U.S. patent application number 11/480634 was filed with the patent office on 2006-11-09 for system and method for generating feeback on physiometry analyzed during automated patient management.
Invention is credited to Gust H. Bardy.
Application Number | 20060253006 11/480634 |
Document ID | / |
Family ID | 26984669 |
Filed Date | 2006-11-09 |
United States Patent
Application |
20060253006 |
Kind Code |
A1 |
Bardy; Gust H. |
November 9, 2006 |
System and method for generating feeback on physiometry analyzed
during automated patient management
Abstract
A system and method for generating feedback on physiometry
analyzed during automated patient management is described. A
patient enrolled in automated patient care is identified, along
with information including at least one of treatment profile and
medical history. Collected device measures are received to provide
raw physiometry for the patient that was regularly monitored and
recorded by a medical device. Derived device measures are
determined to provide derivative physiometry based on the collected
device measures. The collected and derived device measures quantify
feedback including an analyzed pathophysiology indicative of
patient well being.
Inventors: |
Bardy; Gust H.; (Seattle,
WA) |
Correspondence
Address: |
LAW OFFICES OF PATRICK J.S. INOUYE
810 THIRD AVE
STE. 258
SEATTLE
WA
98104
US
|
Family ID: |
26984669 |
Appl. No.: |
11/480634 |
Filed: |
June 30, 2006 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
10422495 |
Apr 23, 2003 |
7070562 |
|
|
11480634 |
Jun 30, 2006 |
|
|
|
09860977 |
May 18, 2001 |
6974413 |
|
|
10422495 |
Apr 23, 2003 |
|
|
|
09476602 |
Dec 31, 1999 |
6270457 |
|
|
09860977 |
May 18, 2001 |
|
|
|
09324894 |
Jun 3, 1999 |
6312378 |
|
|
09476602 |
Dec 31, 1999 |
|
|
|
Current U.S.
Class: |
600/300 ;
705/3 |
Current CPC
Class: |
A61N 1/37258 20130101;
A61B 5/4878 20130101; A61B 5/333 20210101; A61B 5/7275 20130101;
A61B 5/7246 20130101; A61B 5/0002 20130101; A61B 5/021 20130101;
A61B 5/1116 20130101; G16H 15/00 20180101; G16H 50/20 20180101;
A61B 5/02028 20130101; A61B 5/145 20130101; G16H 10/60 20180101;
A61B 5/0205 20130101; A61B 5/0031 20130101; A61B 5/746 20130101;
A61B 5/1118 20130101; A61B 5/686 20130101; A61B 7/02 20130101; A61B
5/0826 20130101; A61B 5/0215 20130101; Y10S 128/92 20130101; A61B
5/0006 20130101; G16H 40/67 20180101; G16H 50/30 20180101; A61N
1/39622 20170801; A61N 1/37282 20130101; A61B 5/0022 20130101 |
Class at
Publication: |
600/300 ;
705/003 |
International
Class: |
A61B 5/00 20060101
A61B005/00 |
Claims
1. Stored feedback on physiometry analyzed during automated patient
management, comprising: information to identify a patient enrolled
in automated patient care and comprising at least one of treatment
profile and medical history; collected device measures to provide
raw physiometry for the patient that was regularly monitored and
recorded by a medical device; and derived device measures to
provide derivative physiometry based on the collected device
measures, wherein the collected and derived device measures
quantify feedback comprising an analyzed pathophysiology indicative
of patient well being.
2. Stored feedback according to claim 1, further comprising: at
least one feedback message selected from the group comprising an
interpretive patient status, a medical diagnosis patient
notification, a medical diagnosis caregiver notification, and
reprogramming instructions for the medical device.
3. Stored feedback according to claim 1, further comprising:
evaluation parameters to define a threshold for the pathophysiology
in terms of the patient well being; and a wellness indicator
determined by analyzing one or more of the collected and derived
device measures against the evaluation parameters.
4. Stored feedback according to claim 3, further comprising: a
trend indicator determined by evaluating a plurality of the
wellness indicators to identify one of an absence, onset,
progression, regression, and status quo of a physiological
disorder.
5. Stored feedback according to claim 1, further comprising: a
baseline pathophysiology quantified by those of the collected and
derived device measures associated with a fixed time period.
6. Stored feedback according to claim 1, further comprising:
quality of life measures to provide qualified voice feedback data
electronically recorded by the patient contemporaneous to
recordation of the collected device measures.
7. Stored feedback according to claim 1, further comprising: an
index physiological disorder determined by evaluating a plurality
of the wellness indicators to order and prioritize multiple
near-simultaneous physiological disorders.
