ArticleJournal of the American Medical Informatics Association : JAMIA2021
Improving the delivery of palliative care through predictive modeling and healthcare informatics.
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. Cited by 20 papers, 1 of them a synthesis that pooled it.
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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
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.
- Implementation of Machine Learning Applications in Health Care Organizations: Systematic Review of Empirical Studies.Journal of medical Internet research · 2024Pooled it
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- Predicting Hospice Use Among American Indian/Alaska Native Persons with End-Stage Kidney Disease.medRxiv : the preprint server for health sciences · 2026Article
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- Critical activities for successful implementation and adoption of AI in healthcare: towards a process framework for healthcare organizations.Frontiers in digital health · 2025Article
- Artificial intelligence in healthcare: a scoping review of perceived threats to patient rights and safety.Archives of public health = Archives belges de sante publique · 2024Article
- Development and validation of a preliminary multivariable diagnostic model for identifying unusual infections in hospitalized patients.Biomolecules & biomedicine · 2024Article
- Minimizing bias when using artificial intelligence in critical care medicine.Journal of critical care · 2024Article
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- Considerations for Quality Control Monitoring of Machine Learning Models in Clinical Practice.JMIR medical informatics · 2024Article
- Using artificial intelligence to promote equitable care for inpatients with language barriers and complex medical needs: clinical stakeholder perspectives.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- A comprehensive overview of barriers and strategies for AI implementation in healthcare: Mixed-method design.PloS one · 2024Review
- Exploring the Potential of Chatbots in Critical Care Nephrology.Medicines (Basel, Switzerland) · 2023Review
- Data Science Implementation Trends in Nursing Practice: A Review of the 2021 Literature.Applied clinical informatics · 2023Review
- Advanced Care Planning for Hospitalized Patients Following Clinician Notification of Patient Mortality by a Machine Learning Algorithm.JAMA network open · 2023Article
- Artificial Intelligence Implementation in Healthcare: A Theory-Based Scoping Review of Barriers and Facilitators.International journal of environmental research and public health · 2022Article
- Palliative Care Exposure Relative to Predicted Risk of Six-Month Mortality in Hospitalized Adults.Journal of pain and symptom management · 2022Article
- Improving Patient Selection and Prioritization for Hospital at Home Through Predictive Modeling.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Expected clinical utility of automatable prediction models for improving palliative and end-of-life care outcomes: Toward routine decision analysis before implementation.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveAccess to palliative care (PC) is important for many patients with uncontrolled symptom burden from serious or complex illness. However, many patients who could benefit from PC do not receive it early enough or at all. We sought to address this problem by building a predictive model into a comprehensive clinical framework with the aims to (i) identify in-hospital patients likely to benefit from a PC consult, and (ii) intervene on such patients by contacting their care team. MATERIALS AND
methodsElectronic health record data for 68 349 inpatient encounters in 2017 at a large hospital were used to train a model to predict the need for PC consult. This model was published as a web service, connected to institutional data pipelines, and consumed by a downstream display application monitored by the PC team. For those patients that the PC team deems appropriate, a team member then contacts the patient's corresponding care team.
resultsTraining performance AUC based on a 20% holdout validation set was 0.90. The most influential variables were previous palliative care, hospital unit, Albumin, Troponin, and metastatic cancer. The model has been successfully integrated into the clinical workflow making real-time predictions on hundreds of patients per day. The model had an "in-production" AUC of 0.91. A clinical trial is currently underway to assess the effect on clinical outcomes.
conclusionsA machine learning model can effectively predict the need for an inpatient PC consult and has been successfully integrated into practice to refer new patients to PC.
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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.