Muge Capan

My current research utilizes data science, stochastic modeling and decision analysis focused on the secondary use of EHR data to inform acute care decisions in the hospital setting. Example research studies focus on: i) diagnosis decisions for hospitalized sepsis patients informed by simultaneous organ system failures using network models, ii) treatment decisions regarding he type and timing of first treatment using a Markov Decision Process model, and iii) display decisions to effectively communicate the clinical risk of adverse outcomes utilizing usability testing and statistical analysis.

The number of older individuals with complex healthcare needs discharged from hospital to home healthcare (HHC) is rising. HHC refers to community-based services to address complex clinical needs (e.g., skilled nursing) through home visits. During HHC, patient assessment data that are not captured in the hospital Electronic Health Records (EHR) are collected. However, HHC data are rarely integrated with hospital EHR, resulting in missed care planning opportunities in the home setting. For example, patient functional impairment identified during HHC combined with hospital EHR data (e.g., diagnoses associated related to functional decline) could inform HHC nursing interventions and visit frequency. In addition to informing HHC care planning, analysis of HHC data in combination with hospital EHR data could identify patient-level features associated with rehospitalization risk.

Moving forward, I am interested in better understanding the continuum of care, learning from health data in the community / home setting (such as sensor and wearables data collected during HHC) for older and/or medically complex patients, informing care planning and resource allocation decisions, and ultimately supporting healthy aging in place.