Acute Care Learning Laboratory – Reducing Threats to Diagnostic Fidelity in Critical Illness
Funding Agency: Agency for Healthcare Research and Quality (AHRQ) Grant Number: 1R18HS026609-01 Mayo Clinic PI: Brian Pickering UF PI: Xiang Zhong
Project Description: This project combines mixed-methods research approaches with systems engineering research approaches to understand the interplay of the multiple factors contributing to diagnostic error and delay. Dr. Zhong is the systems engineer in the team and will work collaboratively with Mayo Clinic physicians from the Multidisciplinary Epidemiology and Translational Research in Intensive Care (METRIC) Lab to develop a “Control Tower” that will be used within the learning laboratory to inform the design, development, evaluation, and refinement of the solutions to diagnostic error and delay. The interventions developed through “Control Tower” have the potential to be shared with multiple practices and adapted to a variety of clinical environments.
Data Analytics for AKI & CHF Risk Prediction and Intervention
Funding agency: Baxter Healthcare Corporation UW-Madison PI: Jingshan Li UF PI: Xiang Zhong
Project Description: This project is aimed to developing data analytic methods to provide risk prediction and patient‐centered intervention strategies for acute kidney injury (AKI) and congestive heart failure (CHF) patients. Dr. Zhong is the UF PI in the team and will work collaboratively with UF Health and Gainesville VA physicians on identifying the risk factors for early diagnosis of CHF. The successful completion of the project will provide efficient and effective risk prediction models for AKI and CHF and individualized patient care plan for physicians and health practitioners. It can also help design and deployment of medical devices targeting at AKI and CHF monitoring and alarming. Moreover, such methodology could be extended to risk prediction and intervention of other critical diseases.