A Focus Group Exploration of Automated Case-Finders to Identify High-Risk Heart Failure Patients Within an Urban Safety Net Hospital.
BACKGROUND
Leveraging "big data" as a means of informing cost-effective care holds potential in triaging high-risk heart failure (HF) patients for interventions within hospitals seeking to reduce 30-day readmissions.


OBJECTIVE
Explore provider's beliefs and perceptions about using an electronic health record (EHR)-based tool that uses unstructured clinical notes to risk-stratify high-risk heart failure patients.


METHODS
Six providers from an inpatient HF clinic within an urban safety net hospital were recruited to participate in a semistructured focus group. A facilitator led a discussion on the feasibility and value of using an EHR tool driven by unstructured clinical notes to help identify high-risk patients. Data collected from transcripts were analyzed using a thematic analysis that facilitated drawing conclusions clustered around categories and themes.


RESULTS
From six categories emerged two themes: (1) challenges of finding valid and accurate results, and (2) strategies used to overcome these challenges. Although employing a tool that uses electronic medical record (EMR) unstructured text as the benchmark by which to identify high-risk patients is efficient, choosing appropriate benchmark groups could be challenging given the multiple causes of readmission. Strategies to mitigate these challenges include establishing clear selection criteria to guide benchmark group composition, and quality outcome goals for the hospital.


CONCLUSIONS
Prior to implementing into practice an innovative EMR-based case-finder driven by unstructured clinical notes, providers are advised to do the following: (1) define patient quality outcome goals, (2) establish criteria by which to guide benchmark selection, and (3) verify the tool's validity and reliability. Achieving consensus on these issues would be necessary for this innovative EHR-based tool to effectively improve clinical decision-making and in turn, decrease readmissions for high-risk patients.
University of Missouri-Kansas City School of Pharmacy.
Mark E
ME
Patterson
North Kansas City Hospital.
Derick
D
Miranda
University of Missouri-Kansas City School of Pharmacy.
Greg
G
Schuman
University of Missouri-Kansas City School of Pharmacy.
Christopher
C
Eaton
University of Missouri-Kansas City School of Pharmacy; Truman Medical Center-Hospital Hill.
Andrew
A
Smith
Quire, Inc.
Brad
B
Silver
Automatic Data Processing; Heart Failure; Information Systems; Risk Adjustment
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