دورية أكاديمية
أعرض في EDS

An Actionable Expert-System Algorithm to Support Nurse-Led Cancer Survivorship Care: Algorithm Development Study.

التفاصيل البيبلوغرافية
العنوان: An Actionable Expert-System Algorithm to Support Nurse-Led Cancer Survivorship Care: Algorithm Development Study.
المؤلفون: Pfisterer KJ; Centre for Digital Therapeutics, University Health Network, Techna Institute, Toronto, ON, Canada.; Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada., Lohani R; Centre for Digital Therapeutics, University Health Network, Techna Institute, Toronto, ON, Canada., Janes E; Centre for Digital Therapeutics, University Health Network, Techna Institute, Toronto, ON, Canada., Ng D; Centre for Digital Therapeutics, University Health Network, Techna Institute, Toronto, ON, Canada., Wang D; Centre for Digital Therapeutics, University Health Network, Techna Institute, Toronto, ON, Canada., Bryant-Lukosius D; School of Nursing, McMaster University, Hamilton, ON, Canada., Rendon R; Department of Urology, Queen Elizabeth II Health Sciences Centre, Halifax, ON, Canada., Berlin A; Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada., Bender J; Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada., Brown I; Niagara Health System, Thorold, ON, Canada., Feifer A; Trillium Health Partners, Mississauga, ON, Canada., Gotto G; Department of Surgery, University of Calgary, Calgary, AB, Canada., Saha S; Centre for Digital Therapeutics, University Health Network, Techna Institute, Toronto, ON, Canada.; Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada., Cafazzo JA; Centre for Digital Therapeutics, University Health Network, Techna Institute, Toronto, ON, Canada.; Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada., Pham Q; Centre for Digital Therapeutics, University Health Network, Techna Institute, Toronto, ON, Canada.; Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada.; Tefler School of Management, University of Ottawa, Ottawa, ON, Canada.
المصدر: JMIR cancer [JMIR Cancer] 2023 Oct 04; Vol. 9, pp. e44332. Date of Electronic Publication: 2023 Oct 04.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: JMIR Publications Country of Publication: Canada NLM ID: 101666844 Publication Model: Electronic Cited Medium: Print ISSN: 2369-1999 (Print) Linking ISSN: 23691999 NLM ISO Abbreviation: JMIR Cancer Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: Toronto, ON : JMIR Publications, [2015]-
مستخلص: 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.
(©Kaylen J Pfisterer, Raima Lohani, Elizabeth Janes, Denise Ng, Dan Wang, Denise Bryant-Lukosius, Ricardo Rendon, Alejandro Berlin, Jacqueline Bender, Ian Brown, Andrew Feifer, Geoffrey Gotto, Shumit Saha, Joseph A Cafazzo, Quynh Pham. Originally published in JMIR Cancer (https://cancer.jmir.org), 04.10.2023.)
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فهرسة مساهمة: Keywords: AI; algorithm development; artificial intelligence–powered decision support; cancer; cancer treatment; digital health; expert system; nurse-led model of care; nursing; patient-reported outcomes; prostate cancer; survivorship
تواريخ الأحداث: Date Created: 20231004 Latest Revision: 20231021
رمز التحديث: 20231021
مُعرف محوري في PubMed: PMC10585445
DOI: 10.2196/44332
PMID: 37792435
قاعدة البيانات: MEDLINE
الوصف
تدمد:2369-1999
DOI:10.2196/44332