ArticlePloS one2015
Sentiment Measured in Hospital Discharge Notes Is Associated with Readmission and Mortality Risk: An Electronic Health Record Study.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
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Who cites it
45 citing papers in PubMed, 1 synthesis or guideline pooled it, 96 citations in OpenAlex.
- Natural Language Processing Applications in the Clinical Neurosciences: A Machine Learning Augmented Systematic Review.Acta neurochirurgica. Supplement · 2022Pooled it
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- Relationship between the sentiment of nursing notes and one-year mortality of elderly sepsis patients.PloS one · 2025Article
- Using natural language processing to explore differences in healthcare professionals' language on Functional Neurological Disorder: a comparative topic and sentiment analysis study.Frontiers in digital health · 2025Article
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- Dimensional Measures of Psychopathology in Children and Adolescents Using Large Language Models.Biological psychiatry · 2024Article
- The Impact of Collaborative Documentation on Person-Centered Care: Textual Analysis of Clinical Notes.JMIR medical informatics · 2024Article
- Identifying data-driven subtypes of major depressive disorder with electronic health records.Journal of affective disorders · 2024Article
- Examining a sentiment algorithm on session patient records in an eating disorder treatment setting: a preliminary study.Frontiers in psychiatry · 2024Article
- Negativity and Positivity in the ICU: Exploratory Development of Automated Sentiment Capture in the Electronic Health Record.Critical care explorations · 2023Observational
- Hospital-wide natural language processing summarising the health data of 1 million patients.PLOS digital health · 2023Article
- Caregivers Attitude Detection From Clinical Notes.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Sentiment analysis of medical record notes for lung cancer patients at the Department of Veterans Affairs.PloS one · 2023Article
- Comparison of Natural Language Processing of Clinical Notes With a Validated Risk-Stratification Tool to Predict Severe Maternal Morbidity.JAMA network open · 2022Article
- Development and validation of prediction model using nursing notes on sentiment scores for prognosis of patients with severe acute kidney injury receiving continuous renal replacement therapy based on computational intelligence algorithms.Annals of translational medicine · 2022Article
- A prediction model with measured sentiment scores for the risk of in-hospital mortality in acute pancreatitis: a retrospective cohort study.Annals of translational medicine · 2022Article
- Domain Adaptation Using Convolutional Autoencoder and Gradient Boosting for Adverse Events Prediction in the Intensive Care Unit.Frontiers in artificial intelligence · 2022Article
- Data-Driven Modeling of Pregnancy-Related Complications.Trends in molecular medicine · 2021Review
- Medical Specialty Recommendations by an Artificial Intelligence Chatbot on a Smartphone: Development and Deployment.Journal of medical Internet research · 2021Article
- Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Natural language processing tools allow the characterization of sentiment--that is, terms expressing positive and negative emotion--in text. Applying such tools to electronic health records may provide insight into meaningful patient or clinician features not captured in coded data alone. We performed sentiment analysis on 2,484 hospital discharge notes for 2,010 individuals from a psychiatric inpatient unit, as well as 20,859 hospital discharges for 15,011 individuals from general medical units, in a large New England health system between January 2011 and 2014. The primary measures of sentiment captured intensity of subjective positive or negative sentiment expressed in the discharge notes. Mean scores were contrasted between sociodemographic and clinical groups in mixed effects regression models. Discharge note sentiment was then examined for association with risk for readmission in Cox regression models. Discharge notes for individuals with greater medical comorbidity were modestly but significantly lower in positive sentiment among both psychiatric and general medical cohorts (p<0.001 in each). Greater positive sentiment at discharge was associated with significantly decreased risk of hospital readmission in each cohort (~12% decrease per standard deviation above the mean). Automated characterization of discharge notes in terms of sentiment identifies differences between sociodemographic groups, as well as in clinical outcomes, and is not explained by differences in diagnosis. Clinician sentiment merits investigation to understand why and how it reflects or impacts outcomes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.