HealthCare Complexity Index (HCIn): A Clinical Decision-Making Tool for Primary Care

Background: Population ageing, multimorbidity, and chronic conditions have increased the demand for person-centred care in Primary Health Care. Current stratification systems rely on diagnoses or resource utilization and fail to capture the multidimensional nature of care complexity, which also depends on functional, social, environmental factors. Objective: To develop a HealthCare Complexity Index using routinely collected electronic health record (EHR) data and the Person-Centred Care Knowledge Model. Methodology: A retrospective, cross-sectional observational study was conducted in Primary Health Care in the Community of Madrid (Spain). Deductive Care Methodology was used to identify functional assessment variables associated with four care dimensions: vulnerability, risk, etiology, and signs/symptoms. A multidisciplinary expert panel selected and validated the variables through iterative consensus rounds. A weighted scoring algorithm, the Care Complexity Index, was designed to quantify care complexity on a 0–100 scale. Relationships between the selected variables and NANDA-I nursing diagnoses were established and validated by an expert in standardized nursing languages. Results: 27 assessment variables were identified and classified into the 4 dimensions of the care model. These variables were integrated into a weighted complexity index with five severity levels ranging from low to critical complexity. Furthermore, 48 NANDA-I nursing diagnoses were linked to the selected variables. Conclusions: The HealthCare Complexity Index provides a standardized, person-centred approach for estimating care complexity using routinely available (EHR) data. It may support early identification of individuals with complex care needs, facilitate individualized care planning, and strengthen clinical decision-making. Future studies should validate its predictive performance and implementation in other clinical practice.