Lessard Lysanne, Michalowski Wojtek, Chen Li Wei, Amyot Daniel, Halwani Fawaz, Banerjee Diponkar
University of Ottawa, Ottawa, Ontario, Canada; Institut de Recherche de l'Hopital Montfort, Ottawa, Ontario, Canada.
University of Ottawa, Ottawa, Ontario, Canada.
AMIA Annu Symp Proc. 2017 Feb 10;2016:772-778. eCollection 2016.
Predictive analytics can provide valuable support to the effective management of pathology facilities. The introduction of new tests and technologies in anatomical pathology will increase the volume of specimens to be processed, as well as the complexity of pathology processes. In order for predictive analytics to address managerial challenges associated with the volume and complexity increases, it is important to pinpoint the areas where pathology managers would most benefit from predictive capabilities. We illustrate common issues in managing pathology facilities with an analysis of the surgical specimen process at the Department of Pathology and Laboratory Medicine (DPLM) at The Ottawa Hospital, which processes all surgical specimens for the Eastern Ontario Regional Laboratory Association. We then show how predictive analytics could be used to support management. Our proposed approach can be generalized beyond the DPLM, contributing to a more effective management of pathology facilities and in turn to quicker clinical diagnoses.
预测分析可为病理设施的有效管理提供有价值的支持。解剖病理学中新技术和检测方法的引入，将增加待处理标本的数量以及病理流程的复杂性。为了让预测分析应对与数量和复杂性增加相关的管理挑战，明确病理管理人员能从预测能力中获得最大益处的领域至关重要。我们通过分析渥太华医院病理与检验医学部（DPLM）的手术标本处理流程，来说明病理设施管理中的常见问题，该部门负责处理安大略东部地区实验室协会的所有手术标本。然后我们展示了预测分析如何用于支持管理工作。我们提出的方法可推广至DPLM以外的地方，有助于更有效地管理病理设施，进而实现更快的临床诊断。
