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Analytics Improves Insight into PMPM, Reduces Liabilities in Rate-Setting Agreements

In the U.S., Medicaid provides health coverage to more than 68 million low-income men, women, and children, and is funded jointly by states and the federal government. Growing at an unsustainable rate, Medicaid programs have left many states with the challenge of finding new ways to create fiscally stable systems of care that also improve health outcomes.

Oregon established an accountable care model unique to the state composed of coordinated care organizations (CCOs) which are local organizations charged with managing care for members of the Oregon Health Plan—Oregon’s Medicaid program—in addition to finding innovative ways to meet the goals of the Triple Aim: better care, smarter spending, and healthier people. Like all CCOs, Health Share of Oregon required accurate and timely data to support forecasting for rate-setting to remain financially solvent and limit liability in this innovative model. Health Share leveraged analytics to obtain a holistic evaluation of the drivers of per member per month (PMPM) payment performance. Through improved access to this strategic and timely data, Health Share has successfully minimized liability, improved the accuracy of rate-setting utilization data, and reduced analyst time spent compiling complex regulatory reports.


  • Timeliness of rate-setting utilization data improved from two years to just a few months.
  • Identified opportunities to effectively reduce liabilities, helping to ensure ongoing financial viability of the organization.
  • Rapid integration of new member cost data.
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Integrating Data and Analytics into Provider Workflow Improves ACO Performance

Reimbursement rates for an Accountable Care Organization (ACO) are based on the quality composite score from the Physician Quality Reporting System Group Practice Reporting Option, examining best practice preventative care and primary care measures. As a result, ACO participants may receive payment adjustments based on their quality composite performance.

U.S. Medical Management (USMM), a leading provider of home-based primary care services for complex patient populations and managed care clients, also operates a multi-state Medicare Shared Savings Program ACO serving over 23,000 complex or fragile Medicare patients. USMM needed to support its providers in meeting their patients’ necessities, while also ensuring they were providing and documenting appropriate best practice preventative and primary care ACO measures.

USMM turned to its analytics platform and analytics applications, implementing the Community Care Advanced Application to aid its efforts. The analytics platform integrates data from the organization’s EMR, billing system, and external claims data, bringing cross-organizational data into focus and delivering specific, actionable interventions needed to improve performance.

After implementing Community Care, USMM achieved a 90th percentile performance for:

  • Tobacco screening and cessation plan.
  • Clinical depression screening and follow-up plan.

The organization also earned an 80th percentile performance for:

  • Influenza immunization.
  • High blood pressure screening and follow-up plan.
  • Screening for future fall risk.
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Improving Accuracy of Clinical Documentation Positively Impacts Risk Adjustment Factor and HCC Coding

The Hierarchical Condition Category (HCC) risk adjustment model is used by CMS to estimate predicted costs for Medicare beneficiaries, and the results directly impact the reimbursement healthcare organizations receive for patients enrolled in a Medicare Advantage plan. CMS requires that all qualifying conditions be identified each year by provider organizations. Documentation that is linked to a non-specific diagnosis, as well as incomplete documentation, negatively affects reimbursement.

Allina Health, a not-for-profit integrated healthcare delivery system serving Minnesota and western Wisconsin, needed to improve its HCC coding and clinical documentation in order to ensure the correct risk adjustment factor (RAF) was applied to its patients, since failing to do so would jeopardize its reimbursement and result in lower than expected compensation. After identifying opportunities for improvement by comparing its HCC risk adjustment coding data to other organizations and vendor metrics, Allina Health improved clinical documentation precision, medical diagnoses accuracy, and ensured eligible patients are seen each calendar year.


  • 10 percent increase in RAF for the target population in one year.
  • 72 percent relative improvement in four key problem list diagnoses.
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Opportunity Analysis Permits Successful Execution of At-Risk Contracts

Growth in the government payer mix and an increased cost burden to the commercial population, decreases in the private payer population, and programs like the Medicare Shared Services Program, have caused joint ventures, partnerships, and co-branding efforts, better known as at-risk contracts, between payers and providers to increase.

Allina Health has three Integrated Health Partnership (IHP) contracts, an accountable care model that incentivizes healthcare providers to take on more financial accountability for the cost of care for Medicaid patients, which cover approximately 90,000 members. To achieve success in its IHP contracts, and avoid losses, Allina Health needed to reduce healthcare costs while improving patient outcomes and experience.

Allina Health has integrated several data sources, including claims and developed the infrastructure required to perform opportunity analysis. Using data and analytics for opportunity analysis has given Allina Health insight into its IHP patient population, supporting the development of interventions to decrease the total cost of care and improve outcomes.

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