Patent Document

BACKGROUND OF THE INVENTION 
     The present disclosure is related to the field of telemetry. More specifically, the present disclosure is related to a system and method for predicting the destination of a patient. 
     Telemetry systems, such as those used in the medical field, are designed to provide continuous physiological monitoring of ambulatory patients. The telemetry system permits ambulatory patients to have the freedom to move around, which has been shown to aid in the recovery process, while being under constant physiological monitoring by either a clinician, an automated monitoring system, or both. The area in which the monitored patients are permitted to move while being monitored is restricted by the areas of the hospital or medical care facility that are designed for, and equipped, with the hardware for telemetry coverage. If a patient moves outside of the telemetry coverage area, continuous monitoring of the patient may lapse, causing delays in treatment should a medical event, such as cardiac arrest, occur during this time. It is also difficult for clinical personnel to locate the patient, when the patient is outside of the telemetry coverage area. The location of a patient within the telemetry coverage area is often determined and/or provided to clinical personnel by location services functionality employed in conjunction with the telemetry system. 
     Currently available technology provides alerts indicative of telemetry signal dropout that are caused when the patient goes out of the telemetry coverage area. However, by the time that these alerts are presented, monitoring coverage of the patient has already lapsed, and the specific location of the patient is unknown. 
     BRIEF DISCLOSURE 
     Therefore, it is desirable to provide clinical personnel with a predictive warning of telemetry signal dropout due to an ambulatory patient leaving a telemetry coverage area. 
     An embodiment of a method of predicting telemetry signal dropout includes defining a telemetry coverage area by locating a telemetry antenna in the telemetry coverage area. Next, a location of a monitored patient is received with the telemetry antenna. A database records the received patient location over time. A processor calculates a trajectory and speed of the monitored patent from the received location and one or more previously received locations. The processor compares the location, trajectory, and speed of the monitored patient to the patient location, trajectory, and speed information previously recorded in the database. The processor then creates a prediction of patient destination. 
     An additional embodiment of a method of predicting telemetry signal dropout includes defining a telemetry coverage area. Next, a patient telemetry signal is continuously received. Then a patient location is continuously received. Next a patient trajectory and a patient speed is computed from the received patient location. Then the patient location, patient trajectory, and patient speed are recorded in a database comprising previously recorded patient locations, patient trajectories, and patient speeds. Then the patient location, patient trajectory, and patent speed are compared to the first database to correlate the patient location, patient trajectory, and patient speed to previously recorded patient locations, trajectories, and speeds. Finally, a probability that the patient will leave the telemetry coverage area is calculated. 
     Also disclosed herein are embodiments of a system for predictive warning of telemetry signal dropout. The system includes a remote unit worn by a patient. The remote unit transmits telemetry information. A plurality of telemetry receivers are distributed throughout a telemetry coverage area. At least one of the plurality of telemetry receivers receives the transmitted telemetry information. A location services manager receives the location signal from the access points and computes the location of the patient, the speed of the patient, and the trajectory of the patient. A patient location database records the computed location, speed, and trajectory of the patient. The patient location database also records location, speed, and trajectory from a plurality of patients in the telemetry coverage area. A location prediction computer compares the computed location, speed, and trajectory of the patient to the locations, speeds, and trajectories stored on the database to predict a patient destination and produces an alarm if the patient destination is outside of the telemetry coverage area. A graphical display receives and presents the patient destination and receives and presents the alarm. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a floor plan of a medical care facility with a telemetry coverage area; 
         FIG. 2  is a system diagram of a telemetry system; 
         FIG. 3  is another floor plan of a medical care facility; and 
         FIG. 4  is a further floor plan of a medical care facility. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       FIG. 1  depicts a diagram of a partial floor plan  10  of a medical care facility. While the floor plan  10  used in the present disclosure is that of a medical care facility, it is understood that the present disclosure is not limited in geography to only medical care facilities, but may be any type of facility within which telemetry monitoring is implemented. These facilities may include a medical care facility such as a hospital or clinic, but may also include any other facilities implementing a telemetry system, including, but not limited to nursing homes, assisted living centers, or schools; however, for the present disclosure the example of a medical care facility is used. The medical care facility includes a plurality of antennas  12  or other signal receiving devices that receive broadcasted telemetry signals from a remote unit (not depicted) worn by, or otherwise associated with, a patient or monitored subject  50 . 
