skip to Main Content

Population Health Comes to the Fore

Population Health Comes To The Fore

While it has been a focus of health plans for years, Population Health is taking on renewed significance with the emergence of the value-based purchasing (VBP) environment. In the past, plans often centered population health on members that posed the greatest risk. Today, population health is looking at all members and seeking to engage them regardless of their relative risk or adversity.

So what has changed?

  • It very much ties to the quality bonus emerging in each line of business. Back in 2010, the Affordable Care Act (ACA) revamped the Star rating program for Medicare Advantage. By any measure, the program has driven huge increases in quality. The measures grade performance across the entire membership base. With the passage of the Medicaid Mega Rule, a similar paradigm will emerge across all state Medicaid programs. If the Exchanges survive, the same type of program will slowly take hold there as well.
  • Health and wellness programs emerged in the commercial world with demands by large employers to reduce the risk of their populations and enhance quality and health among their workers.
  • Technology automation makes it much easier today to focus efforts on the entire membership as opposed to the sickest of the sick.
  • The Centers for Medicare and Medicaid Services (CMS) are more and more recommending a Model of Care for Everyone.

Population Health, too, is morphing into new areas. In the future, it will combine traditional elements and emerging areas:

  • Predictive analytics yielding identification and stratification of member populations will still be important. Various risk tools use algorithms to identify and group members based on risk scores/bands, disease registries, financial utilization, and service utilization. In general, those with the greatest risk (current and future) were identified for engagement. Technology now makes it easier to broaden such predictive analytics to additional data points and complex scenarios. It also provides for expansion to less risky populations through automated interventions.
  • Combining various predictive analytics models with those used for revenue reimbursement (Medicare HCC, Exchange HCC, Medicaid CDPS/MedRX, and more) is the latest trend. These comparisons not only have the capacity to identify other risks but also to help derive greater risk adjustment revenue that may otherwise have been missed.
  • Establishing a reliable and constantly updated disease registry is critical. This can be in conjunction with the predictive analytics tool or separate. Disease management registries and automated interventions have tremendous capacity to increase quality scores by engaging members and providers on disease states and better medication adherence.
  • Personal medication lists gathered during health risk assessments and periodically during transitions of care allow plans to identify potential drug safety interactions, run drug utilization reviews, and track medication adherence.
  • Constantly updated member clinical profiles for medical and pharmacy data points help UM and CM nurses as well as providers understand and identify various risks, disease states and medications.
  • While social determinants again have been a focus for years, study after study indicates that these non-clinical data points (e.g., housing, health literacy, food security, transportation and more) can be as predictive of outcomes as clinical factors. Establishing member social determinant profiles through eligibility data, health risk assessments, census, and other data helps plans further refine outreach approaches.
  • Using traditional NCQA HEDIS and Pharmacy Quality Alliance (PQA) care gaps measures in concert with other risk stratification data is an emerging trend as well. Closing care gaps in risky populations holds great cost-savings and quality outcome potential.
  • Traditionally medical economics analysis at health plans was a retrospective, laborious process. By the time such data was mined, it was largely irrelevant (it came out months late). More and more, real-time medical economics analysis across medical and pharmacy is possible. In this case, cost outlier groups in the area of inpatient utilization, emergency room utilization, outpatient high-cost areas, and pharmacy can be bumped up against predicative analytics data to derive additional data points and risks.
  • Prescriptive Analytics is the newest of frontiers in population health. Once prediction is complete, plans more and more need the “prescription” of the best intervention strategies to address the predicted cost, risk or utilization for a member or groups of members. Previous interventions for the same or similar members or groups of members are interpreted, leading to recommended actions with expected outcomes. At its best, prescriptive analytics offers a menu of appropriate interventions with scaled benefits, costs, quality outcomes (Star results) and return on investment. Over time, plans are able to derive a cost, risk, and outcome matrix from which to assess each future intervention and execute with precision and faith. Plans are able to balance the costs of intervention with reliable outcomes on the cost and quality side.

Suffice it to say that technology continues to mature and creates even greater horizons for population health and seamless engagement of members. Machine learning and artificial intelligence approaches alone will mean prescriptive analytics can constantly be assessed and refined to better member health and improve quality outcomes.

Marc Ryan

Marc S. Ryan serves as MedHOK’s Chief Strategy and Compliance Officer. During his career, Marc has served a number of health plans in executive-level regulatory, compliance, business development, and operations roles. He has launched and operated plans with Medicare, Medicaid, Commercial and Exchange lines of business. Marc was the Secretary of Policy and Management and State Budget Director of Connecticut, where he oversaw all aspects of state budgeting and management. In this role, Marc created the state’s Medicaid and SCHIP managed care programs and oversaw its state employee and retiree health plans. He also created the state’s long-term care continuum program. Marc was nominated by then HHS Secretary Tommy Thompson to serve on a panel of state program experts to advise CMS on aspects of Medicare Part D implementation. He also was nominated by Florida’s Medicaid Secretary to serve on the state’s Medicaid Reform advisory panel.

Marc graduated cum laude from the Edmund A. Walsh School of Foreign Service at Georgetown University with a Bachelor of Science in Foreign Service. He received a Master of Public Administration, specializing in local government management and managed healthcare, from the University of New Haven. He was inducted into Sigma Beta Delta, a national honor society for business, management, and administration.

Back To Top