Abstract:
A system and method for providing voice feedback from sampled physiometry and self-evaluation in conjunction with heart failure assessment is presented. Physiological measures including direct measures recorded by an implantable medical device and measures derived from the direct measures are stored. Voice feedback spoken as speech is recorded contemporaneous to the recordation of the physiological measures and is quantified into quality of life measures by normalizing the speech against a voice grammar and speech vocabulary. Those of the physiological measures, which relate to a same type of physiometry and to a different type of physiometry, are sampled. A status is determined through analysis of those sampled measures assembled from recordation points and those quality of life measures spoken. Trends that are indicated, which might affect cardiac performance of the patient, are identified. Each trend is compared to worsening heart failure indications to generate a notification of parameter violations.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS  
       [0001]     This patent application is a continuation of U.S. patent application Ser. No. 11/104,969, filed on Apr. 12, 2005, pending, which is a divisional of U.S. Pat. No. 6,926,668, issued on Aug. 9, 2005, which is a continuation of U.S. Pat. No. 6,478,737, issued Nov. 12, 2002, which is a divisional of U.S. Pat. No. 6,331,160, issued Dec. 18, 2001, which is a continuation of U.S. Pat. No. 6,203,495, issued Mar. 20, 2001, which is a continuation-in-part of U.S. Pat. No. 6,312,378, issued Nov. 6, 2001, the priority filing dates of which are claimed and the disclosures of which are incorporated by reference. 
     
    
     FIELD  
       [0002]     The present invention relates in general to automated data collection and analysis, and, in particular, to a system and method for analyzing normalized patient voice feedback in an automated collection and analysis patient care system.  
       BACKGROUND  
       [0003]     Implantable pulse generators (IPGs) are medical devices commonly used to treat irregular heartbeats, known as arrhythmias. There are three basic types. Cardiac pacemakers are used to manage bradycardia, an abnormally slow or irregular heartbeat. Bradycardia can cause symptoms such as fatigue, dizziness, and fainting. Implantable cardioverter defibrillators (ICDs) are used to treat tachycardia, heart rhythms that are abnormally fast and life threatening. Tachycardia can result in sudden cardiac death (SCD). Implantable cardiovascular monitors and therapeutic devices are used to monitor and treat structural problems of the heart, such as congestive heart failure, as well as rhythm problems.  
         [0004]     Pacemakers and ICDs are equipped with an on-board, volatile memory in which telemetered signals can be stored for later retrieval and analysis. In addition, a growing class of cardiac medical devices, including implantable heart failure monitors, implantable event monitors, cardiovascular monitors, and therapy devices, are being used to provide similar stored device information. These devices are able to store more than thirty minutes of per heartbeat data. Typically, the telemetered signals can provide patient device information recorded on a per heartbeat, binned average basis, or derived basis from, for example, atrial electrical activity, ventricular electrical activity, minute ventilation, patient activity score, cardiac output score, mixed venous oxygen score, cardiovascular pressure measures, time of day, and any interventions and the relative success of such interventions. Telemetered signals are also stored in a broader class of monitors and therapeutic devices for other areas of medicine, including metabolism, endocrinology, hematology, neurology, muscular disorders, gastroenterology, urology, opthalmology, otolaryngology, orthopedics, and similar medical subspecialties.  
         [0005]     Presently, stored device information is retrieved using a proprietary interrogator or programmer, often during a clinic visit or following a device event. The volume of data retrieved from a single device interrogation “snapshot” can be large and proper interpretation and analysis can require significant physician time and detailed subspecialty knowledge, particularly by cardiologists and cardiac electrophysiologists. The sequential logging and analysis of regularly scheduled interrogations can create an opportunity for recognizing subtle and incremental changes in patient condition otherwise undetectable by inspection of a single “snapshot.” However, present approaches to data interpretation and understanding and practical limitations on time and physician availability make such analysis impracticable.  
         [0006]     Similarly, the determination and analysis of the quality of life issues which typically accompany the onset of a chronic yet stable diseases, such as coronary-artery disease, is a crucial adjunct to assessing patient wellness and progress. However, unlike in a traditional clinical setting, physicians participating in providing remote patient care are not able to interact with their patients in person. Consequently, quality of life measures, such as how the patient subjectively looks and feels, whether the patient has shortness of breath, can work, can sleep, is depressed, is sexually active, can perform activities of daily life, and so on, cannot be implicitly gathered and evaluated.  
         [0007]     A prior art system for collecting and analyzing pacemaker and ICD telemetered signals in a clinical or office setting is the Model 9790 Programmer, manufactured by Medtronic, Inc., Minneapolis, Minn. This programmer can be used to retrieve data, such as patient electrocardiogram and any measured physiological conditions, collected by the IPG for recordation, display and printing. The retrieved data is displayed in chronological order and analyzed by a physician. Comparable prior art systems are available from other IPG manufacturers, such as the Model 2901 Programmer Recorder Monitor, manufactured by Guidant Corporation, Indianapolis, Ind., which includes a removable floppy diskette mechanism for patient data storage. These prior art systems lack remote communications facilities and must be operated with the patient present. These systems present a limited analysis of the collected data based on a single device interrogation and lack the capability to recognize trends in the data spanning multiple episodes over time or relative to a disease specific peer group.  
         [0008]     A prior art system for locating and communicating with a remote medical device implanted in an ambulatory patient is disclosed in U.S. Pat. No. 5,752,976 (&#39;976). The implanted device includes a telemetry transceiver for communicating data and operating instructions between the implanted device and an external patient communications device. The communications device includes a communication link to a remote medical support network, a global positioning satellite receiver, and a patient activated link for permitting patient initiated communication with the medical support network. Patient voice communications through the patient link include both actual patient voice and manually actuated signaling which may convey an emergency situation. The patient voice is converted to an audio signal, digitized, encoded, and transmitted by data bus to a system controller.  
         [0009]     Related prior art systems for remotely communicating with and receiving telemetered signals from a medical device are disclosed in U.S. Pat. Nos. 5,113,869 (&#39;869) and 5,336,245 (&#39;245). In the &#39;869 patent, an implanted AECG monitor can be automatically interrogated at preset times of day to telemeter out accumulated data to a telephonic communicator or a full disclosure recorder. The communicator can be automatically triggered to establish a telephonic communication link and transmit the accumulated data to an office or clinic through a modem. In the &#39;245 patent, telemetered data is downloaded to a larger capacity, external data recorder and is forwarded to a clinic using an auto-dialer and fax modem operating in a personal computer-based programmer/interrogator. However, the &#39;976 telemetry transceiver, &#39;869 communicator, and &#39;245 programmer/interrogator are limited to facilitating communication and transferal of downloaded patient data and do not include an ability to automatically track, recognize, and analyze trends in the data itself. Moreover, the &#39;976 telemetry transceiver facilitates patient voice communications through transmission of a digitized audio signal and does not perform voice recognition or other processing to the patient&#39;s voice.  
