预测挑战会导致临终 (EOL) 癌症患者的提前护理计划 (ACP) 延迟。检查质量改进死亡率预测算法干预对 ACP 记录和 EOL 护理的影响。我们实施了经过验证的死亡风险预测机器学习该模型适用于从急诊科 (ED) 收治至杜克大学医院专门的实体恶性肿瘤病房的实体恶性肿瘤患者。当患者被确定为高风险时，临床医生会收到一封电子邮件。我们比较了通知干预前后的 ACP 文件和 EOL 护理结果。我们排除了在最初 24 小时内入住重症监护病房 (ICU) 的患者。比较涉及卡方/Fisher 精确检验和 Wilcoxon 秩和检验；按医师专业分层的比较采用 Cochran-Mantel-Haenszel 检验。干预前和干预后队列分别包括 88 名患者和 77 名患者。大多数是白人、非西班牙裔/拉丁裔且已婚。干预前的 ACP 对话记录为 2.3%，干预后为 80.5%（P<0.001），如果通知的主治医生是姑息治疗专家（4.1% vs. 84.6%）或肿瘤科医生（0% vs. 76.3%） （P<0.001）。各组之间在住院时间 (LOS)、临终关怀转诊、代码状态变化、ICU 入院或 LOS、30 天再入院、30 天急诊室就诊以及住院患者和 30 天死亡方面没有差异。机器学习带来的高死亡率风险导致记录的 ACP 对话大幅增加，但并未影响 EOL 护理。我们的干预措施有望改变临床医生的行为。该模型正在临床实践中进一步整合。由爱思唯尔公司出版。

Prognostication challenges contribute to delays in advance care planning (ACP) for patients with cancer near the end of life (EOL).Examine a quality improvement mortality prediction algorithm intervention's impact on ACP documentation and EOL care.We implemented a validated mortality risk prediction machine learning model for solid malignancy patients admitted from the emergency department (ED) to a dedicated solid malignancy unit at Duke University Hospital. Clinicians received an email when a patient was identified as high-risk. We compared ACP documentation and EOL care outcomes before and after the notification intervention. We excluded patients with intensive care unit (ICU) admission in the first 24 hours. Comparisons involved chi-square/Fisher's exact tests and Wilcoxon rank sum tests; comparisons stratified by physician specialty employ Cochran-Mantel-Haenszel tests.Preintervention and postintervention cohorts comprised 88 and 77 patients, respectively. Most were White, non-Hispanic/Latino, and married. ACP conversations were documented for 2.3% of hospitalizations preintervention vs. 80.5% postintervention (P<0.001), and if the attending physician notified was a palliative care specialist (4.1% vs. 84.6%) or oncologist (0% vs. 76.3%) (P<0.001). There were no differences between groups in length of stay (LOS), hospice referral, code status change, ICU admissions or LOS, 30-day readmissions, 30-day ED visits, and inpatient and 30-day deaths.Identifying patients with cancer and high mortality risk via machine learning elicited a substantial increase in documented ACP conversations but did not impact EOL care. Our intervention showed promise in changing clinician behavior. Further integration of this model in clinical practice is ongoing.Published by Elsevier Inc.