J Cardiovasc Nurs. 2021 Dec 21. doi: 10.1097/JCN.0000000000000871. Online ahead of print.
ABSTRACT
BACKGROUND: For patients with heart failure (HF), there have been efforts to reduce the risk of 30-day rehospitalization, such as developing predictive models using electronic health records. Few previous studies used clinical notes to predict 30-day rehospitalization.
OBJECTIVE: The aim of this study was to assess the utility of nursing notes versus discharge summaries to predict 30-day rehospitalization among patients with HF.
METHODS: In this pilot study, we used free-text discharge summaries and nursing notes collected from a tertiary hospital. We randomly selected 500 Medicare patients with HF. We followed the natural language processing and machine learning pipeline for data analysis.
RESULTS: Thirty-day rehospitalization risk prediction using discharge summaries (n = 500) produced an area under the receiver operating characteristic curve of 0.74 (Bag of Words + Neural Network). Thirty-day rehospitalization risk prediction using nursing notes (n = 2046) resulted in an area under the receiver operating characteristic curve of 0.85 (Bag of Words + Neural Network).
CONCLUSION: Nursing notes provide a superior input to risk models for 30-day rehospitalization in Medicare patients with HF compared with discharge summaries.
PMID:34935742 | DOI:10.1097/JCN.0000000000000871