     The receiving range  14  of each of the plurality of antennas  12  defines a telemetry coverage area  16 . The receiving range  14  of each of the antennas  12  may be controlled or adjusted based on the antenna receiving strength or the transmission strength of the signals from the remote units. In an example, the same receiving range  14  may be achieved through the use of stronger antennas  12  and weaker transmission remote units as may be achieved through the use of weaker antennas  12  and stronger transmitting remote units. Within the telemetry coverage area  16 , one or more of the antennas  12  receives a telemetry signal broadcasted by the remote unit (not depicted) associated with each of the patients. This telemetry signal may include measured physiological data, physiological data that is derived from the measured physiological data, or patient communications, such as patient initiated alarms or patient subjective physical assessments. 
     The remote unit transmits a location signal that is used to identify the location of the patient within the medical care facility. The location signal may be one that is detected by one or more of the antennas  12 , in order to triangulate the remote unit associated with the patient. In an embodiment, at least three antennas receive a location signal for triangulation of the patient location; however, this is not limiting on the number of antennas  12  distributed through the telemetry coverage area  16  or the overlap of the receiving ranges  14  of the plurality of antennas  12 . Alternatively, the location signal may include information indicative of the location of the patient, such as positional coordinates as determined by a GPS system within the remote unit. Therefore, the location signal may either be indicative of the actual patient location, or may be a signal that is used to derive the location of the patient within the telemetry coverage area  16 . 
     The telemetry coverage area  16  is defined by one or more antennas  12  which may be located on multiple floors within a medical care facility. As noted above, the telemetry coverage area  16  may have antennas  12  distributed to ensure overlap of the receiving ranges of multiple antennas  12 , which aids in patient triangulation. 
       FIG. 2  is a schematic diagram of a telemetry system  18  that may be implemented in a medical care facility. The telemetry system  18  includes the electrical hardware, software, and firmware components that operate the telemetry system  18 . A remote unit  20  is worn by, attached to, or otherwise associated with each of the patients (not depicted) that are being telemetrically monitored. The remote unit  20  transmits one or more signals that include telemetry and/or location information. These signals are received by the antenna  12 , of which a plurality are distributed throughout the medical care facility to define the telemetry coverage area  16  (shown in  FIG. 1 ). However, for the sake of simplicity in  FIG. 2 , only a single antenna  12  is shown. Each antenna  12  is associated with an amplifier  22  that amplifies the signal received from the remote unit  20 . Although not depicted, the amplifier  22  may also include other forms of signal conditioning or processing, including, but not limited to, filtering and/or digitization. 
     The signals from the amplifier  22  are transmitted to a remote closet  24 . The remote closet  24  collects all of the signals received by the plurality of antennas  12  in a defined area of the telemetry coverage area  16 . In one example, the medical care facility includes a telemetry coverage area  16  that expands to multiple floors of the medical care facility. In such an example, a remote closet  24  may be placed at each of the floors in order to collect and process the signals received by the antennas  12  on that floor. The remote closet  24  includes a multiplexer  26  that handles the transmission of the telemetry and location information for a plurality of remote units  20  transmitting to the remove closet  24 . The multiplexer  26  separates the lower frequency telemetry signals from the higher frequency location signals and directs the received signals for further processing. While the telemetry system  18  depicted in  FIG. 1  is a system that places the telemetry and location information on the same antenna  12 , this is not required, and instead of using the multiplexer  26 , separate antenna systems may be implemented to separately obtain the telemetry and location signals. 
     From the multiplexer  26 , the telemetry information is provided to a telemetry remote hub  28  that prepares the telemetry information for transmission from the remote closet  24  to the main closet  30  that collects all of the information from the remote closets  24  distributed throughout the telemetry system  18 . The main closet  30  is centrally or otherwise conveniently located to receive the telemetry and location information from all of the remote closets  24  in the system  18 . The telemetry remote hub  28  may transmit the telemetry information to a telemetry base unit  32  in the main closet  30  that receives and processes the telemetry information. In an embodiment, the transmission of telemetry information from the telemetry remote hub  28  to the telemetry base unit  32  is performed by fiber optic transmission technology and the telemetry remote hub  28  and the telemetry base unit  32  perform the signal conditioning required for the optical fiber conversion necessary for the transmission. 
