HEALTH CARE INCIDENT PREDICTION

• US 20130144816A1
• Filed: 06/06/2012
• Published: 06/06/2013
• Est. Priority Date: 06/06/2011
• Status: Active Grant

• Current Assignee: RLDatix North America Inc. (DATIX (Holdings) Ltd.)
• Original Assignee: Radicalogic Technologies, Inc. (DATIX (Holdings) Ltd.)
• Inventors: Smith, Mike, Malaviya, Sanjay
• Granted Patent: US 8,825,568 B2
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16H 70/00 ICT specially adapted for the handling or processing of medical references

Abstract

Embodiments described herein relate to apparatuses and methods for incident prediction alerts for transmission to a health care organization system by applying rules to data sets. Each rule may define a set of data elements linked to an incident, and a processor may detect one or more sets of data elements in the data sets. The processor may normalize the data feeds, generate rules on historical data, generate prediction alerts by applying rules to near-real time data feeds, train to update rules, validate and error check rules, remove statistical noise, generate visualizations for the data feeds, and receive input and feedback data.

Claims

1. An apparatus comprising a processor and a memory storing instructions which when executed configure the processor to generate incident prediction alerts for transmission to a health care organization system by applying rules to data sets, wherein each rule defines a set of data elements linked to an incident, wherein the processor is further configured to detect one or more sets of data elements in the data sets.

2. The apparatus of claim 1 wherein the data sets comprise historical data sets relating to incidents that have occurred in the health care organization, and wherein the processor is further configured to develop rules by, for each incident, training on the historical data sets to identify a set of data elements that is common to one or more occurrence of the incident in the health care organization.

3. The apparatus of claim 1, wherein each rule is linked to a healthcare incident.

4. The apparatus of claim 1 wherein each data element corresponds to a type and a value.

5. The apparatus of claim 1 wherein the data sets comprise near-real time data feeds regarding machines, devices and patients of the health care organization and other data relevant to the health care organization for incident prediction.

6. The apparatus of claim 2 wherein the processor is further configured to train on the historical data sets to determine the probability of incident occurrence, and wherein incident prediction alerts are generated using the probability of incident occurrence.

7. The apparatus of claim 1 wherein the processor is further configured to provide a visualization of one or more data feeds to receive input data has a guide for training the processor to develop rules.

8. The apparatus of claim 1 wherein an incident is linked to a plurality of rules for generating prediction alerts relating thereto.

9. The apparatus of claim 1 wherein the processor is configured to provide an interface for reporting incident occurrences and receiving data feeds relating to the incident occurrences.

10. The apparatus of claim 1 wherein the processor is configured to periodically train on updated data feeds to update the rules.

11. The apparatus of claim 1 wherein the processor is configured to assign weights to at least one data element of a set of data elements of a rule.

12. The apparatus of claim 1 wherein the processor is configured to query for user input data in response to the incident alerts to further train and update the rules.

13. The apparatus of claim 1 wherein the processor is configured to provide incident prevention messages as part of the incident alerts.

14. The apparatus of claim 1 wherein the processor is configured to monitor user response to the incident alerts to train and update the rules.

15. The apparatus of claim 1 wherein the processor is configured to integrate data feeds from data collected by health care organization and third party organizations.

16. The apparatus of claim 1 wherein the processor is configured to normalize the data feeds, generate rules on historical data, generate prediction alerts by applying rules to near-real time data feeds, train to update rules, validate and error check rules, remove statistical noise, generate visualizations for the data feeds, and receive input and feedback data.

17. A system for health care incident alerts comprising the apparatus of claim 1 connected via a network to a plurality of clients accessed by user stations of the health care organization, and a data repository.

18. The system of claim 17 wherein the processor is configured to connect via the network to devices within the health care organization to receive data feeds therefrom.

19. The system of claim 18 wherein the processor is configured to connect via the network to machines within the health care organization to receive data feeds therefrom.

20. Non-transitory computer readable medium with instructions encoded thereon to configure a processor to generate incident prediction alerts for transmission to a health care organization system by applying rules to data sets, wherein each rule defines a set of data elements linked to an incident, and to further configure the processor to detect one or more sets of data elements of the rules in the data sets.

21. The computer readable medium of claim 20 wherein the data sets comprise historical data sets relating to incidents that have occurred in the health care organization, and to further configure the processor to develop rules by, for each incident, training on the historical data sets to identify a set of data elements that is common to one or more occurrence of the incident in the health care organization.

22. The computer readable medium of claim 20 wherein the data sets comprise near-real time data feeds regarding machines, devices and patients of the health care organization and other data relevant to the health care organization for incident prediction.