Healthcare information technology system for predicting or preventing readmissions

• US 8,949,082 B2
• Filed: 06/06/2011
• Issued: 02/03/2015
• Est. Priority Date: 11/02/2001
• Status: Active Grant

• Current Assignee: Cerner Innovation, Inc. (Oracle Corporation)
• Original Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Inventors: Farooq, Faisal, Yu, Shipeng, Krishnapuram, Balaji, Rao, Bharat R., Rosales, Romer E.
• Primary Examiner(s): ALHIJA, SAIF A
• Application Number:
  US13/153,551
• Publication Number:
  US 20110295622A1
• Time in Patent Office: 1,338 Days
• Field of Search: 703/2
• US Class Current: 703/2
• 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

Hospital readmissions may be prevented. Readmission is prevented by predicting the probability of a given patient to be readmitted. The probability alone may prevent readmission by educating the patient or medical professional. The probability may be predicted during a patient stay and used to generate a workflow action item to reduce the probability, to warn, to output appropriate instructions, and/or assist in avoiding readmission. The probability may be specific to a hospital, physician group, or other entity, allowing prevention to focus on past readmission causes for the given entity.

Claims

1. A system for predicting or preventing hospital readmission, the system comprising:

at least one memory operable to store data for a plurality of readmitted patients of a first hospital; and

a first processor configured to;

identify variables contributing to readmission for the first hospital based on the data for the plurality of the readmitted patients of the first hospital; and

incorporate the variables into a predictor of readmission for a future patient of the first hospital;

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.

2. The system of claim 1 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.

3. The system of claim 1 wherein the processor is configured to associate different workflows with different possible predictions of the predictor.

4. In a non-transitory computer readable storage medium having stored therein data representing instructions executable by a programmed processor for predicting or preventing hospital readmission, the storage medium comprising instructions for:

predicting a probability of readmission of 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;

wherein generating the alert comprises displaying the alert on a display while preventing entry of information until occurrence of an event such that the preventing is temporary, is until the probability is reduced, or is until overridden.

5. The non-transitory computer readable storage medium of claim 4 wherein generating the alert also comprises transmitting a message to a cellular phone.

6. The non-transitory computer readable storage medium of claim 4 wherein generating the alert comprises displaying the alert on a bedside monitor of the patient.

7. The non-transitory computer readable storage medium of claim 4 wherein generating the alert comprises alerting a person with a notice indicating the patient and an indication of risk of the readmission.

8. The non-transitory computer readable storage medium of claim 4 further comprising;

receiving an indication of an event for a patient from a hospital;

triggering application of a predictor of readmission in response to the receiving of the indication;

wherein predicting comprises applying the predictor of the readmission to an electronic medical record of the patient in response to the triggering, the predictor being based on readmission data of the hospital, the probability being based on the applying of the predictor to the electronic medical record of the patient.

9. The method of claim 8 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.

10. The non-transitory computer readable storage medium of claim 9 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.

11. The non-transitory computer readable storage medium of claim 9 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.

12. The non-transitory computer readable storage medium of claim 9 where mining comprises mining as a function of existing knowledge, guidelines, best practices, or about specific institutions regarding readmissions.

13. The non-transitory computer readable storage medium of claim 4 further comprising:

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

14. The non-transitory computer readable storage medium of claim 8 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.

15. The non-transitory computer readable storage medium of claim 4 wherein generating the alert comprises outputting at least one variable having a value for the patient associated with a strongest link to the probability indicating a risk of readmission, the strongest link being relative to links for other values of other variables to the risk.

16. The non-transitory computer readable storage medium of claim 4 wherein generating the alert comprises outputting a mitigation plan associated with the predicting.

17. The non-transitory computer readable storage medium of claim 4 wherein generating the alert comprises outputting based on a criteria set for the hospital.

18. The non-transitory computer readable storage medium of claim 4 wherein generating the alert comprises outputting instructions based on the probability.

19. The non-transitory computer readable storage medium of claim 18 further comprising:

applying a predictor of compliance by the patient with the instructions to an electronic medical record of the patient; and

predicting a probability of compliance of the patient based on the applying of the predictor of compliance.

20. The non-transitory computer readable storage medium of claim 4 wherein preventing comprises preventing being limited to a type of the information.