitoring stations). 
     In another embodiment, first and second medical devices  2405 ,  2410  communicate with each other over a communication bus  2421 . Communication bus  2421  can include any one or more of the communication networks, systems, and methods described above, including the Internet, a hospital WLAN, a LAN, a personal area network, etc. For example, any of the networks describe above with respect to  FIGS. 1, 2, 6, 7, 19 , etc. can be used to facilitate communication between a plurality of medical devices, including first and second medical devices  2405 ,  2410 , discussed above. One such embodiment is illustrated in  FIG. 24B . 
     In  FIG. 24B , first medical device  2405  provides a message to the communication bus  2421 . The message is intended for receipt by the second medical device  2410 ; however, because first and second medical devices  2405 ,  2410  communicate according to different communication protocol format, second medical device  2410  is unable to process the message. 
     Translation module  2415  monitors the communication bus  2421  for such messages. Translation module receives the message and determines that first medical device  2405  is attempting to communicate with second medical device  2410 . Translation module  2415  determines that message translation would facilitate communication between first and second medical devices  2405 ,  2410 . Translation module  2415  therefore utilizes an appropriate translation rule stored in a translation module  2420 . Translation module  2420  can include a memory, EPROM, RAM, ROM, etc. 
     The translation module  2415  translates the message from the first medical device  2405  according to any of the methods described herein. Once translated, the translation module  2415  delivers the translated message to the communication bus  2421 . The second medical device  2410  receives the translated message and responds appropriately. For example, the second medical device may perform a function and/or attempt to communication with the first medical device  2405 . The translation module  2415  facilitates communication from the second medical device  2410  to the first medical device  2405  in a similar manner. 
     The first medical device  2405  and the second medical device  2410  can be, for example, any of the medical devices or systems communicatively coupled to the hospital network  222  illustrated in  FIG. 2 . These medical devices or systems can include, for example, point-of-care devices (such as bedside patient monitors), data storage units or patient record databases, hospital or clinical information systems, central monitoring stations (such as a nurses&#39; monitoring station), and/or clinician devices (such as pagers, cell phones, smart phones, personal digital assistants (PDAs), laptops, tablet PCs, personal computers, pods, and the like). 
     In some embodiments, the first medical device  2405  is a patient monitor for communicatively coupling to a patient for tracking a physiological parameter (e.g., oxygen saturation, pulse rate, blood pressure, etc.), and the second medical device  2410  is a hospital information system (“HIS”) or clinical information system (“CIS”). In some embodiments, the patient monitor can communicate physiological parameter measurements, physiological parameter alarms, or other physiological parameter measurement information generated during the monitoring of a patient to the HIS or CIS for inclusion with the patient&#39;s electronic medical records maintained by the HIS or CIS. 
     In some embodiments, the first medical device  2405  is an HIS or CIS and the second medical device  2410  is a nurses&#39; monitoring station, as described herein. However, the translation module  2415  can facilitate communication between a wide variety of medical devices and systems that are used in hospitals or other patient care facilities. For example, the translation module  2415  can facilitate communication between patient physiological parameter monitoring devices, between a monitoring device and a nurses&#39; monitoring station, etc. 
     Using the translation module  2415 , a patient monitoring sub-system, such as those described herein (e.g., physiological monitoring system  200 ), can push data to the HIS or pull data from the HIS even if the HIS uses a different implementation of the HL7 protocol, or some other electronic medical communication protocol. 
     In certain embodiments, the patient monitoring sub-system can be configured to push/pull data at predetermined intervals. For example, a patient monitor or clinician monitoring station can download patient data automatically from the HIS at periodic intervals so that the patient data is already available when a patient is connected to a patient monitor. The patient data sent from the HIS can include admit/discharge/transfer (“ADT”) information received upon registration of the patient. ADT messages can be initiated by a hospital information system to inform ancillary systems that, for example, a patient has been admitted, discharged, transferred or registered, that patient information has been updated or merged, or that a transfer or discharge has been canceled. 
     In other embodiments, the patient monitoring sub-system can be configured to push/pull data to/from the HIS only when the HIS is solicited by a query. For example, a clinician may make a request for information stored in a patient&#39;s electronic medical records on the HIS. 
     In still other embodiments, the patient monitoring sub-system can be configured to push/pull data to/from the HIS in response to an unsolicited event. For example, a physiological parameter of a patient being monitored can enter an alarm condition, which can automatically be transmitted to the HIS for storing in the patient&#39;s electronic medical records. In yet other embodiments, any combination of the above methods or alternative methods for determining when to communicate messages to and from the HIS can be employed. 
     Example system architectures and example triggers for the communication of messages involving the translation module  2415  have been described. Turning now to the operation of the translation module,  FIGS. 25A-25D  illustrate an example medical message at different phases or steps of a translation process. The translation process will be described in more detail below in connection with  FIGS. 26, 27A and 27B . 
       FIG. 25A  illustrates an example ADT input message  2505  received by the translation module  2415  from an HIS. The ADT input message  2505  is implemented according to the HL7 communication protocol and contains information related to the admission of a patient to a hospital. The ADT message  2505  includes multiple segments, including a message header segment  2506 , an event segment, a patient identification segment, a patient visit segment, role segments, a diagnosis segment, and multiple custom segments. 
     In some embodiments, the message header (“MSH”) segment  2506  defines how the message is being sent, the field delimiters and encoding characters, the message type, the sender and receiver, etc. The first symbol or character after the MSH string can define the field delimiter or separator (in this message, a “caret” symbol). The next four symbols or characters can define the encoding characters. The first symbol defines the component delimiter (“˜”), the second symbol defines the repeatable delimiter (“I”), the third symbol defines the escape delimiter (“\”), and the fourth symbol defines the sub-component delimiter (“&amp;”). All of these delimiters can vary between HL7 implementations. 
     In some embodiments, the example header segment  2506  further includes the sending application (“VAFC PIMS”), the receiving application (“NPTF-508”), the date/time of the message (“20091120104609-0600”), the message type (“ADT˜A01”), the message control ID (“58103”), the processing ID (“P”), and the country code (“USA”). As represented by the consecutive caret symbols, the header segment also contains multiple empty fields. 
       FIG. 25B  illustrates the message header segment  2506  after it has been parsed into fields or elements based on an identified field delimiter (the caret symbol). In certain embodiments, the parsed input message comprises an XML message that is configured to be transformed according to extensible stylesheet language transformation (XSLT) rules. 
     In certain embodiment, the parsed input message can be encoded.  FIG. 25C  illustrates the parsed message header segment of the input message after being encoded (e.g., using a Unicode Transformation Format-8 (“UTF-8”) encoding scheme). 
     The encoded message header segment shows some of the various data types that can be used in the message. For example, the sending application (“VAFC PIMS”) of the third parsed field and the receiving application (“NPTF- 508 ”) of the fifth parsed field are represented using a hierarchic designator (“HD”) name data type. The date/time field (the seventh parsed field) is represented using the time stamp (“TS”) data type. The processing ID field (the eleventh parsed field) is represented using the processing type (“PT”) data type. The fields that do not include a data type identifier are represented using the string (“ST”) data type. Other possible data types include, for example, coded element, structured numeric, timing quantity, text data, date, entry identifier, coded value, numeric, and sequence identification. The data types used for the various fields or attributes of the segments can vary between formatting implementations. 
       FIG. 25D  illustrates an example output message  2510  from the translation module  2415  based on the example input message  2505  of  FIG. 25A . The output message  2510  includes a message acknowledgement segment  2512 . 
     Turning to the operation of the translation module, the translation module  2415  can, for example, create, generate, or produce an output message that is reflective of the input message based on an application of the set of translation rules  2420 . In some embodiments, the translation module  2415  can, for example, translate, transform, convert, reformat, configure, change, rearrange, modify, adapt, alter, or adjust the input message based on a comparison with, and application of, the set of translation rules  2420  to form the output message. In some embodiments, the translation module  2415  can, for example, replace or substitute the input message with an output message that retains the content of the input message but has a new formatting implementation based upon a comparison with, and application of, the set of translation rules  2420 . 
       FIG. 26  illustrates a translation process  2600  for generating an output message based on an input message and a comparison with the set of translation rules  2420  associated with the translation module  2415 . The translation process  2600  starts at block  2602  where the translation module  2415  receives an input message from a first medical device. 