8. Stored feedback according to claim 1, wherein the medical device
comprises one of an implantable medical device and an external
medical device.
9. A patient communicator for generating feedback on physiometry
analyzed during automated patient management, comprising:
information comprising at least one of treatment profile and
medical history for a particular patient enrolled in automated
patient care; collected device measures to provide raw physiometry
for the patient that was regularly monitored and recorded by a
medical device; and derived device measures to provide derivative
physiometry based on the collected device measures, wherein the
collected and derived device measures quantify feedback comprising
an analyzed pathophysiology indicative of patient well being.
10. A patient communicator according to claim 9, further
comprising: at least one feedback message selected from the group
comprising an interpretive patient status, a medical diagnosis
patient notification, a medical diagnosis caregiver notification,
and reprogramming instructions for the medical device.
11. A patient communicator according to claim 9, further
comprising: evaluation parameters to define a threshold for the
pathophysiology in terms of the patient well being; and a wellness
indicator determined by analyzing one or more of the collected and
derived device measures against the evaluation parameters.
12. A patient communicator according to claim 11, further
comprising: a trend indicator determined by evaluating a plurality
of the wellness indicators to identify one of an absence, onset,
progression, regression, and status quo of a physiological
disorder.
13. A patient communicator according to claim 9, further
comprising: a baseline pathophysiology quantified by those of the
collected and derived device measures associated with a fixed time
period.
14. A patient communicator according to claim 9, wherein the
medical device comprises one of an implantable medical device and
an external medical device.
15. An analysis system for generating feedback on physiometry
analyzed during automated patient management, comprising: a
database to store patient records that each comprise: information
to identify a patient enrolled in automated patient care and
comprising at least one of treatment profile and medical history;
and collected device measures to provide raw physiometry for the
patient that was regularly monitored and recorded by a medical
device; an interface to periodically receive the collected device
measures; and a processing module, comprising: a data processor to
determine derived device measures based on the collected device
measures to provide derivative physiometry; and an analyzer to
evaluate the collected and derived device measures to quantify
feedback comprising an analyzed pathophysiology indicative of
patient well being.
16. An analysis system according to claim 15, further comprising: a
feedback module to generate at least one feedback message selected
from the group comprising an interpretive patient status, a medical
diagnosis patient notification, a medical diagnosis caregiver
notification, and reprogramming instructions for the medical
device.
17. An analysis system according to claim 15, further comprising:
evaluation parameters to define a threshold for the pathophysiology
in terms of the patient well being; and a wellness indicator
determined by analyzing one or more of the collected and derived
device measures against the evaluation parameters.
18. An analysis system according to claim 17, further comprising: a
trend indicator determined by evaluating a plurality of the
wellness indicators to identify one of an absence, onset,
progression, regression, and status quo of a physiological
disorder.
19. An analysis system according to claim 15, further comprising: a
baseline pathophysiology quantified by those of the collected and
derived device measures associated with a fixed time period.
20. An analysis system according to claim 15, further comprising:
an index physiological disorder determined by evaluating a
plurality of the wellness indicators to order and prioritize
multiple near-simultaneous physiological disorders.
21. An analysis system according to claim 15, wherein the medical
device comprises one of an implantable medical device and an
external medical device.
22. A structured record to store feedback on physiometry analyzed
during automated patient management, comprising: structured data
for a patient enrolled in automated patient care, comprising at
least one of: collected device measures to provide raw physiometry
for the patient that was regularly monitored and recorded by a
medical device; derived device measures to provide derivative
physiometry based on the collected device measures; and quality of
life measures to provide qualified voice feedback data
electronically recorded by the patient contemporaneous to
recordation of the collected device measures, wherein the collected
and derived device measures quantify and the quality of life
measures qualify an analyzed pathophysiology indicative of patient
well being.
23. A structured record according to claim 22, further comprising:
at least one feedback message selected from the group comprising an
interpretive patient status, a medical diagnosis patient
notification, a medical diagnosis caregiver notification, and
reprogramming instructions for the medical device.
24. A structured record according to claim 22, further comprising:
a wellness indicator determined by analyzing one or more of the
collected and derived device measures against evaluation parameters
that define a threshold for the pathophysiology in terms of the
patient well being.
25. A structured record according to claim 24, further comprising:
a trend indicator determined by evaluating a plurality of the
wellness indicators to identify one of an absence, onset,
progression, regression, and status quo of a physiological
disorder.
