Nurse-led models of care for metabolic syndrome in primary care: A scoping review

AIM

To identify and map out existing nurse-led models of care for treatment and prevention of metabolic syndrome in primary care settings.

DESIGN

A scoping review.

METHODS

Conducted in accordance with the JBI methodology.

DATA SOURCES

A search of the databases PubMed, CINAHL Complete, Cochrane Library, Scopus, handsearch and a grey literature search was conducted in June 2022 and updated in March 2023.

RESULTS

Title and abstract screening was performed on 926 articles resulting in 40 articles for full text screening. Full text screening yielded seven articles that met inclusion criteria.

CONCLUSION

Additional research is needed on nursing models of care to prevent and treat metabolic syndrome. Future studies should concentrate on rigour with clearly defined objective inclusion criteria.

IMPLICATIONS TO CLINICAL PRACTICE

This review contributes a synthesis of the evidence on nurse-led models for metabolic syndrome in primary care.

IMPACT

This scoping review addresses metabolic syndrome, the precursor to non-communicable disease. The review mapped the evidence for nurse-led models of care for metabolic syndrome in the primary care setting. These findings promote the development and evaluation of novel nurse-led models of care which can mitigate the effect of the current epidemic.

REPORTING METHOD

PRISMA checklist for scoping reviews. No patient or public contribution was part of this study.

PROTOCOL REGISTRATION

Open Science Framework accessible at: https://osf.io/jfpw7/.

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

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.