Submission Type

Empirical Research


Automatic Data Processing; Heart Failure; Information Systems; Risk Adjustment


Background: Leveraging ‘big data’ as a means of informing cost-effective care holds potential in triaging high-risk heart failure patients for interventions within hospitals seeking to reduce 30-day readmissions.

Objective: Explore provider’s beliefs and perceptions about using an EHR-based tool that uses unstructured clinical notes to risk-stratify high-risk heart failure patients.

Methods: Six providers from an inpatient heart failure clinic within an urban safety-net hospital were recruited to participate in a semi-structured 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 EMR unstructured text as 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.

Conclusion: Prior to implementing into practice an innovative EMR-based case-finder driven by unstructured clinical notes, providers are advised to 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.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.