Abstract:
A method and an apparatus for use in monitoring are disclosed. The method and system involve the use of a mathematical model. The method and system are particularly useful in the field of patient monitoring when using a physiological mathematical model. A mathematical model can be used to identify an abnormal condition. The mathematical model can also be used to generate an alarm. Also, the mathematical model can be used to generate a suggested treatment for correcting an abnormal condition if an abnormal condition should arise, especially abnormal conditions requiring relatively immediate attention.

Description:
FIELD OF THE INVENTION  
         [0001]    The invention relates to monitoring. More specifically, it relates to methods used to process data obtained during monitoring of a subject.  
         BACKGROUND OF THE INVENTION  
         [0002]    Monitors are used to monitor all sorts of variables to look for the occurrence of certain noteworthy events. Unfortunately, many of these monitors indicate that an event has occurred when in fact no significant event has occurred (false positive). A monitor that can reduce false positive rates without increasing false negative rates would be desirable.  
           [0003]    Many subjects have unique characteristics. A value that would indicate an abnormal event for one subject, may be a normal value for another subject. A monitor that could use limits based on the characteristics of the subject would be preferable.  
           [0004]    Sometimes a change in a value is a significant event. Other times no change in a value is more significant than a change in the value. It would be preferable to have a monitor that could recognize an event based on the absence of a change when the absence of a change is significant. It would be desirable to have a monitor that could both identify an event based on absence of a change when absence is significant, and presence of a change when presence is significant.  
           [0005]    Additionally, in many emergency situations, when abnormal conditions are present, doctors must make quick decisions. Often times, a doctor must look through a large amount of information to make an appropriate decision. A system that can simplify a doctor&#39;s ability to make a decision would be preferable.  
           [0006]    Also, some situations that involve emergency situations may be rare or uncommon for a particular physician. A system that could aid a physician in one of these circumstances would be preferable.  
           [0007]    Additionally, in many on-going monitoring situations, a patient&#39;s potential physiological response is the most important feature for planning an intervention. A mathematical model used to aid in choosing the appropriate response would be beneficial. Further, excess data would likely not be necessary, and would likely add extra expense, to making the choices. Further, excess, marginally relevant, data would only slow down a decision making process. One such set of data that may not be as important for monitoring applications would be a patient&#39;s particular anatomic features. Since many patient monitoring decisions require quick decisions, and do not afford for big, expensive procedures to obtain and process data which may only be marginally relevant, a mathematical model used to monitor a patient preferably can operate based largely on a physiological mathematical model. Specifically, a mathematical model used to aid in the choice of alternative treatments for a patient being monitored preferably would not require incorporation of the anatomic features of the patient in order to aid in the decision making process (anatomic features being features such as the location of certain organs, number of ribs, locations of wounds, and other similar information as opposed to physical characteristics which include age, weight, height, race, sex, etc.).  
           [0008]    The teachings hereinbelow extend to those embodiments which fall within the scope of the appended claims, regardless of whether they accomplish one or more of the above-mentioned needs.  
         SUMMARY OF THE INVENTION  
         [0009]    One embodiment is directed to a method for generating an alarm. The method comprises acquiring data from a subject and generating a comparison based on the data and a mathematical model representing the subject.  
           [0010]    Another embodiment provides a method for generating an alarm using a medical monitoring device. The method comprises acquiring data from a patient and comparing the data to a physiological mathematical model. The comparison can then be used to identify an abnormal condition and generate an alarm. Information relating to the identified abnormal condition may also be displayed.  
           [0011]    Another embodiment provides a method for treating a patient. The method comprises inputting physiological data relating to a patient, and determining an appropriate response based on the physiological data without using an anatomical mathematical model.  
           [0012]    Another embodiment is directed to a medical monitoring system. The system comprises a data acquisition device configured to acquire physiological data relating to a patient. The system also comprises a processor configured to generate a comparison based on physiological data acquired by the data acquisition device and a mathematical model that considers effects of a treatment on a patient. The processor may also be configured to send an alarm signal based on the comparison.  
