ArticleJCO oncology practice2023
Reducing Avoidable Emergency Visits and Hospitalizations With Patient Risk-Based Prescriptive Analytics: A Quality Improvement Project at an Oncology Care Model Practice.
Article in JCO oncology practice, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review of Interventions to Optimize Emergency Department Care of Patients with Cancer.The western journal of emergency medicine · 2026Pooled it
- Review
- Article
- Early Examination of Hospital-Level Performance on Unplanned, Potentially Avoidable Hospital Visits After Chemotherapy, 2018-2022.Health services research · 2026Article
- Non-emergency department (ED) interventions to reduce ED utilization: a scoping review.BMC emergency medicine · 2024Article
- Referral Process Enhancement: Innovative Approaches and Best Practices.Asian Pacific journal of cancer prevention : APJCP · 2024Article
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Authors and funding
7 authors.
Funding
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Abstract
purposeCancer-related emergency department (ED) visits and hospitalizations that would have been appropriately managed in the outpatient setting are avoidable and detrimental to patients and health systems. This quality improvement (QI) project aimed to leverage patient risk-based prescriptive analytics at a community oncology practice to reduce avoidable acute care use (ACU).
methodsUsing the Plan-Do-Study-Act (PDSA) methodology, we implemented the Jvion Care Optimization and Recommendation Enhancement augmented intelligence (AI) tool at an Oncology Care Model (OCM) practice, the Center for Cancer and Blood Disorders practice. We applied continuous machine learning to predict risk of preventable harm (avoidable ACU) and generated patient-specific recommendations that nurses implemented to avert it.
resultsPatient-centric interventions included medication/dosage changes, laboratory tests/imaging, physical/occupational/psychologic therapy referral, palliative care/hospice referral, and surveillance/observation. Nurses contacted patients every 1-2 weeks after initial outreach to assess and maintain adherence to recommended interventions. Per 100 unique OCM patients, monthly ED visits dropped from 13.7 to 11.5 (18%), a sustained month-over-month improvement. Quarterly admissions dropped from 19.5 to 17.1 (13%), a sustained quarter-over-quarter improvement. Overall, the practice realized potential annual savings of $2.8 million US dollars (USD) on avoidable ACU.
conclusionThe AI tool has enabled nurse case managers to identify and resolve critical clinical issues and reduce avoidable ACU. Effects on outcomes can be inferred from the reduction; targeting short-term interventions toward patients most at-risk translates to better long-term care and outcomes. QI projects involving predictive modeling of patient risk, prescriptive analytics, and nurse outreach may reduce ACU.
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