Chen You, Lorenzi Nancy, Nyemba Steve, Schildcrout Jonathan S, Malin Bradley
Department of Biomedical Informatics, School of Medicine, Vanderbilt University, Nashville, TN, USA.
Department of Biomedical Informatics, School of Medicine, Vanderbilt University, Nashville, TN, USA; School of Nursing, Vanderbilt University, Nashville, TN, USA.
Int J Med Inform. 2014 Jul;83(7):495-506. doi: 10.1016/j.ijmedinf.2014.04.006. Epub 2014 Apr 28.
Models of healthcare organizations (HCOs) are often defined up front by a select few administrative officials and managers. However, given the size and complexity of modern healthcare systems, this practice does not scale easily. The goal of this work is to investigate the extent to which organizational relationships can be automatically learned from utilization patterns of electronic health record (EHR) systems.
We designed an online survey to solicit the perspectives of employees of a large academic medical center. We surveyed employees from two administrative areas: (1) Coding & Charge Entry and (2) Medical Information Services and two clinical areas: (3) Anesthesiology and (4) Psychiatry. To test our hypotheses we selected two administrative units that have work-related responsibilities with electronic records; however, for the clinical areas we selected two disciplines with very different patient responsibilities and whose accesses and people who accessed were similar. We provided each group of employees with questions regarding the chance of interaction between areas in the medical center in the form of association rules (e.g., Given someone from Coding & Charge Entry accessed a patient's record, what is the chance that someone from Medical Information Services access the same record?). We compared the respondent predictions with the rules learned from actual EHR utilization using linear-mixed effects regression models.
The findings from our survey confirm that medical center employees can distinguish between association rules of high and non-high likelihood when their own area is involved. Moreover, they can make such distinctions between for any HCO area in this survey. It was further observed that, with respect to highly likely interactions, respondents from certain areas were significantly better than other respondents at making such distinctions and certain areas' associations were more distinguishable than others.
These results illustrate that EHR utilization patterns may be consistent with the expectations of HCO employees. Our findings show that certain areas in the HCO are easier than others for employees to assess, which suggests that automated learning strategies may yield more accurate models of healthcare organizations than those based on the perspectives of a select few individuals.
医疗保健组织（HCOs）的模式通常由少数行政官员和管理人员预先确定。然而，鉴于现代医疗系统的规模和复杂性，这种做法难以轻易推广。本研究的目的是调查从电子健康记录（EHR）系统的使用模式中自动学习组织关系的程度。
我们设计了一项在线调查，以征求一家大型学术医疗中心员工的意见。我们调查了两个行政领域的员工：（1）编码与收费录入，以及（2）医疗信息服务；还有两个临床领域的员工：（3）麻醉学和（4）精神病学。为了检验我们的假设，我们选择了两个与电子记录有工作职责关联的行政单位；然而，对于临床领域，我们选择了两个患者职责差异很大但访问情况和访问人员相似的学科。我们以关联规则的形式向每组员工提供有关医疗中心各区域之间互动可能性的问题（例如，已知编码与收费录入部门的某人访问了患者记录，医疗信息服务部门的某人访问同一记录的可能性是多少？）。我们使用线性混合效应回归模型将受访者的预测与从实际EHR使用情况中得出的规则进行比较。
我们的调查结果证实，当涉及到自己所在区域时，医疗中心的员工能够区分高可能性和非高可能性的关联规则。此外，他们能够对本调查中的任何HCO区域做出这种区分。进一步观察发现，对于高可能性的互动，某些区域的受访者在做出这种区分方面明显优于其他受访者，并且某些区域的关联比其他区域更易于区分。
这些结果表明，EHR的使用模式可能与HCO员工的预期一致。我们的研究结果表明，HCO中的某些区域比其他区域更容易被员工评估，这表明自动化学习策略可能比基于少数人观点的策略产生更准确的医疗保健组织模型。