           [0013]    Other principle features and advantages of the invention will become apparent to those skilled in the art upon review of the following drawings, the detailed description, and the appended claims. 
       
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0014]    [0014]FIG. 1 is a diagram of an exemplary embodiment of a system for monitoring according to one aspect of the invention where a subject is identified, where a monitor is connected to a network, and where a billing record can be generated based on the use of the monitor;  
         [0015]    [0015]FIG. 2 is an exemplary illustration of a flow diagram for monitoring a subject using a mathematical model according to one aspect of the invention;  
         [0016]    [0016]FIG. 3 is an exemplary illustration of a flow diagram for monitoring a subject according to another aspect of the invention where different alarms can be generated and where data is gathered from a plurality of monitors;  
         [0017]    [0017]FIG. 4 is another exemplary embodiment of a system for monitoring a subject according to another aspect of the invention;  
         [0018]    [0018]FIG. 5 is another exemplary illustration of a flow diagram for monitoring a subject using a mathematical model according to another aspect of the invention;  
         [0019]    [0019]FIG. 6 is an exemplary illustration of a comparison between an acquired data stream and a simulated data stream according to one aspect of the invention. 
     
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS  
       [0020]    In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the exemplary embodiments of the present invention. It will be evident, however, to one skilled in the art that the exemplary embodiments may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate description of the exemplary embodiments.  
         [0021]    Referring first to FIG. 1, a monitoring system  8  comprises a monitor  14  and a network  18 . Monitor  14  also comprises a network interface  30  that allows transfer of data to and from network  18 . Network interface  30  is preferably configured to allow wireless transfer of data. More preferably, network interface  30  is configured to transmit data using a radio frequency. Network interface  30  may directly facilitate transfer of data across a network for the monitor, or may facilitate transfer of data by coupling the monitor to some other device that can directly facilitate transfer.  
         [0022]    The data transferred from monitor  14  to network  18  can be raw data and/or processed data. Also, data can be transferred to monitor  14  to aid, configure, or operate a function of monitor  14 , or can serve some other purpose relating to monitor  14 . For instance, a mathematical model  326  (FIG. 4) relating to a specific subject can be transferred to monitor  14  using network  18 .  
         [0023]    Data acquisition device  13  acquires data from subject  10 . The data acquired by data acquisition device  1   3  is preferably physiological data from a patient. Processor  25  can be configured to generate a comparison based on data acquired by the data acquisition device and a mathematical model  326 , and send an alarm signal based on the comparison. For instance, processor  25  may be configured to send an alarm signal if the physiologic values from the patient deviate beyond a threshold amount or a significant amount from what would normally be expected based on the mathematical model.  
         [0024]    Processor  25  may be any signal processing circuitry, such as one or more microprocessors in combination with program logic stored in memory. Processor  25  may be made of a series of sub-processors where each sub-processor performs one of the functions of processor  25 . Further, processor  26  may perform the functions of processor  25 . Further still, processor  26  and processor  25  may be sub-processors of another processor that is responsible for the various functions.  
         [0025]    Physiological mathematical models  326  allow for simulation of a variety of human body compartments and their reaction to treatment processes. A mathematical model that considers effects of events on a subject is any model that represents the working of a subject mathematically, including taking into account what data would be expected from data acquisition device  13  given the events affecting subject  10 . The model may be constructed using finite element alanlysis techniques. For example, a physiological mathematical model  326  may operate by dividing a patient&#39;s bodily system into compartments and tries to represent mathematically what happens, physiologically, in each compartment and how the various compartments interact and respond to medical treatments. This may be used to calculate predicted values for physiologic data if a given event occurs, such as laying in bed, eating, breathing, moving, being injected with anesthesia, taking medicine, reacting to a stimulus, reacting to a therapy, etc. The model  326  preferably can generate predicted values for a plurality of events.  
