一种支持护士主导的癌症幸存者护理的可操作专家系统算法：算法开发研究
An Actionable Expert-System Algorithm to Support Nurse-Led Cancer Survivorship Care: Algorithm Development Study.
作者信息
Pfisterer Kaylen J, Lohani Raima, Janes Elizabeth, Ng Denise, Wang Dan, Bryant-Lukosius Denise, Rendon Ricardo, Berlin Alejandro, Bender Jacqueline, Brown Ian, Feifer Andrew, Gotto Geoffrey, Saha Shumit, Cafazzo Joseph A, Pham Quynh
机构信息
Centre for Digital Therapeutics, University Health Network, Techna Institute, Toronto, ON, Canada.
Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada.
出版信息
JMIR Cancer. 2023 Oct 4;9:e44332. doi: 10.2196/44332.
BACKGROUND
Comprehensive models of survivorship care are necessary to improve access to and coordination of care. New models of care provide the opportunity to address the complexity of physical and psychosocial problems and long-term health needs experienced by patients following cancer treatment.
OBJECTIVE
This paper presents our expert-informed, rules-based survivorship algorithm to build a nurse-led model of survivorship care to support men living with prostate cancer (PCa). The algorithm is called No Evidence of Disease (Ned) and supports timelier decision-making, enhanced safety, and continuity of care.
METHODS
An initial rule set was developed and refined through working groups with clinical experts across Canada (eg, nurse experts, physician experts, and scientists; n=20), and patient partners (n=3). Algorithm priorities were defined through a multidisciplinary consensus meeting with clinical nurse specialists, nurse scientists, nurse practitioners, urologic oncologists, urologists, and radiation oncologists (n=17). The system was refined and validated using the nominal group technique.
RESULTS
Four levels of alert classification were established, initiated by responses on the Expanded Prostate Cancer Index Composite for Clinical Practice survey, and mediated by changes in minimal clinically important different alert thresholds, alert history, and clinical urgency with patient autonomy influencing clinical acuity. Patient autonomy was supported through tailored education as a first line of response, and alert escalation depending on a patient-initiated request for a nurse consultation.
CONCLUSIONS
The Ned algorithm is positioned to facilitate PCa nurse-led care models with a high nurse-to-patient ratio. This novel expert-informed PCa survivorship care algorithm contains a defined escalation pathway for clinically urgent symptoms while honoring patient preference. Though further validation is required through a pragmatic trial, we anticipate the Ned algorithm will support timelier decision-making and enhance continuity of care through the automation of more frequent automated checkpoints, while empowering patients to self-manage their symptoms more effectively than standard care.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2020-045806.
背景
全面的癌症 survivorship 护理模式对于改善医疗服务的可及性与协调性至关重要。新的护理模式为解决癌症治疗后患者所经历的身体和心理社会问题的复杂性以及长期健康需求提供了契机。
目的
本文介绍了我们基于专家意见和规则的 survivorship 算法，以构建由护士主导的 survivorship 护理模式，为前列腺癌（PCa）患者提供支持。该算法名为无疾病证据（Ned），有助于更及时地做出决策、提高安全性并实现护理的连续性。
方法
通过与加拿大各地的临床专家（如护士专家、医生专家和科学家；n = 20）以及患者合作伙伴（n = 3）组成的工作组，制定并完善了初始规则集。算法优先级通过与临床护士专家、护士科学家、执业护士、泌尿肿瘤学家、泌尿科医生和放射肿瘤学家（n = 17）召开的多学科共识会议确定。使用名义小组技术对该系统进行了完善和验证。
结果
建立了四个警报分类级别，由临床实践扩展前列腺癌指数综合调查的响应启动，并通过最小临床重要差异警报阈值、警报历史记录和临床紧急程度的变化进行调节，同时患者自主性会影响临床敏锐度。通过量身定制的教育作为一线应对措施，并根据患者发起的护士咨询请求进行警报升级，来支持患者自主性。
结论
Ned 算法旨在促进以护士为主导且护士与患者比例较高的 PCa 护理模式。这种新颖的基于专家意见的 PCa survivorship 护理算法包含针对临床紧急症状的明确升级路径，同时尊重患者偏好。尽管需要通过务实试验进行进一步验证，但我们预计 Ned 算法将通过更频繁的自动检查点自动化支持更及时的决策，并增强护理的连续性，同时使患者能够比标准护理更有效地自我管理症状。
国际注册报告标识符（IRRID）：RR2 - 10.1136/bmjopen - 2020 - 045806。
Zotero 插件