         [0010]     Thus, there is a need for a system and method for providing continuous retrieval, transferal, and automated analysis of retrieved implantable medical device information, such as telemetered signals, retrieved in general from a broad class of implantable medical devices and, in particular, from IPGs and cardiovascular monitors. Preferably, the automated analysis would include recognizing a trend and determining whether medical intervention is necessary.  
         [0011]     There is a further need for a system and method that would allow consideration of sets of collected measures, both actual and derived, from multiple device interrogations. These collected measures sets could then be compared and analyzed against short and long term periods of observation.  
         [0012]     There is a further need for a system and method that would enable the measures sets for an individual patient to be self-referenced and cross-referenced to similar or dissimilar patients and to the general patient population. Preferably, the historical collected measures sets of an individual patient could be compared and analyzed against those of other patients in general or of a disease specific peer group in particular.  
         [0013]     There is a further need for a system and method for accepting and normalizing live voice feedback spoken by an individual patient while an identifiable set of telemetered signals is collected by a implantable medical device. Preferably, the normalized voice feedback a semi-quantitative self-assessment of an individual patient&#39;s physical and emotional well being at a time substantially contemporaneous to the collection of the telemetered signals.  
       SUMMARY  
       [0014]     The present invention provides a system and method for automated collection and analysis of patient information retrieved from an implantable medical device for remote patient care. The patient device information relates to individual measures recorded by and retrieved from implantable medical devices, such as IPGs and monitors. The patient device information is received on a regular, e.g., daily, basis as sets of collected measures which are stored along with other patient records in a database. The information can be analyzed in an automated fashion and feedback provided to the patient at any time and in any location.  
         [0015]     The present invention also provides a system and method for providing normalized voice feedback from an individual patient in an automated collection and analysis patient care system. As before, patient device information is received on a regular, e.g., daily, basis as sets of collected measures which are stored along with other patient records in a database. Voice feedback spoken by an individual patient is processed into a set of quality of life measures by a remote client substantially contemporaneous to the recordation of an identifiable set of collected device measures by the implantable medical device. The processed voice feedback and identifiable collected device measures set are both received and stored into the patient record in the database for subsequent evaluation.  
         [0016]     An embodiment provides a system and method for processing voice feedback in conjunction with heart failure assessment. Physiological measures, which were directly recorded as data on a substantially continuous basis by an implantable medical device for a patient or indirectly derived from the data, are assembled. Voice feedback spoken by the patient is collected contemporaneous to the recordation of the physiological measures. The voice feedback is quantified into quality of life measures. A status for the patient is determined through sampling and analysis of the physiological measures and the quality of life measures over a plurality of data assembly points. Trends that are indicated by the patient status are identified and each trend is compared to worsening heart failure indications.  
         [0017]     A further embodiment provides a system and method for providing voice feedback from sampled physiometry and self-evaluation in conjunction with heart failure assessment. Physiological measures including at least one of direct measures regularly recorded on a substantially continuous basis by an implantable medical device for a patient and measures derived from the direct measures are stored. Voice feedback spoken as ordinary speech by the patient is recorded contemporaneous to the recordation of the physiological measures. The voice feedback is quantified into quality of life measures by normalizing the ordinary speech against a voice grammar and speech vocabulary. At least one of those of the physiological measures, which each relate to a same type of physiometry, and those of the physiological measures, which each relate to a different type of physiometry are sampled. A status for the patient is determined through analysis of those sampled physiological measures assembled from a plurality of recordation points and those quantified quality of life measures contemporaneously spoken to those points. The sampled physiological measures are evaluated. Trends that are indicated by the patient status, which might affect cardiac performance of the patient, are identified. Each trend is compared to worsening heart failure indications to generate a notification of parameter violations.  
         [0018]     A further embodiment is a system and method for interactively monitoring patient status in an automated patient care system using voice feedback. Physiological measures are monitored for an implant recipient. Device measures are collected through an implantable medical device on a substantially continuous basis from the implant recipient. The device measures are periodically stored as at least one of collected or derived physiological measures into an individual patient care record. Quality of life measures are monitored for the implant recipient. Patient wellness indicators are obtained through voice feedback provided by the implant recipient substantially contemporaneous to the collection of the device measures. The voice feedback is processed against a stored speech grammar and vocabulary. The processed voice feedback is stored as standardized quality of life measures into the patient care record. The physiological measures and the quality of life measures from the patient care record are recurrently evaluated against at least one of other physiological measures and other quality of life measures to generate a patient status indicator.  
         [0019]     The present invention facilitates the gathering, storage, and analysis of critical patient information obtained on a routine basis and analyzed in an automated manner. Thus, the burden on physicians and trained personnel to evaluate the volumes of information is significantly minimized while the benefits to patients are greatly enhanced.  
         [0020]     The present invention also enables the simultaneous collection of both physiological measures from implantable medical devices and quality of life measures spoken in the patient&#39;s own words. Voice recognition technology enables the spoken patient feedback to be normalized to a standardized set of semi-quantitative quality of life measures, thereby facilitating holistic remote, automated patient care.  
         [0021]     Still other embodiments of the present invention will become readily apparent to those skilled in the art from the following detailed description, wherein is described embodiments of the invention by way of illustrating the best mode contemplated for carrying out the invention. As will be realized, the invention is capable of other and different embodiments and its several details are capable of modifications in various obvious respects, all without departing from the spirit and the scope of the present invention. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not as restrictive. 
