Patients crave person centricity and want to be treated as human beings with individual needs and preferences. Furthermore, previous research recognizes the value of person centricity which can be defined as “tailoring offerings to people’s unique preferences, wishes, and needs”. Specifically, person centricity enhances patients’ health outcomes and satisfaction, but also healthcare efficiency and effectiveness. Despite the importance of person centricity, it is currently unclear how the concept could be measured, which hampers its implementation. As marketing researchers have a long tradition of putting customers first by taking their unique preferences and needs into consideration and also have experience with the development of measurement instruments, insights from this discipline may facilitate the transition to person centricity in healthcare. The aim of this paper is to develop a Person Centricity Index (PCI) which provides researchers, healthcare providers and policy makers a relevant and practical instrument for measuring and managing person centricity. An index summarizes a multitude of indicators thereby providing decision makers with an integrated and more informative overview than would otherwise be attainable. The PCI is a weighted composite of relevant indicators that rate the level of person centricity. To develop the PCI, we follow a rigorous approach based on four steps: (1) content specification which implies defining the content domain; (2) indicator specification which means generating an initial pool of indicators; (3) indicator weights which refers to determining the relative importance of each indicator; (4) and nomological validation which means that we linked the PCI to relevant concepts. For the first two steps (content and indicator specification), we conducted an extensive literature review and in-depth interviews (n=). In addition, we assessed content validity and indicator clarity using an expert panel (n=20). These steps resulted in a total of 44 items. We developed a questionnaire based on these items, the outcome variables (satisfaction, loyalty, trust, and stress) and demographic variables. For the next steps (indicator weights and nomological validation), data will be collected in collaboration with a health insurance company in January 2023. The questionnaire will be sent to 20,000 patients who were admitted to a hospital for at least one night during the last year. We aim for a sample size of 1,000 respondents. To analyze the data, we will use Partial Least Squares Structural Equation Modeling (PLS-SEM). Overall, this paper aims to contribute to theory and practice in multiple ways. First, researchers can use the PCI in future empirical studies about person-centric healthcare. Second, healthcare organizations can use the PCI for performance assessment since it complements existing healthcare performance measures (e.g., number of medical errors). Third, policy makers can use the PCI for assessing and benchmarking healthcare organizations.
PIPERS et al. (Sun,) studied this question.