Measuring Collaboration Through Concurrent Electronic Health Record Usage: Network Analysis Study.
Measuring Collaboration Through Concurrent Electronic Health Record Usage: Network Analysis Study.
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DOI：
10.2196/28998
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发表时间：
2021-09-03
影响因子：
3.2
通讯作者：
Chen Y
中科院分区：
文献类型：
--
作者：
Li P;Chen B;Rhodes E;Slagle J;Alrifai MW;France D;Chen Y
合作在医疗机构中至关重要，它允许有效利用集体医疗工作者（HCW）的专业知识。涉及电子健康记录（EHR）的人机交互已经变得普遍，并且作为使用统计和网络分析方法来量化这些协作的途径。我们的目的是衡量HCW的协作和它的特点，通过分析并发EHR的使用。通过从审计日志数据中提取并发EHR使用事件，定义了并发会话。对于每个HCW，我们建立了一个称为并发强度的度量，即并发会话中的EHR活动在所有EHR活动中的比例。统计模型被用来测试HCW之间的并发强度的差异。对于每个患者的访问，从入院到出院，我们测量了所有HCW的并发EHR使用情况，我们称之为时间模式。再次，我们应用统计模型来测试的时间模式的入院，出院，和住院的中间天在工作日和周末之间的差异。利用网络分析来衡量HCW之间的协作关系。我们对专家进行了调查，以确定他们是否可以区分来自并发EHR使用的高可能性和低可能性类别之间的协作关系。聚类用于聚集并发活动来描述并发会话。我们从一家大型学术医疗中心的新生儿重症监护室（NICU）收集了4个月的EHR审计日志数据，以验证我们的框架的有效性。有显著性差异（P<.001）在并发强度中（同时活动的比例：范围从平均值0.07，95% CI 0.06-0.08至平均值0.36，95% CI 0.18-0.54;同时活动花费的时间比例：在EHR中花费时间最多的前13名HCW专业之间的平均值为0.32，95% CI 0.20-0.44，平均值为0.76，95% CI 0.51-1.00）。工作日和周末的时间模式在入院时（每小时并发间期数：11.60 vs 0.54; P<0.001）和出院时（4.72 vs 1.54; P<0.001）有显著差异，但在住院的中间时间没有差异。新生儿护士，研究员，一线供应商，呼吸科医生，顾问，呼吸治疗师，辅助和支持人员的合作关系。NICU专业人员可以区分高可能性合作关系和低可能性合作关系，其比率具有显著性（3.54，95% CI 3.31-4.37 vs 2.64，95% CI 2.46-3.29; P<.001）。我们确定了50组并行活动。超过87%的并发会话可以由单个集群描述，其余13%的会话由多个集群组成。通过审计日志利用并发EHR使用工作流来分析HCW协作可以提高我们对协作患者护理的理解。使用EHR的HCW协作可能会影响患者护理质量、出院及时性和临床医生工作量、压力或倦怠。
Collaboration is vital within health care institutions, and it allows for the effective use of collective health care worker (HCW) expertise. Human-computer interactions involving electronic health records (EHRs) have become pervasive and act as an avenue for quantifying these collaborations using statistical and network analysis methods. We aimed to measure HCW collaboration and its characteristics by analyzing concurrent EHR usage. By extracting concurrent EHR usage events from audit log data, we defined concurrent sessions. For each HCW, we established a metric called concurrent intensity, which was the proportion of EHR activities in concurrent sessions over all EHR activities. Statistical models were used to test the differences in the concurrent intensity between HCWs. For each patient visit, starting from admission to discharge, we measured concurrent EHR usage across all HCWs, which we called temporal patterns. Again, we applied statistical models to test the differences in temporal patterns of the admission, discharge, and intermediate days of hospital stay between weekdays and weekends. Network analysis was leveraged to measure collaborative relationships among HCWs. We surveyed experts to determine if they could distinguish collaborative relationships between high and low likelihood categories derived from concurrent EHR usage. Clustering was used to aggregate concurrent activities to describe concurrent sessions. We gathered 4 months of EHR audit log data from a large academic medical center’s neonatal intensive care unit (NICU) to validate the effectiveness of our framework. There was a significant difference (P<.001) in the concurrent intensity (proportion of concurrent activities: ranging from mean 0.07, 95% CI 0.06-0.08, to mean 0.36, 95% CI 0.18-0.54; proportion of time spent on concurrent activities: ranging from mean 0.32, 95% CI 0.20-0.44, to mean 0.76, 95% CI 0.51-1.00) between the top 13 HCW specialties who had the largest amount of time spent in EHRs. Temporal patterns between weekday and weekend periods were significantly different on admission (number of concurrent intervals per hour: 11.60 vs 0.54; P<.001) and discharge days (4.72 vs 1.54; P<.001), but not during intermediate days of hospital stay. Neonatal nurses, fellows, frontline providers, neonatologists, consultants, respiratory therapists, and ancillary and support staff had collaborative relationships. NICU professionals could distinguish high likelihood collaborative relationships from low ones at significant rates (3.54, 95% CI 3.31-4.37 vs 2.64, 95% CI 2.46-3.29; P<.001). We identified 50 clusters of concurrent activities. Over 87% of concurrent sessions could be described by a single cluster, with the remaining 13% of sessions comprising multiple clusters. Leveraging concurrent EHR usage workflow through audit logs to analyze HCW collaboration may improve our understanding of collaborative patient care. HCW collaboration using EHRs could potentially influence the quality of patient care, discharge timeliness, and clinician workload, stress, or burnout.
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影响因子：
3.2
作者：
Amroze, Azraa;Field, Terry S.;Cutrona, Sarah L.
通讯作者：
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DOI：
10.1093/jamia/ocaa338
发表时间：
2021-02-12
影响因子：
6.4
作者：
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通讯作者：
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影响因子：
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作者：
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作者：
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通讯作者：
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DOI：
10.1093/jamia/ocy009
发表时间：
2018-07-01
影响因子：
6.4
作者：
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通讯作者：
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