Use of Electronic Health Record Access and Audit Logs to Identify Physician Actions Following Noninterruptive Alert Opening: Descriptive Study
Use of Electronic Health Record Access and Audit Logs to Identify Physician Actions Following Noninterruptive Alert Opening: Descriptive Study
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DOI：
10.2196/12650
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发表时间：
2019-01-01
影响因子：
3.2
通讯作者：
Cutrona, Sarah L.
中科院分区：
文献类型：
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作者：
Amroze, Azraa;Field, Terry S.;Cutrona, Sarah L.
背景：电子健康记录 (EHR) 访问和审核日志记录了提供者浏览 EHR 时的行为。这些数据可用于更好地了解提供者对基于 EHR 的临床决策支持 (CDS) 的反应，揭示 CDS 是否有效以及为何有效。 目的：本研究旨在确定使用 EHR 访问和审计日志来跟踪初级保健医生 (PCP) 向 EHR InBaskets 发送的不间断警报的开放和响应的可行性。方法：我们进行了一项描述性研究来评估 EHR InBaskets 的使用情况。 EHR 日志数据用于跟踪提供者行为。我们分析了通过之前的随机对照试验向 75 位 PCP 的 InBasket 发送的 799 个不间断警报打开后记录的数据。三种类型的警报强调了老年患者出院后的新用药问题：仅提供信息 (n=593)、用药建议 (n=37) 和测试建议 (n=169)。我们寻求日志数据来识别打开警报的人以及 PCP 后续 EHR 行动的时间和类型（立即与第二天结束之前）。我们进行了多变量分析，检查警报类型、患者特征、提供者特征和背景因素以及立即或后续 PCP 行动（一般、特定药物或实验室特定行动）的可能性之间的关联。我们描述了日志数据使用的挑战和策略。结果：我们成功识别了 EHR 访问和审核日志中所需的数据。超过四分之三的警报（78.5%，627/799）是由其所针对的 PCP 打开的，使我们能够评估 PCP 的立即行动；其中，208 个警报后立即采取了行动。将我们的分析扩展到包括工作人员或覆盖医生的警报，我们发现 799 个警报中的另外 330 个警报在第二天结束时表明了 PCP 的行动。其余 261 个警报显示 PCP 未采取任何行动。与纯信息警报相比，药物推荐警报的立即行动比值比 (OR) 为 4.03 (95% CI 1.67-9.72)，测试推荐警报的立即行动比值比 (OR) 为 2.14 (95% CI 1.38-3.32)。与纯信息警报相比，第二天结束时的药物特定行动的 OR 显着高于药物建议 (5.59; 95% CI 2.42-12.94) 和测试建议 (1.71; 95% CI 1.09-2.68)。我们发现了实验室特定行动的类似 OR 模式。我们遇到了两个主要挑战：(1) 捕获 EHR 状态的历史快照（警报发送时的 InBasket 消息数量）需要合并几个月前生成的数据并进行纵向跟踪。 (2) 准确解释数据元素需要医生/数据管理团队在 EHR 内采取行动，然后检查审计日志以识别相应的文档进行迭代工作。结论：EHR 日志数据可以为未来的工作提供信息，并在 CDS 干预措施的开发和完善过程中提供有价值的信息。为了应对挑战，应在实施基于电子病历的研究之前规划这些数据的使用。
Background: Electronic health record (EHR) access and audit logs record behaviors of providers as they navigate the EHR. These data can be used to better understand provider responses to EHR-based clinical decision support (CDS), shedding light on whether and why CDS is effective.Objective: This study aimed to determine the feasibility of using EHR access and audit logs to track primary care physicians' (PCPs') opening of and response to noninterruptive alerts delivered to EHR InBaskets.Methods: We conducted a descriptive study to assess the use of EHR log data to track provider behavior. We analyzed data recorded following opening of 799 noninterruptive alerts sent to 75 PCPs' InBaskets through a prior randomized controlled trial. Three types of alerts highlighted new medication concerns for older patients' posthospital discharge: information only (n=593), medication recommendations (n=37), and test recommendations (n=169). We sought log data to identify the person opening the alert and the timing and type of PCPs' follow-up EHR actions (immediate vs by the end of the following day). We performed multivariate analyses examining associations between alert type, patient characteristics, provider characteristics, and contextual factors and likelihood of immediate or subsequent PCP action (general, medication-specific, or laboratory-specific actions). We describe challenges and strategies for log data use.Results: We successfully identified the required data in EHR access and audit logs. More than three-quarters of alerts (78.5%, 627/799) were opened by the PCP to whom they were directed, allowing us to assess immediate PCP action; of these, 208 alerts were followed by immediate action. Expanding on our analyses to include alerts opened by staff or covering physicians, we found that an additional 330 of the 799 alerts demonstrated PCP action by the end of the following day. The remaining 261 alerts showed no PCP action. Compared to information-only alerts, the odds ratio (OR) of immediate action was 4.03 (95% CI 1.67-9.72) for medication-recommendation and 2.14 (95% CI 1.38-3.32) for test-recommendation alerts. Compared to information-only alerts, ORs of medication-specific action by end of the following day were significantly greater for medication recommendations (5.59; 95% CI 2.42-12.94) and test recommendations (1.71; 95% CI 1.09-2.68). We found a similar pattern for OR of laboratory-specific action. We encountered 2 main challenges: (1) Capturing a historical snapshot of EHR status (number of InBasket messages at time of alert delivery) required incorporation of data generated many months prior with longitudinal follow-up. (2) Accurately interpreting data elements required iterative work by a physician/data manager team taking action within the EHR and then examining audit logs to identify corresponding documentation.Conclusions: EHR log data could inform future efforts and provide valuable information during development and refinement of CDS interventions. To address challenges, use of these data should be planned before implementing an EHR-based study.