生物信息学在发展临床实践指南中的作用:原则和机会

Hisham M. F. Sherif
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引用次数: 0

摘要

临床实践指南由专业组织制定,以提供安全、有效、适当、公平和负担得起的诊断和治疗护理标准,以促进以最低成本获得最佳结果;对病人、付款人、卫生保健专业人员以及卫生保健系统。这些准则的一个主要目标是预防疾病过程中的严重和/或有害事件。从过程分析的角度来看,这些事件被认为是“错误”,应该按照事故调查的指导方针和工具进行调查。这些工具包括过程图、根本原因分析、影响图和预测建模方法。基本原则是,所有生物环境都是复杂的环境,涉及遵循多种途径并在多个层面上受到众多因素影响的众多个别过程;而不是石川图的单轴主线。过程制图要求全面了解疾病发生和维持过程中涉及的多线性、相互依赖和重叠的途径。通过这种方法,可以更充分地评估各种因素对最终结果的影响。更健壮地利用注册表数据对于全面和完整的数据收集和组织是必不可少的。基于概率的预测模型可能最适合纳入所有相关因素,包括未来认识到的其他因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The role of bioinformatics in developing clinical practice guidelines: principles and opportunities
Clinical practice guidelines were developed by the professional organizations to provide standards of safe, effective, appropriate, equitable, affordable diagnostic and therapeutic care to promote the best yield at the least cost; to the patient, payers, health care professionals as well as to the healthcare system. One major objective of these guidelines is to prevent serious and/or harmful events in the course of disease. From a process analysis standpoint, such events are considered “errors” and should be investigated in accordance with the guidelines and tools for accident investigation. These tools include Process Mapping, Root-Cause Analysis, Influence Diagrams and Predictive Modeling methodology. The underlying principle is that all biologic environments are complex environments, involving a multitude of individual processes following multiple pathways and under the influence of numerous factors at multiple levels; as opposed to a single-axis main process line of the Ishikawa diagram. Process mapping mandates a thorough understanding of the multi-linear, inter-dependent and overlapping pathways involved in the genesis and maintenance of disease. Through this approach, the impact of various factors on the end-result can be more adequately assessed. More robust utilization of registry data is essential for comprehensive and complete data collection and organization. A probability-based predictive model may be best suited to incorporate all relevant factors, including others recognized in the future.
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