Key Payer Competitive Differentiator: Analytics


Key Payer Competitive DifferentiatorMany health plans are facing uncertainties: the changing health insurance landscape, the speed at which value–based care is approaching, and growing demands from customers, to name a few. But one investment may help executives meet each of these challenges—an investment in analytics. Analytics can be a key payer competitive differentiator setting your organization ahead of the pack. Health plans are data rich, yet those data are not always leveraged to understand what happened and why, or predict what is likely to happen. Health plans that don’t take advantage of their data may risk being disrupted and left behind.

Key findings from the survey and interviews include:

  • Analytics is commonly a critical asset for long–term strategy:
    • 66% agreed analytics is extremely important to their organization as a competitive differentiator.
    • Tangible results (e.g.,
      • increased efficiencies,
      • improved affordability/reduced medical costs,
      • enhanced customer engagement/experience
    • Projected spending on analytics mirrors this view: 33% expect spending on analytics will increase substantially over the next three years.
  • Analytics drivers are often business drivers:
    • Financial goals, such as reducing medical and operating costs, are among the leading drivers of analytics investments among survey respondents.
    • Clinical and customer analytics are priority investments in the next year, particularly in the areas of cost and utilization management and customer experience.
  • Many health plans prefer to buy solutions versus building their own:
    • More than half of survey respondents intend to buy, rent, or use a hybrid approach to acquire solutions to their top analytics priorities in the coming year and over the next three years.
    • Respondents preferred to keep day–to–day analytics activities in house.
  • Data quality, technology, and access to skilled labor are often big barriers to analytics investments and implementation efforts:
    • Data quality is the most commonly cited barrier to health insurance analytics investments and implementation
    • Asset–related barriers follow next
      • tools and technology
      • access to skilled resources
      • funding
  • Analytics functions are generally centralized within an organization:
    • Most survey respondents stated analytics functions related to hardware/infrastructure, data storage, and data preparation are primarily owned by information technology (IT)
    • A closer split of IT and business ownership for the building of reports
    • Majority of respondents stated their analytics functions were generally centralized
Despite many being in a resource–limited environment, interviewers stated that their leadership understands the value that analytics brings to the table and that they support investments. Without enterprise–wide agreement on data definitions and requirements, analytics outputs aren’t trusted and can lead to ineffective insights. Thus, health plans could benefit from investing time and resources to ensure the data they use are valid and meaningful.

Key Payer Competitive Differentiator

How do you become an insight–driven organization? While there is no one–size–fits–all answer, health insurers should consider these initial priorities:

  • Commit leadership: Gain senior leadership alignment on the importance of analytics in driving differentiated business performance in the future. Establish an analytics champion from the senior leadership ranks to sponsor enterprise analytics efforts and work with the leadership team to commit the necessary resources for the journey. Create an enterprise–wide analytics leader, if one is not already in pace, to chart the course and drive day–to–day enterprise analytics forward.
  • Set analytics priorities: Establish the top strategic business priorities for analytics and identify high–value analytics use cases to tackle in the near–term in order to build momentum and excitement around analytics across the company. This should include addressing some foundational reporting/business intelligence needs while also applying advanced analytics techniques to provide new predictive/prescriptive insights to well–trodden business issues.
  • Align the strategies: Align the current data architecture strategy with the future business needs strategy. Introduce new architectural concepts and technologies/tools to support the needs of the business objectives.
  • Address data quality: Establish an enterprise data quality and governance framework to manage the data assets across your organization, helping to create a high level of confidence around the data and the insights derived from it.
  • Organize thoughtfully: Define an operating and organizational model that will best enable analytics within your organization, balancing business intimacy/agility and economies of scale/competency amongst scarce resources. Align governance, processes, and teams to promote reuse and collaboration in building analytics and promoting knowledge sharing.

The Deloitte Center for Health Solutions conducted an online survey of 45 analytics professionals at health plans (with 250,000 or more members) to better understand the priorities and challenges of implementing analytics within a health insurance organization. The Center also conducted 15 interviews with executives at health plans and technology companies to better understand leading practices and lessons learned from insight–driven organizations.

Analytics is more than just technology and tools. An effective insight–driven organization can focus on all of the above areas to begin the data and analytics transformation to drive better insights into executive decision making across the company.

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