         [0026]    The mathematical model  326  can be generic, but is preferably tailored to take into account various properties of the subject. For a patient, the model  326  may take into account age, weight, and/or other criteria. Additionally, the model may take into account properties of a subject by incorporating empirical data relating to the subject. Some possible empirical data to be incorporated could include the results of imaging scans, tests run on the subject, physiological inputs, and various other patient data.  
         [0027]    There are a number of ways to tailor mathematical model  326  to a subject  10 . A user can enter the subject&#39;s attributes into the mathematical model  326 . Such information can be received directly from a subject&#39;s file. In addition to those attributes commonly found in a subject&#39;s file  10 , the file may contain data from registering a pre-treatment data stream from subject  10  to incorporate into the attributes of the subject  10 .  
         [0028]    One example of a mathematical model that may be used as a base model of a patient monitoring system to monitor the physiology of a patient is BODY Simulation for Anesthesia. BODY Simulation is a multi-media interactive anesthesia trainer that has been implemented on a PC. It simulates a patient, an anesthesia workstation, a ventilator and gas delivery circuit, parts of the operating room, and even some operating room personnel. Body Simulation for Anesthesia is based on mathematical models of physiology and pharmacology. When affected by a stimulus (drugs, gases, pain, etc.), the patient&#39;s response is calculated to be as close as possible to that of an average person. This is done using a complex set of mathematical equations.  
         [0029]    Body Simulation can be used to produce real time data plots allowing a user to see different clinical and physiologic parameters graphically displayed. Graphics of drug concentrations and drug mass in  16  different body compartments may be viewed. Dynamic gas displays and X-Y plots of respiration are available. These tools allow the user to see the pressures, flows, resistance, and compliance in the heart, blood vessels, lungs and other organs, as well as drug concentrations and/or masses in the compartments. Scientific data may be viewed in real time as events are occurring during the case.  
         [0030]    The mathematical model  326  may also be adjustable. One manner in which the model  326  may be adjustable is based on results of monitoring. For instance, if the model  326  keeps generating false alarms, the model may adjust to better suit the subject, to be more tolerant, and/or in some other manner to reduce the likelihood of false alarms.  
         [0031]    The model  326  may also be changeable. For instance, the model may be changeable in that if a new drug has been studied with respect to the model, an upgrade can be added to take into account the effects of the new drug. Also, the model may be changeable in that one portion of the model may be used in one instance, but other portions of the model may be used in other instances. This may allow the relevant portions to be applied, while not requiring the lengthy procedure of running through every portion of the model in every instance.  
         [0032]    The alarm signal generated by processor  25  may be based on a tolerance factor where a larger difference is allowed if the tolerance factor is higher. The tolerance factor can be based on a number of different criteria. For example, the tolerance factor may be adjusted by a user, may be adjusted based on information relating to subject  10 , and/or may be adjusted based on the amount of data inputted from subject  10  (the more data that has been inputted, the more likely the mathematical model accurately represents the subject). The tolerance factor may change over time and may be different for different applications of the model to subject  10 .  
         [0033]    Further, the alarm signal sent by processor  25  may be sent to an alarm signaling device  62  physically connected to processor  25 , or may be sent to an alarm signaling device  60  located remote from processor  25 . Remote alarm signaling device  60  may be a part of a pager or some other type of communication device. Remote alarm signaling device  60  could also be located at a discrete location such as at a nurse&#39;s station in a health care facility. The signal from processor  25  would then cause alarm signaling devices  60  and  62  to generate an alarm.  
         [0034]    The alarm generated by alarm signaling devices  60  and  62  may take on any form including, but not limited to, an audible sound, a visual indicator, and/or a vibrating alert. The alarm generated by alarm signaling devices  60  and  62  can include a message indicating the reason for the alarm. The alarms generated by alarm signaling devices  60  and  62  could also be differentiated based on a number of criteria including the type and severity of the event causing the alarm. Further, if a system has more than one alarm signaling device, the device that signals the alarm could be differentiated based on a number of criteria including the type and severity of the event underlying the alarm.  
         [0035]    Processor  25  can also be configured to generate information useful for formulating a response if an abnormal condition (one that might set off an alarm) is identified. An abnormal condition can be identified in a number of manners by a number of different techniques.  
