Detection and Treatment of Patient Deterioration
Develop and implement a machine learning risk score for patient deterioration.
Living Lab
The Living Lab showcases DIHI’s approach to catalyze innovations in health and healthcare. As they evolve from pilot implementations into sustainable solutions, projects become part of the “living lab” of evaluation and iterative innovation to build upon the knowledge gained through the testing of new clinical workflows and use cases.
Using Machine Learning in Emergency Department Patient Flow
Develop a clinical dashboard built on existing DIHI databases and predictive modeling to assist in providing more efficient dispositions for emergency department patients by predicting clinical trajectory and need for admission.
Machine Learning Models to Recognize Early Clinical Deterioration in DUHS Pediatric Inpatients
Develop machine learning methods that will identify children at risk for clinical deterioration.
Telehealth examination versus clinical examination in the detection of rotator cuff tears
Simulated telehealth evaluation of shoulder pain using patient self-examination will be compared to traditional clinical examination of shoulder pain using MRI as the gold standard to determine accuracy in detecting rotator cuff tears.
Transforming Cancer Care: Bringing PCPs “Back” into Cancer Care through Onco-Primary Care
Implement a blood pressure management clinical pathway to better incorporate PCPs as active members of the cancer care team.
High-Sensitivity Cardiac Troponin (hsTn) Decision Aid
Create a chest pain assessment tool which integrates with Epic using FHIR to rule-in or rule-out acute MI.
Machine-learning algorithm to predict ICU readmissions in the cardiothoracic surgical population
Develop predictive models to identify rapidly and accurately CTS patients at high-risk (and high-cost) for postoperative clinical deterioration necessitating readmission from the SDU back to the ICU.
RFID surgical instrument tracking and optimization of the operating room
Implement an RFID system that could be integrated into the OR to measure instrument usage autonomously.
Innovative predictive model to anticipate steroid-induced hyperglycemia and guide insulin regimens
Identify optimal, individualized treatment policies for insulin dosing in the context of systemic glucocorticoids.