     At block  2604 , the translation module  2415  determines the formatting implementation of the input message and the formatting implementation to be used for the output message. In certain embodiments, the input message can include one or more identifiers indicative of the formatting implementation. In some embodiments, the determination of the formatting implementation can be made, for example, by analyzing the message itself by identifying the delimiter or encoding characters used, the field order, the repeatability of segments, fields, or components, the data type of the fields, or other implementation variations. In certain embodiments, the translation module  2415  can separate or parse out the formatting from the content of the message (as shown in  FIG. 25B ) to aid in the determination of the formatting implementation. In some embodiments, the translation module  2415  determines the formatting implementation of the input message by referencing a database that stores the implementation used by each device with which the translation module  2415  has been configured to interface. 
     In certain embodiments, the determination of the formatting implementation required by the output message can also be determined from the input message. For example, the input message can include a field that identifies the intended recipient application, facility, system, device, and/or destination. The input message can alternatively include a field that identifies the type of message being sent (e.g., ADT message) and the translation module  2415  can determine the appropriate recipient from the type of message being sent and/or the sending application, device, or system. The translation module  2415  can then determine the formatting implementation required by the intended recipient of the input message. 
     At decision block  2605 , the translation module  2415  determines whether a rule set has been configured for the translation from the identified formatting implementation of the input message to the identified formatting implementation to be used for the output message. The rule set may have been manually configured prior to installation of the translation module software or may have been automatically configured prior to receipt of the input message. If a rule set has already been configured, then the translation process  2600  continues to block  2606 . If a rule set has not been configured, then a rule set is configured at block  2607 . The configuration of the rule set can be performed as described below in connection with  FIGS. 28 and 29A-29D . The translation process  2600  then continues to block  2608 . 
     At block  2606 , the translation module  2415  identifies the pre-configured rules from the set of translation rules  2420  that govern translation between the determined formatting implementation of the input message and the formatting implementation of the output message. In some embodiments, the identification of the pre-configured rules can be made manually. 
     At block  2608 , the translation module  2415  generates an output message based on the configured rule set(s) of the translation rules  2420 . In certain embodiments, the output message retains all, or at least a portion of, the content of the input message but has the format expected and supported by the intended recipient of the input message. 
     The translation rules  2420  can include, for example, unidirectional rules and/or bidirectional rules. A unidirectional rule is one, for example, that may be applied in the case of a message from a first medical device (e.g.,  2405 ) to a second medical device (e.g.,  2410 ) but is not applied in the case of a message from the second medical device to the first medical device. For example, a unidirectional rule could handle a difference in the delimiters used between fields for two different formatting implementations of, for example, the HL7 communication protocol. The translation module  2415  can apply a field delimiter rule to determine if the field delimiter is supported by the intended recipient of the input message. If the field delimiter of the input message is not supported by the intended recipient, the field delimiter rule can replace the field delimiter of the input message with a field delimiter supported by the intended recipient. 
     For example, an input message from an input medical device can include a formatting implementation that uses a “caret” symbol (“{circle around ( )}”) as the field delimiter or separator. However, the formatting implementation recognized by the intended recipient medical device may use a “pipe” symbol (“|”) as the field delimiter. The translation module  2415  can identify the field delimiter symbol used in the formatting implementation recognized by the intended recipient medical device from the set of translation rules  2420  and generate an output message based on the input message that uses the pipe field delimiter symbol instead of the caret field delimiter symbol used in the input message. The rule to substitute a pipe symbol for a caret symbol would, in this case, only apply to messages that are sent to a recipient device that recognizes the pipe symbol as a field delimiter. This rule could be accompanied by a complementary rule that indicates that a caret symbol should be substituted for a pipe symbol in the case of a message that is intended for a recipient device that is known to recognize the caret symbol as the field delimiter. 
     Another unidirectional rule can handle the presence or absence of certain fields between different formatting implementations. For example, an input message from an input medical device can include fields that would not be recognized by the intended recipient medical device. The translation module  2415  can generate an output message that does not include the unrecognized or unsupported fields. In situations where an input message does not include fields expected by the intended recipient medical device, the set of translation rules  2420  can include a rule to insert null entries or empty “” strings in the fields expected by the intended recipient medical device and/or to alert the recipient device of the absence of the expected field. The sender device may also be notified by the translation module  2415  that the recipient device does not support certain portions of the message. 
     Other unidirectional rules can facilitate, for example, the conversion of one data type to another (for example, string (“ST”) to text data (“TX”) or structured numeric (“SN”) to numeric (“NM”)), and the increase or decrease in the length of various portions of the message. Unidirectional rules can also be used to handle variations in repeatability of portions of the message. For example, the translation module  2415  can apply a field repeatability rule to repeated instances of a segment, field, component, or sub-component of the message to determine how many such repeated instances are supported by the recipient device, if any, and deleting or adding any repeated instances if necessary. For example, a phone number field of a patient identification segment can be a repeatable field to allow for entry of home, work, and cell phone numbers. 
     Bidirectional rules can also be used. Such rules may apply equally to messages between first and second medical devices (e.g.,  2405 ,  2410 ) regardless of which device is the sender and which is the recipient. A bidirectional rule can be used to handle changes in sequence, for example. In certain implementations, an input message from an input medical device can include a patient name field, or fields, in which a first name component appears before a last name component. However, the intended recipient medical device may be expecting an implementation where the last name component appears before the first name component. Accordingly, the set of translation rules  2420  can include a bidirectional rule to swap the order of the first and last name components when communicating between the two medical devices, or between the two formatting implementations. In general, field order rules can be applied to determine whether the fields, components, or sub-components are in the correct order for the intended recipient and rearranging them if necessary. Other bidirectional rules can be included to handle, for example, other sequential variations between formatting implementations or other types of variations. 
     The translation rules  2420  can also include compound rules. For example, a compound rule can include an if-then sequence of rules, wherein a rule can depend on the outcome of another rule. Some translation rules  2420  may employ computations and logic (e.g., Boolean logic or fuzzy logic), etc. 
     As discussed above, the messages communicated over the hospital-based communication network can employ the HL7 protocol.  FIGS. 27A and 27B  illustrate translation processes  2700 A,  2700 B in which HL7 messages are communicated between a HIS and a medical device over a hospital-based communications network or a clinical network. The translation processes  2700 A,  2700 B will be described with the assumption that the rules governing “translation” between the first and second HL7 formats have already been configured. 
       FIG. 27A  illustrates a translation process  2700 A in which the translation module  2415  facilitates communication of an HL7 message, such as the ADT message of  FIG. 25A , from an HIS having a first HL7 format to an intended recipient medical device, such as a patient monitor or a clinician monitoring station, having a second HL7 format. 
     The translation process  2700 A starts at block  2701 , where the translation module  2415  receives an input message having a first HL7 format from the HIS. In certain embodiments, the input message includes information regarding, for example, the admission of a patient and/or patient identification and patient medical history information from an electronic medical records database. 
     At block  2703 , the translation module  2415  determines the formatting implementation of the input message and the formatting implementation to be used for the output message. These determinations can be made in a similar manner to the determinations discussed above in connection with block  2604  of  FIG. 26 . 
     At block  2705 , the translation module  2415  identifies the rules that govern translation between the determined HL7 format of the input message and the HL7 format of the output message and generates an output message having the second HL7 format based on the identified rules. In certain embodiments, the output message retains the content of the input message sent by the HIS but has the format expected and supported by the intended recipient of the input message. 
     At block  2707 , the translation module  2415  can output the output message to the intended recipient over the hospital-based communications network. In certain embodiments, the intended recipient can transmit an acknowledgement message back to the hospital information system acknowledging successful receipt or reporting that an error occurred. 
       FIG. 27B  illustrates a translation process  2700 B in which the translation module  2415  facilitates communication of an HL7 message from a medical device, such as a patient monitor, having a first HL7 format to an HIS having a second HL7 format. For example, the patient monitor can transmit reporting event data m such as patient alarm data, to the HIS to store in the patient&#39;s electronic medical records. 
