Assessment of the Adjusted Clinical Groups system in Dutch primary care using electronic health records: a retrospective cross-sectional study

Abstract

Background Within the Dutch health care system the focus is shifting from a disease oriented approach to a more population based approach. Since every inhabitant in the Netherlands is registered with one general practice, this offers a unique possibility to perform Population Health Management analyses based on general practitioners’ (GP) registries. The Johns Hopkins Adjusted Clinical Groups (ACG) System is an internationally used method for predictive population analyses. The model categorizes individuals based on their complete health profile, taking into account age, gender, diagnoses and medication. However, the ACG system was developed with non-Dutch data. Consequently, for wider implementation in Dutch general practice, the system needs to be validated in the Dutch healthcare setting. In this paper we show the results of the first use of the ACG system on Dutch GP data. The aim of this study is to explore how well the ACG system can distinguish between different levels of GP healthcare utilization. Methods To reach our aim, two variables of the ACG System, the Aggregated Diagnosis Groups (ADG) and the mutually exclusive ACG categories were explored. The population for this pilot analysis consisted of 23,618 persons listed with five participating general practices within one region in the Netherlands. ACG analyses were performed based on historical Electronic Health Records data from 2014 consisting of primary care diagnoses and pharmaceutical data. Logistic regression models were estimated and AUC’s were calculated to explore the diagnostic value of the models including ACGs and ADGs separately with GP healthcare utilization as the dependent variable. The dependent variable was categorized using four different cut-off points: zero, one, two and three visits per year. Results The ACG and ADG models performed as well as models using International Classification of Primary Care chapters, regarding the association with GP utilization. AUC values were between 0.79 and 0.85. These models performed better than the base model (age and gender only) which showed AUC values between 0.64 and 0.71. Conclusion The results of this study show that the ACG system is a useful tool to stratify Dutch primary care populations with GP healthcare utilization as the outcome variable.

Association between Johns Hopkins Adjusted Clinical Groups risk scores and self-reported outcome measures: an observational study among individuals with complex or long-term conditions in Norway

“…Adjusted Clinical Groups (ACGs) system. 7 This system categorises the population into groups with similar expected resource requirements based on a minimum input data requirement of age, sex and registered diagnoses over a specified time period. Another approach for the identification of individuals with the most complex healthcare needs involves the use of self-reported outcome measures (SROMs).…”

“…These tools enable the stratification of the population according to their risk of experiencing adverse outcomes 6. One widely recognised risk stratification software frequently cited in the research literature is the Johns Hopkins Adjusted Clinical Groups (ACGs) system 7. This system categorises the population into groups with similar expected resource requirements based on a minimum input data requirement of age, sex and registered diagnoses over a specified time period.…”

Self, at Home, and Digital if Possible, Drivers to Population Health Management

“…The ACG system has been found to be a useful tool for stratifying Dutch healthcare populations. [5] The value of the data can also be improved by assessing the Kaiser Permanente risk model as the foundation for discussions on shifting to a model of care that emphasizes multimorbidity team work and a digitallybased regional care model.…”