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0022]      FIG. 1  is a block diagram showing a system for automated collection and analysis of patient information retrieved from an implantable medical device for remote patient care in accordance with the present invention;  
         [0023]      FIG. 2  is a block diagram showing the hardware components of the server system of the system of  FIG. 1 ;  
         [0024]      FIG. 3  is a block diagram showing the software modules of the server system of the system of  FIG. 1 ;  
         [0025]      FIG. 4  is a block diagram showing the analysis module of the server system of  FIG. 3 ;  
         [0026]      FIG. 5  is a database schema showing, by way of example, the organization of a cardiac patient care record stored in the database of the system of  FIG. 1 ;  
         [0027]      FIG. 6  is a record view showing, by way of example, a set of partial cardiac patient care records stored in the database of the system of  FIG. 1 ;  
         [0028]      FIG. 7  is a flow diagram showing a method for automated collection and analysis of patient information retrieved from an implantable medical device for remote patient care in accordance with the present invention;  
         [0029]      FIG. 8  is a flow diagram showing a routine for analyzing collected measures sets for use in the method of  FIG. 7 ;  
         [0030]      FIG. 9  is a flow diagram showing a routine for comparing sibling collected measures sets for use in the routine of  FIG. 8 ;  
         [0031]      FIGS. 10A and 10B  are flow diagrams showing a routine for comparing peer collected measures sets for use in the routine of  FIG. 8 ;  
         [0032]      FIG. 11  is a flow diagram showing a routine for providing feedback for use in the method of  FIG. 7 ;  
         [0033]      FIG. 12  is a block diagram showing a system for providing patient status feedback via an automated patient care system with speech-based wellness monitoring;  
         [0034]      FIG. 13  is a block diagram showing the software modules of the remote client of the system of  FIG. 12 ;  
         [0035]      FIG. 14  is a block diagram showing the software modules of the server system of the system of  FIG. 12 ;  
         [0036]      FIG. 15  is a database schema showing, by way of example, the organization of a quality of life record for cardiac patient care stored as part of a patient care record in the database of the system of  FIG. 12 ;  
         [0037]      FIGS. 16A-16B  are flow diagrams showing a method for providing patient status feedback via an automated patient care system with speech-based wellness monitoring;  
         [0038]      FIG. 17  is a flow diagram showing a routine for processing voice feedback for use in the method of  FIGS. 16A-16B ;  
         [0039]      FIG. 18  is a flow diagram showing a routine for requesting a quality of life measure for use in the routine of  FIG. 17 ;  
         [0040]      FIG. 19  is a flow diagram showing a routine for recognizing and translating individual spoken words for use in the routine of  FIG. 17 ; and  
         [0041]      FIG. 20  is a block diagram showing the software modules of the server system in a further embodiment of the system of  FIG. 12 . 
     
    
     DETAILED DESCRIPTION  
       [0042]      FIG. 1  is a block diagram showing a system  10  for automated collection and analysis of patient information retrieved from an implantable medical device for remote patient care in accordance with the present invention. A patient  11  is a recipient of an implantable medical device  12 , such as, by way of example, an IPG or a heart failure or event monitor, with a set of leads extending into his or her heart. The implantable medical device  12  includes circuitry for recording into a short-term, volatile memory telemetered signals, which are stored as a set of collected measures for later retrieval.  
         [0043]     For an exemplary cardiac implantable medical device, the telemetered signals non-exclusively present patient information recorded on a per heartbeat, binned average or derived basis and relating to: atrial electrical activity, ventricular electrical activity, minute ventilation, patient activity score, cardiac output score, mixed venous oxygenation score, cardiovascular pressure measures, time of day, the number and types of interventions made, and the relative success of any interventions, plus the status of the batteries and programmed settings. Examples of pacemakers suitable for use in the present invention include the Discovery line of pacemakers, manufactured by Guidant Corporation, Indianapolis, Ind. Examples of ICDs suitable for use in the present invention include the Gem line of ICDs, manufactured by Medtronic Corporation, Minneapolis, Minn.  
         [0044]     In the described embodiment, the patient  11  has a cardiac implantable medical device. However, a wide range of related implantable medical devices are used in other areas of medicine and a growing number of these devices are also capable of measuring and recording patient information for later retrieval. These implantable medical devices include monitoring and therapeutic devices for use in metabolism, endocrinology, hematology, neurology, muscular disorders, gastroenterology, urology, opthalmology, otolaryngology, orthopedics, and similar medical subspecialties. One skilled in the art would readily recognize the applicability of the present invention to these related implantable medical devices.  
         [0045]     On a regular basis, the telemetered signals stored in the implantable medical device  12  are retrieved. By way of example, a programmer  14  can be used to retrieve the telemetered signals. However, any form of programmer, interrogator, recorder, monitor, or telemetered signals transceiver suitable for communicating with an implantable medical device  12  could be used, as is known in the art. In addition, a personal computer or digital data processor could be interfaced to the implantable medical device  12 , either directly or via a telemetered signals transceiver configured to communicate with the implantable medical device  12 .  
         [0046]     Using the programmer  14 , a magnetized reed switch (not shown) within the implantable medical device  12  closes in response to the placement of a wand  13  over the location of the implantable medical device  12 . The programmer  14  communicates with the implantable medical device  12  via RF signals exchanged through the wand  13 . Programming or interrogating instructions are sent to the implantable medical device  12  and the stored telemetered signals are downloaded into the programmer  14 . Once downloaded, the telemetered signals are sent via an internetwork  15 , such as the Internet, to a server system  16  which periodically receives and stores the telemetered signals in a database  17 , as further described below with reference to  FIG. 2 .  
         [0047]     An example of a programmer  14  suitable for use in the present invention is the Model 2901 Programmer Recorder Monitor, manufactured by Guidant Corporation, Indianapolis, Ind., which includes the capability to store retrieved telemetered signals on a proprietary removable floppy diskette. The telemetered signals could later be electronically transferred using a personal computer or similar processing device to the internetwork  15 , as is known in the art.  
         [0048]     Other alternate telemetered signals transfer means could also be employed. For instance, the stored telemetered signals could be retrieved from the implantable medical device  12  and electronically transferred to the internetwork  15  using the combination of a remote external programmer and analyzer and a remote telephonic communicator, such as described in U.S. Pat. No. 5,113,869, the disclosure of which is incorporated herein by reference. Similarly, the stored telemetered signals could be retrieved and remotely downloaded to the server system  16  using a world-wide patient location and data telemetry system, such as described in U.S. Pat. No. 5,752,976, the disclosure of which is incorporated herein by reference.  
         [0049]     The received telemetered signals are analyzed by the server system  16 , which generates a patient status indicator. The feedback is then provided back to the patient  11  through a variety of means. By way of example, the feedback can be sent as an electronic mail message generated automatically by the server system  16  for transmission over the internetwork  15 . The electronic mail message is received by a remote client  18 , such as a personal computer (PC), situated for local access by the patient  11 . Alternatively, the feedback can be sent through a telephone interface device  19  as an automated voice mail message to a telephone  21  or as an automated facsimile message to a facsimile machine  22 , both also situated for local access by the patient  11 . In addition to a remote client  18 , telephone  21 , and facsimile machine  22 , feedback could be sent to other related devices, including a network computer, wireless computer, personal data assistant, television, or digital data processor. Preferably, the feedback is provided in a tiered fashion, as further described below with reference to  FIG. 3 .  
         [0050]      FIG. 2  is a block diagram showing the hardware components of the server system  16  of the system  10  of  FIG. 1 . The server system  16  consists of three individual servers: network server  31 , database server  34 , and application server  35 . These servers are interconnected via an intranetwork  33 . In the described embodiment, the functionality of the server system  16  is distributed among these three servers for efficiency and processing speed, although the functionality could also be performed by a single server or cluster of servers. The network server  31  is the primary interface of the server system  16  onto the internetwork  15 . The network server  31  periodically receives the collected telemetered signals sent by remote implantable medical devices over the internetwork  15 . The network server  31  is interfaced to the internetwork  15  through a router  32 . To ensure reliable data exchange, the network server  31  implements a TCP/IP protocol stack, although other forms of network protocol stacks are suitable.  
