Mario Teran
$999,999
UNIVERSITY OF MICHIGAN AT ANN ARBOR
Michigan
Heart failure (HF) is the most common hospital discharge diagnosis among older adults in the United States. Strikingly, 40% of patients are readmitted within 1-year following their first HF admission. This results in significant potentially avoidable costs to our already strained healthcare system, since hospitalizations result in 70% of yearly HF management costs. One of the most common causes of HF readmission is lack of chronic medication optimization. Despite widespread knowledge that guideline-directed medication therapy (GDMT) reduces hospital admission and mortality in HF with reduced ejection fraction (HFrEF, also referred to as systolic heart failure), medications are often not optimized in clinical practice. During the COVID-19 pandemic, missed visits and virtual visits without a physical exam have further disrupted care for HFrEF. The American College of Cardiology recommends the use of electronic health records (EHR) to reduce errors, improve decision support, and facilitate GDMT for HFrEF. Yet currently there are no effective patient centered EHR tools that can assess clinical characteristics and provide adaptive recommendations to optimize GDMT. This represents a significant gap in knowledge that limits the benefits of GDMT. Therefore, there is an immediate need to rigorously test EHR tools that can increase appropriate prescribing of GDMT. This proposed project will determine the effectiveness of an adaptive web application to facilitate GDMT optimization and builds on our work from previous research. Our central hypothesis is that the web application can improve the prescribing of GDMT in HFrEF patients. The rationale for this project is that a new model for disease management – placing patients in control of their condition – will have a substantial impact on HF outcomes. Our objectives are to: (1) determine the effects of an adaptive medication optimization web application on guideline-directed medication prescribing in HF, (2) assess concordance between the recommendations provided by the medication optimization algorithm and the medications prescribed, and (3) identify the patient and provider characteristics that moderate the effectiveness of the medication optimization web application.