A systematic review of risk stratification tools internationally used in primary care settings

• Main Authors: Shelley‐Ann M. Girwar, Robert Jabroer, Marta Fiocco, Stephen P. Sutch, Mattijs E. Numans, Marc A. Bruijnzeels
• Format: Article
• Language: English
• Published: Wiley 2021-09-01
• Series: Health Science Reports
• Subjects: population health management; primary healthcare; risk assessment
• Online Access: https://doi.org/10.1002/hsr2.329

Abstract Background and Aims In our current healthcare situation, burden on healthcare services is increasing, with higher costs and increased utilization. Structured population health management has been developed as an approach to balance quality with increasing costs. This approach identifies sub‐populations with comparable health risks, to tailor interventions for those that will benefit the most. Worldwide, the use of routine healthcare data extracted from electronic health registries for risk stratification approaches is increasing. Different risk stratification tools are used on different levels of the healthcare continuum. In this systematic literature review, we aimed to explore which tools are used in primary healthcare settings and assess their performance. Methods We performed a systematic literature review of studies applying risk stratification tools with health outcomes in primary care populations. Studies in Organisation for Economic Co‐operation and Development countries published in English‐language journals were included. Search engines were utilized with keywords, for example, “primary care,” “risk stratification,” and “model.” Risk stratification tools were compared based on different measures: area under the curve (AUC) and C‐statistics for dichotomous outcomes and R2 for continuous outcomes. Results The search provided 4718 articles. Specific election criteria such as primary care populations, generic health utilization outcomes, and routinely collected data sources identified 61 articles, reporting on 31 different models. The three most frequently applied models were the Adjusted Clinical Groups (ACG, n = 23), the Charlson Comorbidity Index (CCI, n = 19), and the Hierarchical Condition Categories (HCC, n = 7). Most AUC and C‐statistic values were above 0.7, with ACG showing slightly improved scores compared with the CCI and HCC (typically between 0.6 and 0.7). Conclusion Based on statistical performance, the validity of the ACG was the highest, followed by the CCI and the HCC. The ACG also appeared to be the most flexible, with the use of different international coding systems and measuring a wider variety of health outcomes.