Walter Jonathan, Ma Jessica, Platt Alyssa, Acker Yvonne, Sendak Mark, Gao Michael, Gardner Matt, Balu Suresh, Setji Noppon
Department of Medicine Duke University School of Medicine.
Geriatric Research, Education, and Clinical Center, Durham VA Health System.
J Brown Hosp Med. 2024 Jul 2;3(3):120907. doi: 10.56305/001c.120907. eCollection 2024.
Advance care planning (ACP) is an important aspect of patient care that is underutilized. Machine learning (ML) models can help identify patients appropriate for ACP. The objective was to evaluate the impact of using provider notifications based on an ML model on the rate of ACP documentation and patient outcomes.
This was a pre-post QI intervention study at a tertiary academic hospital. Adult patients admitted to general medicine teams identified to be at elevated risk of mortality using an ML model were included in the study. The intervention consisted of notifying a provider by email and page for a patient identified by the ML model.
A total of 479 encounters were analyzed of which 282 encounters occurred post-intervention. The covariate-adjusted proportion of higher-risk patients with documented ACP rose from 6.0% at baseline to 56.5% (Risk Ratio (RR)= 9.42, 95% CI: 4.90 - 18.11). Patients with ACP were more than twice as likely to have code status reduced when ACP was documented (29.0% vs. 10.8% RR=2.69, 95% CI: 1.64 - 4.27). Additionally, patients with ACP had twice the odds of hospice referral (22.2% vs. 12.6% Odds Ratio=2.16, 95% CI: 1.16 - 4.01). However, patients with ACP documented had a longer mean LOS (9.7 vs. 7.6 days, Event time ratio = 1.29, 95% CI: 1.10 - 1.53).
Provider notifications using an ML model can lead to an increase in completion of ACP documentation by frontline clinicians in the inpatient setting.
预先护理计划（ACP）是患者护理的一个重要方面，但目前未得到充分利用。机器学习（ML）模型有助于识别适合进行ACP的患者。本研究的目的是评估基于ML模型的医护人员通知对ACP文件记录率和患者结局的影响。
这是一项在三级学术医院进行的前后对照质量改进干预研究。使用ML模型确定为死亡风险升高的普通内科团队收治的成年患者纳入本研究。干预措施包括通过电子邮件和传呼通知医护人员ML模型识别出的患者。
共分析了479次诊疗，其中282次诊疗发生在干预后。经协变量调整后，记录有ACP的高风险患者比例从基线时的6.0%升至56.5%（风险比（RR）=9.42，95%置信区间：4.90-18.11）。记录有ACP时，有ACP的患者降低急救状态的可能性增加两倍多（29.0%对10.8%，RR=2.69，95%置信区间：1.64-4.27）。此外，有ACP的患者接受临终关怀转诊的几率增加两倍（22.2%对12.6%，优势比=2.16，95%置信区间：1.16-4.01）。然而，记录有ACP的患者平均住院时间更长（9.7天对7.6天，事件时间比=1.29，95%置信区间：1.10-1.53）。
在住院环境中，使用ML模型通知医护人员可使一线临床医生完成ACP文件记录的情况增加。