         [0036]    Possible reasons that an abnormal condition exists could include an actual abnormal condition, a malfunction in equipment, or improper set up of the equipment (originally or caused to be improper by some later event—such as patient movement).  
         [0037]    Processor  25  can process the data inputted from various sensors and display information based on the inputted data when an abnormal condition exists. The information displayed could be listing the data that resulted in the determination that an abnormal condition exits, could be displaying the reasons that an abnormal condition was indicated (such as the data and the calculations made based on the data), could be suggesting reasons why an abnormal condition might exist, could be suggesting an appropriate reaction to the fact that an abnormal condition was indicated, and/or could be some other information relating to the abnormal condition.  
         [0038]    In a health care setting, a mathematical model is preferably used to determine an appropriate response to the abnormal condition that was identified from the monitoring of the patient. Such an abnormal condition would likely have an immediate adverse effect on the patient. A mathematical model can be used to identify the response that will best alleviate the abnormal condition by determining the likely effect of administering different treatments in response to the abnormal condition. Such a system could include balancing the longer term effectiveness of a treatment against the short term need to alleviate the immediate adverse effects of the abnormal condition.  
         [0039]    When applied to a patient, processor  25  can input various physiological data relating to a patient to look for an abnormal condition. The physiological data that is inputted can be applied to a physiological or pathophysiological based mathematical model. Such a model may be useful for ongoing monitoring of patients such as occur in a critical care facility.  
         [0040]    Storage  22  may include a database that stores a mathematical model. The stored mathematical model may be a generic model, or may be a model that had previously been customized to subject  10 . Data from storage  22  may be transferred to monitor  14 , and data from monitor  14  may be transferred to storage  22 .  
         [0041]    Monitoring system  8  may also include an event monitor  66 . Event monitor  66  can monitor the occurrence of an event that might affect the predicted values based on the mathematical model being used by processor  25 . For example, a patient may receive medication intravenously so event monitor  66  can monitor the rate, and using the concentration of the medication, also monitor the amount of medication being administered. Also, event monitor  66  could be used to indicate that subject  10  is moving or lying down, and even the rate at which subject  10  is moving or for how long subject  10  has been laying down. There are also a large number of other events that could be monitored by event monitor  66 . Event monitor  66  would then send a signal based on its monitoring of subject  10 . The event monitor signal could then be included in the calculation of the predict values based on the mathematical model.  
         [0042]    Referring to FIG. 2, a process for monitoring a subject using a mathematical model includes identifying a subject at step  104 . The identification at step  1   04  can be performed manually (an operator enters a patient ID code into monitor  14 , an operator inserts a patient ID card, etc.), or automatically (patient is identified wirelessly using a wireless detector). Based on the subject identified, characteristics of the identified subject can be imported at step  110 . These characteristics can be used along with the stored base mathematical model to form an adjusted model at step  108 . This can be done by modifying parameters of the mathematical model to reflect characteristics of the subject. The base mathematical model stored at step  102  can be a generic model or can be a model that had previously been tailored to the subject. For a patient, the base mathematical model preferably includes a physiological mathematical model.  
         [0043]    Also, data is acquired from the subject at step  100 . For a patient, the data preferably includes physiological data collected by a monitor. The data can be from one source or can be from multiple sources. A comparison is generated at step  106  based on the adjusted model from step  108  and the data acquired at step  100 . The comparison preferably includes comparing at least one value of the acquired data to a value predicted using the mathematical model, and determining the difference between the values.  
         [0044]    At block  112  the comparison generated at block  106  is used to determine whether an alarm should be generated. Determining whether an alarm should be generated can be based on any number of criteria. Further, different alarm types/levels can be generated based on different criteria. If an alarm is not generated then data is acquired at step  100 . If an alarm should be generated, then an alarm is generated at step  116 .  