26. A structured record according to claim 22, further comprising:
a baseline pathophysiology quantified by those of the collected and
derived device measures associated with a fixed time period.
27. A structured record according to claim 22, further comprising:
an index physiological disorder determined by evaluating a
plurality of the wellness indicators to order and prioritize
multiple near-simultaneous physiological disorders.
28. A structured record according to claim 22, wherein the medical
device comprises one of an implantable medical device and an
external medical device.
29. A method for generating feedback on physiometry analyzed during
automated patient management, comprising: identifying a patient
enrolled in automated patient care with information comprising at
least one of treatment profile and medical history; receiving
collected device measures to provide raw physiometry for the
patient that was regularly monitored and recorded by a medical
device; and determining derived device measures to provide
derivative physiometry based on the collected device measures,
wherein the collected and derived device measures quantify feedback
comprising an analyzed pathophysiology indicative of patient well
being.
30. A method according to claim 29, further comprising: generating
at least one feedback message selected from the group comprising an
interpretive patient status, a medical diagnosis patient
notification, a medical diagnosis caregiver notification, and
reprogramming instructions for the medical device.
31. A method according to claim 29, further comprising: specifying
evaluation parameters to define a threshold for the pathophysiology
in terms of the patient well being; and determining a wellness
indicator by analyzing one or more of the collected and derived
device measures against the evaluation parameters.
32. A method according to claim 31, further comprising: determining
a trend indicator by evaluating a plurality of the wellness
indicators to identify one of an absence, onset, progression,
regression, and status quo of a physiological disorder.
33. A method according to claim 29, further comprising: determining
a baseline pathophysiology by quantifying those of the collected
and derived device measures associated with a fixed time
period.
34. A method according to claim 29, further comprising: determining
an index physiological disorder by evaluating a plurality of the
wellness indicators to order and prioritize multiple
near-simultaneous physiological disorders.
35. A method according to claim 29, wherein the medical device
comprises one of an implantable medical device and an external
medical device.
36. A computer-readable storage medium holding code for performing
the method according to claim 29.
Description
CROSS-REFERENCES TO RELATED APPLICATIONS
[0001] This patent application is a continuation of U.S. patent
application Ser. No. 10/422,495, filed Apr. 23, 2003, pending;
which is a divisional of U.S. Pat. No. 6,974,413, issued Dec. 13,
2005; which is a continuation of U.S. Pat. No. 6,270,457, issued
Aug. 7, 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 OF THE INVENTION
[0002] The present invention relates in general to automated
patient management, and, in particular, to a system and method for
generating feedback on physiometry analyzed during automated
patient management.
BACKGROUND OF THE INVENTION
[0003] A broad class of medical subspecialties, including
cardiology, endocrinology, hematology, neurology, gastroenterology,
urology, ophthalmology, and otolaryngology, to name a few, rely on
accurate and timely patient information for use in aiding health
care providers in diagnosing and treating diseases and disorders.
Often, proper medical diagnosis requires information on
physiological events of short duration and sudden onset, yet these
types of events are often occur infrequently and with little or no
warning. Fortunately, such patient information can be obtained via
external, implantable, cutaneous, subcutaneous, and manual medical
devices, and combinations thereof. For example, in the area of
cardiology, implantable pulse generators (IPGs) are commonly used
to treat irregular heartbeats, known as arrhythmias. There are
three basic types of IPGs. 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).
Finally, implantable cardiovascular monitors and therapeutic
devices are used to monitor and treat structural problems of the
heart, such as congestive heart failure and rhythm problems.
[0004] Pacemakers and ICDs, as well as other types of implantable
and external medical devices, 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. In addition, many such devices can have multiple
sensors, or several devices can work together, for monitoring
different sites within a patient's body.
[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] 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.
[0007] 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 ('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.
[0008] 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 ('869) and 5,336,245 ('245).
In the '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 '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 '976 telemetry transceiver,
'869 communicator, and '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.
[0009] In addition, the uses of multiple sensors situated within a
patient's body at multiple sites are disclosed in U.S. Pat. No.
5,040,536 ('536) and U.S. Pat. No. 5,987,352 ('352). In the '536
patent, an intravascular pressure posture detector includes at
least two pressure sensors implanted in different places in the
cardiovascular system, such that differences in pressure with
changes in posture are differentially measurable. However, the
physiological measurements are used locally within the device, or
in conjunction with any implantable device, to effect a therapeutic
treatment. In the '352 patent, an event monitor can include
additional sensors for monitoring and recording physiological
signals during arrhythmia and syncopal events. The recorded signals
can be used for diagnosis, research or therapeutic study, although
no systematic approach to analyzing these signals, particularly
with respect to peer and general population groups, is
presented.