     After the telemetry information is transmitted from the telemetry remote hub  28  to the telemetry base unit  32 , the telemetry base unit  32  processes the fiber optic signal to extract the telemetry information embedded thereon. The telemetry base unit  23  sends the telemetry information to a telemetry receiver  33  that receives the telemetry information and further directs the telemetry information to the telemetry server  40 . 
     In the remote closet  24 , the separated location signals from the multiplexer  26  are provided to an access point  29 . The access point  29  measures the strength of the location signal from the base unit  20  received by one or more antenna  12 . In a telemetry system  18  wherein a plurality of antennas  12  are distributed throughout the telemetry coverage area, the signal strengths determined by the access point  29  can be used to triangulate the remote unit  20  as the varying signal strength from a plurality of antennas  12  may be used to determine the patient location with reference to each of the antennas receiving the location signal. 
     The access point  29  of the remote closet  24  provides the location information, including the received signal strengths to the main closet  30  through any number of information transmission technologies, including wire, wireless, or fiber optic technologies. An access point (AP) controller  34  is connected to each of the access points  29  if a plurality of remote closets  24  exist in the telemetry system  18 . The AP controller  34  coordinates the transmission and reception of the location information from the access points  29  of each of the remote closets  24 . 
     The location information is provided from the AP control  34  to a location services (LS) computer  36 . The LS computer includes computer readable code stored on a computer readable medium (not depicted) that embodies software as detailed further herein for calculating location information regarding a patient. Software implemented by the LS computer  36  may also include software required to operate an advanced neural network (ANN), as disclosed in embodiments herein. 
     The LS computer  36  is further connected to a location database  38  that stores the location information from the LS computer  36  for later retrieval and reference by the software operating on the LS computer  36  in determining patient location information. 
     The main closet  30  transmits both the telemetry information and the location information to a telemetry server  40  that coordinates the telemetry and location information with other patient, facility, and services information that is required for the operation of other features of the telemetry system  18  that are not central to the present disclosure. Such additional telemetry system functionalities include patient medical history and electronic medical record (EMR) access, clinical staff information, medical care facility availability, and facility capacity. 
     The telemetry server  40  may also perform analysis of the received telemetry information, such as to process measured physiological data, derive additional physiological data from the measured physiological data, and/or apply institutional diagnostic rules such as to perform automatic or automated diagnostic tests. The telemetry server  40  transmits all of the telemetry information, and location information to the central station  42 . The central station  42  may otherwise be known as the telemetry command center, or “war room.” The central station  42  is where one or more clinical staff are presented with the telemetry and location information for all of the patients currently under monitoring in the telemetry system. The telemetry information is presented to the clinical staff such that the clinical staff can remotely monitor the physiological condition of the telemetrically monitored patients depending upon changes in the monitored physiological condition of the remotely located patients, the clinical staff may electronically update a patient&#39;s diagnosis or treatment regimen, or may initiate intervention by other clinical staff with the patient. In the event that physiological conditions indicate one or more alarm conditions, the clinical staff at the central station  42  may evaluate the alarm conditions and initiate the proper response based upon those conditions. 
     While the above description of the telemetry system  18  has been made with respect to a large number of hardware components that operate software or firmware in order to form the functionality, data processing, and communication as disclosed above, it is understood to one of ordinary skill in the art that depending on the specific implementation of the telemetry system  18  individual components described herein may be combined into a single piece of hardware or may be implemented as a smaller module of a larger control system software. Additionally, one of ordinary skill in the art would also recognize that the communication aspects disclosed herein are merely an exemplary embodiment and that the communication and data transmission would be modified to the specific needs of the telemetry system  18  implemented within a medical care facility. 
     The telemetry system  18  can provide a cost effective and convenient way to monitor ambulatory patients. This benefits the patients as the ability of a recovering patient to move about the patient&#39;s surroundings has been found to aid in recovery times; however, while patients are recovering from illness or a medical procedure, they are at increased risk of being afflicted by a severe medical condition. Examples of severe medical conditions include a heart attack or stroke. Thus, these ambulatory patients still require constant monitoring. A problem arises if a telemetrically monitored patient moves outside of the telemetry coverage area  16  ( FIG. 1 ), the telemetry system  18  both no longer receives the critical physiological data required to continuously monitor the patient, but also the location of the patient becomes unknown, putting the patient at risk of delayed clinician intervention or treatment, should the patient develop a serious medical condition. 