         [0045]    A determination is then made at step  118  as to whether the alarm is a valid alarm. This determination can be made by a user who sends an input if the alarm should not have been generated, can be made by determining if other sources for monitoring the subject indicate that an alarm should be generated, can be made using some combination of these criteria, or can be made using some other criteria. If the alarm is not valid, the mathematical model is adjusted at step  108  in an attempt to make the model function as a better predictor of appropriate alarms. If the alarm is valid, a record of the alarm is made at step  114  and data is acquired at step  100 .  
         [0046]    Referring to FIG. 3, data relating to a subject is acquired at block  200  from a plurality of monitors. The data from the plurality of monitors is correlated at block  204  to form a correlated data set. The correlated data set could refer to only one monitored characteristic of the subject, or could refer to multiple monitored characteristics of the subject. At block  206  the correlated data is used to generate a comparison between the correlated data set and a mathematical model of the subject. The comparison could comprise comparing the data from each of-the monitors individually to predicted values based on the mathematical model, or may comprise comparing the correlated data set as a whole to predicted values based on the mathematical model.  
         [0047]    At block  208 , the comparison of block  206  is used to determine if conditions are severe enough to generate a severe alarm at block  210 . If conditions are not severe enough, the comparison of block  206  is used at block  212  to determine if conditions are such that a moderate alarm should be generated at block  210 . If conditions do not warrant a moderate alarm, the comparison of block  206  is used at block  216  to determine if conditions are such that a moderate alarm should be generated at block  218 . The severity of the alarm generated may depend on the amount of difference between the predicted value and the data, may be based on the number of data values that differ from the predicted values, etc.  
         [0048]    If no alarm is generated, data is acquired at block  200 . If an alarm is sent at blocks  210 ,  214 , or  218 , an indication of the reason for the alarm is generated at block  202 . The indication could be made in any number of forms. Further, the indication may indicate what values are not appropriate and/or what monitors are giving readings indicating the alarm. Further still, the values leading to the alarm may be grouped together to give a user a better indication of the reason for the alarm (rather than needing to view a plurality of different locations to find the appropriate values).  
         [0049]    Referring now to FIG. 4, patient physiologic monitoring assembly  310  includes a controller  312  in communication with a patient sensor  314  in order to receive a real-time physiologic data stream  316 . It is contemplated that the patient sensor  314  and real-time physiologic data stream  316  may encompass a wide variety of patient monitoring physiologic characteristics. These characteristics include, but are not limited to, heart rate, arterial blood pressure, StO 2 , CO 2 , EtC 2 , respiratory rate, and a variety of other patient physiologic responses. It should be understood that a wide variety of such responses and sensors  314  designed to receive them could be used. Similarly, a host of amplifiers, filters, and digitization elements may be utilized in combination with the sensors  314  as would be understood by one skilled in the art. The controller  312  may be utilized in combination with a variety of interactive elements such as a display  318  and control features  320  as would be comprehended by one skilled in the art.  
         [0050]    The controller  312  includes a logic  322  adapted to perform a plurality of functions as is illustrated in FIG. 5. It should be understood that although the terms controller  312  and logic  322  are utilized in the singular vernacular, a plurality of individualized controllers  312  and logics  322  could be used and are contemplated as incorporated into the chosen vernacular. By way of example, an independent physiologic emulation system  324  may be utilized to perform various functions. The logic  322  is adapted to develop a physiologic mathematical model  410  of the patient.  
         [0051]    The logic  322  registers initiation of a treatment procedure  450 . A wide variety of treatment procedures may be used. By way of example, one contemplated treatment procedure anticipates the administration of anesthesia to a patient prior to surgery. Other treatment procedures, however, may encompass a wide range of procedures including, but not limited to, drug injections, gas treatment, and even simply monitored care. The initiation of the treatment procedure  450  is intended to encompass a plurality of simultaneous individual treatments. The initiation of treatment procedure  450  is coordinated with a simulation of the treatment procedure on a physiologic mathematical model  460 . As stated, the physiologic mathematical model  326  is a simulation of a human anatomical system that allows simulation of treatment and predictive responses to such treatment.  