[0010] Thus, there is a need for a system and method for providing
continuous retrieval, transferal, and automated analysis of
retrieved medical device information, such as telemetered signals,
retrieved in general from a broad class of implantable and external
medical devices. Preferably, the automated analysis would include
recognizing a trend indicating disease absence, onset, progression,
regression, and status quo 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.
SUMMARY OF THE INVENTION
[0013] 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.
[0014] An embodiment of the present invention is a system and
method for analyzing patient information for use in automated
patient care. One or more physiological measures relating to
individual patient information recorded on a substantially
continuous basis are retrieved from a patient care record. The
physiological measures retrieved from one such patient care record
are analyzed to determine a patient status. Each physiological
measure is representative of at least one of measured and derived
patient information.
[0015] A further embodiment is a system and method for collecting
physiological measures for use in automated patient care. One or
more physiological measures relating to individual patient
information are obtained from a medical device having a sensor for
monitoring and recording from an anatomical site at least one of
directly and derivatively. The physiological measures are stored in
patient care records.
[0016] A further embodiment is a system and method for providing
tiered patient feedback for use in automated patient care.
Physiological measures are retrieved from one such patient care
record are analyzed to determine a patient status. Each
physiological measure is representative of at least one of measured
and derived patient information recorded on a substantially
continuous basis. Tiered feedback is provided to an individual
patient responsive to the patient status.
[0017] A further embodiment is a system and method for generating
feedback on physiometry analyzed during automated patient
management. A patient enrolled in automated patient care is
identified, along with information including at least one of
treatment profile and medical history. Collected device measures
are received to provide raw physiometry for the patient that was
regularly monitored and recorded by a medical device. Derived
device measures are determined to provide derivative physiometry
based on the collected device measures. The collected and derived
device measures quantify feedback including an analyzed
pathophysiology indicative of patient well being.
[0018] 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
[0019] 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;
[0020] FIG. 2 is a block diagram showing the hardware components of
the server system of the system of FIG. 1;
[0021] FIG. 3 is a block diagram showing the software modules of
the server system of the system of FIG. 1;
[0022] FIG. 4 is a block diagram showing the analysis module of the
server system of FIG. 3;
[0023] 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;
[0024] 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;
[0025] 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;
[0026] FIG. 8 is a flow diagram showing a routine for analyzing
collected measures sets for use in the method of FIG. 7;
[0027] FIG. 9 is a flow diagram showing a routine for comparing
sibling collected measures sets for use in the routine of FIG.
8;
[0028] FIGS. 10A and 10B are flow diagrams showing a routine for
comparing peer collected measures sets for use in the routine of
FIG. 8; and
[0029] FIG. 11 is a flow diagram showing a routine for providing
feedback for use in the method of FIG. 7;
[0030] FIG. 12 is a block diagram showing a system for automated
collection and analysis of regularly retrieved patient information
for remote patient care in accordance with a further embodiment of
the present invention;
[0031] FIG. 13 is a block diagram showing the analysis module of
the server system of FIG. 12;
[0032] FIG. 14 is a database schema showing, by way of example, the
organization of a quality of life and symptom measures set record
for care of patients stored as part of a patient care record in the
database of the system of FIG. 12;
[0033] FIG. 15 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. 12;
[0034] FIG. 16 is a Venn diagram showing, by way of example, peer
group overlap between the partial patient care records of FIG.
15;
[0035] FIGS. 17A-17B are flow diagrams showing a method for
automated collection and analysis of regularly retrieved patient
information for remote patient care in accordance with a further
embodiment of the present invention; and
[0036] FIG. 18 is a flow diagram showing a routine for analyzing
collected measures sets for use in the method of FIGS. 17A-17B.
DETAILED DESCRIPTION
[0037] 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.
[0038] For an exemplary cardiac implantable medical device, the
telemetered signals non-exclusively present patient information
relating to: atrial electrical activity, ventricular electrical
activity, time of day, activity level, cardiac output, oxygen
level, cardiovascular pressure measures, the number and types of
interventions made, and the relative success of any interventions
made on a per heartbeat or binned average basis, 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 Ventak line of ICDs, also manufactured by
Guidant Corporation, Indianapolis, Ind.
[0039] 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, muscularology, gastro-intestinalogy, genital-urology,
ocular, auditory, and similar medical subspecialties. One skilled
in the art would readily recognize the applicability of the present
invention to these related implantable medical devices.
[0040] 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.
