Healthcare Information Technology System for Predicting and Preventing Adverse Events

• US 20110295621A1
• Filed: 06/06/2011
• Published: 12/01/2011
• Est. Priority Date: 11/02/2001
• Status: Abandoned Application

• Current Assignee: Cerner Health Services Incorporated (Oracle Corporation)
• Original Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Inventors: Farooq, Faisal, Krishnapuram, Balaji, Rosales, Romer E., Yu, Shipeng, Rao, Bharat R.
• Application Number:
  US13/153,526
• Publication Number:
  US 20110295621A1
• Time in Patent Office: Days
• US Class Current: 705/3
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 40/20 for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• 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 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

An adverse event may be prevented by predicting the probability of a given patient to have or undergo the adverse event. The probability alone may prevent the adverse event by educating the patient or medical professional. The probability may be predicted at any time, such as upon entry of information for the patient, periodic analysis, or at the time of admission. The probability may be used to generate a workflow action item to reduce the probability, to warn, to output appropriate instructions, and/or assist in avoiding adverse event. The probability may be specific to a hospital, physician group, or other medical entity, allowing prevention to focus on past adverse event causes for the given entity.

Claims

1. A method for predicting or preventing medical entity related adverse events, the method comprising:

receiving an indication of a patient event for a patient of a medical entity;

triggering application of a predictor of an adverse event in response to the receiving of the indication;

applying, by a processor, the predictor of the adverse event to an electronic medical record of the patient in response to the triggering, the predictor being based on adverse event data of other patients;

predicting, by the processor, a probability of the adverse event of the patient based on the applying of the predictor to the electronic medical record of the patient, the probability being a value greater than 0% and less than 100%; and

outputting as a function of the probability.

2. The method of claim 1 further comprising:

mining the electronic medical record of the patient; and

populating a feature vector used for predicting the probability from the mining;

wherein applying the predictor comprises applying the predictor to the feature vector.

3. The method of claim 2 wherein mining comprises mining from a first data source of the electronic medical record and mining from a second data source of the electronic medical record, the first data source comprising structured data and the second data source comprising unstructured data, the mining outputting values for the feature vector in a structured format from the first and second data sources.

4. The method of claim 2 wherein mining comprises inferring a value for each of a plurality of variables, each value inferred by probabilistic combination of probabilities associated with different possible values from different sources, the inferred values for the variables comprising the feature vector.

5. The method of claim 2 where mining comprises mining as a function of existing knowledge, guidelines, best practices, or about specific institutions regarding adverse events.

6. The method of claim 1 wherein outputting comprises generating a cell phone alert, a bedside monitor alert, an alert associated with prevention of data entry, or combinations thereof.

7. The method of claim 1 further comprising:

automatically scheduling a job entry in a workflow of a case manager, the job entry being for examination to avoid the adverse event.

8. The method of claim 1 wherein applying the predictor comprises applying a machine-learnt classifier, and wherein predicting comprises obtaining an output of the machine-learnt classifier, the machine-learnt classifier comprising a statistical model trained on the adverse event data for the other patients of the medical entity.

9. The method of claim 1 wherein outputting comprises outputting at least one variable having a value for the patient associated with a strongest link to the probability indicating a risk of the adverse event, the strongest link being relative to links for other values of other variables to the risk.

10. The method of claim 1 wherein outputting comprises outputting a mitigation plan associated with the predicting.

11. The method of claim 1 wherein outputting comprises outputting based on a criteria set for the medical entity.

12. The method of claim 1 wherein predicting comprises predicting the risk of acquiring an infection, and wherein outputting comprises outputting an alert about the risk of acquiring the infection during a patient stay of the patient at the medical entity.

13. The method of claim 1 wherein predicting comprises predicting the risk of a patient fall of the patient, and wherein outputting comprises outputting an alert about the risk of the patient fall during the patient stay of the patient at the medical entity.

14. The method of claim 1 wherein predicting comprises predicting the risk of a contrast induced illness of the patient, and wherein outputting comprises outputting an alert about the contrast induced illness during the patient stay of the patient at the medical entity.

15. A system for predicting or preventing adverse events associated with a first medical entity, the system comprising:

at least one memory operable to store data for a plurality of patients, whom have had an adverse event of a first type, of the first medical entity; and

a first processor configured to;

identify variables contributing to the adverse events for the patients of the first medical entity, the identification based on the data for the plurality of the patients of the first medical entity; and

incorporate the variables into a predictor of adverse events of the first type for a future patient of the first medical entity.

16. The system of claim 15 wherein the processor is configured to identify and incorporate by machine learning a statistical model from the data, the predictor comprising a matrix of the statistical model.

17. The system of claim 15 wherein the processor is configured to mine the data including mining unstructured information, the mining providing values for the variables, the values inferred from different possible values in the data and probabilities assigned to the possible values.

18. The system of claim 15 wherein the processor is configured to associate different workflows with different possible predictions of the predictor.

19. The system of claim 15 wherein the processor is configured to incorporate the variables into the predictor of acquiring an infection, patient fall, nephrogenic systemic fibrosis, contrast induced nephropathy, or combinations thereof.

20. In a non-transitory computer readable storage medium having stored therein data representing instructions executable by a programmed processor for predicting or preventing adverse events associated with a medical entity, the storage medium comprising instructions for:

predicting a probability of an adverse event to a patient, the predicting occurring during a patient stay;

comparing the probability to a threshold; and

generating an alert based on the comparing, the generating occurring during the patient stay.

21. The non-transitory computer readable storage medium of claim 20 wherein generating the alert comprises displaying the alert on a display while preventing entry of information.

22. The non-transitory computer readable storage medium of claim 20 wherein generating the alert comprises transmitting a message to a cellular phone.

23. The non-transitory computer readable storage medium of claim 20 wherein generating the alert comprises displaying the alert on a bedside monitor of the patient.

24. The non-transitory computer readable storage medium of claim 20 wherein generating the alert comprises alerting a person with a notice indicating the patient and an indication of risk of the adverse event.

25. The non-transitory computer readable storage medium of claim 20 wherein predicting comprises predicting a risk of acquiring an infection, a patient fall, nephrogenic systemic fibrosis, contrast induced nephropathy, or combinations thereof, and wherein generating comprises generating the alert during the patient stay of the patient at the medical entity.