Stratifying the population based on health risk: identification of patient key health risk factors through consensus techniques - Carolina Castagna; Andrew Huff; Aaron Douglas; Matteo Garofano; Massimo Fabi; Richard Hass; Vittorio Maio (BMC Primary Care)
Abstract Background Risk stratification is a population health management approach that classifies patients according to their health risks and projected care needs. This strategy is especially valuable in primary care, where timely interventions for high-risk individuals can lead to better health outcomes, reduced healthcare expenditures, and a more sustainable healthcare system. The goal of this study was to establish expert consensus on the clinical and sociodemographic patient factors that should be incorporated into a primary care risk stratification tool. Methods A multidisciplinary expert panel of 24 healthcare professionals, including primary care providers (PCPs), specialists, and allied health professionals, was convened in June 2024 by Local Health Authority of Parma, Italy. Using the Nominal Group Technique, the panel was asked to define ‘health risk’ and identify contributing factors based on clinical and social relevance and data availability in patients’ PCP electronic medical records. A modified Delphi process, following ACCORD guidelines for consensus-based methods, was conducted in three rounds (July–October 2024) to derive numerical weights for the factors. Survey questions rated the perceived importance of factors using a Likert scale (1 = no importance to 9 = critical importance). Consensus, defined as ≥ 75% agreement among panelists, set each factor’s weight to the median importance rating. Results Health risk was defined as “the likelihood of a progressive deterioration of an individual’s health status due to medical and/or psychosocial-welfare conditions that could lead to hospitalization or death within a year.” A total of 31 clinical and social factors were identified, and consensus about importance was achieved for all factors. Higher-weighted factors included advanced age, excessive polypharmacy, cancer, cognitive impairment, and social-psychological distress, followed by clinical conditions such as renal failure, stroke, and heart failure, and previous hospitalizations and emergency room visits. Conclusions The tool provides a robust framework for population health risk stratification in primary care, aligning with Italy’s healthcare reform goals. Future phases will validate the tool’s predictive performance using patient-level PCP data and assess its implications for policy and practice.