ArticleJournal of the American Medical Informatics Association : JAMIA2021
A framework for making predictive models useful in practice.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07167173 (Predictive Model for Multidrug Resistance in Patients Admitted to the Emergency Department With Sepsis), which is not on this map. Cited by 39 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Predictive Model for Multidrug Resistance in Patients Admitted to the Emergency Department With Sepsis
Who cites it
39 citing papers in PubMed, 2 syntheses or guidelines pooled it, 76 citations in OpenAlex.
- Assessment of Adherence to Reporting Guidelines by Commonly Used Clinical Prediction Models From a Single Vendor: A Systematic Review.JAMA network open · 2022Pooled it
- Predictive Models to Assess Risk of Persistent Opioid Use, Opioid Use Disorder, and Overdose.Journal of addiction medicinePooled it
- Loss function influence on hyperparameter optimization for observational healthcare prediction models.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Applications, Challenges, and Future Directions of Large Language Models in Health Care Communication: Scoping Review.Journal of medical Internet research · 2026Article
- Prospective validation and real-time implementation of an automated machine learning postoperative mortality prediction model.British journal of anaesthesia · 2026Article
- Combining Machine Learning Models and Screening to Enhance Suicide Risk Identification for American Indian Patients: Retrospective Cohort Study.Journal of medical Internet research · 2026Observational
- Ethical challenges and opportunities for integrating predictive analytics in community-based overdose prevention.Lancet regional health. Americas · 2026Review
- Curated and Structure-Based Drug-Target Interactions Improve Underprediction of Drug Side Effects in Network Models.Journal of chemical information and modeling · 2026Article
- Artificial intelligence-based approaches for advance care planning: a scoping review.BMC palliative care · 2025Article
- Racial/ethnic discrimination and racial trauma: Concurrent evaluation among Black adults who smoke in the United States.Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors · 2025Article
- Applications of Large Language Models in the Field of Suicide Prevention: Scoping Review.Journal of medical Internet research · 2025Article
- In Silico Evaluation of Algorithm-Based Clinical Decision Support Systems: Protocol for a Scoping Review.JMIR research protocols · 2025Article
- Patient perspective on predictive models in healthcare: translation into practice, ethical implications and limitations?BMJ health & care informatics · 2025Article
- The promises and limitations of artificial intelligence for quality improvement, patient safety, and research in hospital medicine.Journal of hospital medicine · 2025Article
- Developing a Research Center for Artificial Intelligence in Medicine.Mayo Clinic proceedings. Digital health · 2024Article
- Preclinical side effect prediction through pathway engineering of protein interaction network models.CPT: pharmacometrics & systems pharmacology · 2024Article
- Ensuring useful adoption of generative artificial intelligence in healthcare.Journal of the American Medical Informatics Association : JAMIA · 2024Review
- Towards proactive palliative care in oncology: developing an explainable EHR-based machine learning model for mortality risk prediction.BMC palliative care · 2024Article
- Artificial Intelligence in Geriatrics: Riding the Inevitable Tide of Promise, Challenges, and Considerations.The journals of gerontology. Series A, Biological sciences and medical sciences · 2024Article
- Leveraging physiology and artificial intelligence to deliver advancements in health care.Physiological reviews · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo analyze the impact of factors in healthcare delivery on the net benefit of triggering an Advanced Care Planning (ACP) workflow based on predictions of 12-month mortality. MATERIALS AND
methodsWe built a predictive model of 12-month mortality using electronic health record data and evaluated the impact of healthcare delivery factors on the net benefit of triggering an ACP workflow based on the models' predictions. Factors included nonclinical reasons that make ACP inappropriate: limited capacity for ACP, inability to follow up due to patient discharge, and availability of an outpatient workflow to follow up on missed cases. We also quantified the relative benefits of increasing capacity for inpatient ACP versus outpatient ACP.
resultsWork capacity constraints and discharge timing can significantly reduce the net benefit of triggering the ACP workflow based on a model's predictions. However, the reduction can be mitigated by creating an outpatient ACP workflow. Given limited resources to either add capacity for inpatient ACP versus developing outpatient ACP capability, the latter is likely to provide more benefit to patient care. DISCUSSION: The benefit of using a predictive model for identifying patients for interventions is highly dependent on the capacity to execute the workflow triggered by the model. We provide a framework for quantifying the impact of healthcare delivery factors and work capacity constraints on achieved benefit.
conclusionAn analysis of the sensitivity of the net benefit realized by a predictive model triggered clinical workflow to various healthcare delivery factors is necessary for making predictive models useful in practice.
Indexed as
Identifiers
What OpenQuestion holds
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.