ArticlePsychosomatics
Linking electronic health record-extracted psychosocial data in real-time to risk of readmission for heart failure.
Article in Psychosomatics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Utility of models to predict 28-day or 30-day unplanned hospital readmissions: an updated systematic review.BMJ open · 2016Pooled it
- Impact of social factors on risk of readmission or mortality in pneumonia and heart failure: systematic review.Journal of general internal medicine · 2013Pooled it
- Combining text mining with clinical decision support in clinical practice: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Article
- Article
- Development of Electronic Health Record-Based Prediction Models for 30-Day Readmission Risk Among Patients Hospitalized for Acute Myocardial Infarction.JAMA network open · 2021Article
- Efficient goal attainment and engagement in a care manager system using unstructured notes.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review.JMIR medical informatics · 2019Review
- Analysis of an Internet Community about Pneumothorax and the Importance of Accurate Information about the Disease.The Korean journal of thoracic and cardiovascular surgery · 2018Article
- Hospital Readmission and Social Risk Factors Identified from Physician Notes.Health services research · 2018Observational
- Article
- Leveraging Electronic Health Care Record Information to Measure Pressure Ulcer Risk in Veterans With Spinal Cord Injury: A Longitudinal Study Protocol.JMIR research protocols · 2017Article
- Predicting all-cause readmissions using electronic health record data from the entire hospitalization: Model development and comparison.Journal of hospital medicine · 2016Observational
- Comparing Perspectives of Patients, Caregivers, and Clinicians on Heart Failure Management.Journal of cardiac failure · 2016Article
- Patient Characteristics Predicting Readmission Among Individuals Hospitalized for Heart Failure.Medical care research and review : MCRR · 2016Review
- Assessing Risk and Preventing 30-Day Readmissions in Decompensated Heart Failure: Opportunity to Intervene?Current heart failure reports · 2015Review
- Predicting Non-Adherence with Outpatient Colonoscopy Using a Novel Electronic Tool that Measures Prior Non-Adherence.Journal of general internal medicine · 2015Observational
- Development and implementation of a real-time 30-day readmission predictive model.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2014Article
- Assessing hospital readmission risk factors in heart failure patients enrolled in a telemonitoring program.International journal of telemedicine and applications · 2013Article
- Patient Perspectives on Home-Based Care and Remote Monitoring in Heart Failure: A Qualitative Study.Journal of primary care & community healthArticle
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Authors and funding
8 authors.
Funding
Abstract
backgroundKnowledge of psychosocial characteristics that helps to identify patients at increased risk for readmission for heart failure (HF) may facilitate timely and targeted care.
objectiveWe hypothesized that certain psychosocial characteristics extracted from the electronic health record (EHR) would be associated with an increased risk for hospital readmission within the next 30 days.
methodsWe identified 15 psychosocial predictors of readmission. Eleven of these were extracted from the EHR (six from structured data sources and five from unstructured clinical notes). We then analyzed their association with the likelihood of hospital readmission within the next 30 days among 729 patients admitted for HF. Finally, we developed a multivariable predictive model to recognize individuals at high risk for readmission.
resultsWe found five characteristics-dementia, depression, adherence, declining/refusal of services, and missed clinical appointments-that were associated with an increased risk for hospital readmission: the first four features were captured from unstructured clinical notes, while the last item was captured from a structured data source.
conclusionsUnstructured clinical notes contain important knowledge on the relationship between psychosocial risk factors and an increased risk of readmission for HF that would otherwise have been missed if only structured data were considered. Gathering this EHR-based knowledge can be automated, thus enabling timely and targeted care.
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