         [0051]     The database server  34  organizes the patient care records in the database  17  and provides storage of and access to information held in those records. A high volume of data in the form of collected measures sets from individual patients is received. The database server  34  frees the network server  31  from having to categorize and store the individual collected measures sets in the appropriate patient care record.  
         [0052]     The application server  35  operates management applications and performs data analysis of the patient care records, as further described below with reference to  FIG. 3 . The application server  35  communicates feedback to the individual patients either through electronic mail sent back over the internetwork  15  via the network server  31  or as automated voice mail or facsimile messages through the telephone interface device  19 .  
         [0053]     The server system  16  also includes a plurality of individual workstations  36  (WS) interconnected to the intranetwork  33 , some of which can include peripheral devices, such as a printer  37 . The workstations  36  are for use by the data management and programming staff, nursing staff, office staff, and other consultants and authorized personnel.  
         [0054]     The database  17  consists of a high-capacity storage medium configured to store individual patient care records and related health care information. Preferably, the database  17  is configured as a set of high-speed, high capacity hard drives, such as organized into a Redundant Array of Inexpensive Disks (RAID) volume. However, any form of volatile storage, non-volatile storage, removable storage, fixed storage, random access storage, sequential access storage, permanent storage, erasable storage, and the like would be equally suitable. The organization of the database  17  is further described below with reference to  FIG. 3 .  
         [0055]     The individual servers and workstations are general purpose, programmed digital computing devices consisting of a central processing unit (CPU), random access memory (RAM), non-volatile secondary storage, such as a hard drive or CD ROM drive, network interfaces, and peripheral devices, including user interfacing means, such as a keyboard and display. Program code, including software programs, and data are loaded into the RAM for execution and processing by the CPU and results are generated for display, output, transmittal, or storage. In the described embodiment, the individual servers are Intel Pentium-based server systems, such as available from Dell Computers, Austin, Tex., or Compaq Computers, Houston, Tex. Each system is preferably equipped with 128 MB RAM, 100 GB hard drive capacity, data backup facilities, and related hardware for interconnection to the intranetwork  33  and internetwork  15 . In addition, the workstations  36  are also Intel Pentium-based personal computer or workstation systems, also available from Dell Computers, Austin, Tex., or Compaq Computers, Houston, Tex. Each workstation is preferably equipped with 64 MB RAM, 10 GB hard drive capacity, and related hardware for interconnection to the intranetwork  33 . Other types of server and workstation systems, including personal computers, minicomputers, mainframe computers, supercomputers, parallel computers, workstations, digital data processors and the like would be equally suitable, as is known in the art.  
         [0056]     The telemetered signals are communicated over an internetwork  15 , such as the Internet. However, any type of electronic communications link could be used, including an intranetwork link, serial link, data telephone link, satellite link, radio-frequency link, infrared link, fiber optic link, coaxial cable link, television link, and the like, as is known in the art. Also, the network server  31  is interfaced to the internetwork  15  using a T-1 network router  32 , such as manufactured by Cisco Systems, Inc., San Jose, Calif. However, any type of interfacing device suitable for interconnecting a server to a network could be used, including a data modem, cable modem, network interface, serial connection, data port, hub, frame relay, digital PBX, and the like, as is known in the art.  
         [0057]      FIG. 3  is a block diagram showing the software modules of the server system  16  of the system  10  of  FIG. 1 . Each module is a computer program written as source code in a conventional programming language, such as the C or Java programming languages, and is presented for execution by the CPU as object or byte code, as is known in the arts. The various implementations of the source code and object and byte codes can be held on a computer-readable storage medium or embodied on a transmission medium in a carrier wave. There are three basic software modules, which functionally define the primary operations performed by the server system  16 : database module  51 , analysis module  53 , and feedback module  55 . In the described embodiment, these modules are executed in a distributed computing environment, although a single server or a cluster of servers could also perform the functionality of the modules. The module functions are further described below in more detail beginning with reference to  FIG. 7 .  
         [0058]     For each patient being provided remote patient care, the server system  16  periodically receives a collected measures set  50  which is forwarded to the database module  51  for processing. The database module  51  organizes the individual patent care records stored in the database  52  and provides the facilities for efficiently storing and accessing the collected measures sets  50  and patient data maintained in those records. An exemplary database schema for use in storing collected measures sets  50  in a patient care record is described below, by way of example, with reference to  FIG. 5 . The database server  34  (shown in  FIG. 2 ) performs the functionality of the database module  51 . Any type of database organization could be utilized, including a flat file system, hierarchical database, relational database, or distributed database, such as provided by database vendors, such as Oracle Corporation, Redwood Shores, Calif.  
         [0059]     The analysis module  53  analyzes the collected measures sets  50  stored in the patient care records in the database  52 . The analysis module  53  makes an automated determination of patient wellness in the form of a patient status indicator  54 . Collected measures sets  50  are periodically received from implantable medical devices and maintained by the database module  51  in the database  52 . Through the use of this collected information, the analysis module  53  can continuously follow the medical well being of a patient and can recognize any trends in the collected information that might warrant medical intervention. The analysis module  53  compares individual measures and derived measures obtained from both the care records for the individual patient and the care records for a disease specific group of patients or the patient population in general. The analytic operations performed by the analysis module  53  are further described below with reference to  FIG. 4 . The application server  35  (shown in  FIG. 2 ) performs the functionality of the analysis module  53 .  
         [0060]     The feedback module  55  provides automated feedback to the individual patient based, in part, on the patient status indicator  54 . As described above, the feedback could be by electronic mail or by automated voice mail or facsimile. Preferably, the feedback is provided in a tiered manner. In the described embodiment, four levels of automated feedback are provided. At a first level, an interpretation of the patient status indicator  54  is provided. At a second level, a notification of potential medical concern based on the patient status indicator  54  is provided. This feedback level could also be coupled with human contact by specially trained technicians or medical personnel. At a third level, the notification of potential medical concern is forwarded to medical practitioners located in the patient&#39;s geographic area. Finally, at a fourth level, a set of reprogramming instructions based on the patient status indicator  54  could be transmitted directly to the implantable medical device to modify the programming instructions contained therein. As is customary in the medical arts, the basic tiered feedback scheme would be modified in the event of bona fide medical emergency. The application server  35  (shown in  FIG. 2 ) performs the functionality of the feedback module  55 .  
         [0061]      FIG. 4  is a block diagram showing the analysis module  53  of the server system  16  of  FIG. 3 . The analysis module  53  contains two functional submodules: comparison module  62  and derivation module  63 . The purpose of the comparison module  62  is to compare two or more individual measures, either collected or derived. The purpose of the derivation module  63  is to determine a derived measure based on one or more collected measures which is then used by the comparison module  62 . For instance, a new and improved indicator of impending heart failure could be derived based on the exemplary cardiac collected measures set described with reference to  FIG. 5 . The analysis module  53  can operate either in a batch mode of operation wherein patient status indicators are generated for a set of individual patients or in a dynamic mode wherein a patient status indicator is generated on the fly for an individual patient.  