     The translation process  2700 B starts at block  2702 , where the translation module  2415  receives an input message having a first HL7 format from the medical device. In certain embodiments, the input message includes patient monitoring data or alarm data regarding one or more physiological parameters of the patient being monitored for storage in an electronic medical records database associated with the HIS. 
     At block  2704 , the translation module  2415  determines the formatting implementation of the input message and the formatting implementation to be used for the output message. These determinations can be made in a similar manner to the determinations discussed above in connection with block  2604  of  FIG. 26 . 
     At block  2706 , the translation module  2415  identifies the rules that govern translation between the determined HL7 format of the input message and the HL7 format of the output message and generates an output message having the second HL7 format based on the identified rules. In certain embodiments, the output message retains the content of the input message sent by the medical device but has the format expected and supported by the HIS. 
     At block  2708 , the translation module  2415  can output the output message to the hospital information system over the hospital-based communications network. In certain embodiments, the HIS can transmit an acknowledgement message back to the medical device acknowledging successful receipt or reporting that an error occurred. 
       FIGS. 26, 27A and 27B  described the operation of the translator module  2415 .  FIGS. 28 and 29A-29D  will be used to illustrate the description of the configuration of the translation rules  2420 . 
     The translation rules  2420  can be implemented as one or more stylesheets, hierarchical relationship data structures, tables, lists, other data structures, combinations of the same, and/or the like. In certain embodiments, the translation rules  2420  can be stored in local memory within the translation module  2415 . In other embodiments, the translation rules  2420  can be stored in external memory or on a data storage device communicatively coupled to the translation module  2415 . 
     The translation module  2415  can include a single rule set or multiple rule sets. For example, the translation module  2415  can include a separate rule set for each medical device/system and/or for each possible communication pair of medical devices/systems coupled to the network or capable of being coupled to the network. In some embodiments, the translation module  2415  can include a separate rule set for each possible pair of formatting implementations that are allowed under a medical communication protocol such as, for example, the HL7 protocol. 
     In certain embodiments, the translation rules  2420  can be manually inputted using, for example, the messaging implementation software tool  2800  illustrated in  FIG. 28 . For example, the software developer for a particular hospital network can determine the protocol message formats used by the devices and/or systems that are or can be coupled to the hospital network and then manually input rules to facilitate “translation” between the various protocol message formats supported or recognized by the devices and/or systems. 
       FIG. 28  illustrates an example screenshot from a messaging implementation software tool  2800  for manually configuring translation rules  2420  to be used by the translation module  2415 . The screenshot from the messaging implementation software tool  2800  illustrates various parameters that may differ between formatting implementations of an electronic medical communication protocol, such as HL7. The screenshot also includes areas where a user can input information that defines, or is used to define, translation rules for converting between different HL7 implementations. In some embodiments, the messaging implementation software tool  2800  stores a variety of pre-configured rule sets based, for example, on known communication protocol implementations of various medical devices. In such embodiments, a user may configure one or more translation rules  2420  to be used in communications involving such devices by entering identification information, such as the device manufacturer, model number, etc. Based on this identification information, the messaging implementation tool  2800  can identify a pre-configured set of translation rules for communication with that device. 
     In other embodiments, the translation rules  2420  can be automatically generated. For example, the automatic generation of a new set, or multiple sets, of rules can be triggered by the detection of a newly recognized “communicating” medical device or system on a network. In certain embodiments, the automatic generation of a new set or multiple sets of rules can occur at the time a first message is received from or sent to a new “communicating” medical device or system coupled to the network. In still other embodiments, the automatic generation of rule sets includes updating or dynamically modifying a pre-existing set of rules. 
     The automatic generation of translation rule sets can be carried out in a variety of ways. For example, in some embodiments, the translation module  2415  can automatically initiate usage of a pre-configured set of translation rules  2420  based upon, for example, the make and model of a new device that is recognized on the network. In certain embodiments, the translation module  2415  can request one or more messages from the new device or system and then analyze the messages to determine the type of formatting being implemented, as illustrated by the automatic rule configuration process  2900 A of  FIG. 29A . The automatic rule configuration process  2900 A starts at block  2901 , where the translation module  2415  receives one or more messages from a detected medical device or system on the network. The messages can be received upon transmission to an intended recipient medical device or system or in response to a query sent by the translation module  2415  or another medical device or system coupled to the network. 
     At block  2903 , the translation module  2415  determines the protocol of the one or more received messages by, for example, analyzing the message or by consulting a database that indicates what communication protocol/format is implemented by each medical device or system on the network. In certain embodiments, the translation module  2415  is configured to handle medical messages implemented using a single common protocol, such as HL7. Accordingly, if a determination is made that the received messages are implemented using a non-supported or non-recognized protocol, the translation module can ignore the messages received from the detected medical device or system, output an alert or warning, or allow the messages to be sent without being translated. 
     At block  2905 , the translation module  2415  determines the formatting implementation of the received message(s). In certain embodiments, the received messages can include one or more identifiers indicative of the formatting implementation. In other embodiments, the determination of the formatting implementation can be made, for example, by analyzing the message itself by checking field order, the delimiter or encoding characters used, or other implementation variations. In certain embodiments, the translation module  2415  can separate or parse out the formatting from the content of the message to aid in the determination of the formatting implementation. 
     At block  2907 , the translation module  2415  configures one or more rules or rule sets to handle messages received from and/or sent to the detected medical device or system. In certain embodiments, the configuration of the rules involves the creation or generation of new rules. In other embodiments, the configuration of the rules involves the alteration or updating of existing rules. The configured rules or rule sets can be included with the translation rules  2420 . If a set of rules already exists for the formatting implementation used by the new device or system, then the configuration of new translation rules may not be required. Instead, existing translation rules can be associated with the new device or system for use in communication involving that device or system. In other embodiments, the translation module  2415  can create a new set of rules geared specifically for the new device or system or can modify an existing set of rules based on subtle formatting variations identified. 
     In other embodiments, the translation module  2415  can generate test message(s) that may be useful in identifying the communication protocol and implementation used by a device or system. For example, the translation module can generate test messages to cause the newly detected device or system to take a particular action (e.g., store information) and then query information regarding the action taken by the newly detected device to determine whether or how the test message was understood. This is illustrated by the automatic rule configuration process  2900 B of  FIG. 29B . 
     The automatic rule configuration process  2900 B starts at block  2902 , where the translation module  2415  transmits one or more test, or initialization, messages to a remote device or system detected on a network. The test messages can be configured, for example, to instruct the remote device or system to take a particular action (e.g., store patient information). In certain embodiments, the test messages can be configured to generate a response indicative of the type of formatting recognized or supported by the remote device or system. In other embodiments, the test messages can be configured such that only devices or systems supporting a particular formatting implementation will understand and properly act on the test messages. 
     At block  2904 , the translation module  2415  queries the remote device or system to receive information regarding the action taken based on the test message sent to the remote device or system to determine whether the test message was understood. For example, if the test message instructed the remote device or system to store patient information in a particular location, the translation module  2415  can query the information from the location to determine whether the test message was understood. If the test message was not understood, the translation module  2415  can, for example, continue sending test messages of known formatting implementations until a determination is made that the test message has been understood. 
     At block  2906 , the translation module  2415  determines the protocol and formatting implementation based on the information received. As an example, in certain embodiments, the test message can include an instruction to store patient name information. The test message can include a patient name field having a first name component followed by a surname component. The translation module  2415  can then query the remote device or system to return the patient surname. Depending on whether the patient surname or the first name is returned, this query can be useful in determining information about the order of fields in the formatting implementation being used by the remote device or system. As another example, the test messages can instruct the detected device or system to store repeated instances of a component. The translation module  2415  can then query the device or system to return the repeated instances to see which, if any, were stored. This repeatability information can also be useful in determining whether certain fields are allowed to be repeated in the formatting implementation being used by the remote device for system, and, if so, how many repeated instances are permitted. 
     At block  2908 , the translation module  2415  configures one or more rules to handle messages received from and/or sent to the detected medical device or system. For example, the rules can convert messages from the message format used by a first medical device to that used by a second medical device, as described herein. In certain embodiments, the configuration of the rules involves the creation or generation of new rules. In other embodiments, the configuration of the rules involves the alteration or updating of existing rules. If a set of rules already exists for the formatting implementation used by the new device or system, then the configuration of new translation rules may not be required. 