[0041] 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 14. 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.
[0042] 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.
[0043] 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.
[0044] 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 personal computer 18 (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 personal computer 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.
[0045] 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.
[0046] 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.
[0047] 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.
[0048] 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.
[0049] 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.
[0050] 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.
[0051] 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.
[0052] 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.
[0053] 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 patient 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..
[0054] 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.
[0055] 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'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.
[0056] 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.
[0057] 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.
[0058] 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.
[0059] 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 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.
[0060] 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 patient, 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.
[0061] 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.
[0062] 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.sub.n, X.sub.n-1, X.sub.n-2, . . . X.sub.0,
or sibling-to-sibling measures for a single snapshot, for instance,
X.sub.n, Y.sub.n, and Z.sub.n, or projected over time, for
instance, X.sub.n, Y.sub.n, Z.sub.n, X.sub.n-1, Y.sub.n-1,
Z.sub.n-1, X.sub.n-2, Y.sub.n-2, Z.sub.n-2, . . . X.sub.0, Y.sub.0,
Z.sub.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.sub.n,
X.sub.n', X.sub.n'', X.sub.n-1, X.sub.n-1', X.sub.n-1'', X.sub.n-2,
X.sub.n-2', X.sub.n-2'', . . . X.sub.0, X.sub.0', X.sub.0'', or
comparing the individual patient's measures to an average from the
group. Similarly, these comparisons might be sibling-to-sibling
measures for single snapshots, for instance, X.sub.n, X.sub.n',
X.sub.n'', Y.sub.n, Y.sub.n', Y.sub.n'', and Z.sub.n, Z.sub.n',
Z.sub.n'', or projected over time, for instance, X.sub.n, X.sub.n',
X.sub.n'', Y.sub.n, Y.sub.n', Y.sub.n'', Z.sub.n, Z.sub.n',
Z.sub.n'', X.sub.n-1, X.sub.n-1', X.sub.n-1'', Y.sub.n-1,
Y.sub.n-1', Y.sub.n-1'', Z.sub.n-1, Z.sub.n-1', Z.sub.n-1'',
X.sub.n-2, X.sub.n-2', X.sub.n-2'', Y.sub.n-2, Y.sub.n-2',
Y.sub.n-2'', Z.sub.n-2, Z.sub.n-2', Z.sub.n-2'' . . . X.sub.0,
X.sub.0', X.sub.0'', Y.sub.0, Y.sub.0', Y.sub.0'', and Z.sub.0,
Z.sub.0', Z.sub.0''. Other forms of comparisons are feasible.
[0063] 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.
[0064] 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.
[0065] 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.
[0066] 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.
[0067] 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.
[0068] 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.
[0069] 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.
[0070] 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.
[0071] 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.
[0072] 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.
[0073] 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.
[0074] 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.
[0075] 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.
[0076] Finally, 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's IPG.
[0077] FIG. 12 is a block diagram showing a system for automated
collection and analysis of regularly retrieved patient information
for remote patient care 200 in accordance with a further embodiment
of the present invention. The system 200 provides remote patient
care in a manner similar to the system 10 of FIG. 1, but with
additional functionality for diagnosing and monitoring multiple
sites within a patient's body using a variety of patient sensors
for diagnosing one or more disorder. The patient 201 can be the
recipient of an implantable medical device 202, as described above,
or have an external medical device 203 attached, such as a Holter
monitor-like device for monitoring electrocardiograms. In addition,
one or more sites in or around the patient's body can be monitored
using multiple sensors 204a, 204b, such as described in U.S. Pat.
Nos. 4,987,897; 5,040,536; 5,113,859; and 5,987,352, the
disclosures of which are incorporated herein by reference. Other
types of devices with physiological measure sensors, both
heterogeneous and homogenous, could be used, either within the same
device or working in conjunction with each other, as is known in
the art.
[0078] As part of the system 200, the database 17 stores patient
care records 205 for each individual patient to whom remote patient
care is being provided. Each patient care record 205 contains
normal patient identification and treatment profile information, as
well as medical history, medications taken, height and weight, and
other pertinent data (not shown). The patient care records 205
consist primarily of monitoring sets 206 storing device and derived
measures (D&DM) sets 207 and quality of life and symptom
measures (QOLM) sets 208 recorded and determined thereafter on a
regular, continuous basis. The organization of the device and
derived measures sets 205 for an exemplary cardiac patient care
record is described above with reference to FIG. 5. The
organization of the quality of life and symptom measures sets 208
is further described below with reference to FIG. 14.