     Therefore, as disclosed further herein, the LS computer  36  may provide with the location information, a prediction if an ambulatory patient will move out of the telemetry coverage area  16 , thus causing telemetry signal dropout. Alternatively, the prediction of patient destination may be created using a separate location prediction computer (not depicted). 
     Referring back to  FIG. 1 , the floor plan  10  of  FIG. 1  is also representative of an embodiment of the information displayed by a graphical display of the central station  42 . The central station  42  may present the patient location information graphically, such as using a floor plan representation, like  FIG. 1 , that indicates both the monitored patients and their potential destinations. Alternatively, the central station  42  may present the patient location information and destination predictions in tabulative or textual formats. In still further embodiments, the destination prediction may only be presented as an alarm, when it is predicted that the probability of the patient leaving the telemetry coverage area  16  meets a predetermined threshold probability. 
     In  FIG. 1 , a patient  50  is indicated by a graphical representation, such as an arrow, the arrow graphically represents both the location of the patient  50  within the floor plan  10  and also indicates the patient&#39;s direction of travel. In alternative embodiments, it is understood that additional indications of other telemetrically monitored patients may be made on the same display. Additionally, the patient speed may be indicated such as through the use of a tail (not depicted) or progressively fading indication of the patient  50  location at previous time intervals, such as two second intervals or one second intervals. 
     As noted with respect to  FIG. 2 , a remote unit  20  is associated with the patient  50 . The remote unit  20  transmits its location information as picked up by one or more of the antennas  12  in the telemetry coverage area  16 . By monitoring this patient location information, the LS computer  36  can compute the patient&#39;s location within the telemetry coverage area  16 , the speed that the patient is traveling, and the trajectory of the patient, or the direction the patient is traveling. The LS computer  36  records this information in a location database  38  for each of a plurality of monitored patients in the telemetry coverage area  16 . The data in a location database  38  includes not only current patients within the telemetry coverage area, but the location database  38  also stores the location information for previously telemetrically monitored patients in the telemetry coverage area  16 . 
     Computer  36  uses the previously recorded patient location information in the location database  38  to identify the instance rates of patients moving from a current location to a variety of destinations. These instance rates or probabilities may then be further detailed using artificial intelligence techniques such as artificial neural networks (ANN) or fuzzy logic in order to correlate not only the patient location, but the calculated patient trajectory and speed to the previously recorded patient location information. ANN or fuzzy logic implementations may be used to computer historical patient movement trends throughout the telemetry coverage area  16 . This allows for the destination predictions to be correlated to the location information presently received and computed for the monitored patient. Therefore, the present telemetry system  18  provides improved prediction of patient destination using both currently measured and computed patient location information with historical patient movement trends obtained from the historical location information of other patients and/or the monitored patient in the same telemetry coverage area  16 . 
     As noted above, the floor plan  10  of  FIG. 1  may represent an embodiment of the information presented by the central station  42 . In this example, a patient  50  is indicated as moving through the telemetry coverage area  16  in a hallway  52 . The location information transmitted by the remote unit  20  associated with the patient  50  received by the telemetry system  18  is used to determine the location, speed, and trajectory of the patient  50 . The LS computer  36  records the patient&#39;s actual location and path in the location database  38  for reference in future destination determinations. The LS computer  36  also performs a current destination prediction. In this destination prediction, the LS computer  36  identifies by analyzing previous destinations of patients in the same telemetry coverage area  16  to determine historical patient movement trends and comprising these historical patient movement trends to the current received/measured/calculated location, trajectory, and speed of the patient  50 . In this example, the LS computer  36  identifies that there are five potential destinations of the patient  50 . These potential destinations are indicated on the floor plan  10  as destinations A, B, C, D, and E. 
     The LS computer  36  further determinates a probability that the patient  50  will go to each of these destinations. 
     As an example, the LS computer  36  may determine, based on the historical patient movement trends and the current location, trajectory, and speed of the patient  50 , that the following probabilities exist that the patient will move to each of the identified destinations: 
     
