BACKGROUND Activity or audit log data are required for EHR privacy and security management but may also be useful for understanding desktop workflow. OBJECTIVE We determined if the EHR audit log file, a rich source of complex time-stamped data on desktop activities, could be processed to derive primary care provider (PCP) level workflow measures. METHODS We analyzed audit log data on 876 PCPs across 17,455 ambulatory care encounters that generated 578,394 time-stamped records. Each individual record represents a user interaction (e.g., point and click) that reflects all or part of a specific activity (e.g., order entry access). No dictionary exists to define how to combine clusters of sequential audit log records to represent identifiable PCP tasks. We determined if PARAFAC2 tensor factorization could: (1) learn to identify audit log record clusters that specifically represent defined PCP tasks; and (2) identify variation in how tasks are completed without the need for ground-truth labels. To interpret the result, we used the following PARAFAC2 factors: a matrix representing the task definitions and a matrix containing the frequency measure of each task for each encounter. RESULTS PARAFAC2 automatically identified 4 clusters of audit log records that represent 4 common clinical encounter tasks: (1) medications' access, (2) notes' access, (3) order entry access, and (4) diagnosis modification. PARAFAC2 also identified the most common variants in how PCPs accomplish these tasks. It discovered variation in how the notes' access task was done, including identification of 9 distinct variants of notes access that explained 77% of the input data variation for notes. The discovered variants mapped to two known workflows for notes' access and to two distinct PCP user groups who accessed notes by either using the Visit Navigator or the Wrap-Up option. CONCLUSIONS Our results demonstrate that EHR audit log data can be rapidly processed to create higher-level constructed features that represent time-stamped PCP tasks.

中文翻译：

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在EHR审核数据上使用PARAFAC2张量分解以了解PCP桌面工作。

背景技术活动或审核日志数据是EHR隐私和安全管理所必需的，但对于理解桌面工作流程也可能有用。目的我们确定是否可以处理EHR审核日志文件（台式机活动的大量带有时间戳的复杂数据的源），以得出初级保健提供者（PCP）级别的工作流程度量。方法我们分析了17455次门诊就诊中876名PCP的审核日志数据，这些记录生成了578394条带时间戳的记录。每个单独的记录表示反映特定活动的全部或部分（例如，订单输入访问）的用户交互（例如，点击）。没有字典可以定义如何组合顺序审核日志记录的群集来表示可识别的PCP任务。我们确定了PARAFAC2张量分解是否可以：（1）学习识别专门代表已定义的PCP任务的审核日志记录群集；（2）识别出如何完成任务而无需地面标签的变化。为了解释结果，我们使用了以下PARAFAC2因素：代表任务定义的矩阵和包含每次遭遇每个任务的频率测量值的矩阵。结果PARAFAC2自动识别了代表4个常见临床遇到任务的4个审核日志记录群集：（1）药物访问，（2）便笺访问，（3）订单输入访问和（4）诊断修改。PARAFAC2还确定了PCP如何完成这些任务的最常见变体。它发现了便签访问任务的完成方式有所不同，包括识别注释访问的9个不同变体，这些变体解释了注释输入数据变化的77％。发现的变量映射到两个已知的工作流以进行便笺访问，并且映射到了两个不同的PCP用户组，这些用户通过使用“访问导航器”或“包裹”选项来访问便笺。结论我们的结果表明，可以快速处理EHR审核日志数据，以创建代表带时间戳的PCP任务的更高级别的构建功能。