     Instead, existing translation rules can be associated with the new device or system for use in communication involving that device or system. 
       FIGS. 29C and 29D  illustrate automatic rule configuration processes performed by the translation module  2415  for messages utilizing the HL7 protocol. The HL7 protocol can be used, for example, to communicate electronic messages to support administrative, logistical, financial, and clinical processes. For example, HL7 messages can include patient administration messages, such as ADT messages, used to exchange patient demographic and visit information across various healthcare systems. 
     The automatic rule configuration process  2900 C illustrated in  FIG. 29C  is similar to the process  2900 A illustrated in  FIG. 29A . At block  2911 , the translation module  2415  receives one or more messages from an HL7 medical device. At block  2915 , the translation module  2415  determines the formatting implementation of the HL7 medical device from the one or more messages received. As discussed above, the determination of the formatting implementation can be made, for example, by checking field order or sequence, field delimiter characters, repeatability, cardinality, and other HL7 implementation variations. 
     At block  2917 , the translation module  2415  configures one or more rules to handle messages received from and/or sent to the HL7 medical device. In certain embodiments, the configuration of the rules involves the creation or generation of new rules for the detected formatting implementation. In other embodiments, the configuration of the rules involves the dynamic alteration or updating of existing rules. If a set of rules already exists for the formatting implementation used by the new HL7 medical device, then the configuration of new translation rules may not be required. Instead, existing translation rules can be associated with the new HL7 medical device for use in communication involving that device. 
     The automatic rule configuration process  2900 D illustrated in  FIG. 29D  is similar to the process  2900 B illustrated in  FIG. 29B . At block  2912 , the translation module  2415  transmits one or more test, dummy, or initialization messages to an HL7 medical device. In other embodiments, the translation module  2415  can cause one or more test messages to be transmitted to the new HL7 medical device from another HL7 medical device. As described above, the test messages can include messages having known HL7 formats configured to determine whether the HL7 device understands the test messages. The test messages can include test ADT messages, for example. 
     At block  2914 , the translation module  2415  queries the HL7 medical device to receive information regarding an action taken or information stored in response to the test message. At block  2916 , the translation module  2415  determines the formatting implementation of the HL7 device based on the information received. In certain embodiments, the translation module  2415  can analyze the information received to determine whether the test message or messages were properly understood. If none of the test messages were properly understood, the translation module  2415  can send additional test messages having other known HL7 formats and repeat blocks  2914  and  2916 . 
     At block  2918 , the translation module  2415  configures one or more translation rules to handle messages received from and/or sent to the detected HL7 medical device. In certain embodiments, the configuration of the translation rules involves the creation or generation of new translation rules. In other embodiments, the configuration of the rules involves the alteration or updating of existing rules. If a set of translation rules already exists for the formatting implementation used by the new HL7 medical device, then the configuration of new translation rules may not be required. Instead, existing translation rules can be associated with the new HL7 medical device for use in communication involving that HL7 medical device. 
     The automatic rule configuration processes described above can be triggered by the detection of a network device or system by the translation module  2415 . The medical devices referred to in  FIGS. 29A-29D  can include any of the devices or systems illustrated in  FIG. 2  and discussed above. 
     In some embodiments, the automatic generation of translation rules can advantageously occur post-installation and post-compilation of the messaging sub-system software, which includes the translation module  2415 . In certain embodiments, the automatic generation or dynamic modification of the translation rules  2420  can occur without having to recompile or rebuild the translation module software. This feature can be advantageous in terms of efficiently complying with U.S. Food and Drug Administration (“FDA”) requirements regarding validation of software used in healthcare environments. 
     Take, for example, a situation where a medical device manufacturer plans to use the translation module  2415  to facilitate communication between a particular medical device or system that is to be installed in a hospital (e.g., a patient monitoring system, as described herein), or other patient care facility, and other devices or systems that are already installed at the hospital (e.g., the HIS or CIS). Any software required for the operation of the new medical device to be installed may be at least partially validated for FDA compliance prior to installation at the hospital despite the fact that, for example, the HL7 implementations of other existing devices or systems at the hospital may still be unknown. For example, any aspects of the software for the new medical device that are dependent upon receiving messages from other hospital devices can be validated pre-installation as being capable of fully and correctly operating when the expected message format is received. Then, once the medical device is installed at the hospital, the validation of the software can be completed by showing that the translation module  2415  is able to provide messages of the expected format to the newly installed device. In this way, FDA validation tasks can be apportioned to a greater extent to the pre-installation timeframe where they can be more easily carried out in a controlled manner rather than in the field. 
     In addition, the translation module  2415  can further help streamline FDA validation, for example, when a medical device or system is expected to be installed at different hospitals whose existing devices use, for example, different implementations of the HL7 protocol. Normally, this type of situation could impose the requirement that the entire functionality of the software for the new medical device be completely validated at each hospital. However, if the translation module  2415  is used to interface between the new medical device and the hospital&#39;s existing devices, then much of the software functionality could possibly be validated a single time prior to installation, as just described. Then, once installed at each hospital, the software validation for the medical device can be completed by validating that correct message formats are received from the translation module (the translation rules for which are field-customizable). This may result in making on-site validation procedures significantly more efficient, which will advantageously enable more efficient FDA compliance in order to bring life-saving medical technology to patients more quickly by the use of field-customizable translation rules. 
     Patient Monitoring Reports 
     Devices and methods for monitoring physiological parameters such as blood oxygen saturation, pulse rate, blood pressure, and many others, are described herein. Such medical monitoring devices are often programmed with alarm limits to automatically detect when a physiological parameter has a value that is, for example, outside the range of values considered safe or healthy for that particular physiological parameter. In some embodiments, when such an alarm condition is detected, various actions can be taken. For example, the bedside medical monitor can emit an audible or visual alarm. In addition, in some cases, after the alarm condition has persisted for some set amount of time (e.g., 5 sec.), the alarm condition can be displayed at, for example, a central patient monitoring station, as described herein. Moreover, if the alarm condition continues to persist for some set amount of time (e.g., 10 sec.), the clinician assigned to care for the patient who is experiencing the alarm condition can be notified by, for example, a pager or other notification device. 
     The number of detected alarm conditions is, of course, dependent upon the settings for the alarm criteria that indicate an alarm condition. In some embodiments, such alarm criteria can include a threshold value, which may indicate the boundary between values for a physiological parameter that are considered safe or normal, and those that are considered to indicate a medical condition which may require attention from a clinician. The nearer such an alarm threshold is set to values that are common for that particular physiological parameter in healthy individuals under normal circumstances, the larger the number of alarm events that will be expected to be detected. Generally speaking, the closer the alarm criteria come to being satisfied by the normal expected range of values for a given physiological parameter, then the greater the odds of detecting any deviation from the normal range of values that may indicate that the patient is in need of some type of medical intervention (e.g., administration of drugs, CPR, ventilator, etc.). This can be desirable in the sense that it becomes less likely that a patient will experience medical duress without triggering an alarm, which can be referred to as a false negative. 
     Reduction of false negatives does not come without a cost, however. Namely, alarm criteria for physiological parameters that are successful in reducing false negatives may also increase the rate of false positives, where alarm conditions are detected even though the patient may not be experiencing any clinically significant medical duress. If false positives become too frequent, they can become burdensome to clinicians, who are responsible for investigating alarm conditions, resetting the monitoring devices from the alarm state, etc. In addition, frequent false positives can even put patients at risk by reducing the importance assigned to alarm events by clinicians, whether consciously or subconsciously. Thus, it is desirable to determine alarm criteria for medical monitoring applications that strike a satisfactory balance that limits false negatives to an acceptable rate without unduly increasing false positive alarm events. In some cases, false positives may be preferred to false negatives, especially in circumstances where the consequences of a false negative would be severe to the patient. Such a preference for maintaining the occurrence of false negatives at a relatively low rate can be reflected in the choice of alarm limit criteria. It is not necessarily the case, however, that false positives are always preferred to false negatives. Moreover, alarm criteria that may be satisfactory for one type of patient may be unsatisfactory for other types of patients. The appropriate balance between false positives and false negatives may vary for different medical monitoring applications. 
