Duke Virtual Assistant (Chatbot)
A Virtual Assistant to help patients understand whether they should go to the ER immediately rather than sending the email they are about to send to their physician.
Innovation Science and Implementation
With its mission in mind, DIHI strives to implement each of its innovations to positively impact clinical care in the real world. DIHI has designed, led, and measured impact for numerous innovation implementations. Moreover, DIHI applies its evolving methodologies into the design and implementation of each new innovation, thereby increasing the speed to deliver value and expanding the potential for achievable transformative innovation.
Detection and Treatment of Patient Deterioration
Develop and implement a machine learning risk score for patient deterioration.
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.
Duke Health Data Pipeline
A foundational, fully-automated data curation tool enabling data liquidity which accelerates quality improvement, learning health, research and innovation projects.