System and method for supporting health management services

• US 9,955,869 B2
• Filed: 06/14/2013
• Issued: 05/01/2018
• Est. Priority Date: 06/04/2013
• Status: Active Grant

• Current Assignee: Purdue Pharma L.P.
• Original Assignee: Purdue Pharma L.P.
• Inventors: Meltzer, Brian, Natarajan, Sayee
• Primary Examiner(s): Downey, John R
• Assistant Examiner(s): JIAN, SHIRLEY XUEYING
• Application Number:
  US13/918,560
• Publication Number:
  US 20140357961A1
• Time in Patent Office: 1,782 Days
• Field of Search: 600300-301
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/01 Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/1112 Global tracking of patients, e.g. by using GPS
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/112 Gait analysis
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/4833 Assessment of subject's compliance to treatment
• CPC Class Codes: A61B 5/4842 Monitoring progression or stage of a disease
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/747 in case of emergency, i.e. alerting emergency services
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

A method in support of health management services acts upon active and passive data, in which the active data is communicated by a patient or healthcare professional and in which the passive data is detected by at least one sensor detectably coupled to the patient. These data are correlated with each other and if a predefined correlation is not met, further data can be solicited, patient-specific queries can be generated, alerts can be communicated, or a combination of these can be done in embodiments of the invention. Any Irregularity or trend in the data can be identified and processed in embodiments of the invention. Also in some embodiments, these data can be used in connection with intelligent, machine-based decisions to send communications concerning the provision of further healthcare resources to patients, such as in view of their relative acuity rank. Systems can be constructed to implement the methods described herein.

Claims

1. A method in support of health management services for a patient, the method performed by a hardware device having a processor and memory having processor executable code that, when executed by the processor, causes the hardware device to:

(a) monitor the patient by;

receiving passive data of a first type detected by at least one patient sensor from the patient, the passive data being associated with metadata concerning at least a time of the passive data detection;

receiving active data that represents an affirmative response to a first patient-specific query that has been provided to a user interface of a device associated with the patient, the active data being associated with metadata concerning at least a time of active data receipt; and

storing the received passive data, the received active data, and the metadata associated with the received passive data and the received active data;

(b) validate the received passive data and the received active data by;

comparing a portion of the received passive data to a portion of the received active data on the basis of the time in the metadata associated with the passive data and the active data, respectively; and

analyzing the data that was compared on the basis of the time in the metadata relative to a correlation threshold in order to assess whether the compared data correlate with the correlation threshold, wherein the correlation threshold comprises a threshold value, set of values or value range of a change relative to a baseline value or range;

(c) repeat the monitoring and validating steps if the compared data correlate with the correlation threshold, else, if the compared data does not correlate with the correlation threshold;

receive further passive data of a second type that is different than the first type and which is detected by a different sensor;

determine compliance and/or patient veracity in connection with health management based on the analysis of the compared data and an analysis of the received further passive data; and

in the event of the determination being either non-compliance or lack of patient veracity, generate a non-correlation notification and provide the non-correlation notification to a respective device associated with at least one of the patient, a healthcare professional, and a third-party.

2. The method of claim 1, wherein the hardware device is further configured to dynamically generate the correlation threshold based on the received passive data, the received active data, or both.

3. The method of claim 1, wherein the received further passive data includes metadata concerning a time of the further passive data detection, and wherein the code, when executed the processor, causes the hardware to repeat the monitoring and validating in the event that the compared data does not correlate with the correlation threshold, further causes the hardware to:

generate a second patient-specific query which corresponds with at least the received further passive data;

deliver the second patient-specific query to the device associated with the patient to be provided to the patient in the user interface; and

receive further active data and metadata concerning the time of the active data receipt, the further active data representing an affirmative response to the second patient-specific query in the user interface,

wherein the determination of compliance and/or patient veracity in connection with health management is based on the analysis of the compared data, the analysis of the received further passive data, and an analysis of the further active data.

4. The method of claim 3, wherein the second patient-specific query generation comprises:

regenerating the first patient-specific query and the processor executing code to generate the second patient-specific query.

5. The method of claim 1, wherein the generation of the non-correlation notification includes providing information reflecting the non-compliance or lack of patient veracity.