     For example, in the case of blood oxygen saturation monitoring, typical SpO 2  values of healthy individuals may fall in the range of 95-100%. Therefore, if a patient monitoring device were configured with an SpO 2  alarm threshold of 94%, the number of false positive alarm events may be relatively high. In contrast, if the SpO 2  alarm threshold were set at 92%, then the number of false positives would likely be reduced, but the number of false negatives may increase beyond a satisfactory level in some medical monitoring applications. Therefore, devices and methods for providing data that would aid in the selection of an alarm threshold that would reduce false positives while still maintaining false negatives at or below a satisfactory level would be very useful. Such devices and methods could be used for establishing alarm criteria for a wide variety of physiological parameters. 
       FIG. 30  is an example graph  3000  of the distribution of alarm events for a given physiological parameter as a function of alarm limit values. The graph  3000  plots the number of detected alarm conditions versus a range of alarm limit values. The graph  3000  reflects, for example, a hypothetical situation where physiological parameter alarm data is collected from a statistically-significant number of patients of a particular type (e.g., cardiac patients) over the course of a statistically-significant period of time using a range of different alarm limit values. Of course, the distribution of alarm events as a function of alarm limit values will generally vary for different physiological parameters. 
     The graph  3000  shows a set of linearly increasing alarm limit values on the x-axis. The corresponding number of detected alarm conditions for each alarm limit value is plotted on the y-axis. As illustrated, for this particular physiological parameter, the number of detected alarm conditions generally decreases as the alarm limit value is increased. Each bar in the graph  3000  may be representative of, for example, a combination of false positive alarm events and correctly detected alarm events (e.g., detection of an alarm event when the patient was actually in need of medical assistance). 
     The dashed vertical line  3002  represents one possible alarm limit threshold value. When the physiological parameter value is above the threshold indicated by the dashed vertical line  3002 , for example, an alarm condition is detected, whereas when the physiological parameter value is below the threshold, no alarm condition is detected. The dashed vertical line  3004  represents another possible alarm limit value. 
     As shown on the graph  3000 , the illustrated alarm limit values  3002 ,  3004  are only separated by two values on the x-axis. However, the number of alarms detected using each of the two illustrated alarm thresholds  3002 ,  3004  is approximately halved in going from the first alarm threshold  3002  to the second alarm threshold  3004 . Thus, in this case, the number of alarm thresholds is non-linearly related to variation in the alarm limit values. This is illustrative of the realization that, in some cases, a hospital or other patient care facility could make relatively small changes to the alarm criteria used in monitoring a physiological parameter while disparately impacting the number of detected alarms and false positives. In some cases, the number of detected alarms could be significantly reduced, for example, by reducing the number of false positives without necessarily increasing the risk of false negatives in a clinically-significant way. Even if, however, no disproportionate change in the number of false positives can be achieved with a relatively small adjustment to alarm criteria (e.g., an alarm threshold value), the techniques described herein may still be useful in some circumstances for incrementally reducing the number of false positives in a safe manner. Of course, changes to the alarm criteria used for monitoring patients are not to be taken lightly; generally speaking, hospital administrators or other responsible personnel should authorize any changes to alarm criteria. 
     In some embodiments, a device and/or system is provided for collecting medical monitoring information from patients in a patient care domain. For example, the medical monitoring information can be collected from a clinically-significant number of patients over a clinically-significant period of time. In some embodiments, the patient care domain is a group of patients of a similar type, or a group of patients who exhibit similar medical characteristics, conditions, defects, etc., and, as such, can also be expected to undergo monitoring alarm conditions for similar reasons. For example, the patient care domain could consist of a group of cardiac patients on a hospital floor, etc. 
     In some embodiments, a number of bedside patient monitors are used to collect physiological signals from the patients. The raw physiological signals can be processed by the bedside patient monitors. For example, the bedside patient monitors may perform averaging of the raw signals, filtering, etc. The bedside patient monitors may also perform computations to calculate the value of a physiological parameter. The bedside patient monitors may then output an indication of a physiological parameter value (e.g., SpO 2 , pulse rate, blood pressure, etc.) and its trending over time. Physiological information such as the raw physiological signals, processed physiological signals, and/or calculated physiological parameter values, for example, for each of the patients can then be transmitted to, and stored by, for example, a central repository. In some embodiments, this information is stored by a networked database such as, for example, the round-robin database  722  described herein. In some embodiments, the central repository can store medical monitoring information for the patients in a particular domain (e.g., a hospital ward) over a period of time such as a week, or a month, for example. 
     At the initial time of monitoring, an algorithm, or algorithms, may be applied to the raw physiological signals, processed physiological signals, and/or computed physiological parameter values for detecting whether a first set of alarm criteria are satisfied. This can be done by, for example, each bedside patient monitor for each patient in the patient care domain. The first set of alarm criteria are, for example, those criteria implemented in the patient monitoring devices that perform real-time monitoring functions to detect alarm conditions. If the alarm criteria are satisfied, then an alarm can be generated, as described herein. The central repository can also be used to store the occurrences of alarm conditions for each patient. 
     In some embodiments, once a statistically-significant amount of patient monitoring data has been collected at the central repository, a reporting module can access the central repository and use these data to simulate the alarm events that would have been detected had the patient monitoring devices in the patient care domain used a different set of alarm criteria than those that were actually used at the time of monitoring. 
     In some embodiments, the reporting module is used in conjunction with the patient monitoring systems described herein (e.g., those shown in  FIGS. 1, 2, 6, 7, 19 , and others). In some embodiments, the reporting module is a server or other computing device communicatively coupled to a network of bedside patient monitoring devices, a central monitoring station, a database, and other devices that can form a patient monitoring system. The reporting module can include a processor for analyzing patient monitoring data. The reporting module can also include, for example, electronic memory for storing patient monitoring data. 
     In some embodiments, if the central repository includes, for example, physiological parameter trend data for each of the patients, then the reporting module can access the trend data and can re-analyze it using, for example, the same algorithm, or algorithms, previously used by the bedside patient monitoring devices for detecting whether alarm criteria are satisfied. However, in this case a second alarm criteria can be used that is different from the first alarm criteria that was used to detect alarm conditions, for example, in real time when the patient monitoring data was actually collected. In some embodiments, the reporting module re-analyzes the stored patient monitoring data using multiple different new alarm criteria. Thus, the reporting module can generate information showing how the number of alarms detected changes as a function of changing alarm criteria. 
       FIG. 31  is a flow chart that illustrates a method  3100  for determining the variation in identified alarm conditions resulting from varying alarm criteria. The method  3100  begins at block  3102  where physiological parameter data is collected from a group of patients in a patient care domain. For example, the physiological parameter data can be collected by a number of different bedside patient monitoring devices distributed throughout a patient care facility. The collected physiological parameter data can include, for example, any type of information relevant to the physiological parameter being monitored and the patient from whom the physiological parameter data is being collected. Again, some examples of physiological parameter data that can be collected are raw physiological signals, processed physiological signals, calculated values of a physiological parameter, etc. 
     At block  3104 , the physiological parameter data is analyzed to identify alarm conditions based upon a first set of alarm criteria. The alarm criteria can be configurable so as to modify the physiological conditions that will trigger an alarm. In some embodiments, the analysis of the physiological parameter data is performed in substantially real-time by, for example, the bedside patient monitoring devices in order to detect alarm conditions as they occur. The alarm criteria will generally depend upon the particular physiological parameter being monitored. In some embodiments, the alarm criteria is a single threshold value. In some embodiments, the alarm criteria includes multiple threshold values that define, for example, an enclosed range of safe or normal values for the physiological parameter. Other types of alarm criteria can also be used. 
     At block  3106 , the physiological parameter data is stored at, for example, a central repository (e.g., the round-robin database  722 ). In some embodiments, the central repository stores all, or substantially all, of the physiological parameter data that was collected at block  3102 . For example, the central repository can store a physiological information such as the raw physiological signals from each patient, or physiological signals that have already been processed or altered to some extent by, for example, the bedside patient monitoring devices. In addition, the central repository can store information about any alarm conditions that were detected for each patient at block  3104 . For example, the central repository can store the timing and type of each alarm condition for each patient. 
