March 20 2019 | 0 Comments | 87 reads Average Rating: 3

Agreeing on Outcomes is Good, Achieving Outcomes with Data Analytics is Even Better

by John Pagliuca in Health Analytics

Outcomes-based contracts make plenty of sense, as they are designed to get organizations to work toward common goals. Consider the following publicly disclosed outcomes-based contract terms and it’s easy to see how these deals get payers and pharma companies on the same page:

  • Additional rebate given if the congestive heart failure drug Entresto (sacubitril/valsartan) does not achieve the heart failure admissions reductions it achieved in clinical trials.
  • For the Hyper-cholesterolemia drug Repatha (evolocumab), a full rebate awarded if the patient has a heart attack or stroke.
  • For the diabetes drugs Januvia (sitagliptin) and Janumet (sitagliptin and metformin HCl), rebates given if a specified A1c blood sugar level is not met in the patient population.1

Agreeing on outcomes, however, doesn’t automatically result in achieving said outcomes for payer and pharma companies. To move toward desired outcomes, organizations need to leverage data analytics in order to:

1. Understand how to interact with various patients. Socioeconomic, demographic, clinical, and behavioral data can be combined to categorize patients according to their personas, such as healthy and affluent, balanced adults, high utilizers, quality driven, cost conscious, chronic older adults, and high-cost baby boomers. These groupings can help you gain the insight needed to make their interactions with each persona group more effective.

2. Develop risk scores. Data analytics sheds light on the number of chronic conditions, inpatient utilization, emergency room utilization, inpatient and emergency room paid per-member-per-month metrics, allowing you to assess how likely it is for members of the population to become diagnosed with a condition or how likely members who already have a certain condition are to get worse.

3. Target the subset of the population that presents the greatest impactability. For example, when working with a specific population, you could determine if an increase in the frequency of case management services would actually make a difference in the utilization of the emergency department or the number of inpatient admissions – or if it would merely be a “nice to have”.

4. Access engagement potential. Data analysis can help you determine the probability for compliance vs. non-compliance with various persona groups. For example, you can determine how likely or unlikely certain personas are to be with medication regimens or various other interventions such as educational programs.

5. Determine the type of rewards that would result in compliance. For example, if the targeted population consists primarily of young college graduates ages 21 to 30, it would be safe to assume that these members frequently eat out. So, it could be worthwhile to encourage compliance by offering a gift certificate to a popular restaurant when certain milestones are reached.

This blog is based on a byline recently authored by John Pagliuca titled “Outcomes-Based Contracting: How Pharma Companies and Payers Can Make It Work”.

References:

1. E. Seeley and A. S. Kesselheim, Outcomes-Based Pharmaceutical Contracts: An Answer to High U.S. Drug Spending? The Commonwealth Fund, September 2017. https://www.commonwealthfund.org/publications/issue-briefs/2017/sep/outcomes-based-pharmaceutical-contracts-answer-high-us-drug?redirect_source=/publications/issue-briefs/2017/sep/outcomes-based-contracts-high-drug-spending

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Author
John Pagliuca
Vice President, Life Sciences

John is responsible for leading the commercialization efforts of SCIO’s SaaS solution suite and advanced analytics within the US Life Science vertical. He has more than 15 years of sales, marketing and technology experience, with particular expertise in quantitative analytics and SaaS solutions for Life Sciences.

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