Objectives: The objective of this study was to explore the phenomenon and determinants of healthcare service utilization in Chinese older adults with multimorbidity.
Methods: We adopted a mixed-methods explanatory design from July 2022 to May 2023. The quantitative research was a social network analysis to explore the phenomenon of healthcare service utilization in target participants. The quantitative results were further interpreted as the participant's propensity for healthcare services and the potential for information sharing between healthcare providers through shared patients. Logistic regression was conducted to identify individual determinants for healthcare service utilization. The quantitative research was followed by qualitative interviews with stakeholders to deeply understand the phenomenon of interest from the individual, healthcare system, and societal perspectives.
Results: We recruited 321 participants for the quantitative study. They preferred using medication services from primary healthcare providers, pharmacists at private pharmacies, and hospital specialists, and preferred using other services from hospital specialists. Dense relationships arose from shared patients among healthcare providers across various professions and settings, making it possible to share patient information. Primary healthcare providers were particularly important in the process, as they were closely related to others through patient sharing. Health status was identified through logistic regression and qualitative interviews as an individual determinant for healthcare service utilization. More determinants were explored in qualitative interviews with 30 stakeholders, including trust, resource allocation, healthcare accessibility, medical treatment process, and healthcare awareness.
Discussion: Strategies should be proposed to intervene with patients' nonoptimal propensity toward healthcare services and promote information sharing among healthcare providers.
Keywords: Descriptive qualitative research; Health service utilization; Multiple chronic conditions; Patient sharing; Social network analysis.
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