背景:全面的生存护理模式对于改善护理的获取和协调是必要的。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机会。
目的:本文介绍了我们的专家,基于规则的生存算法,以建立护士主导的生存护理模型,以支持患有前列腺癌(PCa)的男性。该算法称为“无疾病证据”(Ned),支持更及时的决策,增强安全性,和护理的连续性。
方法:通过加拿大各地的临床专家工作组制定和完善了初始规则集(例如,护士专家,内科医生专家,和科学家;n=20),和患者伴侣(n=3)。通过与临床护士专家的多学科共识会议来定义算法优先级,护士科学家,执业护士,泌尿外科肿瘤学家,泌尿科医师,和放射肿瘤学家(n=17)。使用标称组技术对系统进行了改进和验证。
结果:建立了四个级别的警报分类,由临床实践调查扩大前列腺癌综合指数的回应发起,并通过最小临床重要的不同警报阈值的变化来介导,警报历史记录,和临床紧迫性,患者自主性影响临床视力。通过量身定制的教育作为反应的第一线,支持患者的自主性。并根据患者发起的护士咨询请求进行警报升级。
结论:Ned算法旨在促进PCa护士主导的护理模式,并具有较高的护患比例。这种新颖的专家知情的PCa生存护理算法包含针对临床紧急症状的定义的升级途径,同时尊重患者的偏好。尽管需要通过务实的试验进一步验证,我们预计Ned算法将支持更及时的决策,并通过更频繁的自动化检查点的自动化来增强护理的连续性,同时使患者能够比标准护理更有效地自我管理症状。
■RR2-10.1136/bmjopen-2020-045806。
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.
UNASSIGNED: RR2-10.1136/bmjopen-2020-045806.