Background: Knowledge management in global health is essential in response to ageing populations, increasing morbidity, and rising expectations of care. The Knowledge Model about Person Care promotes health systems organized around individuals rather than diseases. Within this framework, vulnerability—understood as the risk of physical or moral harm—can be assessed through Basic Care Variables (BVC) that determine individuals’ need and capacity for self-care. Primary care health information systems provide an opportunity to operationalize these variables at the population level. Methods: This study applies Deductive Methodology to extrapolate community-level health indicator data to population-level vulnerability measures. Using the electronic Primary Care Objective Monitoring tool (e-SOAP) from the Community of Madrid, we analyzed health and social care indicators derived from primary care clinical information systems. The mathematical architecture of selected indicators was used as an approximation to Model-Based Systems Engineering. Results: Primary care indicators enabled the identification and aggregation of community-level data reflecting BCV. The system supports multi-level analysis (regional, managerial, institutional, and professional), facilitating grouped and anonymized data extraction for future vulnerability assessment. Conclusion: A minimum set of primary care indicators can effectively estimate community vulnerability, supporting person-centred health system management and informed decision-making.

Background: Knowledge management in global health is essential in response to aging populations, increasing morbidity, and rising expectations of care. The Knowledge Model about Person Care promotes health systems organized around individuals rather than diseases. Within this framework, vulnerability—understood as the risk of physical or moral harm—can be assessed through basic care variables (BCV) that determine individuals’ need and capacity for self-care. Primary care health information systems provide an opportunity to operationalize these variables at the population level. Methods: This study applies deductive methodology to extrapolate community-level health indicator data to population-level vulnerability measures. Using the electronic primary care objective monitoring tool (e-SOAP) from the Community of Madrid, we analyzed health and social care indicators derived from primary care clinical information systems. The mathematical architecture of selected indicators was used as an approximation to model-based systems engineering. Results: Primary care indicators enabled the identification and aggregation of community-level data reflecting BCVs. The system supports multi-level analysis (regional, managerial, institutional, and professional), facilitating grouped and anonymized data extraction for future vulnerability assessment. Conclusions: A minimum set of primary care indicators can effectively estimate community vulnerability, supporting person-centered health system management and informed decision-making.

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.