[0079] Optionally, the patient care records 205 can further include
a reference baseline 209 storing a special set of device and
derived reference measures sets 210 and quality of life and symptom
measures sets 211 recorded and determined during an initial
observation period, such as described in the related,
commonly-owned U.S. Pat. No. 6,280,380, issued Aug. 28, 2001, the
disclosure of which is incorporated herein by reference. Other
forms of database organization are feasible.
[0080] Finally, simultaneous notifications can also be delivered to
the patient's physician, hospital, or emergency medical services
provider 212 using feedback means similar to that used to notify
the patient. As described above, the feedback could be by
electronic mail or by automated voice mail or facsimile. The
feedback can also include normalized voice feedback, such as
described in the related, commonly-owned U.S. Pat. No. 6,261,230,
issued Jul. 17, 2001, the disclosure of which is incorporated
herein by reference.
[0081] FIG. 13 is a block diagram showing the analysis module 53 of
the server system 16 of FIG. 12. The peer collected measures sets
60 and sibling collected measures sets 61 can be organized into
site specific groupings based on the sensor from which they
originate, that is, implantable medical device 202, external
medical device 203, or multiple sensors 204a, 204b. The
functionality of the analysis module 53 is augmented to iterate
through a plurality of site specific measures sets 215 and one or
more disorders.
[0082] As an adjunct to remote patient care through the monitoring
of measured physiological data via implantable medical device 202,
external medical device 203 and multiple sensors 204a, 204b,
quality of life and symptom measures sets 208 can also be stored in
the database 17 as part of the monitoring sets 206. A quality of
life measure is a semi-quantitative self-assessment of an
individual patient's physical and emotional well-being and a record
of symptoms, such as provided by the Duke Activities Status
Indicator. These scoring systems can be provided for use by the
patient 11 on the personal computer 18 (shown in FIG. 1) to record
his or her quality of life scores for both initial and periodic
download to the server system 16. FIG. 14 is a database schema
showing, by way of example, the organization of a quality of life
and symptom measures set record 220 for care of patients stored as
part of a patient care record 205 in the database 17 of the system
200 of FIG. 12. The following exemplary information is recorded for
a patient: overall health wellness 221, psychological state 222,
chest discomfort 223, location of chest discomfort 224,
palpitations 225, shortness of breath 226, exercise tolerance 227,
cough 228, sputum production 229, sputum color 230, energy level
231, syncope 232, near syncope 233, nausea 234, diaphoresis 235,
time of day 91, and other quality of life and symptom measures as
would be known to one skilled in the art.
[0083] Other types of quality of life and symptom measures are
possible, such as those indicated by responses to the Minnesota
Living with Heart Failure Questionnaire described in E. Braunwald,
ed., "Heart Disease--A Textbook of Cardiovascular Medicine," pp.
452-454, W.B. Saunders Co. (1997), the disclosure of which is
incorporated herein by reference. Similarly, functional
classifications based on the relationship between symptoms and the
amount of effort required to provoke them can serve as quality of
life and symptom measures, such as the New York Heart Association
(NYHA) classifications I, II, III and IV, also described in
Ibid.
[0084] The patient may also add non-device quantitative measures,
such as the six-minute walk distance, as complementary data to the
device and derived measures sets 207 and the symptoms during the
six-minute walk to quality of life and symptom measures sets
208.
[0085] FIG. 15 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 200 of FIG. 12. Three patient care records are again
shown for Patient 1, Patient 2, and Patient 3 with each of these
records containing site specific measures sets 215, grouped as
follows. First, the patient care record for Patient 1 includes
three site specific measures sets A, B and C, corresponding to
three sites on Patient 1's body. Similarly, the patient care record
for Patient 2 includes two site specific measures sets A and B,
corresponding to two sites, both of which are in the same relative
positions on Patient 2's body as the sites for Patient 1. Finally,
the patient care record for Patient 3 includes two site specific
measures sets A and D, also corresponding to two medical device
sensors, only one of which, Site A, is in the same relative
position as Site A for Patient 1 and Patient 2.