         [0062]     The comparison module  62  receives as inputs from the database  17  two input sets functionally defined as peer collected measures sets  60  and sibling collected measures sets  61 , although in practice, the collected measures sets are stored on a per sampling basis. Peer collected measures sets  60  contain individual collected measures sets that all relate to the same type of patient information, for instance, atrial electrical activity, but which have been periodically collected over time. Sibling collected measures sets  61  contain individual collected measures sets that relate to different types of patient information, but which may have been collected at the same time or different times. In practice, the collected measures sets are not separately stored as “peer” and “sibling” measures. Rather, each individual patient care record stores multiple sets of sibling collected measures. The distinction between peer collected measures sets  60  and sibling collected measures sets  61  is further described below with reference to  FIG. 6 .  
         [0063]     The derivation module  63  determines derived measures sets  64  on an as-needed basis in response to requests from the comparison module  62 . The derived measures  64  are determined by performing linear and non-linear mathematical operations on selected peer measures  60  and sibling measures  61 , as is known in the art.  
         [0064]      FIG. 5  is a database schema showing, by way of example, the organization of a cardiac patient care record stored  70  in the database  17  of the system  10  of  FIG. 1 . Only the information pertaining to collected measures sets are shown. Each patient care record would also contain normal identifying and treatment profile information, as well as medical history and other pertinent data (not shown). Each patient care record stores a multitude of collected measures sets for an individual patient. Each individual set represents a recorded snapshot of telemetered signals data which was recorded, for instance, per heartbeat or binned average basis by the implantable medical device  12 . For example, for a cardiac patient, the following information would be recorded as a collected measures set: atrial electrical activity  71 , ventricular electrical activity  72 , time of day  73 , activity level  74 , cardiac output  75 , oxygen level  76 , cardiovascular pressure measures  77 , pulmonary measures  78 , interventions made by the implantable medical device  78 , and the relative success of any interventions made  80 . In addition, the implantable medical device  12  would also communicate device specific information, including battery status  81  and program settings  82 . Other types of collected measures are possible. In addition, a well-documented set of derived measures can be determined based on the collected measures, as is known in the art.  
         [0065]      FIG. 6  is a record view showing, by way of example, a set of partial cardiac patient care records stored in the database  17  of the system  10  of  FIG. 1 . Three patient care records are shown for Patient 1, Patient 2, and Patient 3. For each patent, three sets of measures are shown, X, Y, and Z. The measures are organized into sets with Set 0 representing sibling measures made at a reference time t=0. Similarly, Set n-2, Set n-1 and Set n each represent sibling measures made at later reference times t=n-2, t=n-1 and t=n, respectively.  
         [0066]     For a given patient, for instance, Patient 1, all measures representing the same type of patient information, such as measure X, are peer measures. These are measures, which are monitored over time in a disease-matched peer group. All measures representing different types of patient information, such as measures X, Y, and Z, are sibling measures. These are measures which are also measured over time, but which might have medically significant meaning when compared to each other within a single set. Each of the measures, X, Y, and Z, could be either collected or derived measures.  
         [0067]     The analysis module  53  (shown in  FIG. 4 ) performs two basic forms of comparison. First, individual measures for a given patient can be compared to other individual measures for that same patient. These comparisons might be peer-to-peer measures projected over time, for instance, X n , X n-1 , X n-2 , . . . X 0 , or sibling-to-sibling measures for a single snapshot, for instance, X n , Y n , and Z n , or projected over time, for instance, X n , Y n , Z n , X n-1 , Y n-1 , Z n-1 , X n-2 , Y n-2 , Z n-2 , . . . X 0 , Y 0 , Z 0 . Second, individual measures for a given patient can be compared to other individual measures for a group of other patients sharing the same disease-specific characteristics or to the patient population in general. Again, these comparisons might be peer-to-peer measures projected over time, for instance, X n , X n′ , X n″ , X n-1 , X n-1′ , X n-1″ , X n-2 , X n-2′ , X n-2″  . . . X 0 , X 0′ , X 0″ , or comparing the individual patient&#39;s measures to an average from the group. Similarly, these comparisons might be sibling-to-sibling measures for single snapshots, for instance, X n , X n′ , X n″ , Y n , Y n′ , Y n″  and Z n , Z n′ , Z n″ , or projected over time, for instance, X n , X n′ , X n″ , Y n , Y n′ , Y n″ , Z n , Z n′ , Z n″ , X n-1 , X n-1′ , X n-1″ , Y n-1 , Y n-1′ , Y n-1″ , Z n-1 , Z n-1′ , Z n-1″ , X n-2 , X n-2′ , X n-2″ , Y n-2 , Y n-2′ , Y n-2″ , Z n-2 , Z n-2′ , Z n-b-2″  . . . X 0 , X 0′ , X 0″ , Y 0 , Y 0′ , Y 0″ , and Z 0 , Z 0′ , Z 0″ . Other forms of comparisons are feasible.  
         [0068]      FIG. 7  is a flow diagram showing a method  90  for automated collection and analysis of patient information retrieved from an implantable medical device  12  for remote patient care in accordance with the present invention. The method  90  is implemented as a conventional computer program for execution by the server system  16  (shown in  FIG. 1 ). As a preparatory step, the patient care records are organized in the database  17  with a unique patient care record assigned to each individual patient (block  91 ). Next, the collected measures sets for an individual patient are retrieved from the implantable medical device  12  (block  92 ) using a programmer, interrogator, telemetered signals transceiver, and the like. The retrieved collected measures sets are sent, on a substantially regular basis, over the internetwork  15  or similar communications link (block  93 ) and periodically received by the server system  16  (block  94 ). The collected measures sets are stored into the patient care record in the database  17  for that individual patient (block  95 ). One or more of the collected measures sets for that patient are analyzed (block  96 ), as further described below with reference to  FIG. 8 . Finally, feedback based on the analysis is sent to that patient over the internetwork  15  as an email message, via telephone line as an automated voice mail or facsimile message, or by similar feedback communications link (block  97 ), as further described below with reference to  FIG. 11 .  
         [0069]      FIG. 8  is a flow diagram showing the routine for analyzing collected measures sets  96  for use in the method of  FIG. 7 . The purpose of this routine is to make a determination of general patient wellness based on comparisons and heuristic trends analyses of the measures, both collected and derived, in the patient care records in the database  17 . A first collected measures set is selected from a patient care record in the database  17  (block  100 ). If the measures comparison is to be made to other measures originating from the patient care record for the same individual patient (block  101 ), a second collected measures set is selected from that patient care record (block  102 ). Otherwise, a group measures comparison is being made (block  101 ) and a second collected measures set is selected from another patient care record in the database  17  (block  103 ). Note the second collected measures set could also contain averaged measures for a group of disease specific patients or for the patient population in general.  
