Girgis Afaf, Durcinoska Ivana, Koh Eng-Siew, Ng Weng, Arnold Anthony, Delaney Geoff P
Afaf Girgis, Ivana Durcinoska, and Geoff P. Delaney, The University of New South Wales, Sydney; Eng-Siew Koh, Weng Ng, and Geoff P. Delaney, Liverpool Hospital, Liverpool; and Anthony Arnold, Wollongong Hospital, Wollongong, NSW, Australia.
JCO Clin Cancer Inform. 2018 Dec;2:1-13. doi: 10.1200/CCI.18.00024.
High-quality symptom management and supportive care are essential components of comprehensive cancer care. We aimed to describe the development of an evidence-based automated decisional algorithm for patients with cancer that had specific, actionable, clinical, evidence-based recommendations to improve patient care, communication, and management.
We reviewed existing literature and clinical practice guidelines to identify priority domains of patient care and potential clinical recommendations. Two multidisciplinary clinical advisory groups used a two-stage consensus decision-making approach to determine domains of care and patient-reported outcome (PRO) measures and subsequently developed automated algorithms with clear clinical recommendations amendable to intervention in clinical settings.
Algorithms were developed to inform management of patient symptoms, distress, and unmet needs. Three PRO measures were chosen: Distress Thermometer and problem checklist, Edmonton Symptom Assessment Scale, and the Supportive Care Needs Survey-Screening Tool 9. PRO items were mapped to five domains of patient well-being: physical, emotional, practical, social and family, and maintenance of well-being. A total of 15 actionable clinical recommendations tailored to specific issues of concern were established.
Using automated algorithms and clinical recommendations provides a platform for streamlining and systematizing the use of PROs to inform risk-stratified guideline-informed care. The series of algorithms, which set out systematized care pathways for the clinical care of patients with cancer, can be used to potentially inform patient-centered care.
高质量的症状管理和支持性护理是综合癌症护理的重要组成部分。我们旨在描述一种基于证据的自动化决策算法的开发，该算法针对癌症患者，具有具体、可操作、基于临床证据的建议，以改善患者护理、沟通和管理。
我们回顾了现有文献和临床实践指南，以确定患者护理的优先领域和潜在的临床建议。两个多学科临床咨询小组采用两阶段共识决策方法来确定护理领域和患者报告结局（PRO）指标，随后开发了具有明确临床建议的自动化算法，这些建议适用于临床环境中的干预。
开发了算法以指导患者症状、痛苦和未满足需求的管理。选择了三项PRO指标：痛苦温度计和问题清单、埃德蒙顿症状评估量表以及支持性护理需求调查-筛查工具9。PRO项目被映射到患者福祉的五个领域：身体、情感、实际、社会和家庭以及福祉维持。共制定了15条针对特定关注问题的可操作临床建议。
使用自动化算法和临床建议为简化和系统化PRO的使用提供了一个平台，以指导基于风险分层的指南指导护理。这一系列算法为癌症患者的临床护理设定了系统化的护理路径，可用于潜在地指导以患者为中心的护理。