[0086] The analysis module 53 (shown in FIG. 13) performs two
further forms of comparison in addition to comparing the individual
measures for a given patient to other individual measures for that
same patient or to other individual measures for a group of other
patients sharing the same disease-specific characteristics or to
the patient population in general. First, the individual measures
corresponding to each body site for an individual patient can be
compared to other individual measures for that same patient, a peer
group or a general patient population. Again, these comparisons
might be peer-to-peer measures projected over time, for instance,
comparing measures for each site, A, Band C, for Patient 1,
X.sub.n.sub.A, X.sub.n'.sub.A, X.sub.n''.sub.A, X.sub.n-1.sub.A,
X.sub.n-1'.sub.A, X.sub.n-1''.sub.A, X.sub.n-2.sub.A,
X.sub.n-2'.sub.A, X.sub.n-2''.sub.A . . . X.sub.0.sub.A,
X.sub.0'.sub.A, X.sub.0''.sub.A, X.sub.n.sub.B, X.sub.n'.sub.B,
X.sub.n''.sub.B, X.sub.n-1.sub.B, X.sub.n-1'.sub.B,
X.sub.n-1''.sub.B, X.sub.n-2.sub.B, X.sub.n-2'.sub.B,
X.sub.n-2''.sub.A . . . X.sub.0.sub.B, X.sub.0'.sub.B,
X.sub.0''.sub.B, X.sub.n.sub.C, X.sub.n'.sub.C, X.sub.n''.sub.C,
X.sub.n-1.sub.C, X.sub.n-1'.sub.C, X.sub.n-1''.sub.C,
X.sub.n-2.sub.C, X.sub.n-2'.sub.C, X.sub.n-2''.sub.C . . .
X.sub.0.sub.C, X.sub.0'.sub.C, X.sub.0''.sub.C; comparing
comparable measures for Site A for the three patients,
X.sub.n.sub.A, X.sub.n'.sub.A, X.sub.n''.sub.A, X.sub.n-1.sub.A,
X.sub.n-1'.sub.A, X.sub.n-1''.sub.A, X.sub.n-2.sub.A,
X.sub.n-2'.sub.A, X.sub.n-2''.sub.A . . . X.sub.0.sub.A,
X.sub.0'.sub.A, X.sub.0''.sub.A; or comparing the individual
patient's measures to an average from the group. Similarly, these
comparisons might be sibling-to-sibling measures for single
snapshots, for instance, comparing comparable measures for Site A
for the three patients, X.sub.n.sub.A, X.sub.n'.sub.A,
X.sub.n''.sub.A, Y.sub.n.sub.A, Y.sub.n'.sub.A, Y.sub.n''.sub.A,
and Z.sub.n.sub.A, Z.sub.n'.sub.A, Z.sub.n''.sub.A, or comparing
those same comparable measures for Site A projected over time, for
instance, X.sub.n.sub.A, X.sub.n'.sub.A, X.sub.n''.sub.A,
Y.sub.n.sub.A, Y.sub.n'.sub.A, Y.sub.n''.sub.A, Z.sub.n.sub.A,
Z.sub.n'.sub.A, Z.sub.n''.sub.A, X.sub.n-1.sub.A, X.sub.n-1'.sub.A,
X.sub.n-1''.sub.A, Y.sub.n-1.sub.A, Y.sub.n-1'.sub.A,
Y.sub.n-1''.sub.A, Z.sub.n-1.sub.A, Z.sub.n-1'.sub.A,
Z.sub.n-1''.sub.A, X.sub.n-2.sub.A, X.sub.n-2'.sub.A,
X.sub.n-2''.sub.A, Y.sub.n-2.sub.A, Y.sub.n-2'.sub.A,
Y.sub.n-2''.sub.A, Z.sub.n-2.sub.A, Z.sub.n-2'.sub.A,
Z.sub.n-2''.sub.A . . . X.sub.0.sub.A, X.sub.0'.sub.A,
X.sub.0''.sub.A, Y.sub.0.sub.A, Y.sub.0'.sub.A, Y.sub.0''.sub.A,
and Z.sub.0.sub.A, Z.sub.0'.sub.A, Z.sub.0''.sub.A.
Other forms of site-specific comparisons, including comparisons
between individual measures from non-comparable sites between
patients, are feasible.
[0087] Second, the individual measures can be compared on a
disorder specific basis. The individual measures stored in each
cardiac patient record can be logically grouped into measures
relating to specific disorders and diseases, for instance,
congestive heart failure, myocardial infarction, respiratory
distress, and atrial fibrillation. The foregoing comparison
operations performed by the analysis module 53 are further
described below with reference to FIGS. 17A-17B.
[0088] FIG. 16 is a Venn diagram showing, by way of example, peer
group overlap between the partial patient care records 205 of FIG.
15. Each patient care record 205 includes characteristics data 250,
251, 252, including personal traits, demographics, medical history,
and related personal data, for patients 1, 2 and 3, respectively.