         [0070]     Next, if a sibling measures comparison is to be made (block  104 ), a routine for comparing sibling collected measures sets is performed (block  105 ), as further described below with reference to  FIG. 9 . Similarly, if a peer measures comparison is to be made (block  106 ), a routine for comparing sibling collected measures sets is performed (block  107 ), as further described below with reference to  FIGS. 10A and 10B .  
         [0071]     Finally, a patient status indicator is generated (block  108 ). By way of example, cardiac output could ordinarily be approximately 5.0 liters per minute with a standard deviation of ± 1 . 0 . An actionable medical phenomenon could occur when the cardiac output of a patient is ±3.0-4.0 standard deviations out of the norm. A comparison of the cardiac output measures  75  (shown in  FIG. 5 ) for an individual patient against previous cardiac output measures  75  would establish the presence of any type of downward health trend as to the particular patient. A comparison of the cardiac output measures  75  of the particular patient to the cardiac output measures  75  of a group of patients would establish whether the patient is trending out of the norm. From this type of analysis, the analysis module  53  generates a patient status indicator  54  and other metrics of patient wellness, as is known in the art.  
         [0072]      FIG. 9  is a flow diagram showing the routine for comparing sibling collected measures sets  105  for use in the routine of  FIG. 8 . Sibling measures originate from the patient care records for an individual patient. The purpose of this routine is either to compare sibling derived measures to sibling derived measures (blocks  111 - 113 ) or sibling collected measures to sibling collected measures (blocks  115 - 117 ). Thus, if derived measures are being compared (block  110 ), measures are selected from each collected measures set (block  111 ). First and second derived measures are derived from the selected measures (block  112 ) using the derivation module  63  (shown in  FIG. 4 ). The first and second derived measures are then compared (block  113 ) using the comparison module  62  (also shown in  FIG. 4 ). The steps of selecting, determining, and comparing (blocks  111 - 113 ) are repeated until no further comparisons are required (block  114 ), whereupon the routine returns.  
         [0073]     If collected measures are being compared (block  110 ), measures are selected from each collected measures set (block  115 ). The first and second collected measures are then compared (block  116 ) using the comparison module  62  (also shown in  FIG. 4 ). The steps of selecting and comparing (blocks  115 - 116 ) are repeated until no further comparisons are required (block  117 ), whereupon the routine returns.  
         [0074]      FIGS. 10A and 10B  are a flow diagram showing the routine for comparing peer collected measures sets  107  for use in the routine of  FIG. 8 . Peer measures originate from patient care records for different patients, including groups of disease specific patients or the patient population in general. The purpose of this routine is to compare peer derived measures to peer derived measures (blocks  122 - 125 ), peer derived measures to peer collected measures (blocks  126 - 129 ), peer collected measures to peer derived measures (block  131 - 134 ), or peer collected measures to peer collected measures (blocks  135 - 137 ). Thus, if the first measure being compared is a derived measure (block  120 ) and the second measure being compared is also a derived measure (block  121 ), measures are selected from each collected measures set (block  122 ). First and second derived measures are derived from the selected measures (block  123 ) using the derivation module  63  (shown in  FIG. 4 ). The first and second derived measures are then compared (block  124 ) using the comparison module  62  (also shown in  FIG. 4 ). The steps of selecting, determining, and comparing (blocks  122 - 124 ) are repeated until no further comparisons are required (block  115 ), whereupon the routine returns.  
         [0075]     If the first measure being compared is a derived measure (block  120 ) but the second measure being compared is a collected measure (block  121 ), a first measure is selected from the first collected measures set (block  126 ). A first derived measure is derived from the first selected measure (block  127 ) using the derivation module  63  (shown in  FIG. 4 ). The first derived and second collected measures are then compared (block  128 ) using the comparison module  62  (also shown in  FIG. 4 ). The steps of selecting, determining, and comparing (blocks  126 - 128 ) are repeated until no further comparisons are required (block  129 ), whereupon the routine returns.  
         [0076]     If the first measure being compared is a collected measure (block  120 ) but the second measure being compared is a derived measure (block  130 ), a second measure is selected from the second collected measures set (block  131 ). A second derived measure is derived from the second selected measure (block  132 ) using the derivation module  63  (shown in  FIG. 4 ). The first collected and second derived measures are then compared (block  133 ) using the comparison module  62  (also shown in  FIG. 4 ). The steps of selecting, determining, and comparing (blocks  131 - 133 ) are repeated until no further comparisons are required (block  134 ), whereupon the routine returns.  
         [0077]     If the first measure being compared is a collected measure (block  120 ) and the second measure being compared is also a collected measure (block  130 ), measures are selected from each collected measures set (block  135 ). The first and second collected measures are then compared (block  136 ) using the comparison module  62  (also shown in  FIG. 4 ). The steps of selecting and comparing (blocks  135 - 136 ) are repeated until no further comparisons are required (block  137 ), whereupon the routine returns.  
         [0078]      FIG. 11  is a flow diagram showing the routine for providing feedback  97  for use in the method of  FIG. 7 . The purpose of this routine is to provide tiered feedback based on the patient status indicator. Four levels of feedback are provided with increasing levels of patient involvement and medical care intervention. At a first level (block  150 ), an interpretation of the patient status indicator  54 , preferably phrased in lay terminology, and related health care information is sent to the individual patient (block  151 ) using the feedback module  55  (shown in  FIG. 3 ). At a second level (block  152 ), a notification of potential medical concern, based on the analysis and heuristic trends analysis, is sent to the individual patient (block  153 ) using the feedback module  55 . At a third level (block  154 ), the notification of potential medical concern is forwarded to the physician responsible for the individual patient or similar health care professionals (block  155 ) using the feedback module  55 . Finally, at a fourth level (block  156 ), reprogramming instructions are sent to the implantable medical device  12  (block  157 ) using the feedback module  55 .  
         [0079]      FIG. 12  is a block diagram showing a system  200  for providing normalized voice feedback from an individual patient  11  in an automated collection and analysis patient care system, such as the system  10  of  FIG. 1 . The remote client  18  includes a microphone  201  and a speaker  202  which is interfaced internally within the remote client  18  to sound recordation and reproduction hardware. The patient  11  provides spoken feedback into the microphone  201  in response to voice prompts reproduced by the remote client  18  on the speaker  202 , as further described below with reference to  FIG. 13 . The raw spoken feedback is processed into a normalized set of quality of life measures which each relate to uniform self-assessment indicators, as further described below with reference to  FIG. 15 . Alternatively, in a further embodiment of the system  200 , the patient  11  can provide spoken feedback via a telephone network  203  using a standard telephone  203 , including a conventional wired telephone or a wireless telephone, such as a cellular telephone, as further described below with reference to  FIG. 20 . In the described embodiment, the microphone  201  and the speaker  202  are standard, off-the-shelf components commonly included with consumer personal computer systems, as is known in the art.  