     At block  3108 , the physiological parameter data that was previously stored can be analyzed to identify alarm conditions based on a second alarm criteria that is different from the first criteria used at block  3104 . This analysis can be performed by, for example, the reporting module described herein. If, for example, in the case of blood oxygen saturation monitoring, detected pulse oximetry signals were analyzed at the actual time of monitoring using an alarm threshold of 94% oxygen saturation, then later at block  3108 , the pulse oximetry signals can be re-analyzed using an alarm threshold of 93% oxygen saturation, or 92% oxygen saturation, etc. This analysis of the previously-collected physiological parameter data can be used to simulate the effect of a new alarm threshold in a riskless manner, since patients can still be monitored at, for example, blocks  3102 ,  3104  using alarm criteria that are already accepted and validated. This ability to simulate the effect of changing alarm criteria on the alarm conditions that are identified from physiological data is advantageous to hospitals and other patient care facilities as a means of adjusting alarm criteria to be specifically adapted for that particular hospital or patient care facility. Specially adapted alarm criteria are advantageous because alarm criteria that work well at one hospital, or for one type of patient, are not necessarily guaranteed to work well at another hospital, or for another type of patient. This can be due to differences in the type of monitoring equipment that is used, differences in patient population, differences in the type of medical care offered, differences in medical procedures implemented by clinicians, etc. 
     In some embodiments, the algorithm, or algorithms, that are applied by the reporting module to the collected physiological parameter data at block  3108  are the same as, or substantially similar to, those which were applied at the time of monitoring in order to detect real-time alarm conditions, though this may not be required in all embodiments. In addition, in some embodiments, the physiological parameter data stored at the central repository is the same as, or substantially similar to, the physiological parameter data to which alarm detection algorithms were applied by, for example, bedside patient monitors at the time of collection of the data. In this way, different alarm criteria can be simulated as if they had actually been used at the time of collection of the physiological parameter data to detect real-time alarm conditions. 
     At block  3110 , the reporting module can analyze the effect of the simulated alarm criteria on alarm conditions that are detected. For example, the reporting module can analyze the change, if any, in the number of detected alarm conditions using the new simulated alarm criteria. This information can be provided for each patient and/or for the combined group of patients, for example. In addition, the reporting module can analyze differences in the timing at which alarm conditions were detected. Generally speaking, the reporting module can analyze any change in the number, type, timing, duration, etc. of alarm conditions that are detected when using the second alarm criteria as compared to the alarm conditions detected using the first alarm criteria that were applied at the time of monitoring. 
     At block  3112 , the reporting module can output a report that identifies, explains, summarizes, or otherwise bears upon the effect of the simulated alarm criteria. This report can be beneficial to, for example, hospital administrators in determining whether any changes to the alarm criteria used by, for example, the bedside patient monitors are warranted. For example, as described herein, in some circumstances the alarm criteria could be changed so as to reduce the number of false positives that are detected. The reporting module enhances the ability of hospital administrators to make such decisions because it can provide information about the effect that such changes would have had if they had been previously implemented. Generally speaking, hospital administrators will have the final responsibility for determining whether changes to the alarm criteria can be safely made in order to, for example, reduce false positives without unacceptably increasing false negatives. 
       FIG. 32  illustrates an example report with a table  3200  showing how simulated alarm criteria affect alarm detection events. The table  3200  includes row entries for five different simulated alarm criteria, though any number of new alarm criteria could be simulated. The table  3200  includes column entries for the number of alarms detected using each simulated alarm criteria. The number of alarms could be broken down, for example, according to patient, or listed as a total sum of alarms detected for all of the patients for whom physiological parameter data was collected. 
     The table  3200  also includes column entries for the change in the number of alarms that were detected using each of the simulated alarm criteria as compared to the number of alarms that were detected using the actual alarm criteria applied at the time of collection of the physiological parameter data. This change could be indicated as the difference in the number of alarms, the percent difference, etc. 
     Many other types of information and information formats exist for reporting the effect of the simulated alarm criteria.  FIG. 32  illustrates only an example report that could be generated by the reporting module based upon the simulated alarm criteria. It should be understood that such reports could include a wide variety of information relating to the impact of the simulated alarm criteria to help hospital administrators make a decision as to whether changes to alarm criteria should be made. In addition, such reports can be presented in a wide variety of formats, including tables, charts, graphs, lists, spreadsheets, etc. 
       FIG. 33  is a flow chart that illustrates another method  3300  for determining the variation in identified alarm conditions that occur as a result of varying alarm criteria. The method  3300  is similar to the method  3100  illustrated in  FIG. 31 , however, the method  3300  additionally involves determinations of, for example, the expected effect of simulated alarm limits on false positive alarms and false negative alarms. 
     The method  3300  can proceed through blocks  3302  and  3304  as described above with respect to the method  3100  and blocks  3102 ,  3104  illustrated in  FIG. 31 . At block  3306 , however, the method  3300  further includes collection of medical intervention data. The medical intervention data can include, for example, records of whether a patient required some type of medical intervention at any point in time while the physiological parameter was being monitored. Such medical interventions could include, for example, the administration of a drug, attention from a physician or nurse (e.g., non-routine attention), attention from a rapid response team, administration of a treatment or procedure, etc. The medical intervention data can also include any pertinent information about the medical intervention such as, for example, the type, the time, and the duration of the medical intervention, the medical cause that necessitated the intervention, relationship to detect alarm events, etc. 
     In some embodiments, the medical intervention data that is collected at block  3306  is used to determine which, if any, of the alarm conditions detected at block  3304  were false positive alarms and/or which were alarms that represented true indications of medical duress. Later, this information can be used, for example, to determine whether various simulated alarm criteria would have eliminated any identified false positive alarms or whether the simulated alarm criteria would have resulted in non-detection of any alarms that actually did indicate a need for medical intervention (e.g., resulting in a false negative). In addition, the medical intervention data can be used to identify false negatives and to determine whether simulated alarm criteria would have resulted in detection of such false negatives. This information can be analyzed and presented in a report to further aid hospital administrators in making a determination of whether to change alarm criteria used by patient monitoring devices based upon simulated alarm criteria, as described herein. 
     The medical intervention data can be obtained in a variety of ways. For example, medical intervention data can be recorded by clinicians as medical interventions become necessary. These records can then be manually imported into the central repository that also stores the collected physiological parameter data. Medical intervention data can be automatically imported into the central repository from the patient&#39;s electronic medical record stored in, for example, a Hospital Information System or a Clinical Information System. In some embodiments, the bedside patient monitoring devices can be configured so as to prompt clinicians to enter medical intervention data, for example, after an alarm is disabled. Other techniques for obtaining records of medical interventions can also be used. 
     If a record of a medical intervention that has been performed on behalf of the patient is, for example, temporally associated with the timing of a detected alarm condition (e.g., they are separated by some length of time less than a pre-determined threshold), this can be taken as a sign of an accurately detected alarm condition. For example, if a detected alarm condition is followed by a medical intervention relatively shortly thereafter, then it can be presumed that the alarm condition required medical attention. If, however, a record of a medical intervention that has been performed is not temporally associated with the timing of any detected alarm condition for that patient, then this can be an indication of a false negative since the medical condition that necessitated the intervention did not trigger an alarm. Later in the method  3300 , after various new alarm criteria have been simulated, it can be determined whether such simulated criteria would have detected the false negative, or whether the new simulated criteria would have still detected the alarm condition that was accurately detected by the alarm criteria in place at the time of monitoring. 
     In some embodiments, medical intervention data can include an automated estimation of whether or not a medical intervention for a given patient has taken place. An estimation of whether or not a medical intervention was required after an alarm detection event can be automatically made based upon, for example, the length of time that a clinician spent with the patient after responding to an alarm event, or whether a physician came to check on the patient within some time limit of a detected alarm event. This information can be collected using the clinician proximity detection devices and systems described herein. For example, in some embodiments, a patient monitoring device can start a timer after an alarm detection event has occurred. If the presence of a physician (e.g., as identified by a clinician token, as described herein) is detected within some predetermined amount of time, then an estimation can be made that the physician visit was in response to the alarm event. As such, the physician visit can be identified as a medical intervention. Similarly, a patient monitoring device can track the amount of time that a clinician (e.g., a nurse) spends in proximity to the patient after silencing an alarm. If the amount of time with the patient exceeds a certain threshold, then it can be inferred that some type of medical intervention was necessary in response to the alarm event. 