For example, the characteristics data 250 for patient 1 might
include personal traits which include gender and age, such as male
and an age between 40-45; a demographic of resident of New York
City; and a medical history consisting of anterior myocardial
infraction, congestive heart failure and diabetes. Similarly, the
characteristics data 251 for patient 2 might include identical
personal traits, thereby resulting in partial overlap 253 of
characteristics data 250 and 251. Similar characteristics overlap
254, 255, 256 can exist between each respective patient. The
overall patient population 257 would include the universe of all
characteristics data. As the monitoring population grows, the
number of patients with personal traits matching those of the
monitored patient will grow, increasing the value of peer group
referencing. Large peer groups, well matched across all monitored
measures, will result in a well known natural history of disease
and will allow for more accurate prediction of the clinical course
of the patient being monitored. If the population of patients is
relatively small, only some traits 256 will be uniformly present in
any particular peer group. Eventually, peer groups, for instance,
composed of 100 or more patients each, would evolve under
conditions in which there would be complete overlap of
substantially all salient data, thereby forming a powerful core
reference group for any new patient being monitored.
[0089] FIGS. 17A-17B are flow diagrams showing a method for
automated collection and analysis of regularly retrieved patient
information for remote patient care 260 in accordance with a
further embodiment of the present invention. 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. As before, the patient care records are organized in
the database 17 with a unique patient care record assigned to each
individual patient (block 261). Next, the individual measures for
each site are iteratively obtained in a first processing loop
(blocks 262-267) and each disorder is iteratively analyzed in a
second processing loop (blocks 268-270). Other forms of flow
control are feasible, including recursive processing.
[0090] During each iteration of the first processing loop (blocks
262-267), the collected measures sets for an individual patient are
retrieved from the medical device or sensor located at the current
site (block 263) 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 264) and
periodically received by the server system 16 (block 265). The
collected measures sets are stored into the patient care record 205
in the database 17 for that individual patient (block 266).
[0091] During each iteration of the second processing loop (blocks
268-270), one or more of the collected measures sets for that
patient are analyzed for the current disorder (block 269), as
further described below with reference to FIG. 18. 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 above with
reference to FIG. 11.
[0092] FIG. 18 is a flow diagram showing a routine for analyzing
collected measures sets 270 for use in the method 260 of FIGS.
17A-17B. The purpose of this routine is to make a determination of
general patient wellness based on comparisons and heuristic trends
analyses of the device and derived measures and quality of life and
symptom measures in the patient care records 205 in the database
17. A first collected measures set is selected from a patient care
record in the database 17 (block 290). The selected measures set
can either be compared to other measures originating from the
patient care record for the same individual patient or to measures
from a peer group of disease specific patients or for the patient
population in general (block 291). If the first collected measures
set is being compared within an individual patient care record
(block 291), the selected measures set can either be compared to
measures from the same site or from another site (block 292). If
from the same site (block 292), a second collected measures set is
selected for the current site from that patient care record (block
293). Otherwise, a second collected measures set is selected for
another site from that patient care record (block 294). Similarly,
if the first collected measures set is being compared within a
group (block 291), the selected measures set can either be compared
to measures from the same comparable site or from another site
(block 295). If from the same comparable site (block 295), a second
collected measures set is selected for a comparable site from
another patient care record (block 296). Otherwise, a second
collected measures set is selected for another site from another
patient care record (block 297). 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.
[0093] Next, if a sibling measures comparison is to be made (block
298), the routine for comparing sibling collected measures sets is
performed (block 105), as further described above with reference to
FIG. 9. Similarly, if a peer measures comparison is to be made
(block 299), the routine for comparing sibling collected measures
sets is performed (block 107), as further described above with
reference to FIGS. 10A and 10B.
[0094] Finally, a patient status indicator is generated (block
300), as described above with reference to FIG. 8. In addition, the
measures sets can be further evaluated and matched to diagnose
specific medical disorders, such as congestive heart failure,
myocardial infarction, respiratory distress, and atrial
fibrillation, as described in related, commonly-owned U.S. Pat. No.
6,336,903, issued Jan. 8, 2002; U.S. Pat. No. 6,368,284, issued
Apr. 9, 2002; U.S. Pat. No. 6,398,728, issued Jun. 4, 2002; and
U.S. Pat. No. 6,411,840, issued Jun. 25, 2002, the disclosures of
which are incorporated herein by reference. In addition, multiple
near-simultaneous disorders can be ordered and prioritized as part
of the patient status indicator as described in the related,
commonly-owned U.S. Pat. No. 6,440,066, issued Aug. 27, 2002, the
disclosure of which is incorporated herein by reference.
[0095] 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.
* * * * *