Analytical validation of Exandra: a clinical decision support system for promoting guideline-directed therapy of type-2 diabetes in primary care - a collaborative study with experts from Diabetes Canada.

IF 3.8 3区 医学 Q2 MEDICAL INFORMATICS BMC Medical Informatics and Decision Making Pub Date : 2025-02-12 DOI:10.1186/s12911-025-02881-4
Klaudia Grechuta, Pedram Shokouh, Valentina Bayer, Henrich Kraemer, Jeremy Gilbert, Susie Jin, Ahmad Alhussein
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Abstract

Background: Individuals with type 2 diabetes (T2D) have a high prevalence of cardiovascular and renal comorbidities. Despite clinical practice guidelines recommending the use of cardiorenal protective medications, many people with T2D are not prescribed these medications. A clinical decision support system called Exandra was developed to provide treatment recommendations for individuals with T2D based on current clinical practice guidelines from Diabetes Canada. The current study aimed to medically validate Exandra via review by external medical experts in T2D.

Methods: Validation of Exandra took place in two phases. Test cases using simulated clinical scenarios and recommendations were generated by Exandra. In Phase 1 of the validation, reviewers evaluated whether they agreed with Exandra's recommendations with a "yes," "no," or "not sure" response. In Phase 2, reviewers were interviewed about their "no" and "not sure" responses to determine possible reasons and potential fixes to the Exandra system. The primary outcome was the precision rate of Exandra following the interviews and final adjudication of the cases. The target precision rate was 90%.

Results: Exandra displayed an overall precision rate of 95.5%. A large proportion of cases that were initially labeled "no" or "not sure" by reviewers were changed to "yes" following the interview phase. This was largely due to the validation using a simplified user interface compared with the complexity of the actual Exandra system, and reviewers needing clarification of how the outputs would be displayed on the Exandra platform.

Conclusion: Exandra displayed a high level of accuracy and precision in providing guideline-directed recommendations for managing T2D and its common comorbidities. The results of this study indicate that Exandra is a promising tool for improving the management of T2D and its comorbidities.

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Exandra的分析验证:促进2型糖尿病初级保健指导治疗的临床决策支持系统-与加拿大糖尿病专家的合作研究。
背景:2型糖尿病(T2D)患者心血管和肾脏合并症的患病率很高。尽管临床实践指南建议使用心肾保护药物,但许多T2D患者没有处方这些药物。开发了一个名为Exandra的临床决策支持系统,根据加拿大糖尿病协会目前的临床实践指南,为T2D患者提供治疗建议。目前的研究旨在通过外部医学专家对T2D的审查在医学上验证Exandra。方法:分两个阶段进行验证。使用模拟临床场景和建议的测试用例由Exandra生成。在验证的第一阶段,审稿人用“是”、“否”或“不确定”来评估他们是否同意Exandra的建议。在第二阶段,对评审人员进行了“不”和“不确定”的采访,以确定可能的原因和对Exandra系统的潜在修复。主要结果是Exandra在案件访谈和最终裁决后的准确率。目标准确率为90%。结果:Exandra的总准确率为95.5%。很大一部分最初被评论者标记为“否”或“不确定”的案例在访谈阶段之后被更改为“是”。这在很大程度上是由于与实际的Exandra系统的复杂性相比,使用简化的用户界面进行验证,并且审稿人需要澄清输出将如何在Exandra平台上显示。结论:Exandra在为T2D及其常见合并症的治疗提供指导性建议方面显示出较高的准确性和精确性。本研究结果表明,Exandra是改善T2D及其合并症管理的有前途的工具。
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来源期刊
CiteScore
7.20
自引率
5.70%
发文量
297
审稿时长
1 months
期刊介绍: BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.
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