ArticleHealth services research2020
Predicting preventable hospital readmissions with causal machine learning.
Article in Health services research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 15 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.
The trial behind it
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
15 citing papers in PubMed.
- Renal function as an effect modifier of intensive glucose control in delaying cognitive function decline among individuals with type 2 diabetes: A revisit to the ACCORD MIND trial.Diabetes, obesity & metabolism · 2024Trial
- Effectiveness of oseltamivir treatment on clinical failure in hospitalized patients with lower respiratory tract infection.BMC infectious diseases · 2021Trial
- Machine Learning in Causal Effect Estimation and Causal Evaluation of ML-Enabled Interventions in Nursing-Related Research: A Scoping Review.Nursing & health sciences · 2026Article
- Circadian syndrome (CircS) and cognitive trajectory deterioration in middle-aged and older adults: A national cohort study with causal forest analysis.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Expanding care coordination in an integrated health system through causal machine learning.NPJ digital medicine · 2025Article
- Predictive Modeling of Heterogeneous Treatment Effects in RCTs: A Scoping Review.JAMA network open · 2025Article
- Reformulating patient stratification for targeting interventions by accounting for severity of downstream outcomes resulting from disease onset: a case study in sepsis.Journal of the American Medical Informatics Association : JAMIA · 2025Observational
- Potential clinical impact of predictive modeling of heterogeneous treatment effects: scoping review of the impact of the PATH Statement.medRxiv : the preprint server for health sciences · 2025Article
- Clinical Applications of Machine Learning.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2024Review
- Big Data Analytics to Reduce Preventable Hospitalizations-Using Real-World Data to Predict Ambulatory Care-Sensitive Conditions.International journal of environmental research and public health · 2023Article
- Prospective evaluation of social risks, physical function, and cognitive function in prediction of non-elective rehospitalization and post-discharge mortality.BMC health services research · 2022Article
- Article
- Evaluation of an intervention targeted with predictive analytics to prevent readmissions in an integrated health system: observational study.BMJ (Clinical research ed.) · 2021Observational
- Correction to "Predicting preventable hospital readmissions with causal machine learning".Health services research · 2021Article
- Predicting preventable hospital readmissions with causal machine learning.Health services research · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
Abstract
objectiveTo assess both the feasibility and potential impact of predicting preventable hospital readmissions using causal machine learning applied to data from the implementation of a readmissions prevention intervention (the Transitions Program). DATA SOURCES: Electronic health records maintained by Kaiser Permanente Northern California (KPNC). STUDY
designRetrospective causal forest analysis of postdischarge outcomes among KPNC inpatients. Using data from both before and after implementation, we apply causal forests to estimate individual-level treatment effects of the Transitions Program intervention on 30-day readmission. These estimates are used to characterize treatment effect heterogeneity and to assess the notional impacts of alternative targeting strategies in terms of the number of readmissions prevented. DATA COLLECTION: 1 539 285 index hospitalizations meeting the inclusion criteria and occurring between June 2010 and December 2018 at 21 KPNC hospitals. PRINCIPAL
findingsThere appears to be substantial heterogeneity in patients' responses to the intervention (omnibus test for heterogeneity p = 2.23 × 10
conclusionsCausal machine learning can be used to identify preventable hospital readmissions, if the requisite interventional data are available. Moreover, our results suggest a mismatch between risk and treatment effects.
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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.