6. The method of claim 1, wherein the hardware is further configured to:

generate a correlation notification if the compared data correlates with or within the correlation threshold; and

provide the correlative notification to a respective device associated with at least one of the patient, a healthcare professional, and a third-party.

7. The method of claim 6, wherein the hardware is further configured to:

receive diagnosis/treatment data responsive to providing the notification to the healthcare professional;

wherein the diagnosis/treatment data represents at least one of a diagnosis, a treatment plan, a healthcare professional-query, and an observation relating to the patient;

store in the memory the diagnosis/treatment data and at least one of the received active data, the received passive data, compared data, and correlation data generated by the analyzing the compared data relative to the correlation threshold of the received active data with the received passive data;

anonymize the diagnosis/treatment data and the at least one of the received active data, the received passive data, compared data, and correlation data into a form that has patient-identifiable elements removed;

correlate the anonymized diagnosis/treatment data and the anonymized at least one of the received active data, the received passive data, and the first correlation data;

correlate the anonymized diagnosis/treatment data with other anonymized diagnosis/treatment data of at least one other healthcare professional;

generate anonymized diagnosis/treatment data reflective of data generated by the correlation of the anonymized diagnosis/treatment data of the healthcare professional with the other anonymized diagnosis/treatment data of the at least one other healthcare professional; and

present the anonymized diagnosis/treatment data to at least one of the patient, the healthcare professional, the at least one other healthcare professional, an accountable care organization to which the healthcare professional belongs, and a third-party payor.

8. The method of claim 6, wherein the hardware is further configured to:

determine based on predefined scheduling criteria whether to schedule a patient appointment with the healthcare professional;

upon a determination not to schedule a patient appointment with the healthcare professional;

generate a healthcare professional-query; and

deliver the healthcare professional-query to a device associated with the healthcare professional; and

upon a determination to schedule a patient appointment with the healthcare professional;

generate a critical-level indicator associated with the patient;

receive patient-calendar information associated with the patient;

coordinate the patient-calendar information with received healthcare-professional scheduling information responsive to the critical-level indicator;

wherein a first patient is prioritized to be seen by a healthcare professional sooner in time than a second patient, when the first patient has a higher associated critical-level indicator than the second patient; and

determine an agreed-upon appointment.

9. The method of claim 1, wherein the code, when executed the processor, causes the hardware to monitor the received passive data further causes the hardware to:

identify any predefined irregularities in the received active data and the received passive data; and

generate a second patient-specific query which corresponds with at least the irregularities in the received passive data,

wherein the monitoring of the received active data comprises receiving the second patient-specific query active data which corresponds to an affirmative response to the second patient-specific query.

10. The method of claim 9, wherein, upon identifying at least one least one irregularity and in the event of the determination being either non-compliance or lack of patient veracity the notification generation step includes, further code configures the hardware to generate an irregularity notification reflecting the irregularity or failure.

11. The method of claim 1, wherein the validation step further comprises:

anonymizing at least the compared data into a form that has patient-identifiable elements removed;

wherein the analyzing step includes correlating the anonymized data relative to an anonymous correlation threshold to assess whether the anonymized data correlates with or within the anonymous threshold correlation, wherein the anonymous threshold correlation value comprises a threshold value, set of values, or value range of change relative to a base line value or range relative to at least one other patient, and

wherein the hardware is further configured to provide at least the anonymized data and whether the anonymized data correlates with the anonymous threshold correlation to a respective device associated with at least one of the patient, a healthcare professional, and a third-party.

12. The method of claim 1, wherein the hardware device is further configured to, in regard to the monitor of the patient:

generate the first patient-specific query by selecting and providing the first patient-specific query and delivering the first patient-specific query to the device associated with the patient.

13. The method of claim 1, wherein the metadata associated with at least one of the passive data and the active data further concerns GPS location data, and further wherein the correlation threshold is further defined by a distance range, and still further wherein the compared data does not correlate with the correlated threshold when distance represented by the GPS location data is outside of the distance range.

14. The method of claim 1, wherein one of the received passive data and the received further passive data include GPS location data.