Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Nov 2;9(11):e20570.
doi: 10.2196/20570.

Web-Based Patient Segmentation in Finnish Primary Care: Protocol for Clinical Validation of the Navigator Service in Patients With Diabetes

Affiliations

Web-Based Patient Segmentation in Finnish Primary Care: Protocol for Clinical Validation of the Navigator Service in Patients With Diabetes

Riikka Riihimies et al. JMIR Res Protoc. .

Abstract

Background: An aging population and increasing multimorbidity challenge health care systems worldwide. Patient segmentation aims to recognize groups of patients with similar needs, offer targeted services to these groups, and reduce the burden of health care. In this study, the unique Finnish innovation Navigator, a web-based service for patient segmentation, is presented. Both patients and health care professionals complete the electronic questionnaire concerning patients' coping in everyday life and health state. Thus, it considers the patient perspective on self-care. One of four customership-strategy (CS) groups (self-acting, community, cooperating, and network) is then proposed in response to the answers given. This resulting strategy helps both professionals to coordinate patient health care and patients to utilize appropriate health services.

Objective: This study aims to determine the feasibility, validity, and reliability of the Navigator service in the segmentation of patients with diabetes into four CS groups in a primary care setting. Patient characteristics concerning demographic status, chronic conditions, disabilities, health-related quality of life, and well-being in different CS groups will be described. We hypothesize that patients in the network group will be older, have more illnesses, chronic conditions or disabilities, and require more health care services than patients in the self-acting group.

Methods: In this mixed methods study, data collection was based on questionnaires (user experience of Navigator, demographic and health status, World Health Organization Disability Assessment Schedule 2.0, EuroQol 5D, Wellbeing Questionnaire 12, and the Diabetes Treatment Satisfaction Questionnaire) issued to 300 patients with diabetes and on user-experience questionnaires for and semistructured focus-group interviews with 12 nurses. Navigator-database reports and diabetes-care values (blood pressure, BMI, HbA1c, low-density lipoprotein, albumin-creatinine, smoking status) were collected. Qualitative and descriptive analyses were used to study the feasibility, content, concurrent, and face validity of Navigator. While criterion and concurrent validity were examined with correlations, reliability was examined by calculating Cohen kappa and Cronbach alpha. Construct validity is studied by performing exploratory-factor analysis on Navigator data reports and by hypothesis testing. The values, demographics, and health status of patients in different groups were described, and differences between groups were studied by comparing means. Linear regression analysis was performed to assess which variables affect CS group variation.

Results: Data collection was completed in September 2019, and the first feasibility results are expected by the end of 2020. Further results and publications are expected in 2021 and 2022.

Conclusions: This is the first scientific study concerning Navigator's psychometric properties. The study will examine the segregation of patients with diabetes into four CS groups in a primary care setting and the differences between patients in groups. This study will assist in Navigator's further development as a patient segmentation method considering patients' perspectives on self-care. This study will not prove the effectiveness or efficacy of Navigator; therefore, it is essential to study these outcomes of separate care pathways.

International registered report identifier (irrid): DERR1-10.2196/20570.

Keywords: Navigator; coordination of care; eHealth; equality; health care services; patient segmentation; primary care; psychometric properties; questionnaires.

PubMed Disclaimer

Conflict of interest statement

Conflicts of Interest: TK is a part-time salaried employee, and EK is a former part-time salaried employee of the Center of General Practice in Pirkanmaa. They were not involved in the development of Navigator.

Figures

Figure 1
Figure 1
Dimensions of Navigator and four CS groups.
Figure 2
Figure 2
Psychometric properties of Navigator, data collection methods and statistical analysis used in study, and timeline of data collection process.
Figure 3
Figure 3
Navigator's first three questions for patients, showing VAS.

Similar articles

Cited by

References

    1. National Institute of Aging. National Institutes of Health. US Department of Health and Human Services . Global Health and Aging. Geneva: World Health Organization; 2011. Oct, [2020-07-14]. https://www.who.int/ageing/publications/global_health.pdf.
    1. Multimorbidity: Technical Series on Safer Primary Care. Geneva: World Health Organization; 2016.
    1. GBD 2017 Disease and Injury Incidence and Prevalence Collaborators Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018 Dec 10;392(10159):1789–1858. doi: 10.1016/S0140-6736(18)32279-7. https://linkinghub.elsevier.com/retrieve/pii/S0140-6736(18)32279-7 - DOI - PMC - PubMed
    1. Garin N, Koyanagi A, Chatterji S, Tyrovolas S, Olaya B, Leonardi M, Lara E, Koskinen S, Tobiasz-Adamczyk B, Ayuso-Mateos JL, Haro JM. Global Multimorbidity Patterns: A Cross-Sectional, Population-Based, Multi-Country Study. J Gerontol A Biol Sci Med Sci. 2016 Feb;71(2):205–14. doi: 10.1093/gerona/glv128. doi: 10.1093/gerona/glv128. - DOI - DOI - PMC - PubMed
    1. Smith SM, Soubhi H, Fortin M, Hudon C, O'Dowd T. Managing patients with multimorbidity: systematic review of interventions in primary care and community settings. BMJ. 2012;345:e5205. - PMC - PubMed

LinkOut - more resources