Piloting and Evaluating the Johns Hopkins ACG® System within Republika Srpska

In the second half of 2012, The Agency for Certification, Accreditation and Health Care Quality Improvement of the Republic of Srpska started a pilot-project with Health Services Research and Development Center of Johns Hopkins University, Bloomberg School of Public Health from Baltimore, Maryland, USA.

The Project “Piloting and Evaluating the Johns Hopkins ACG® System within Republika Srpska” introduces the ACG System that will be used for measurement of the co-morbidity of populations in the Republic of Srpska and to calculate expected health care needs. The patient population of different primary health care centres will be adjusted for co-morbidity and the appropriateness of their populations’ use of higher levels of health care services will be evaluated. This will be a pilot study of how the ACG System could be used for more appropriate resource distribution between different Primary Health Centres. The specific objective of this Project is to pilot the ACG System for electronic monitoring of performance of 20 family medicine teams in two health centres (population of 30.000) with adequate IT infrastructure capable for electronic reporting and with FMTs with experience in utilization of existing electronic record (software application).

ACG® System

The ACG System is an approach to measuring the morbidity burden of patients and populations. It relies on diagnostic code information and pharmaceutical data to stratify patients’ morbidity status into 93 distinct groups – Adjusted Clinical Groups (ACGs), thus allowing a more accurate representation of the overall constellation of morbidities. As opposed to systems that base grouping on episodes-of-care, the ACGs account for a person's mix of illnesses that extend across visits, facilities, and providers. The system is based on the premise that accounting for co-morbidities is essential for population-based perspectives because illnesses are well known to cluster both within individuals and populations. It is currently used or being tested in over 12 countries worldwide to improve accuracy and fairness in evaluating provider performance, forecasting healthcare utilization and setting equitable payment rates.