Mrazek Cornelia, Haschke-Becher Elisabeth, Felder Thomas K, Keppel Martin H, Oberkofler Hannes, Cadamuro Janne
Department of Laboratory Medicine, Paracelsus Medical University Salzburg, A-5020 Salzburg, Austria.
Diagnostics (Basel). 2021 Jun 23;11(7):1141. doi: 10.3390/diagnostics11071141.
Inappropriate laboratory test selection in the form of overutilization as well as underutilization frequently occurs despite available guidelines. There is broad approval among laboratory specialists as well as clinicians that demand management strategies are useful tools to avoid this issue. Most of these tools are based on automated algorithms or other types of machine learning. This review summarizes the available demand management strategies that may be adopted to local settings. We believe that artificial intelligence may help to further improve these available tools.
尽管有可用的指南，但以过度使用和使用不足形式出现的不适当实验室检查选择仍经常发生。实验室专家和临床医生普遍认可需求管理策略是避免这一问题的有用工具。这些工具大多基于自动化算法或其他类型的机器学习。本综述总结了可适用于当地情况的现有需求管理策略。我们认为人工智能可能有助于进一步改进这些现有工具。