     In addition, an estimate of whether or not medical intervention was required, for example, after an alarm event can be determined by analyzing the physiological parameter data collected for that patient. For example, the reporting module can analyze the trend values for the physiological parameter and determine whether the physiological parameter continued to worsen after the alarm event was detected. In some embodiments, the reporting module can analyze the trend data to determine whether the patient&#39;s condition, as indicated by the trend values of the physiological parameter, was worse  1  min. after the alarm detection event, whether it was worse  5  min. later, and/or whether it was worse  10  min. later. Different time limits can of course also be used. If such an analysis indicates that the patient&#39;s condition deteriorated after the alarm event was detected, then this can be taken as an indication that the alarm did in fact indicate that the patient was experiencing medical duress and that the alarm was not a false positive. 
     As just described, the medical intervention data used in the method  3300  can come from actual records of medical interventions that occurred. Alternatively, or additionally, the medical intervention data used in the method  3300  can be estimated based upon factors such as, for example, the amount of time clinicians spent with the patient in the wake of a detected alarm event or the behavior of the physiological parameter within some relevant time after a detected alarm event. Other factors and methods for estimating the occurrence of a medical intervention can also be used. While medical intervention data that results from actual clinician records may be more accurate and reliable, some such occurrences of medical interventions may go unreported. Estimated medical intervention data can be useful since the reliance upon clinicians to maintain accurate records is reduced, though the estimates may be somewhat less reliable than actual clinician records. 
     At block  3308 , the collected physiological parameter data and the medical intervention data can be stored in, for example, the central repository (e.g., the round-robin database  722 ) for later analysis by the reporting module. The reporting module can include logic used for correlating the collected medical intervention data with the detected alarm events. For example, the logic can include rules or criteria for determining whether or not a given medical intervention for a patient was related to an alarm condition experienced by that patient. For example, in the case of medical intervention data obtained from actual clinician records, a particular medical intervention for a patient can be correlated with a detected alarm event for that patient if the medical intervention and the alarm event occurred within a certain amount of time of one another. Other methods are also possible for matching medical intervention data with corresponding detected alarm events that were possibly related to the medical intervention. For example, such a correlation can be based upon the type of medical intervention that was performed and the type of physiological parameter for which monitoring data has been obtained. Some medical interventions may be viewed as being particularly likely to be related to a specific physiological parameter. In such cases, the reporting module logic may be configured to make it more likely that such a medical intervention will be marked as being correlated with alarm events triggered by that physiological parameter. 
     At block  3310 , the reporting module analyzes the physiological parameter data using second alarm criteria, for example, as described with respect to  FIG. 31  (e.g., block  3108 ). At block  3312 , the reporting module can analyze any differences between those alarm conditions identified using the first alarm criteria versus those alarm conditions identified using simulated second alarm criteria. For example, after determining the alarm conditions that would have been detected by the second alarm criteria, the reporting module can determine how many of the true alarm conditions that were correctly identified at the actual time of monitoring using the first alarm criteria would have still been detected if the simulated alarm criteria had instead been implemented. It is desirable that such true alarm conditions still be detected so as to avoid increasing the number of false negatives. Accordingly, information regarding the number of true alarm conditions that would go undetected using a given simulated alarm criteria can be provided to hospital administrators to aid in determining whether a proposed change to the alarm criteria should be adopted. 
     In addition, the reporting module can analyze the effect of the simulated alarm criteria on any false negatives that were identified based on medical intervention data. In some embodiments, the reporting module determines whether the simulated alarm criteria would have detected any false negatives that were not identified by the first alarm criteria actually used by the patient monitoring devices. This can be done, for example, by executing logic designed to determine whether any alarm conditions detected using the simulated alarm criteria are temporally correlated with a previously-identified false negative event. If, for example, an alarm condition identified by the simulated alarm criteria precedes the timing of the identified false negative by some period of time less than a given threshold, then this can be taken as an indication that the alarm condition would have been an indicator of the false negative. Other logical tests can also be applied to correlate alarm conditions detected using the simulated alarm criteria with false negatives that have been identified based on medical intervention data. 
     At block  3314 , the reporting module outputs a report that identifies, explains, summarizes, or otherwise bears upon the effect of the simulated alarm criteria. In some embodiments, the report can provide an indication of the effect that the simulated alarm criteria would be expected to have on not only the number of detected alarm events but also the number, percentage, proportion, etc. of, for example, previously undetected false negatives that may have been detected using the simulated alarm criteria. The report can also include an indication of, for example, the number, percentage, proportion, etc. of actual alarm conditions that were correctly identified using the first alarm criteria but may not have been identified using the second alarm criteria. The report can also include other information as well. 
       FIG. 34  illustrates an example report with a table  3400  showing how simulated alarm criteria affect the total number of alarm detection events as well as how the simulated alarm criteria affect, for example, false negatives and false positives. The table  3400  is similar to the table  3200  illustrated in  FIG. 32 , and includes row entries for five different simulated alarm criteria. The table  3400  includes column entries for the number of alarms detected using each simulated alarm criteria. The table  3200  also includes column entries for the change in the number of alarms that were detected using each of the simulated alarm criteria as compared to the number of alarms that were detected using the actual alarm criteria applied at the time of collection of the physiological parameter data. 
     In addition, the table  3400  includes column entries for the estimated number or percentage of false negatives that previously went undetected but would have been detected using a particular simulated alarm criteria. The table  3400  also includes column entries for the estimated number or percentage of true alarm conditions that were correctly identified using the first alarm criteria but would not have been identified using a particular simulated alarm criteria (i.e., new false negatives resulting from the simulated alarm criteria). These values can be determined or estimated by the reporting module, as described herein. The table  3400  could also include information regarding change in false positives, for example, the number of false positives that were detected by the first alarm criteria that would not have been detected by the simulated alarm criteria, or vice versa. 
     Again,  FIG. 34  illustrates only an example report that could be generated by the reporting module based upon the simulated alarm criteria. It should be understood that such reports could include a wide variety of information to help hospital administrators make a decision as to whether changes to alarm criteria should be made. In addition, such reports can be presented in a wide variety of formats, including tables, charts, graphs, lists, spreadsheets, etc. 
     In addition to simulating alarm criteria, as described herein, the reporting module can also simulate the effect of other configuration changes in the bedside patient monitoring devices and/or a central patient monitoring station. For example, the reporting module can simulate the effect of different alarm notification delay times. As discussed herein, in some embodiments, when an alarm condition is detected, bedside patient monitors may be configured to wait until a predetermined alarm notification delay time has elapsed before transmitting notification of the alarm event to either a clinician or to a central monitoring station. In addition, the central monitoring station can likewise be configured to wait until a predetermined alarm notification delay time has elapsed before actually transmitting a notification of the detected alarm to a clinician by, for example, a page or other notification method. 
     These notification delay times can be useful in reducing the frequency of false positive alarm notification events when alarm conditions only transiently persist. Such transient alarm conditions may be triggered by, for example, sudden exertion or emotion. The reporting module can be useful in simulating the effect of differing notification delay times on alarm notification events. This can be useful because, for example, relatively slight modifications to the notification delay times could result in an important reduction in the number of false positives to which clinicians must respond. 
       FIG. 35  is a flow chart that illustrates a method  3500  for determining the variation in alarm notification events that occurs as a result of varying alarm notification delay times. The method  3500  begins at block  3502  where patients are monitored for physiological parameter alarm events, as described herein. 
     The method  3500  proceeds to block  3504  where alarm notification events are identified based upon a first alarm notification delay time. For example, an alarm notification event may be a notification by a bedside patient monitor to a central monitoring station of an alarm condition. In this case, the first alarm notification delay time could be measured as the elapsed time between when an alarm condition was detected at the bedside monitor and when notification of the alarm was sent to the central monitoring station. In addition, an alarm notification event may be a notification from a patient monitoring device to a clinician of an alarm condition. In this case, the first alarm notification delay time can be measured as the elapsed time between when an alarm condition was detected and when the clinician was notified. 