         [0052]    It is contemplated that a separate step in logic  322  of coordinating the simulated treatment with the physical treatment procedure  470  may also be incorporated. The coordination logic  470  is intended to encompass a wide variety of embodiments.  
         [0053]    In one embodiment, it is contemplated that a clinician after selecting a treatment and parameters within the mathematical model  326  will activate the simulation procedure at approximately the same time as the physical procedure is beginning.  
         [0054]    In another embodiment, however, it is contemplated that the simulated procedure (mathematical model  326 ) can be placed in communication with a treatment device  328 , or group of such devices, such that the activation of the treatment device  328  can automatically effectuate the start of simulated treatment. In still another contemplated embodiment, the communication between the treatment device  328  and the mathematical model  326  allows for accurate real-time treatment information to be supplied to the mathematical model  326 . For example, type, quantity, and flow rate of anesthesia may be automatically communicated from the treatment device  328  to the mathematical model  326  such that the simulated treatment accurately reflect the physical treatment without requiring excessive interaction from a clinician.  
         [0055]    The mathematical model  326  is utilized to generate a simulated physiologic data stream  480  in response to the simulated treatment  460 . It should be understood that the simulated physiologic data stream  330  need not represent a moment-to-moment exact prediction of patient physiologic data but may also be represented by ranges of predicted responses over time. The logic  322  also is adapted to receive a real-time physiologic data stream  490  from the patient. As stated, the real-time physiologic data stream  316  is intended to comprise a wide variety of different patient physiologic characteristics. The logic  322  is adapted to compare the real-time physiologic data stream with the simulated physiologic data stream  495 . This allows the real-time physiologic data stream  316  to be compared to the simulated physiologic data stream  330  to verify the patient is responding to treatment as predicted by the mathematical model  326 . The logic  322  then checks for divergence  500  between the real-time physiologic data stream  316  and the simulated physiologic data stream  330  to determine if the patient&#39;s response to treatment is different from that predicted by the mathematical model  326 . If a divergence  332  is discovered, the logic  322  is adapted to generate an alarm warning  510 . The alarm warning is intended to comprise both audible alarms as well as clinical guidance statements.  
         [0056]    It is contemplated that a wide variety of approaches to checking for divergence  500  may be utilized. In one contemplated embodiment, the divergence  332  may simply represent a hard threshold value in relation to the simulated physiologic data stream  330  that once crossed by the real-time physiologic data stream  316  sets off the alarm warning. In other embodiments, the divergence  332  may be registered when the real-time physiologic data stream  316  begins to move in a direction opposite that predicted by the simulated physiologic data stream  330 . Thus, if the simulated physiologic data stream  330  predicts heart rate to drop and the real-time physiologic data stream  316  rises or remains the same, a divergence  332  is registered by the logic  322 . As a practical example, if a patient is undergoing surgery, anesthesia is commonly given. The patient&#39;s blood pressure commonly drops quickly in response to the anesthesia. During a portion of the surgery, however, the surgery is aggravating and effectuates a rise in blood pressure. Thus, the effective blood pressure would remain the same. Normal monitoring systems have no way to determine that this non-change in blood pressure should generate a warning alarm (as the drop in blood pressure due to anesthesia is desired). Here, when the blood pressure remains the same, a divergence  332  is registered and an alarm can be sounded  510 .  
         [0057]    A variety of features intended to reduce the occurrence of undesired alarm warnings may also be incorporated. One such feature contemplates the development of a baseline data-averaged physiologic data stream  520 . The baseline data averaged value  334  is a mean rate of the real-time physiologic data stream  316 . The term data averaged and mean rate are intended to encompass any of a wide variety of data averaging and tracking techniques. By way of example, one such embodiment compares each new physiologic data sample and compares it to the running baseline  334  and increments or decrements the next point in the baseline  334  by a predetermined amount. Thus, utilizing this technique, or a variety of others, the baseline data averaged value  334  can track true physiologic changes that are consistent over time.  
