A Statistical Framework for Person-centered Analysis of Digital Service Use in Public Health and Social Care

Secondary use of health and social care registry data enables evaluation of service effectiveness. The focus is shifting from organization-based metrics to person-centered, system-level approaches that understand outcomes as the combined result of interacting actors. In this context, digital service use offers an important lens for understanding accessibility, participation, and equity within public services.The aim of this study is to develop an interpretable analysis approach that uses a pre-existing person-centered, registry-based data model to identify individual variables associated with clients' digital service use.This registry-based study in a Finnish wellbeing services county applied a person-centered data model linking variables across health care, social services, and client experience. Digital service use was analyzed across 17 reason-for-encounter groups defined by the International Classification of Primary Care (ICPC) and one system-generated supplementary category arising from the dataset's internal structure. Subgroup analysis covered single and paired variables. Effect size was quantified using lift values from Market Basket Analysis, and generalizability was assessed aggregating uncorrected directional test outcomes across ICPC groups.We developed a subgroup-based dual-loop analysis method to identify variable-specific associations across 18 ICPC categories. The method showed that digital service use was most common among younger individuals, women, and urban residents. Low service need and mental-health or family-center-related variables were associated with a higher likelihood of choosing a digital channel, whereas chronic illnesses, intensive care needs, and continuity of care were associated with a lower likelihood of digital channel selection.Registry data and a person-centered model reveal how individual variables influence digital service choices. The method enabled interpretable insights into single variables and their combinations. The findings support more equitable digital services and emphasize the need to tailor solutions to meet diverse client needs.