电子健康记录（EHR）中的临床决策支持（CDS）工具通常用作支持临床设置中质量改进计划的核心策略。监测这些工具产生的预期和非预期影响对于方案评估和调整至关重要。现有监测方法通常依赖于医疗保健提供者的自我报告或对临床工作流程的直接观察，这需要大量的数据收集工作，并容易出现报告偏见。本研究旨在开发一种新的监测方法，利用EHR活动数据并展示其在监测由国家癌症研究所癌症中心戒烟计划（C3I）赞助的CDS工具的使用方面的应用。我们开发了基于EHR的度量标准，以监测两个CDS工具的实施：（1）筛查警报，提醒诊所工作人员完成吸烟评估，（2）支持警报，提示医疗保健提供者讨论支持和治疗选择，包括转诊到戒烟诊所。使用EHR活动数据，我们测量了CDS工具的完成情况（就诊级警报完成率）和负担（在完成前触发的警报次数和处理警报所花费的时间）。我们报告了在C3I中心的7个癌症诊所（2个诊所实施了筛查警报，5个诊所同时实施了两个警报）实施后的12个月跟踪的度量标准，并确定改善警报设计和采用的领域。在实施后的12个月中，筛查警报在5121次就诊中触发。就诊级警报完成率（诊所工作人员承认在EHR中完成筛查：0.55；诊所工作人员完成了筛查结果的EHR文件编写：0.32）随时间保持稳定但在不同诊所之间变化很大。支持警报在12个月中的1074个接触中触发。提供者在87.3%（n = 938）的接触中采取了行动（即未推迟支持警报），在12%（n = 129）的接触中发现做好准备戒烟的患者，并在2%（n = 22）的接触中要求转诊到戒烟诊所。就警报负担而言，平均而言，两个警报在完成前都触发了2次以上（筛查警报：2.7;支持警报：2.1），推迟筛查警报所花费的时间与完成警报所花费的时间相似（52秒对53秒），而推迟支持警报所花费的时间比完成所花费的时间长，每次接触的时间差为67和50秒。这些发现有助于改善四个方面的警报设计和使用：（1）通过本地适应改善警报采纳和完成情况，（2）通过额外策略包括提供者-患者沟通培训来提高支持警报效力，（3）提高有关警报完成情况的跟踪准确性，（4）平衡警报效力与负担。EHR活动度量标准能够监测戒烟警报的成功和负担，从而更加全面地了解警报实施可能存在的权衡。这些度量标准可用于指导实施调整，并可在不同环境中进行扩展。 ©Jinying Chen，Sarah L Cutrona，Ajay Dharod，Stephanie C Bunch，Kristie L Foley，Brian Ostasiewski，Erica R Hale，Aaron Bridges，Adam Moses，Eric C Donny，Erin L Sutfin，Thomas K Houston，iDAPT癌症控制实施科学中心。最初发表于《JMIR Medical Informatics》（https://medinform.jmir.org），2023年2月3日。

Clinical decision support (CDS) tools in electronic health records (EHRs) are often used as core strategies to support quality improvement programs in the clinical setting. Monitoring the impact (intended and unintended) of these tools is crucial for program evaluation and adaptation. Existing approaches for monitoring typically rely on health care providers' self-reports or direct observation of clinical workflows, which require substantial data collection efforts and are prone to reporting bias.This study aims to develop a novel monitoring method leveraging EHR activity data and demonstrate its use in monitoring the CDS tools implemented by a tobacco cessation program sponsored by the National Cancer Institute's Cancer Center Cessation Initiative (C3I).We developed EHR-based metrics to monitor the implementation of two CDS tools: (1) a screening alert reminding clinic staff to complete the smoking assessment and (2) a support alert prompting health care providers to discuss support and treatment options, including referral to a cessation clinic. Using EHR activity data, we measured the completion (encounter-level alert completion rate) and burden (the number of times an alert was fired before completion and time spent handling the alert) of the CDS tools. We report metrics tracked for 12 months post implementation, comparing 7 cancer clinics (2 clinics implemented the screening alert and 5 implemented both alerts) within a C3I center, and identify areas to improve alert design and adoption.The screening alert fired in 5121 encounters during the 12 months post implementation. The encounter-level alert completion rate (clinic staff acknowledged completion of screening in EHR: 0.55; clinic staff completed EHR documentation of screening results: 0.32) remained stable over time but varied considerably across clinics. The support alert fired in 1074 encounters during the 12 months. Providers acted upon (ie, not postponed) the support alert in 87.3% (n=938) of encounters, identified a patient ready to quit in 12% (n=129) of encounters, and ordered a referral to the cessation clinic in 2% (n=22) of encounters. With respect to alert burden, on average, both alerts fired over 2 times (screening alert: 2.7; support alert: 2.1) before completion; time spent postponing the screening alert was similar to completing (52 vs 53 seconds) the alert, and time spent postponing the support alert was more than completing (67 vs 50 seconds) the alert per encounter. These findings inform four areas where the alert design and use can be improved: (1) improving alert adoption and completion through local adaptation, (2) improving support alert efficacy by additional strategies including training in provider-patient communication, (3) improving the accuracy of tracking for alert completion, and (4) balancing alert efficacy with the burden.EHR activity metrics were able to monitor the success and burden of tobacco cessation alerts, allowing for a more nuanced understanding of potential trade-offs associated with alert implementation. These metrics can be used to guide implementation adaptation and are scalable across diverse settings.©Jinying Chen, Sarah L Cutrona, Ajay Dharod, Stephanie C Bunch, Kristie L Foley, Brian Ostasiewski, Erica R Hale, Aaron Bridges, Adam Moses, Eric C Donny, Erin L Sutfin, Thomas K Houston, iDAPT Implementation Science Center for Cancer Control. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 02.03.2023.