         [0058]    Additionally, the use of a baseline data averaged value  334  is beneficial in ignoring noise and other generated artifacts. In embodiments utilizing the baseline data averaged values  334 , the comparison of the real-time physiologic data stream to the simulated physiologic data stream  490  is accomplished by comparing the simulated physiologic data stream  330  to the baseline data averaged value  334 . Although a single method of reducing unwanted alarms has been disclosed, a wide variety of methods and approaches may be utilized.  
         [0059]    A variety of features could be added to extend the usefulness of the monitoring system  8  within a medical setting. One such additional feature is achieved by adapting the logic  322  to generate a prediction of simulated physiologic response to proposed treatment  530 . This allows a clinician to check what a patient&#39;s response to a treatment will be prior to actual initiation of treatment  450 . The unique advantage of this feature is that it allows a clinician to access such predictive capabilities directly from the monitoring system  310  in the treatment room during treatments such as operations. Thus, instantaneous predictive advice is available during surgery and other treatment options that has previously been unavailable.  
         [0060]    An additional feature comprises a plurality of networked monitors  336  in communication with the monitoring system  310 . These networked monitors  336  allows the patient to be moved to any of the monitors within the network and still retain the ability to compare the real-time physiologic data stream  316  with the simulated physiologic data stream  330 . By way of example, a patient may be subjected to anesthesia during a surgical procedure. After the surgical procedure, the patient is commonly moved into a recovery room. Through the use of the networked monitors  336  in communication with the monitoring system  310 , mathematical model  326  may continue to produce a simulated physiologic data stream  330  that can be compared with the real-time physiologic data stream  316 . Therefore, as the model predicts the adjustment of the physiologic data in response to the gradual emergence from the effects of the anesthesia, it can be compared to the real-time physiologic data stream  316  to monitor if the patient experiences problems in recovery. Thus, if the patient does not properly emerge from the anesthesia as desired, a warning alarm can be sounded to draw a clinician for further analysis. Although a single example has been provided, it should be understood that a wide variety of procedures may make use of the networked monitors  336 .  
         [0061]    Referring again to FIG. 1, monitor  14  comprises an identity detector device  16  configured to identify a subject  10 . Identity detector device  16  can identify subject  10  by detecting an identification device  12  associated with a subject of interest  10 . Identification device  12  can be a card or other object associated with the subject. Identification device  12  is can be configured to allow wireless detection by identity detector device  16 .  
         [0062]    Network  18  can be any type of network across which data can be transferred. For example, network  18  can be a local area network, a wide area network, and an internet. Network  18  is coupled to a report generator  20 , a data storage device  22 , a record keeping device  24 , a processor, and a display. Report generator  20  can generate a report based on, data storage device  22  can store, record keeping device  24  can make or add to a record based on, processor  26  can process, and display  28  can display data acquired by a data acquisition device  1   3  of monitor  14 .  
         [0063]    Additionally, a bill generator  32  can generate a bill based on the use of monitor  14 . Bill generator  32  can generate a bill for the use of monitor  14 , or can integrate the use of monitor  14  into a larger bill to be sent. Bill generator  32  can also monitor the usage of monitor  14 , and generate reports based on usage of monitor  14 . Bill generator  32  can also be used to send a notice to a person across network  18  indicating that monitor  14  is being used and billed. People that may desire receiving such a notice might include a patient&#39;s primary physician, a treating physician, an insurance carrier, and a patient. Delivering a notice to an insurance carrier may allow faster approval for sudden, unexpected usage of monitor  14 . This would allow a hospital to collect funds sooner, and would allow a patient to worry less about obtaining coverage after treatment. Once the bill is generated, it can then be sent physically or electronically to a recipient. The recipient may be a computer at an insurance company that calculates the extent of coverage and the amount to be paid based on the usage of monitor  14 .  
         [0064]    The invention has been described with reference to various specific and illustrative embodiments and techniques. However, it should be understood that many variations and modifications may be made while remaining within the spirit and scope of the invention. For instance, while the invention is particularly useful for patient monitoring, some aspects of the invention are applicable to other monitoring activities.