       
         
               
               
               
             
           
               
                   
                   
               
               
                   
                 DESTINATION 
                 PROBABILITY % 
               
               
                   
                   
               
             
             
               
                   
                 A 
                 60%  
               
               
                   
                 B 
                 22%  
               
               
                   
                 C 
                 8% 
               
               
                   
                 D 
                 4% 
               
               
                   
                 E 
                 6% 
               
               
                   
                   
               
             
          
         
       
     
     Thus from the exemplary Table above, it can be determined that the patient  50  has a 94% probability of moving forward. The patient  50  also has a 60% probability of moving to destination A, while only having a 22% probability of moving outside of the telemetry coverage area  16 , to designated destination D. Therefore, the patient  50  at the specified location, speed, and trajectory will be regarded as a 22% risk for telemetry signal dropout based upon the patient leaving the telemetry coverage area  16  at destination D. 
     The medical care facility may define its own alarm definitions for telemetry signal dropout risk as well as define the responses that are initiated by clinical staff at the central station  42  upon the meeting of these predefined probability criteria. Some institutions may be highly risk adverse and therefore would desire to intervene any time the destination probability of the destination outside the telemetry coverage area  16  crosses a minimal threshold percentage. This threshold percentage may be relatively low, such as 10-20% likelihood, or lower, based at the discretion of the medical care facility. Alternatively, a progression of patient interactions may escalate as the probability that the patient will leave the telemetry coverage area  16  increases. These intervention escalations may begin with a page or other audible or textual alert that is sent to the remote unit  20  associated with the patient  50 . This may be escalated to the dispatch of clinical staff to the location of the patient  50  or to the patient&#39;s predicted destination in order to intercept the patient  50  before the patient  50  leaves the telemetry coverage area  16 . It is further understood that in alternative embodiments some or all of these responses may be automated or automatedly initiated responses and do not require clinician action in order to initiate or carry out. 
     The LS computer  36  ( FIG. 2 ) may simplify the destination prediction by dividing the telemetry coverage area  16  into a plurality of destination areas. Therefore, the LS computer  36  may more easily define historical patient movement trends through ANN or fuzzy logic techniques. These or other data processing techniques may be used to process the large amount of stored patient location information. The division of the telemetry coverage area  16  into discrete destinations (A, B, C, D, E) help to identify a probability that the patient will enter one of these destinations. 
     Referring now to  FIG. 3 , it depicts the floor plan  10  with two different alternative locations, location  52  and location  54 , for the patient  50  to proceed from the location in  FIG. 1 . From both locations  52  and  54 , the patient  50  may move to the same five destinations A, B, C, D, and E. If the patient  50  moves to location  52 , as the patient  50  moves, the LS computer  36  continuously updates the destination prediction, taking into account the updated patient location, trajectory, and speed, as well as the historical patient movement trends stored in the location database  38 . In the present example, by the time the patient  50  moves to location  52 , the destination probabilities have changed to: 
     
