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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.
“Bridging the Continuum of Care”
April 22-25, 2018
The Westin Riverwalk, San Antonio, Texas
Join the Johns Hopkins ACG System team and meet with hundreds of system users from around the world at the ACG System 2018 International Conference, along the beautiful Riverwalk, in San Antonio, Texas.
Arden & GEM CSU were winners at the Health Business Awards this month in the ‘Patient Data’ category for their work to develop a Risk Stratification solution which helps organizations predict behaviors such as unplanned admissions, and intervene earlier to improve patient care and reduce the burden on emergency services.
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.”
Working with claims and EHR data from a large integrated provider system, the ACG System team assessed the “credibility” of risk scores derived from several EHR-only database providers.
These new data sources, increasingly used in the United States, have great potential as a source of information for the risk measurement and predictive modeling community. This published study represents a first step in understanding the risk measurement properties of these new streams of data without complimentary claims data.
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.
Highlights include enhancements to the Utilization Profile Report, new reports and export files, expanded modeling and processing options, technical enhancements, documentation improvements, and more.
HealthPartners, the largest consumer-governed, nonprofit health care organization in the United States, used the ACG System to develop summary measures to identify and address conditions and factors that have the greatest impact on population health. The method addresses a persistent need in population health measurement for improvement. The article is a joint publication initiative between Preventing Chronic Disease and the National Academy of Medicine.