     At the initial time of monitoring, an algorithm, or algorithms, may be applied to the raw physiological signals, processed physiological signals, and/or computed physiological parameter values for detecting whether an alarm condition has persisted for the duration of the first alarm notification delay time. This can be done by, for example, each bedside patient monitor for each patient in the patient care domain. If an alarm condition persists for the duration of the first alarm notification delay time, then an alarm notification event can be recognized. 
     At block  3506 , physiological parameter data is collected and stored at, for example, a central repository (e.g., the round-robin database  722 ), as described herein. At block  3508 , the physiological parameter data is re-analyzed by, for example, the reporting module using a second alarm notification delay time that is different from the first alarm notification delay time. If, for example, the first alarm notification delay time used by the patient monitoring device at block  3504  were  5  sec., the physiological parameter data could be re-analyzed using an alarm notification delay time of, for example,  6  sec., or  7  sec., etc. Shorter delay times could also be simulated. 
     In some cases, if the alarm condition is only transient in nature, a relatively small lengthening of the alarm notification delay time could result in the alarm condition ceasing before an alarm notification event is generated. In this way, adjustment of the alarm notification delay time can potentially safely reduce the number of alarm notification events to which clinicians must respond. This can in turn increase the effectiveness of patient care by allowing clinicians to focus their time on attending to alarm events that are non-transient. Of course, any change to alarm notification delay times should generally be approved by hospital administrators or other responsible personnel to ensure that, for example, increases in the alarm notification delay times do not unacceptably put patients at risk by increasing the amount of elapsed time between a detected alarm and the arrival of a clinician. 
     The analysis of the previously-collected physiological parameter data by the reporting module can be used to simulate the effect of a new alarm notification delay time in a riskless manner since patients can still be monitored at, for example, blocks  3502 ,  3504  using a delay time that has already been accepted and validated. This ability to simulate the effect that new alarm notification delay times would have, without necessarily actually implementing them, is advantageous to hospitals and other patient care facilities as a means of adjusting alarm notification delay times to be specifically adapted for that particular hospital or patient care facility. As described herein with respect to alarm criteria, a change in the alarm notification delay times may result in significantly fewer alarm notification events without necessarily increasing the risk to patients. 
     At block  3510 , the reporting module can analyze differences between clinician notification events that are detected using the first alarm notification delay time as compared to those that are detected using the second alarm notification delay time. For example, the reporting module may determine whether the total number of alarm notification events decreases or increases, and by how much, in response to a change in the alarm notification delay time. This information can be presented to hospital administrators in the form of tables, charts, spreadsheets, etc. to assist them in determining whether a change in the alarm notification delay times implemented by the patient monitoring devices would be advantageous. 
     Clinician response time data can also be collected and stored for analysis by the reporting module. Clinician response time can be measured as, for example, the elapsed time between when a clinician is notified of an alarm condition and when the clinician arrives at the patient&#39;s room to shutoff the alarm and check the patient&#39;s status. This elapsed time can be measured by, for example, the bedside patient monitoring devices and transmitted to the central repository of data. Clinician response times can be stored for each clinician and/or for a group of clinicians as a whole. As a result, the reporting module can output information regarding, for example, the maximum, minimum, and average response times for each clinician, and/or for a group of clinicians as a whole. This data may be useful to hospital administrators as an indicator of the performance of an individual clinician, or a group of clinicians, in responding to monitoring alarms in a prompt manner. Display Features 
       FIGS. 36A-B  illustrate displays having layout zones including zones for parameters  3610 , a plethysmograph  3620 , a prompt window  3630 , patient information  3640 , monitor settings  3650 , monitor status  3660 , user profiles  3670 , a parameter well  3680 , pulse-to-pulse signal quality bars  3690  and soft key menus  3695 . Advantageously, each zone dynamically scales information for readability of parameters most important to the proximate user. Also, the prompt window  3630  utilizes layered messaging that temporarily overwrites a less critical portion of the display. Further, the parameter well  3680  contains parameters that the proximate user has chosen to minimize until they alarm. These and other display efficiency features are described below. 
       FIGS. 37A-F  illustrate displays that vary layouts and font sizes according to the number of installed parameters. Horizontal and vertical display formats are shown for displaying eight parameters ( FIG. 37A ); seven parameters ( FIG. 37B ); six parameters ( FIG. 37C ); five parameters ( FIG. 37D ); four parameters ( FIG. 37E ); and three parameters ( FIG. 37F ). Advantageously, font size increases with fewer installed parameters. Further, parameter layout varies according to the number of rows and spacing according to the number of installed parameters. Also, the plethysmograph display increases in size with few installed parameters. In addition, font size of text information scales according to the amount of information displayed, e.g. patient name is displayed in a smaller font when date and time information is added. 
       FIGS. 38A-B  illustrate displays  3800  having parameter wells  3810 . In particular, parameter values are displayed in either a main display portion or in a parameter well. Through a menu selection or by user profile activated by user proximity, a parameter is minimized to the parameter well. Advantageously, one or more parameters in the parameter well are displayed in a relatively small font. However, when a minimized parameter alarms, it is removed from the parameter well and return in a relatively larger font to the main display. 
       FIGS. 39A-B  illustrate enlarged parameter displays  3900 ,  3901  that increase the font size of alarming parameters. In normal conditions, all parameters are display in a same sized font. When an alarm occurs, the violating parameter&#39;s actual value and limit values are displayed in a larger font and also blink to draw attention to the violation. In another embodiment, where all parameters are displayed at or near the maximum-sized font, then the alarming parameter will increase only slightly in size while all other parameters are reduced in size. Thus, the effect is an appearance that the alarming parameter is enlarged. In an embodiment, if either a single parameter alarms ( FIG. 39A ) or all parameters alarm ( FIG. 39B ), the background color also blinks at the same frequency so as to contrast with the blinking font, such as between a red background color and a soft red background color. 
       FIGS. 40A-B ,  41 ,  42 ,  43 A-B illustrate additional display embodiments having various advantageous features.  FIGS. 40A-B  illustrate trend displays  4000  having colored alarm zones  4010  so that a user can readily identify the historical severity of a patient condition that triggers an alarm.  FIG. 41  illustrate displays that invert arrow keys to match the cursor.  FIGS. 43A-B  illustrate trend displays and corresponding set-up screens. 
       FIG. 42  illustrates a display having user-selectable jump-screens. In particular, through a menu option choice, a user can choose one of multiple jump screens, such as the seven choices shown, that they can access from the home page. In an embodiment, the default behavior for the button is the Trend-Toggle button  4231 . Other buttons are Alarm Limits  4232 , Compressed Waveform View or PI &amp; PVI trend overlay  4233 , Mode Sensitivity  4234 , Patient Assess  4235 , Parameter Detail Toggle  4236  and User Profile Login  4237 . 
     Information and signals described herein can be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that can be referenced throughout the above description can be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof. 
     The various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans can implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention. 
     Depending on the embodiment, certain acts, events, or functions of any of the methods described herein can be performed in a different sequence, may be added, merged, or left out all together (e.g., not all described acts or events are necessary for the practice of the method). Moreover, in certain embodiments, acts or events may be performed concurrently, e.g., through multi-threaded processing, interrupt processing, or multiple processors, rather than sequentially. 
     The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein can be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor can be a microprocessor, conventional processor, controller, microcontroller, state machine, etc. A processor can also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. In addition, the term “processing” is a broad term meant to encompass several meanings including, for example, implementing program code, executing instructions, manipulating signals, filtering, performing arithmetic operations, and the like. 
     The steps of a method or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, a DVD, or any other form of storage medium known in the art. A storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium can reside in an ASIC. The ASIC can reside in a user terminal. In the alternative, the processor and the storage medium can reside as discrete components in a user terminal. 
     The modules can include, but are not limited to, any of the following: software or hardware components such as software object-oriented software components, class components and task components, processes, methods, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, or variables. 
     In addition, although this invention has been disclosed in the context of certain preferred embodiments, it should be understood that certain advantages, features and aspects of the systems, devices, and methods may be realized in a variety of other embodiments. Additionally, it is contemplated that various aspects and features described herein can be practiced separately, combined together, or substituted for one another, and that a variety of combination and subcombinations of the features and aspects can be made and still fall within the scope of the invention. Furthermore, the systems and devices described above need not include all of the modules and functions described in the preferred embodiments.