       
         
               
               
               
             
           
               
                   
                   
               
               
                   
                 DESTINATION 
                 PROBABILITY % 
               
               
                   
                   
               
             
             
               
                   
                 A 
                 59%  
               
               
                   
                 B 
                 35%  
               
               
                   
                 C 
                 2% 
               
               
                   
                 D 
                 2% 
               
               
                   
                 E 
                 2% 
               
               
                   
                   
               
             
          
         
       
     
     By referencing the above Table, it can be seen that as the patient  50  turned in the direction away from the telemetry coverage area  16  boundary and destination D, the probability that the patient would enter this destination is drastically reduced. The reduction in this destination probability of the destination D would be due to the fact that patients historically at location  52  on the trajectory and speed of patient  50 , rarely turn around and head out of the telemetry coverage zone  16  to destination D. 
     However, in an alternative example, if the patient  50  moves from location in  FIG. 1  to location  54  depicted in  FIG. 2 , then as the patient moves between those two locations, the LS computer  36  will compute the new destination probabilities, such that by the time the patient  50  reaches location  54 , the destination probabilities are: 
     
       
         
               
               
               
             
           
               
                   
                   
               
               
                   
                 DESTINATION 
                 PROBABILITY % 
               
               
                   
                   
               
             
             
               
                   
                 A 
                 5% 
               
               
                   
                 B 
                 5% 
               
               
                   
                 C 
                 5% 
               
               
                   
                 D 
                 80%  
               
               
                   
                 E 
                 5% 
               
               
                   
                   
               
             
          
         
       
     
     By reference to the above Table, it can be seen that by the time the patient  50  reaches location  54 , it becomes very likely that the patient  50  will leave the telemetry coverage area  16  and move to destination D. This escalation of the probability that the patient&#39;s telemetry signal will be lost due to moving out of the telemetry coverage area  16 , may trigger an appropriate response from the clinical staff at the central station  42 . The clinical staff at central station  42  would dispatch clinical staff to location  54  or destination D in an attempt to first intercept the patient  50  before the patient leaves the telemetry coverage area  16 , or if the clinical staff response arrives too late, the patient  50  is recovered at or near destination D with minimal telemetry signal dropout. 
     Referring back to  FIG. 1 , if the patient  50  moves to destination D, both the patient&#39;s telemetry signal and location signal would be lost. The patient would no longer appear on the floor plan  10 . In this instance, the LS computer  36  saves the patient location, trajectory, and speed at the time of the telemetry and location signal dropout. In an additional functionality of the LS computer  36 , the LS computer  36  uses patient location information stored in the location database  38  to additionally predict a destination outside of the telemetry coverage area  16  that the patient  50  may be most likely to be found. 
     The patient location information used to determine probability of patient location outside of the telemetry coverage area  16  may be based upon reporting by clinical staff that find telemetry patients outside of the telemetry coverage area  16 . The reporting of clinical staff may be analyzed and compiled in order to determine probabilities of where patients leaving the telemetry coverage area  16  may be headed after signal dropout occurs. 
     In  FIG. 1 , locations F and G represent two locations outside of the telemetry coverage area  16  that may be deemed as likely patient destinations outside of the telemetry coverage area  16 . Locations F and G may be specific destinations of patients leaving the telemetry coverage area  16  due to features about these locations. For example, location F may be the site of a point of interest such as vending machines, or a fish tank that attract patients, while location G may be an outdoor park or sitting area. 
     The LS computer  36  computes a probability determination for the likelihood that the patient leaving the telemetry coverage area  16  may be found at one of locations F or G. This probability may be similar to that previously calculated with respect to patient destination predictions. The calculated probability is transmitted to the central station  42  to be presented on a graphical display. Thus, if the patient  50  leaves the telemetry coverage area  16 , the graphical display of the central station  42  may present an indication that there is a 25% likelihood that the patient  50  is at destination F while there is a 50% probability that the patient  50  is at destination G. The probabilities provided in this determination may or may not add up to 100% due to rounding, or the consideration of other locations. For the sake of simplicity, in some embodiments, only those locations that are above a predetermined probability threshold are presented as likely options. Alternatively, the system could present all the calculated probabilities. 
     It is to be understood that the effectiveness of this type of location prediction outside of the telemetry coverage area  16  may be dependent upon a clinical staff reporting system, whereby the patient location information is collected that is indicative of where the clinical staff actually locate the patient  50  outside of the telemetry coverage area  16 . This type of reporting identifies the locations outside of the telemetry coverage area  16  where the patients are likely to go after telemetry signal dropout. 
     In an additional aspect, the location database  38  keeps track of all interventions on patient movement. Often, these are recorded by clinical staff after intervening on patient movement. If left unreported or unaccounted for, these interventions may skew the probabilities of the patients leaving the telemetry coverage area  16 , such as to under report the actual instance of patient signal dropout, in instances where no intervention is initiated. Therefore, the LS computer  36  may credit an interaction as full or partial consideration that the patient left the telemetry coverage area. 
     With respect to  FIG. 1 , in a still further embodiment, an exemplary table of destination probabilities presented by the central station  42  is: 
     
