Identifying Patient Satisfaction from Electronic Health Record Data – A Retrospective Register Study

The aim of this study was to review the potential of electronic health record (EHR) data to automatically identify key concepts related to patient satisfaction in cardiac care. A randomised sample of EHR data from 500 cardiac patients were screened for feature extraction, identifying 187 terms describing patient satisfaction. The predictive positive value (PPV) for positive descriptions was high, indicating the value of EHRs and narrative documentation when developing tools to measure patient satisfaction. The high number of false positives resulting from the automated screening and the low PPVs for negative descriptions call for more sophisticated methods to identify not only the used terms, but also their context to increase the reliability of future tools aimed at automated monitoring of patient satisfaction.

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Publication Details

Journal
Studies in health technology and informatics
Published
2025-05-15
DOI
https://doi.org/10.3233/shti250313
Primary Topic
Patient Satisfaction in Healthcare
Type
article
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article

Identifying Patient Satisfaction from Electronic Health Record Data – A Retrospective Register Study

Ville Kytö, Hanna von Gerich, Julia Walsh, Matej Medvecký et al.
1 links
Studies in health technology and informatics
Patient Satisfaction in Healthcare
article

Identifying Patient Satisfaction from Electronic Health Record Data – A Retrospective Register Study

Ville Kytö, Hanna von Gerich, Julia Walsh, Matej Medvecký, Laura‐Maria Peltonen
article en

Abstract

The aim of this study was to review the potential of electronic health record (EHR) data to automatically identify key concepts related to patient satisfaction in cardiac care. A randomised sample of EHR data from 500 cardiac patients were screened for feature extraction, identifying 187 terms describing patient satisfaction. The predictive positive value (PPV) for positive descriptions was high, indicating the value of EHRs and narrative documentation when developing tools to measure patient satisfaction. The high number of false positives resulting from the automated screening and the low PPVs for negative descriptions call for more sophisticated methods to identify not only the used terms, but also their context to increase the reliability of future tools aimed at automated monitoring of patient satisfaction.

Studies in health technology and informaticsVol. 327
University of Turku (FI), Turku University Hospital (FI), University of Warwick (GB)
Openalex Percentile: Top 70%
Patient Satisfaction in Healthcare
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