Reducing Avoidable Emergency Visits and Hospitalizations With Patient Risk-Based Prescriptive Analytics: A Quality Improvement Project at an Oncology Care Model Practice

Abstract

PURPOSE Cancer-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). METHODS Using 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. RESULTS Patient-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. CONCLUSION The 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. PURPOSE Problem Cancer-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 care systems. As occurs with about 50% of ED visits, acute care use (ACU) is avoidable if determined manageable in a clinic, urgent care, or physician office setting. 1-7 Patients may be treated by staff less experienced with cancer-related symptoms; consequently, care may be substandard. 3-5 Moreover, acute care accounts for nearly half of on-cologic costs in the United States. 3 Specific Aim This quality improvement (QI) project aimed to leverage patient risk-based prescriptive analytics at an Oncology Care Model (OCM) practice to reduce avoidable ACU. Following Plan-Do-Study-Act (PDSA) methodology, 8 we implemented the Jvion Care Optimization and Recommendation Enhancement augmented intelligence (AI) tool, which applied continuous machine learning (ML) to predict risk of preventable harm (avoidable ACU) and generated patient-specific recommendations. 9-12 At their discretion, nurses contacted at-risk patients with interventions to avert the ACU. Brief Literature Approximately 50% of ED visits and 40% of admissions are related to complications of chemotherapy (eg, pain, nausea, and infection). 1,3,4 Studies considering patient age, sex, functional status, comorbidities, outpatient care access, and ED arrival time have not identified objective factors associated with avoidable ASSOCIATED CONTENT Appendix