       
         
               
               
               
             
           
               
                   
                   
               
               
                   
                 DESTINATION 
                 PROBABILITY % 
               
               
                   
                   
               
             
             
               
                   
                 A 
                 60%  
               
               
                   
                 B 
                 22%  
               
               
                   
                 C 
                 8% 
               
               
                   
                 D 
                 4% 
               
               
                   
                 E 
                 6% 
               
               
                   
                   
               
             
          
         
       
     
     Referring to the table above, based upon the location, trajectory, and speed of the patient  50 , and historical patient movement trends, the LS computer  36  may compute that the patient  50  is relatively unlikely to leave the telemetry coverage area  16  to go to destination D. In this instance, the patient  50  is likely to pass very close to the edge of the telemetry coverage area  16  and there is a potential for the patient to leave the telemetry coverage area  16  resulting in telemetry signal dropout. However, based upon the historical patient movement trends and the monitored patient location, trajectory, and speed, the LS computer  36  indicates to the clinical staff at the central station  42  a low probability that the patient will leave the telemetry coverage area  16 . Therefore, no intervention, or a low intervention, may be initiated, thus conserving resources and not interrupting the ambulatory movement of the patient  50  or the current tasks being performed by clinicians. 
     In embodiments of the telemetry system  18 , the LS computer  36  may further be communicatively coupled to a database of patient demographic information (not depicted), or alternatively, the location database  38  may also include patient demographic information that may be further used to increase the accuracy of the destination predictions of the telemetry system  18 . The stored demographic information may correlate the patient&#39;s age, gender, or ethnicity with particular historical patient movement trends or behavior patterns. In one such example, referring to  FIG. 1 , if the patient  50  leaves the telemetry coverage area  16  by moving to destination D, if destination F represents a fish tank and destination G represents a sitting area or park, the LS computer  36  may determine that there is a correlation that patients below a certain age are more likely to go to location F, presumably to view the fish in the fish tank while patients above a certain age are move likely to be found at the sitting area G. 
     In a still further embodiment of the telemetry system  18 , the location database  38  may also store the historical movement trends for each individual patient  50  separately from the group of all patients as a whole. Thus, the LS computer  36  may use the specific movement history of each patient in order to more accurately predict where that patient is going. This additional personalized movement trend determination may help to reduce false positives, resulting in fewer interventions or intervention escalations, requiring the medical care facility resources and staff time. One such example of a personalized patient historical movement trend would be that if patient  50  every morning goes to location B for a particular treatment, therapy, or to visit another particular patient. Despite the fact that the historical patient movement trends on a whole may indicate that a generic patient at the patient&#39;s  50  location, trajectory, and speed is likely to leave the telemetry coverage area  16  and move to destination D, the probability of this particular patient  50  following that movement path is comparatively low. Alternatively, the additional personalized movement trend determination may help to proactively warn clinicians of patients at greater risk of leaving the telemetry coverage area  16  than the general patient population. 
     Referring now to  FIG. 4 ,  FIG. 4  depicts a floor plan  60  that is similar to the other figures, but depicts an alternative potential display presented by the central station  42 . If the destination predictions are presented by the central station  42  as merely numerical, textual or escalatory results, then the floor plan  60  of  FIG. 4  is a pictorial representation of the logic that may be used by the LS computer  36  in this embodiment. The floor plan  60  of  FIG. 4  is different from that depicted in  FIG. 1  in that the floor plan  10  of  FIG. 1  depicted only the nearest extrapolation of potential patient destination. Therefore, in the embodiment of  FIG. 1 , the predictive capability of these destination predictions are limited to a next destination of the patient. However, additional warning time of potential telemetry signal dropout beyond a simple “next destination” may be provided in some embodiments. Therefore, in the floor plan  60  of  FIG. 4 , additional destinations F-J are included in the floor plan  60 . These additional destinations extend from destination B and C in the original floor plan  10 . Thus, the probability that the patient moves to any of destination F-J, would first require that the patient move through destination B or C. Therefore, the probabilities of destinations F-J are a subset of the probability that the patient move to destinations B or C. 
     In an embodiment of this patient destination prediction scheme, the destination probabilities may appear at: 
     
