ArticlePloS one2022
Predicting Length of Stay using machine learning for total joint replacements performed at a rural community hospital.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools.Anesthesiology · 2024Pooled it
- Trustworthy AI models to predict prolonged hospital length-of-stay in orthopedic surgery.npj health systems · 2026Article
- Community medical centers struggle to produce well-calibrated clinical prediction models: Data augmentation can help.Journal of biomedical informatics · 2026Article
- Gaps in artificial intelligence research for rural health in the United States: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Predictive Model for Estimating the Length of Stay in Hip Arthroplasty Patients by Machine Learning-H.I.P.P.O Score.Life (Basel, Switzerland) · 2025Article
- Emerging Diagnostic Approaches for Musculoskeletal Disorders: Advances in Imaging, Biomarkers, and Clinical Assessment.Diagnostics (Basel, Switzerland) · 2025Review
- Article
- Predicting extended hospital stay following revision total hip arthroplasty: a machine learning model analysis based on the ACS-NSQIP database.Archives of orthopaedic and trauma surgery · 2024Article
- Predicting hospital length of stay using machine learning on a large open health dataset.BMC health services research · 2024Article
- Are social determinants of health associated with an increased length of hospitalization after revision total hip and knee arthroplasty? A comparison study of social deprivation indices.Archives of orthopaedic and trauma surgery · 2024Article
- Can machine learning models predict prolonged length of hospital stay following primary total knee arthroplasty based on a national patient cohort data?Archives of orthopaedic and trauma surgery · 2023Article
- Rural implementation of the perioperative surgical home: A case-control study.World journal of orthopedics · 2023Article
- Predicting risk factors for pediatric mortality in clinical trial research: A retrospective, cross-sectional study using a Healthcare Cost and Utilization Project database.Journal of clinical and translational science · 2023Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPredicting patient's Length of Stay (LOS) before total joint replacement (TJR) surgery is vital for hospitals to optimally manage costs and resources. Many hospitals including in rural areas use publicly available models such as National Surgical Quality Improvement Program (NSQIP) calculator which, unfortunately, performs suboptimally when predicting LOS for TJR procedures.
objectiveThe objective of this research was to develop a Machine Learning (ML) model to predict LOS for TJR procedures performed at a Perioperative Surgical Home implemented rural community hospital for better accuracy and interpretation than the NSQIP calculator.
methodsA total of 158 TJR patients were collected and analyzed from a rural community hospital located in Montana. A random forest (RF) model was used to predict patient's LOS. For interpretation, permuted feature importance and partial dependence plot methods were used to identify the important variables and their relationship with the LOS.
resultsThe root mean square error for the RF model (0.7) was lower than the NSQIP calculator (1.21). The five most important variables for predicting LOS were BMI, Duke Activity Status-Index, diabetes, patient's household income, and patient's age.
conclusionThis pilot study is the first of its kind to develop an ML model to predict LOS for TJR procedures that were performed at a small-scale rural community hospital. This pilot study contributes an approach for rural hospitals, making them more independent by developing their own predictions instead of relying on public models.
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