Department of Computer Science, Dartmouth College, Hanover, New Hampshire, USA.
J Am Med Inform Assoc. 2014 Jan-Feb;21(1):117-31. doi: 10.1136/amiajnl-2012-001419. Epub 2013 Jun 25.
To model inconsistencies or distortions among three realities: patients' physical reality; clinicians' mental models of patients' conditions, laboratories, etc; representation of that reality in electronic health records (EHR). To serve as a potential tool for quality improvement of EHRs.
Using observations, literature, information technology (IT) logs, vendor and US Food and Drug Administration reports, we constructed scenarios/models of how patients' realities, clinicians' mental models, and EHRs can misalign to produce distortions in comprehension and treatment. We then categorized them according to an emergent typology derived from the cases themselves and refined the categories based on insights gained from the literature of interactive sociotechnical systems analysis, decision support science, and human computer interaction. Typical of grounded theory methods, the categories underwent repeated modifications.
We constructed 45 scenarios of misalignment between patients' physical realities, clinicians' mental models, and EHRs. We then identified five general types of misrepresentation in these cases: IT data too narrowly focused; IT data too broadly focused; EHRs miss critical reality; data multiplicities-perhaps contradictory or confusing; distortions from data reflected back and forth across users, sensors, and others. The 45 scenarios are presented, organized by the five types.
With humans, there is a physical reality and actors' mental models of that reality. In healthcare, there is another player: the EHR/healthcare IT, which implicitly and explicitly reflects many mental models, facets of reality, and measures thereof that vary in reliability and consistency. EHRs are both microcosms and shapers of medical care. Our typology and scenarios are intended to be useful to healthcare IT designers and implementers in improving EHR systems and reducing the unintended negative consequences of their use.
构建患者的物理现实、临床医生对患者病情、实验室等的心理模型以及电子健康记录（EHR）中对该现实的描述之间的不一致或扭曲模型。旨在作为改善 EHR 的潜在工具。
通过观察、文献、信息技术（IT）日志、供应商和美国食品和药物管理局的报告，我们构建了患者的现实、临床医生的心理模型以及 EHR 如何产生偏差从而导致理解和治疗产生扭曲的场景/模型。然后，我们根据案例本身得出的一个新兴分类法对它们进行分类，并根据交互社会技术系统分析、决策支持科学和人机交互的文献中获得的见解对这些类别进行细化。与扎根理论方法一样，这些类别经历了反复修改。
我们构建了 45 个患者的物理现实、临床医生的心理模型和 EHR 之间的失配场景。然后，我们确定了这些案例中存在的五种代表性的失实类型：IT 数据过于狭隘；IT 数据过于宽泛；EHR 遗漏关键现实；数据多重性——可能相互矛盾或混淆；来自数据的扭曲在用户、传感器和其他各方之间来回反馈。通过呈现这 45 个场景，按照这五种类型进行组织。
人类有一个物理现实和该现实的心理模型。在医疗保健领域，还有另一个参与者：EHR/医疗保健 IT，它隐含和显式地反映了许多心理模型、现实的各个方面以及可靠性和一致性不同的各种测量结果。EHR 既是微观世界，也是医疗保健的塑造者。我们的分类法和场景旨在为医疗保健 IT 设计人员和实施人员提供有用的信息，以改善 EHR 系统并减少其使用带来的意外负面影响。
