Abstract: Systems, methods, and computer-readable media are provided for predicting patient qualification for application of coordinated healthcare resources. In aspects, an indication of a patient encounter associated with a target patient is received. Feature values may be extracted from an electronic health record of the target patient stored in an electronic health record system. The extracted feature values may be input into a trained machine learning model to programatically determine whether the target patient qualifies for application of coordinated healthcare resources. Based on a determination that the patient qualifies for application of coordinated healthcare resources, a care protocol, which may include a coordinated allocated of resources, associated with application of coordinated healthcare resources may be initiated.

Abstract: System, methods and computer storage media are disclosed for providing a decision support tool for reducing readmissions of AMI patients through early prediction of readmission. An AMI patient may be identified using a working diagnosis and/or an elevated troponin level. One or more machine learning models may be utilized to predict readmission at a time prior to discharge. Based on the prediction, an intervening action may be automatically initiated. Further embodiments include training machine learning model(s) to predict readmission of an AMI patient. In some embodiments, a first model may be trained using reference patient data as it existed at a predetermined time following the patient's admission (e.g., 12 hours after admission), and a second model may be trained using reference patient data as it existed at a later time (e.g., discharge). Readmission risk scores from each model may be combined to determine an overall risk for an AMI patient.

Abstract: Technologies are provided for leveraging machine learning to predict the likelihood of near-future OUD for patients presenting in an emergency department. A model is trained with data corresponding to one or more of: gender, age, prior opioid use disorder diagnosis, prior opioid use disorder events, prior opioids, prior emergency department encounters, prior inpatient encounters, other medications, drug screening tests, hepatitis C tests, tobacco use questionnaires, prior results from medical tests, social history questionnaires, or other diagnoses to predict opioid use disorder risk for a population of patients. Upon receiving information available at an emergency department triage for a patient, the trained model is utilized to predict the opioid use disorder risk for the patient over a predetermined period of time.