启发式改进健康信息技术系统设计的力量

ACI open Pub Date : 2021-09-22 DOI:10.1055/s-0042-1758462
Don Roosan, Justin Clutter, Brian Kendall, C. Weir
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引用次数: 1

摘要

摘要背景 如果卫生系统设计与专业临床医生较高的认知技能不匹配,临床决策可能容易出错。在理解启发式在临床决策中的重要性的必要性方面存在差距。启发式方法可以为复杂病例设计直观的健康信息系统提供认知支持。目的探讨传染病(ID)临床医生的复杂决策,重点是快速和节俭的启发式方法。我们假设ID临床医生使用简单的启发式方法来利用他们的经验来理解复杂的病例。方法采用认知任务分析法和启发式决策模型。我们进行了认知访谈,并为临床医生提供了一种快速而节俭的树算法,将复杂的信息转换为简单的决策算法。我们进行了一种基于批判性决策方法的分析,从成绩单中生成if-then逻辑句子。我们对启发式算法进行了主题分析,并计算了完成的平均时间和决策节点中关键信息的数量。结果通过对数据的分析,共生成了27个if-then逻辑启发式语句。建造这些快速而节俭的树木的平均时间为1.65 ± 0.37 分钟,临床医生关注的关键信息的平均数量为5.4条 ± 3.1.结论临床医生使用快捷的心理模型将复杂的病例简化为简单的心理模型算法。人工智能的创新使用可以使临床决策支持系统专注于创造性和直观的界面设计,与专业临床医生的更高认知技能相匹配。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Power of Heuristics to Improve Health Information Technology System Design
Abstract Background  Clinical decision-making can be prone to error if health system design does not match expert clinicians' higher cognitive skills. There is a gap in understanding the need for the importance of heuristics in clinical decision-making. The heuristic approach can provide cognitive support in designing intuitive health information systems for complex cases. Objective We explored complex decision-making by infectious diseases (ID) clinicians focusing on fast and frugal heuristics. We hypothesized that ID clinicians use simple heuristics to understand complex cases using their experience. Methods The study utilized cognitive task analysis and heuristics-based decision modeling. We conducted cognitive interviews and provided clinicians with a fast-and-frugal tree algorithm to convert complex information into simple decision algorithms. We conducted a critical decision method–based analysis to generate if–then logic sentences from the transcript. We conducted a thematic analysis of heuristics and calculated the average time to complete and the number of crucial information in the decision nodes. Results A total of 27 if–then logic heuristics sentences were generated from analyzing the data. The average time to construct the fast-and-frugal trees was 1.65 ± 0.37 minutes, and the average number of crucial pieces of information clinicians focused on was 5.4 ± 3.1. Conclusion Clinicians use shortcut mental models to reduce complex cases into simple mental model algorithms. The innovative use of artificial intelligence could allow clinical decision support systems to focus on creative and intuitive interface design matching the higher cognitive skills of expert clinicians.
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