Effect of Integrating Machine Learning Mortality Estimates With Behavioral Nudges to Clinicians on Serious Illness Conversations Among Patients With Cancer: A Stepped-Wedge Cluster Randomized Clinical Trial.
Effect of Integrating Machine Learning Mortality Estimates With Behavioral Nudges to Clinicians on Serious Illness Conversations Among Patients With Cancer: A Stepped-Wedge Cluster Randomized Clinical Trial.
复制标题
DOI：
10.1001/jamaoncol.2020.4759
复制
发表时间：
2020-12-01
期刊：
影响因子：
28.4
通讯作者：
Patel MS
中科院分区：
文献类型：
--
作者：
Manz CR;Parikh RB;Small DS;Evans CN;Chivers C;Regli SH;Hanson CW;Bekelman JE;Rareshide CAL;O'Connor N;Schuchter LM;Shulman LN;Patel MS
向肿瘤临床医生提供带有行为推动的机器学习死亡率预测对与癌症患者进行严重疾病对话的比率有什么影响？在这项包括14607名癌症患者的阶梯楔形随机临床试验中，干预导致严重疾病对话从所有患者的约1%增加到5%，并且从约4%增加到15%，患者具有高预测死亡风险。机器学习死亡率预测与临床医生的行为推动相结合，导致癌症患者的严重疾病对话率增加。严重疾病对话（SIC）是临床医生和患者之间关于预后，治疗目标和临终偏好的结构化对话。增加肿瘤临床医生和患者之间SIC发生率的干预措施可能会改善目标一致性护理和患者结局。确定将机器学习死亡率预测与行为轻推相结合的临床医生指导干预对激励临床医生-患者SIC的影响。这项阶梯楔形群随机临床试验在宾夕法尼亚州一个大型学术卫生系统的9个肿瘤内科诊所（8个亚专科肿瘤科和1个普通肿瘤科诊所）进行了20周（从2019年6月17日至11月1日）。将2个最小的专科诊所的临床医生分组在一起，导致8个诊所组被随机分配到4个干预楔形期。意向治疗分析的参与者包括78名接受SIC培训的肿瘤临床医生及其在研究期间有门诊肿瘤就诊的患者（N = 14 607）。(1)每周向肿瘤临床医生发送电子邮件，其中包含SIC性能反馈和同行比较;（2）使用经验证的机器学习算法估计的下周计划的最多6名高风险患者（≥10%的180天死亡率预测风险）的列表;以及（3）在患者预约日选择退出短信提示临床医生考虑SIC。对照组的临床医生接受常规护理，包括每周电子邮件和累积SIC性能。干预组与常规护理（对照）组中发生SIC的患者百分比。样本包括78名临床医生和14607名患者。患者的平均（SD）年龄为61.9（14.2）岁，53.7%为女性，70.4%为白色。对于所有接触，对照组中1.3%和干预组中4.6%的人进行了SIC，差异显著（调整后的差异为3.3个百分点; 95% CI，2.3-4.5; P <0.001）。在4124名高危患者中，对照组和干预组分别有3.6%和15.2%的患者进行了SIC，差异有显著性（调整后的差异为11.6个百分点; 95%CI，8.2-12.5; P <0.001）。在这项阶梯楔群随机临床试验中，一项向肿瘤临床医生提供机器学习死亡率预测和行为轻推的干预措施显着增加了所有患者和干预措施针对的高死亡风险患者的SIC率。行为推动与机器学习死亡率预测相结合可以积极影响临床医生的行为，并可以更广泛地应用于改善生命末期的护理。ClinicalTrials.gov 标识符：NCT 03984773这项阶梯楔形群随机临床试验评估了临床医生指导的干预措施的效果，该干预措施将机器学习死亡率预测与行为轻推相结合，与常规护理相比较，可以激发临床医生和癌症患者之间的严重疾病对话。
What is the effect of delivering machine learning mortality predictions with behavioral nudges to oncology clinicians on the rate of serious illness conversations with patients with cancer? In this stepped-wedge cluster randomized clinical trial that included 14 607 patients with cancer, the intervention led to a significant increase in serious illness conversations from approximately 1% to 5% of all patient encounters and from approximately 4% to 15% of encounters with patients having high predicted mortality risk. Machine learning mortality predictions combined with behavioral nudges to clinicians led to an increased rate of serious illness conversations for patients with cancer. Serious illness conversations (SICs) are structured conversations between clinicians and patients about prognosis, treatment goals, and end-of-life preferences. Interventions that increase the rate of SICs between oncology clinicians and patients may improve goal-concordant care and patient outcomes. To determine the effect of a clinician-directed intervention integrating machine learning mortality predictions with behavioral nudges on motivating clinician-patient SICs. This stepped-wedge cluster randomized clinical trial was conducted