Cardinal Health, Dublin, OH.
Hematology-Oncology Associates of CNY, East Syracuse, NY.
JCO Oncol Pract. 2023 May;19(5):e725-e731. doi: 10.1200/OP.22.00307. Epub 2023 Mar 13.
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).
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.
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.
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.
在门诊环境下本可得到妥善管理的癌症相关急诊就诊和住院是可以避免的，且对患者和医疗系统有害。本质量改进（QI）项目旨在利用社区肿瘤学实践中的基于患者风险的规定性分析来减少可避免的急性护理使用（ACU）。
使用计划-执行-研究-行动（PDSA）方法，我们在肿瘤学护理模式（OCM）实践中心癌症和血液疾病实践中实施了 Jvion Care Optimization 和推荐增强人工智能（AI）工具。我们应用连续机器学习来预测可预防伤害（可避免的 ACU）的风险，并生成护士实施以避免伤害的患者特定建议。
以患者为中心的干预措施包括药物/剂量变化、实验室检查/影像学、物理/职业/心理治疗转诊、姑息治疗/临终关怀转诊以及监测/观察。护士在初始联系后每 1-2 周联系患者，以评估和维持对推荐干预措施的依从性。每 100 名独特的 OCM 患者中，每月急诊就诊次数从 13.7 次减少到 11.5 次（18%），逐月持续改善。每季度入院次数从 19.5 次减少到 17.1 次（13%），逐季持续改善。总体而言，该实践在可避免的 ACU 方面实现了潜在的 280 万美元（USD）的年度节省。
人工智能工具使护士个案经理能够识别和解决关键临床问题并减少可避免的 ACU。从减少中可以推断出对结果的影响；针对风险最高的患者实施短期干预措施可转化为更好的长期护理和结果。涉及患者风险预测、规定性分析和护士外联的 QI 项目可能会减少 ACU。
