ArticleHealth care management science2016
Assessing risk of hospital readmissions for improving medical practice.
Article in Health care management science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 25 citations in OpenAlex.
- "Using network analysis modularity to group health code systems and decrease dimensionality in machine learning models".Exploratory research in clinical and social pharmacy · 2024Article
- An AI-driven clinical care pathway to reduce 30-day readmission for chronic obstructive pulmonary disease (COPD) patients.Scientific reports · 2022Article
- Neural networks and hospital length of stay: an application to support healthcare management with national benchmarks and thresholds.Cost effectiveness and resource allocation : C/E · 2021Article
- Application of machine learning in predicting hospital readmissions: a scoping review of the literature.BMC medical research methodology · 2021Article
- A continuous-time Markov model for estimating readmission risk for hospital inpatients.Journal of applied statistics · 2021Article
- Implementation of Artificial Intelligence-Based Clinical Decision Support to Reduce Hospital Readmissions at a Regional Hospital.Applied clinical informatics · 2020Article
- Nationwide prediction of type 2 diabetes comorbidities.Scientific reports · 2020Article
- Efficiency Analysis of Integrated Public Hospital Networks in Outpatient Internal Medicine.Journal of medical systems · 2017Article
- Contextual, organizational and ecological effects on the variations in hospital readmissions of rural Medicare beneficiaries in eight southeastern states.Health care management science · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We compare statistical approaches for predicting the likelihood that individual patients will require readmission to hospital within 30 days of their discharge and for setting quality-control standards in that regard. Logistic regression, neural networks and decision trees are found to have comparable discriminating power when applied to cases that were not used to calibrate the respective models. Significant factors for predicting likelihood of readmission are the patient's medical condition upon admission and discharge, length (days) of the hospital visit, care rendered during the hospital stay, size and role of the medical facility, the type of medical insurance, and the environment into which the patient is discharged. Separately constructed models for major medical specialties (Surgery/Gynecology, Cardiorespiratory, Cardiovascular, Neurology, and Medicine) can improve the ability to identify high-risk patients for possible intervention, while consolidated models (with indicator variables for the specialties) can serve well for assessing overall quality of care.
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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.