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Updated: Jun 6, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Use of admission MDS data to capture unrecognized cognitive impairment in heart failure: Implications for community
Candace C Harrington1, Shuying Sha1, Sarah Markgraf1
1University of Louisville School of Nursing in Louisville, KY, United States.
Background:
Skilled Nursing residents who have cognitive impairment (CI) in heart failure(HF) have a significantly higher mortality rate. These residents' ability to self-manage their complex care upon discharge is critical for positive health outcomes.
Method:
We conducted a secondary data analysis of admission MDS records for 79 residents admitted for HF care from 2021 to 2022.
Results:
Seventy-nine eligible admission MDS records were included in the study. Only one additional with DC diagnoses of CI in HF was captured upon discharge. Twenty-seven (35.1 %) records affirming CI in HF were omitted from the discharge diagnosis list or discharge summary.
Conclusion:
This secondary data analysis of admission MDS records in two large mid-south metropolitan nursing facilities uncovered quality improvement opportunities, including improving facility interprofessional communication, opportunities to capture and improve diagnostic accuracy, the potential value of an evidence-based discharge planning program, opportunity for improved hand-offs back to community primary care providers.
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