         [0080]     The system  200  continuously monitors and collects sets of device measures from the implantable medical device  12 . To augment the on-going monitoring process with a patient&#39;s self-assessment of physical and emotional well-being, a quality of life measures set can be recorded by the remote client  18  Importantly, each quality of life measures set is recorded substantially contemporaneous to the collection of an identified collected device measures set. The date and time of day at which the quality of life measures set was recorded can be used to correlate the quality of life measures set to the collected device measures set recorded closest in time to the quality of life measures set. The pairing of the quality of life measures set and an identified collected device measures set provides medical practitioners with a more complete picture of the patient&#39;s medical status by combining physiological “hard” machine-recorded data with semi-quantitative “soft” patient-provided data.  
         [0081]      FIG. 13  is a block diagram showing the software modules of the remote client  18  of the system  200  of  FIG. 12 . As with the software modules of the system  10  of  FIG. 1 , each module here is also a computer program written as source code in a conventional programming language, such as the C or Java programming languages, and is presented for execution by the CPU as object or byte code, as is known in the arts. There are two basic software modules, which functionally define the primary operations performed by the remote client  18  in providing normalized voice feedback: audio prompter  210  and speech engine  214 . The remote client  18  includes a secondary storage  219 , such as a hard drive, a CD ROM player, and the like, within which is stored data used by the software modules. Conceptually, the voice reproduction and recognition functions performed by the audio prompter  210  and speech engine  214  can be described separately, but those same functions could also be performed by a single voice processing module, as is known in the art.  
         [0082]     The audio prompter  210  generates voice prompts  226  which are played back to the patient  11  on the speaker  202 . Each voice prompt is in the form of a question or phrase seeking to develop a self-assessment of the patient&#39;s physical and emotional well being. For example, the patient  11  might be prompted with, “Are you short of breath?” The voice prompts  226  are either from a written script  220  reproduced by speech synthesizer  211  or pre-recorded speech  221  played back by playback module  212 . The written script  220  is stored within the secondary storage  219  and consists of written quality of life measure requests. Similarly, the pre-recorded speech  221  is also stored within the secondary storage  219  and consists of sound “bites” of recorded quality of life measure requests in either analog or digital format.  
         [0083]     The speech engine  214  receives voice responses  227  spoken by the patient  11  into the microphone  201 . The voice responses  227  can be unstructured, natural language phrases and sentences. A voice grammar  222  provides a lexical structuring for use in determining the meaning of each spoken voice response  227 . The voice grammar  222  allows the speech engine  214  to “normalize” the voice responses  227  into recognized quality of life measures  228 . Individual spoken words in each voice response  227  are recognized by a speech recognition module  215  and translated into written words. In turn, the written words are parsed into tokens by a parser  216 . A lexical analyzer  217  analyzes the tokens as complete phrases in accordance with a voice grammar  222  stored within the secondary storage  219 . Finally, if necessary, the individual words are normalized to uniform terms by a lookup module  218  which retrieves synonyms maintained as a vocabulary  223  stored within the secondary storage  218 . For example, in response to the query, “Are you short of breath?,” a patient might reply, “I can hardly breath,” “I am panting,” or “I am breathless.” The speech recognition module  215  would interpret these phrases to imply dyspnea with a corresponding quality of life measure indicating an awareness by the patient of abnormal breathing. In the described embodiment, the voice reproduction and recognition functions can be performed by the various natural voice software programs licensed by Dragon Systems, Inc., Newton, Mass. Alternatively, the written script  220 , voice grammar  222 , and vocabulary  223  could be expressed as a script written in a voice page markup language for interpretation by a voice browser operating on the remote client  18 . Two exemplary voice page description languages include the VoxML markup language, licensed by Motorola, Inc., Chicago, Ill., and described at http://www.voxml.com, and the Voice eXtensible Markup Language (VXML), currently being jointly developed by AT&amp;T, Motorola, Lucent Technologies, and IBM, and described at http://www.vxmlforum.com. The module functions are further described below in more detail beginning with reference to  FIGS. 16A-16B .  
         [0084]      FIG. 14  is a block diagram showing the software modules of the server system  16  of the system  200  of  FIG. 12 . The database module  51 , previously described above with reference to  FIG. 3 , also receives the collected quality of life measures set  228  from the remote client  18 , which the database module  51  stores into the appropriate patient care record in the database  52 . The date and time of day  236  (shown in  FIG. 15 ) of the quality of life measures set  228  is matched to the date and time of day  73  (shown in  FIG. 5 ) of the collected measures set  50  recorded closest in time to the quality of life measures set  228 . The matching collected measures set  50  is identified in the patient care record and can be analyzed with the quality of life measures set  228  by the analysis module  53 , such as described above with reference to  FIG. 8 .  
         [0085]      FIG. 15  is a database schema showing, by way of example, the organization of a quality of life record  230  for cardiac patient care stored as part of a patient care record in the database  17  of the system  200  of  FIG. 12 . A quality of life score is a semi-quantitative self-assessment of an individual patient&#39;s physical and emotional well being. Non-commercial, non-proprietary standardized automated quality of life scoring systems are readily available, such as provided by the Duke Activities Status Indicator. For example, for a cardiac patient, the quality of life record  230  stores the following information: health wellness  231 , shortness of breath  232 , energy level  233 , chest discomfort  235 , time of day  234 , and other quality of life measures as would be known to one skilled in the art. Other types of quality of life measures are possible.  
         [0086]     A quality of life indicator is a vehicle through which a patient can remotely communicate to the patient care system how he or she is subjectively feeling. The quality of life indicators can include symptoms of disease. When tied to machine-recorded physiological measures, a quality of life indicator can provide valuable additional information to medical practitioners and the automated collection and analysis patient care system  200  not otherwise discernible without having the patient physically present. For instance, a scoring system using a scale of 1.0 to 10.0 could be used with 10.0 indicating normal wellness and 1.0 indicating severe health problems. Upon the completion of an initial observation period, a patient might indicate a health wellness score  231  of 5.0 and a cardiac output score of 5.0. After one month of remote patient care, the patient might then indicate a health wellness score  231  of 4.0 and a cardiac output score of 4.0 and a week later indicate a health wellness score  231  of 3.5 and a cardiac output score of 3.5. Based on a comparison of the health wellness scores  231  and the cardiac output scores, the system  200  would identify a trend indicating the necessity of potential medical intervention while a comparison of the cardiac output scores alone might not lead to the same prognosis.  
