Learn how to put “Big Data” to work
Learn how to put “Big Data” to work in health care at the 2016 Johns Hopkins ACG System International Conference. Meet and interact with ACG System users from across the globe and discover new features of Version 11.1 in person.
REGISTER FOR 2016 ACG SYSTEM CONFERENCE
Held in sunny San Diego, the 2016 ACG System International Conference will take a global view of the ACG System’s applications, and address how the advent of “Big Data” has made tools like the ACG System, the most widely used population-based case-mix system in the world, invaluable to providers and insurers alike.
Health care, like many other industries, has arrived in the Information Age. The passage of the Affordable Care Act and the summary creation of Accountable Care Organizations have led to a new, data-centric era in U.S. health care. Reams of data are being collected at hospitals, doctor’s offices and by insurance groups, in hopes of finding new ways to improve the health of patients and populations.
But more than just amassing data, health care professionals must put it to work. Analytical tools like the ACG System are needed to help transform data into actionable information. With this information, the ACG System helps users worldwide:
- Identify at risk patients
- Assess performance of practice networks
- Appropriately distribute limited financial resources
- Inform decision makers on policy reform
The 2016 ACG System International Conference will bring together users from around the world, to discuss their experiences with each other, and talk with the developers of the ACG System about new features available in Version 11.1.
About The ACG System
The Johns Hopkins Adjusted Clinical Groups® (ACG®) System offers a unique approach to measuring morbidity that improves accuracy and fairness in evaluating provider performance, identifying patients at high risk, forecasting healthcare utilization and setting equitable payment rates.
The ACG System measures the morbidity burden of patient populations based on disease patterns, age and gender. It relies on the diagnostic and/or pharmaceutical code information found in insurance claims or other electronic medical records. This provides the user with a more accurate representation of the morbidity burden of populations, subgroups or individual patients – as a constellation of morbidities, not as individual diseases.