across 20 weeks (from June 17 to November 1, 2019) at 9 medical oncology clinics (8 subspecialty oncology and 1 general oncology clinics) within a large academic health system in Pennsylvania. Clinicians at the 2 smallest subspecialty clinics were grouped together, resulting in 8 clinic groups randomly assigned to the 4 intervention wedge periods. Included participants in the intention-to-treat analyses were 78 oncology clinicians who received SIC training and their patients (N = 14 607) who had an outpatient oncology encounter during the study period. (1) Weekly emails to oncology clinicians with SIC performance feedback and peer comparisons; (2) a list of up to 6 high-risk patients (≥10% predicted risk of 180-day mortality) scheduled for the next week, estimated using a validated machine learning algorithm; and (3) opt-out text message prompts to clinicians on the patient’s appointment day to consider an SIC. Clinicians in the control group received usual care consisting of weekly emails with cumulative SIC performance. Percentage of patient encounters with an SIC in the intervention group vs the usual care (control) group. The sample consisted of 78 clinicians and 14 607 patients. The mean (SD) age of patients was 61.9 (14.2) years, 53.7% were female, and 70.4% were White. For all encounters, SICs were conducted among 1.3% in the control group and 4.6% in the intervention group, a significant difference (adjusted difference in percentage points, 3.3; 95% CI, 2.3-4.5; P < .001). Among 4124 high-risk patient encounters, SICs were conducted among 3.6% in the control group and 15.2% in the intervention group, a significant difference (adjusted difference in percentage points, 11.6; 95% CI, 8.2-12.5; P < .001). In this stepped-wedge cluster randomized clinical trial, an intervention that delivered machine learning mortality predictions with behavioral nudges to oncology clinicians significantly increased the rate of SICs among all patients and among patients with high mortality risk who were targeted by the intervention. Behavioral nudges combined with machine learning mortality predictions can positively influence clinician behavior and may be applied more broadly to improve care near the end of life. ClinicalTrials.gov Identifier: NCT03984773 This stepped-wedge cluster randomized clinical trial assesses the effect of a clinician-directed intervention combining machine learning mortality predictions with behavioral nudges vs usual care on motivating serious illness conversations between clinicians and patients with cancer.
登录
查看更多内容
影响因子：
15.8
作者：
Moher D;Liberati A;Tetzlaff J;Altman DG;PRISMA Group
通讯作者：
PRISMA Group
影响因子：
158.5
作者：
Llovet, Josep M.;Ricci, Sergio;Bruix, Jordi
通讯作者：
Bruix, Jordi
影响因子：
158.5
作者：
Finn, Richard S.;Qin, Shukui;Cheng, Ann-Lii
通讯作者：
Cheng, Ann-Lii
影响因子：
168.9
作者：
Bruix, Jordi;Qin, Shukui;Han, Guohong
通讯作者：
Han, Guohong
索拉非尼对亚太地区晚期肝细胞癌患者的疗效和安全性：一项 III 期随机、双盲、安慰剂对照试验
影响因子：
51.1
作者：
Cheng, Ann-Lii;Kang, Yoon-Koo;Guan, Zhongzhen
通讯作者：
Guan, Zhongzhen