         [0087]      FIGS. 16A-16B  are flow diagrams showing a method  239  for providing normalized voice feedback from an individual patient  11  in an automated collection and analysis patient care system  200 . As with the method  90  of  FIG. 7 , this method is also implemented as a conventional computer program and performs the same set of steps as described with reference to  FIG. 7  with the following additional functionality. First, voice feedback spoken by the patient  11  into the remote client  18  is processed into a quality of life measures set  228  (block  240 ), as further described below with reference to  FIG. 17 . The voice feedback is spoken substantially contemporaneous to the collection of an identified device measures set  50 . The appropriate collected device measures set  50  can be matched to and identified with (not shown) the quality of life measures set  228  either by matching their respective dates and times of day or by similar means, either by the remote client  18  or the server system  16 . The quality of life measures set  228  and the identified collected measures set  50  are sent over the internetwork  15  to the server system  16  (block  241 ). Note the quality of life measures set  228  and the identified collected measures set  50  both need not be sent over the internetwork  15  at the same time, so long as the two sets are ultimately paired based on, for example, date and time of day. The quality of life measures set  228  and the identified collected measures set  50  are received by the server system  16  (block  242 ) and stored in the appropriate patient care record in the database  52  (block  243 ). Finally, the quality of life measures set  228 , identified collected measures set  50 , and one or more collected measures sets  50  are analyzed (block  244 ) and feedback, including a patient status indicator  54  (shown in  FIG. 14 ), is provided to the patient (block  245 ).  
         [0088]      FIG. 17  is a flow diagram showing the routine for processing voice feedback  240  for use in the method of  FIGS. 16A-16B . The purpose of this routine is to facilitate a voice interactive session with the patient  11  during which is developed a normalized set of quality of life measures. Thus, the remote client  18  requests a quality of life measure via a voice prompt (block  250 ), played on the speaker  202  (shown in  FIG. 13 ), as further described below with reference to  FIG. 18 . The remote client  18  receives the spoken feedback from the patient  11  (block  251 ) via the microphone  201  (shown in  FIG. 13 ). The remote client  18  recognizes individual words in the spoken feedback and translates those words into written words (block  252 ), as further described below with reference to  FIG. 19 . The routine returns at the end of the voice interactive session.  
         [0089]      FIG. 18  is a flow diagram showing the routine for requesting a quality of life measure  251  for use in the routine  240  of  FIG. 17 . The purpose of this routine is to present a voice prompt  226  to the user via the speaker  202 . Either pre-recorded speech  221  or speech synthesized from a written script  220  can be used. Thus, if synthesized speech is employed by the remote client  18  (block  260 ), a written script, such as a voice markup language script, specifying questions and phrases which with to request quality of life measures is stored (block  261 ) on the secondary storage  219  of the remote client  18 . Each written quality of life measure request is retrieved by the remote client  18  (block  262 ) and synthesized into speech for playback to the patient  11  (block  263 ). Alternatively, if pre-recorded speech is employed by the remote client  18  (block  260 ), pre-recorded voice “bites” are stored (block  264 ) on the secondary storage  219  of the remote client  18 . Each pre-recorded quality of life measure request is retrieved by the remote client  18  (block  265 ) and played back to the patient  11  (block  266 ). The routine then returns.  
         [0090]      FIG. 19  is a flow diagram showing the routine for recognizing and translating individual spoken words  252  for use in the routine  240  of  FIG. 17 . The purpose of this routine is to receive and interpret a free-form voice response  227  from the user via the microphone  201 . First, a voice grammar consisting of a lexical structuring of words, phrases, and sentences is stored (block  270 ) on the secondary storage  219  of the remote client  18 . Similarly, a vocabulary of individual words and their commonly accepted synonyms is stored (block  271 ) on the secondary storage  219  of the remote client  18 . After individual words in the voice feedback are recognized (block  272 ), the individual words are parsed into tokens (block  273 ). The voice feedback is then lexically analyzed using the tokens and in accordance with the voice grammar  222  (block  274 ) to determine the meaning of the voice feedback. If necessary, the vocabulary  223  is referenced to lookup synonyms of the individual words (block  275 ). The routine then returns.  
         [0091]      FIG. 20  is a block diagram showing the software modules of the server system in a further embodiment of the system  200  of  FIG. 12 . The functionality of the remote client  18  in providing normalized voice feedback is incorporated directly into the server system  16 . The system  200  of  FIG. 12  requires the patient  11  to provide spoken feedback via a locally situated remote client  18 . However, the system  280  enables a patient  11  to alternatively provide spoken feedback via a telephone network  203  using a standard telephone  203 , including a conventional wired telephone or a wireless telephone, such as a cellular telephone. The server system  16  is augmented to include the audio prompter  210 , the speech engine  214 , and the data stored in the secondary storage  219 . A telephonic interface  280  interfaces the server system  16  to the telephone network  203  and receives voice responses  227  and sends voice prompts  226  to and from the server system  16 . Telephonic interfacing devices are commonly known in the art.  
         [0092]     Therefore, through the use of the collected measures sets, the present invention makes possible immediate access to expert medical care at any time and in any place. For example, after establishing and registering for each patient an appropriate baseline set of measures, the database server could contain a virtually up-to-date patient history, which is available to medical providers for the remote diagnosis and prevention of serious illness regardless of the relative location of the patient or time of day.  
         [0093]     Moreover, the gathering and storage of multiple sets of critical patient information obtained on a routine basis makes possible treatment methodologies based on an algorithmic analysis of the collected data sets. Each successive introduction of a new collected measures set into the database server would help to continually improve the accuracy and effectiveness of the algorithms used. In addition, the present invention potentially enables the detection, prevention, and cure of previously unknown forms of disorders based on a trends analysis and by a cross-referencing approach to create continuously improving peer-group reference databases.  
         [0094]     Similarly, the present invention makes possible the provision of tiered patient feedback based on the automated analysis of the collected measures sets. This type of feedback system is suitable for use in, for example, a subscription based health care service. At a basic level, informational feedback can be provided by way of a simple interpretation of the collected data. The feedback could be built up to provide a gradated response to the patient, for example, to notify the patient that he or she is trending into a potential trouble zone. Human interaction could be introduced, both by remotely situated and local medical practitioners. Finally, the feedback could include direct interventive measures, such as remotely reprogramming a patient&#39;s IPG.  
         [0095]     Finally, the present invention allows “live” patient voice feedback to be captured simultaneously with the collection of physiological measures by their implantable medical device. The voice feedback is normalized to a standardized set of quality of life measures which can be analyzed in a remote, automated fashion. The voice feedback could also be coupled with visual feedback, such as through digital photography or video, to provide a more complete picture of the patient&#39;s physical well-being.  
         [0096]     While the invention has been particularly shown and described as referenced to the embodiments thereof, those skilled in the art will understand that the foregoing and other changes in form and detail may be made therein without departing from the spirit and scope of the invention.