METHOD AND SYSTEM TO PREDICT PHYSIOLOGIC AND CLINICAL STATUS CHANGES

• US 20150006088A1
• Filed: 12/07/2012
• Published: 01/01/2015
• Est. Priority Date: 12/21/2011
• Status: Abandoned Application

• Current Assignee: Koninklijke Philips N.V.
• Original Assignee: Koninklijke Philips N.V.
• Inventors: Eshelman, Larry James, Gross, Brian David, Chiofolo, Caitlyn Marie, Lee, Kwok Pun, Cao, Hanqing, Nielsen, Larry, Saeed, Mohammed, Flower, Abigail Acton, Frassica, Joseph James
• Application Number:
  US14/366,876
• Publication Number:
  US 20150006088A1
• Time in Patent Office: Days
• US Class Current: 702/19
• 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 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: F04C 2270/041 Controlled or regulated
• CPC Class Codes: G06N 5/025 Extracting rules from data
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

A clinical decision support system (16) monitors one or more patients. The system (16) includes one or more processors (84) programmed to receive patient data for the patients. For each patient, one or more monitoring rules are selected from a plurality of monitoring rules based on patient data availability and/or patient context. A determination is made as to whether a patient is deteriorating using the selected monitoring rules for the patient. In response to determining the patient is deteriorating, an alert is generated.

Claims

1. A clinical support system for monitoring one or more patients, said system comprising:

one or more processors programmed to;

receive patient data for the patients;

for each patient, select one or more monitoring rules from a plurality of monitoring rules based on patient data availability and/or patient context;

determine or predict whether a patient is deteriorating using the selected monitoring rules for the patient; and

,

in response to determining the patient is deteriorating, generate an alert, wherein the processors are further programmed to;

calculate a baseline vital signs instability index (VIX) for the patient from VIX values within a past predetermined amount of time;

compare the baseline VIX to the VIX threshold; and

in response to the VIX exceeding the VIX threshold and the baseline VIX exceeding a predetermined fraction of the VIX threshold, determine the patient is deteriorating.

2. The clinical decision support system according to claim 1, wherein the monitoring rules for each patient are further selected based upon patient data source.

3. The clinical decision support system according to claim 1, wherein a patient'"'"'s context includes one or more of where the patient is in a care process, a problem list of the patient, clinical interventions for the patient, demographics of the patient, and laboratory tests performed on the patient.

4. The clinical decision support system according to claim 1, wherein the processors are further programmed to:

select a predictive model of stability of a physiological condition for the patient based on patient data availability and/or patient context; and

,

calculate a vital signs index (VIX) for the patient from the patient data using the predictive model;

wherein the determining includes comparing the VIX to a VIX threshold of instability.

5. The clinical decision support system according to claim 1, wherein the clinical decision support system adapts to available data and/or natively processes patient data format differently between hosts.

8. The clinical decision support system according to claim 1, wherein the processors are further programmed to:

filter the received patient data to remove patient data failing to meet one or more of normalcy ranges, time criteria for usability, and cross parameter checks.

9. The clinical decision support system according to claim 1, wherein the processors are further programmed to:

in response to determining the patient is deteriorating from an index or parameter of stability, disarm future alerts for the same deterioration until at least one of a plurality of rearming conditions are met, the rearming conditions including;

a first rearming condition, which includes;

a first predetermined amount of time passed;

no intervention was administered during the first predetermined amount of time; and

,

the index or parameter worsened by a predetermined amount compared to the value of the index or parameter when the patient deteriorated; and

,

a second rearming condition, which includes;

the first predetermined amount of time passed;

intervention was administered during the first predetermined amount of time; and

,

a second predetermined amount of time passed.

6. (canceled)

7. The clinical decision support system according to claim 6, wherein the baseline VIX is a maximum VIX during the past predetermined amount of time.

10. A method for monitoring one or more patients, said method comprising:

receiving patient data for the patients;

for each patient, selecting one or more monitoring rules from a plurality of monitoring rules based on patient data availability and/or patient context;

determining whether a patient is deteriorating using the selected monitoring rules for the patient; and

,

in response to determining the patient is deteriorating, generating an alert further including;

calculating a baseline VIX for the patient from VIX values within a past predetermined amount of time;

comparing the baseline VIX to the VIX threshold; and

,

in response to the VIX exceeding the VIX threshold and the baseline VIX exceeding a predetermined fraction of the VIX threshold, determining the patient is deteriorating.

11. The method according to claim 10, wherein a patient'"'"'s context includes one or more of where the patient is in a care process, a problem list of the patient, clinical interventions for the patient, demographics of the patient, and laboratory tests performed on the patient.

12. The method according to claim 10, further including:

selecting a predictive model of stability of a physiological condition for the patient based on patient data availability and/or patient context; and

,

calculating a vital signs index (VIX) for the patient from the patient data using the predictive model;

wherein the determinating includes comparing the VIX to a VIX threshold of instability.

14. One or more processors programmed to perform the method according to claim 10.

13. (canceled)

15. A system for assessing the stability of a physiological condition of a patient, said system comprising:

one or more processors programmed to;

receive patient data for the patient, the patient data including monitoring data and contextual data;

calculate a vital signs index (VIX) for the physiological condition from the monitoring data using a predictive model;

determine a VIX threshold of instability from the contextual data;

determine whether the patient is unstable at least partially by comparing the VIX to the VIX threshold; and

,

in response to determining the patient is unstable, generate an alert wherein the processors are further programmed to;

calculate a baseline VIX for the patient from VIX values within a past predetermined amount of time;

compare the baseline VIX to the VIX threshold; and

in response to the VIX exceeding the VIX threshold and the baseline VIX exceeding a predetermined fraction of the VIX threshold, determine the patient is unstable.

18. The system according to claim 15, wherein the predictive model is generated using one or more of logistic regression, multinomial logistic regression, linear regression, and a support vector machine, and/or

wherein the VIX threshold is determined using a machine learning algorithm, such as a decision tree algorithm, and/or

wherein the contextual data includes laboratory data and/or the monitoring data includes physiological data.

16. (canceled)

17. The system according to claim 16, wherein the baseline VIX is a maximum VIX during the past predetermined amount of time.

19. (canceled)

20. (canceled)