       
         
               
               
               
             
           
               
                   
                   
               
               
                   
                 DESTINATION 
                 PROBABILITY % 
               
               
                   
                   
               
             
             
               
                   
                 A 
                 47% 
               
               
                   
                 B 
                 22% 
               
               
                   
                 C 
                 20% 
               
               
                   
                 D 
                  5% 
               
               
                   
                 E 
                  6% 
               
               
                   
                 F 
                 12% 
               
               
                   
                 G 
                  8% 
               
               
                   
                 H 
                  7% 
               
               
                   
                 I 
                  5% 
               
               
                   
                 J 
                 10% 
               
               
                   
                   
               
             
          
         
       
     
     A feature of the embodiment of floor plan  60  is apparent from this example in that it may be noted that the patient  50  has a greater probability of leaving the telemetry coverage area  16  at destination J, causing telemetry and location signal dropout, than the much closer destination D. Thus, clinical staff at the central station  42  are provided with a warning of a possibly counter intuitive destination prediction and may monitor the location of the patient  50  more closely, or provide the necessary intervention, or intervention escalation with respect to the more probable destination causing signal dropout. 
     As stated previously, the embodiments of the floor plan  10 ,  60  are merely exemplary as to the type of graphical presentation that may be made by the central station  42  to clinical staff. Alternative to the graphical depiction of these figures, graphical indications that only focus on the possible patient point of departure from the telemetry coverage area  16  may be implemented. These embodiments may only track the location, speed, and trajectory of the patient  50 , while noting only those paths and probabilities that lead to telemetry signal dropout. Alternatively, rather than specific patient vectors and discrete destination locations, a scatter plot or heat map or other type of graphical representation of probability may be used to graphically depict the likelihood that the patient  50  would move to a particular destination. 
     Finally, as mentioned above, the central station  42  may rather present the destination predictions in a more simplistic numeral or textural form such, as in the non-limiting example, the tables presented above, or may only be presented to the clinical staff at the central station  42  only upon meeting one or more probability thresholds for clinical staff intervention, or intervention escalation. 
     This written description uses examples to disclose various embodiments, including the best mode, and also to enable any person skilled in the art to make and use these embodiments. The patentable scope is defined by the claims may extend to include other examples not explicitly listed that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent elements with insubstantial differences from the literal languages of the claims. 
     Various alternatives and embodiments are contemplated as being with in the scope of the following claims, particularly pointing out and distinctly claiming the subject matter of the present disclosure.

Technology Category: 3