ArticleJournal of general internal medicine2019
Electronic Health Record Mortality Prediction Model for Targeted Palliative Care Among Hospitalized Medical Patients: a Pilot Quasi-experimental Study.
Article in Journal of general internal medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 2 of them syntheses that pooled it.
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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
48 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of the assessment model for palliative care needs in cancer patients.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Pooled it
- Enhancing goals of care discussions: a systematic review and meta-analysis of clinician-directed behavioural nudges.Age and ageing · 2026Pooled it
- Impact of Prognostic Notifications on Inpatient Advance Care Planning: A Cluster Randomized Trial.Journal of pain and symptom management · 2025Trial
- Trial
- Improving End-of-Life Screening in the Emergency Department With Collaborative Artificial Intelligence.Annals of emergency medicine · 2026Article
- Ethics of artificial intelligence prognostication in palliative care: perspectives from a national survey of palliative care physicians.BMJ supportive & palliative care · 2026Article
- Clinician Distress in the Care of Seriously Ill Hospitalized Patients: A Mixed Methods Integration of Qualitative Findings and a Quantitative Typology.Research square · 2026Article
- Recognising dying: Will artificial intelligence (AI) help improve clinical accuracy?Future healthcare journal · 2026Article
- Evaluating Clinical Implementation of Risk Prediction Based Interventions Using Difference-In-Differences.Health services research · 2026Article
- Making Community-Based Palliative Care Eligibility Determinations: Palliative Care Team Member Perspectives on Access to Information and Algorithm Use.The American journal of hospice & palliative care · 2025Article
- Targeted Digital Health Intervention in End-of-Life and Hospice Care: A Scoping Review.Journal of advanced nursing · 2025Article
- Performance of electronic medical record tool in predicting 6-month mortality in hospitalized patients with cancer.Journal of hospital medicine · 2025Article
- Measuring Goal-Concordant Care Using Electronic Clinical Notes.JAMA network open · 2025Article
- External validation of a proprietary risk model for 1-year mortality in community-dwelling adults aged 65 years or older.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Exploring the Utility of the Modified Hospitalized-Patient One-Year Mortality Risk Score to Trigger Referrals to Palliative Care for Inpatients With Cancer.Cancer medicine · 2024Article
- Leveraging the electronic health record to identify delivery of goal-concordant care.medRxiv : the preprint server for health sciences · 2024Article
- Classification of Documented Goals of Care Among Hospitalized Patients with High Mortality Risk: a Mixed-Methods Feasibility Study.Journal of general internal medicine · 2024Article
- Article
- Hospital-Onset Sepsis Warrants Expanded Investigation and Consideration as a Unique Clinical Entity.Chest · 2024Review
- Applying Machine Learning Techniques to Implementation Science.Online journal of public health informatics · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDevelopment of electronic health record (EHR) prediction models to improve palliative care delivery is on the rise, yet the clinical impact of such models has not been evaluated.
objectiveTo assess the clinical impact of triggering palliative care using an EHR prediction model.
designPilot prospective before-after study on the general medical wards at an urban academic medical center.
participantsAdults with a predicted probability of 6-month mortality of ≥ 0.3.
interventionTriggered (with opt-out) palliative care consult on hospital day 2. MAIN MEASURES: Frequencies of consults, advance care planning (ACP) documentation, home palliative care and hospice referrals, code status changes, and pre-consult length of stay (LOS). KEY
resultsThe control and intervention periods included 8 weeks each and 138 admissions and 134 admissions, respectively. Characteristics between the groups were similar, with a mean (standard deviation) risk of 6-month mortality of 0.5 (0.2). Seventy-seven (57%) triggered consults were accepted by the primary team and 8 consults were requested per usual care during the intervention period. Compared to historical controls, consultation increased by 74% (22 [16%] vs 85 [63%], P < .001), median (interquartile range) pre-consult LOS decreased by 1.4 days (2.6 [1.1, 6.2] vs 1.2 [0.8, 2.7], P = .02), ACP documentation increased by 38% (23 [17%] vs 37 [28%], P = .03), and home palliative care referrals increased by 61% (9 [7%] vs 23 [17%], P = .01). There were no differences between the control and intervention groups in hospice referrals (14 [10] vs 22 [16], P = .13), code status changes (42 [30] vs 39 [29]; P = .81), or consult requests for lower risk (< 0.3) patients (48/1004 [5] vs 33/798 [4]; P = .48).
conclusionsTargeting hospital-based palliative care using an EHR mortality prediction model is a clinically promising approach to improve the quality of care among seriously ill medical patients. More evidence is needed to determine the generalizability of this approach and its impact on patient- and caregiver-reported outcomes.
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