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AMGA’s Anceta Collaborative Data Warehouse illuminates the relation­ships between process of care, outcomes, and cost, enabling participating medical groups to enhance quality, safety and cost-efficiency. Anceta, a subsidiary of the American Medical Group Association (AMGA), facilitates data-driven collaboration among multi-specialty medical groups across the nation, based on detailed comparative data and meaningful benchmarks.

Anceta extends AMGA’s tradition of collaborative quality improvement and sharing best practices among AMGA member medical groups. Quality and cost-efficiency are at the top of the national health care agenda. Value-based purchasing and comparative effectiveness are now front and center.

Unlike other benchmarking initiatives that provide drill-down on a table of static “outcome” measures, Anceta analytics focus on controllable aspects of the process of care—medications prescribed, patterns of visits and referrals, patient outreach activities, and patients’ interactions with health coaches, educators, and other members of the care team.

Anceta facilitates data-driven collaboration among participating medical groups, within a state-of-the-art, HIPAA-compliant, and highly secure environment. We high­light opportunities for improvement, identifying and disseminating best practices, and tracking the results of changes made as part of a process improvement cycle.

Anceta gives participating medical groups the tools to demonstrate value to payors and employers by showing how their processes and outcomes stack up against the best in the nation. At the national level, AMGA will use Anceta data to demonstrate the benefits of patient-centered, team-based care.

Anceta has partnered with Humedica, a Boston-based, next generation clinical informatics firm, to aggregate and analyze detailed com­parative longitudinal data among participating AMGA member medical groups. Humedica’s innovative solutions connect processes, outcomes, and cost-efficiency, so you can learn how medical groups achieve the best outcomes at the lowest possible cost.

We obtain detailed data with minimal impact on each medical group’s IT shop. We integrate available claims, clinical detail from EHR and e-prescribing systems, and data about important process steps from systems like appointment scheduling and patient registries. We apply standardized costs to provide a relative measure of resource efficiency, and we selectively map each group’s data to a robust ontology.

We enable reliable, apples-to-apples comparisons, based on identifying comparable cohorts of patients in the aggregate data, a “precision match” for each physician’s patient population. We develop consensus methods for patient attribution and other “denominator” issues, to ensure that the data are credible to individual physicians and valuable for internal QI initiatives.