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The Johns Hopkins ACG System models and predicts an individual’s health over time using existing data from medical claims, electronic medical records, and demographics like age and gender.
You gain the insights you need to evaluate and compensate providers, stratify risk, identify patients who would benefit from care management and forecast health care utilization.
For more than 30 years Johns Hopkins statisticians, economists and health care providers have collaborated with users to continually improve the ACG System’s ability to describe population health.
Close to 200 million lives are impacted by the ACG System worldwide as health systems, health organizations, insurance companies, accountable care organizations (ACOs) as well as individual clinics and clinicians rely on the ACG System for health care analytics and insights into population health.
From the beginning, the ACG System has provided a more accurate representation of the health of the general population by transforming health care data into actionable information.
By capturing the morbidity burden of populations, the Johns Hopkins ACG System helps explain and predict how health care resources are delivered and consumed. Click on below categories to explore these different application types in detail, or visit our Applications page to see more application types.
The team at the Johns Hopkins University Center for Population Health Information Technology (CPHIT) and colleagues at the JHU Center for Drug Safety and Effectiveness have published research in BMC Medicine that reports on the development and testing of three measures of ‘high-risk prescription opioid use’ that can be derived from pharmacy claims data.
Medicaid pilot program uses the ACG System to identify patients at high or rising risk. Care coordinators then offer appropriate support to those patients.
Winning project created a new type of health care program that is delivered within a primary care setting
The ACG System is the population-based case mix system with the largest footprint in the world. Professor Jonathan Weiner and Mark Cochran reflect on where the ACG System has been…and where it is going in the April edition of Health Data Management.
The Center for Population Health Information Technology and the ACG SystemTeam recently published a breakthrough article in Medical Care presenting and evaluating the ACG System’s new expanded Geriatric Risk/Frailty Risk metrics for predictive modeling derived from both ‘structured’ and ‘free text’ EHRs.
Medrave Software, along with partner Ensolution (Johns Hopkins’ Nordic distributor) have won a contract to supply their risk stratification solution to 6 hospitals and 40 primary care practices in Norway. The solution, which includes the Johns Hopkins ACG® System, will identify high risk patients and then help to manage them more effectively. Kumar Subramaniam, executive director of Population Health Analytics at Johns Hopkins HealthCare Solutions says: “We are delighted that the ACG System will be used in a third Nordic country. Already in use in Sweden and Iceland, we are pleased that the software is going to be used to help Norwegian doctors and their teams support high risk patients more effectively.”
Professor Jonathan Weiner and Kumar Subramaniam share their thoughts on practical ways for health care executives to tap their data, as well as the specific challenges it poses for population health analytics.
In what its authors believe is “the first article to assess the impact of integrating EMR-based ‘e-prescribing’ information into the more conventional claims database when undertaking risk adjustment and predictive modeling,” a team of researchers –that included Predictive Modeling News Editorial Advisory Board member Jonathan P. Weiner DrPH, Professor of Health Policy & Management and of Health Informatics; Director, Center for Population Health Information Technology (CPHIT); ACG System co-developer and Executive Director of Research– had this to say: “We found that medication fill rates enhance the performance of some base models more than others. These improvements were lower when base models already included diagnostic codes or diagnostic-derived scores, thus signifying the potential usability of medication fill rates for risk adjustment in operational settings that have incomplete diagnostic information.”
Three recently published studies applied the ACG System to pediatric populations. They use the ACG System to measure child health, to examine health care resource use and to gain insight into risk factors associated with repeat tests.