Department of Biochemistry, Faculty of Medicine, Başkent University, Ankara, Turkey.
Department of Medical Biochemistry, Faculty of Medicine, Gazi University, Ankara, Turkey.
Clin Biochem. 2021 Jul;93:90-98. doi: 10.1016/j.clinbiochem.2021.03.018. Epub 2021 Apr 5.
Autoverification is the process of evaluating and validating laboratory results using predefined computer-based algorithms without human interaction. By using autoverification, all reports are validated according to the standard evaluation criteria with predefined rules, and the number of reports per laboratory specialist is reduced. However, creating and validating these rules are the most demanding steps for setting up an autoverification system. In this study, we aimed to develop a model for helping users establish autoverification rules and evaluate their validity and performance.
DESIGN & METHODS: The proposed model was established by analyzing white papers, previous study results, and national/international guidelines. An autoverification software (myODS) was developed to create rules according to the model and to evaluate the rules and autoverification rates. The simulation results that were produced by the software were used to demonstrate that the determined framework works as expected. Both autoverification rates and step-based evaluations were performed using actual patient results. Two algorithms defined according to delta check usage (Algorithm A and B) and three review limits were used for the evaluation.
Six hundred seventeen rules were created according to the proposed model. 1,976 simulation results were created for validation. Our results showed that manual review limits are the most critical step in determining the autoverification rate, and delta check evaluation is especially important for evaluating inpatients. Algorithm B, which includes consecutive delta check evaluation, had higher AV rates.
Systemic rule formation is a critical factor for successful AV. Our proposed model can help laboratories establish and evaluate autoverification systems. Rules created according to this model could be used as a starting point for different test groups.
自动验证是使用预定义的基于计算机的算法评估和验证实验室结果而无需人工交互的过程。通过使用自动验证，所有报告都根据标准评估标准和预定义规则进行验证，并且减少了每个实验室专家的报告数量。但是，创建和验证这些规则是设置自动验证系统最具挑战性的步骤。在这项研究中，我们旨在开发一种帮助用户建立自动验证规则并评估其有效性和性能的模型。
通过分析白皮书、先前的研究结果和国家/国际指南，建立了所提出的模型。开发了一个自动验证软件（myODS），根据模型创建规则，并评估规则和自动验证率。软件生成的模拟结果用于证明确定的框架按预期工作。使用实际患者的结果进行自动验证率和基于步骤的评估。使用 delta 检查使用情况（算法 A 和 B）定义了两种算法和三个审核限制进行评估。
根据所提出的模型创建了 617 条规则。创建了 1976 个模拟结果进行验证。我们的结果表明，手动审核限制是确定自动验证率的最关键步骤，delta 检查评估对于评估住院患者尤为重要。包括连续 delta 检查评估的算法 B 具有更高的 AV 率。
系统的规则形成是自动验证成功的关键因素。我们提出的模型可以帮助实验室建立和评估自动验证系统。根据该模型创建的规则可以用作不同测试组的起点。
