How Stakeholders Can Maximize the Value of Real-World Data Analytics

Organizations across the health care continuum are increasingly leveraging real-world data (RWD) and real-world evidence (RWE) to inform strategy and decision-making with the goal of reducing costs and improving patient outcomes.¹ However, payer organizations have lagged behind other players, such as health care providers and pharma companies, in maximizing the potential of RWD.

Payer organizations have a wealth of internal claims data that can be analyzed to better understand patient populations, design improved programs, and establish new initiatives. Now, forward-thinking payers are beginning to take advantage of these resources and many plan to expand their use of analytics in the coming years.

Finding the right resources and tools

Most payers recognize the importance of analytics, but many internal analytics teams are challenged by a lack of time and resources. In fact, 83% of payers believe that analytics are an important part of their strategy, yet only 17% feel that they have adequate skills and technology to effectively leverage data.²

Common barriers include staff constraints, difficulty interpreting complex data, and slow output of the analysis, all of which prohibit payers from maximizing the value of analytics. Because of these challenges, 84% of payer organizations outsource some of their analytical work to outside organizations.³ However, using an effective analytics platform can help payer organizations overcome these barriers, enabling them to conduct effective analytics in-house instead of outsourcing this work.

The first step toward maximizing the potential of analytics is gathering the right data. The most common data source, used by 92% of payer organizations who conduct analytics, is internal claims data. Payers can also use external claims data, as well as other external sources, including electronic medical record and prescription data.

In terms of software, payers should have an end-to-end solution that enables them to build data sets, run analyses, and share their findings all in a single platform. Often, payers utilize separate solutions for analysis and reporting, which results in less comprehensive analytics and less robust insights. The best platforms will be scalable and easy to use even for staff without a technical background, allowing organizations to overcome challenges with staffing and hiring in technical roles.

Use cases for payers

Progressive payer organizations have already started to leverage data analytics to improve processes and save on costs. Analytics can allow payers to gain a better understanding of the needs of their member populations and the barriers they face in accessing care, which can help them design better programs and data-backed initiatives to meet these needs. Among payers who are currently using analytics, a few of the most common use cases are program design and evaluation, service utilization benchmarking, and benefit design.

According to Panalgo’s 2022 Payor Benchmarking Report, 80% of payers who conduct health care analytics use it to inform program design and evaluation. Payers can use analytical tools to statistically model the performance of different care programs, compare patient outcomes pre- and post-program, and create benchmarks for treated versus non-treated patients. Payers can also use analytics to assess individual patients in terms of resource use, adherence, and outcomes, and decide whether certain members should be included in programs.

For example, when creating disease management programs, analytics can first help payers identify patients for inclusion based on certain criteria. Once patients are enrolled, payers can track program success based on metrics including the number of downstream procedures and hospital visits. These insights can allow payers to make more informed decisions about how to allocate resources or where to expand programming, such as clinical staff outreach and information sharing.

Payers can also use analytics for benchmarking and identifying trends in service utilization, including the frequency and volume of high-cost procedures and spend on specific drugs. This type of analysis allows them to discover which clinical areas are costing the health plan the most while also understanding out-of-pocket costs for patients in those areas.

Using these insights, payers can design improved care programs that direct patients away from high-cost options to lower cost procedures or venues of care, such as outpatient instead of inpatient care. By leveraging analytics for service utilization benchmarking, payers can not only cut their own costs but can also reduce patients’ financial burden.

More than half of payers who currently use analytics also leverage these insights for benefit design. When designing benefits programs for employers, payers can use analytics to address unmet medical needs by estimating the expected usage of certain benefits and the average costs associated with them.

This information can help them decide which procedures and services to include in programs, whether to expand certain benefits, and how to establish parameters, such as benefit duration and number of visits. Many employers are increasingly interested in expanding benefits programs to retain top talent, and insights coming from analytics can enable them to do so intelligently and strategically.

The future of data analytics for payers

Some payers have already made major investments in analytics, particularly with the adoption of health care data solutions, and many plan to increase their spend in this area. In 2021, the US health care payer analytics market was valued at $3.2 billion and is expected to expand at a high rate of growth between now and 2030.⁴

By investing in data analytics now, payer organizations can position themselves to compete with other payers by designing effective programs to meet the evolving needs of patients. And, as staffing shortages continue to threaten health care, using the right tools will allow payers to up-level their analytics teams and optimize their resources, while keeping the analytical work in-house.

Despite the use cases described earlier, there is still much room for payers to expand their usage of analytics. RWD holds unlimited potential for generating insights that can lead to real change in health care. It’s time for payers to embrace this opportunity and take action.

About the Author

Matthew Marshall is a solutions engineer at Panalgo.

References

1. Real-World Evidence. U.S. Food & Drug Administration. https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence
2. Healthcare Payers Are Keen on Analytics, But Feel Unprepared. Health IT Analytics. https://healthitanalytics.com/news/healthcare-payers-are-keen-on-analytics-but-feel-unprepared
3. 2022 Panalgo Payor Benchmarking Report. Panalgo. https://info.panalgo.com/panalgo-payorbenchmarkingreport-website
4. U.S. Healthcare Payer Analytics Market Size, Share, And Trend Analysis Report By Analytics Type (Descriptive Analytics), By Component (Software), By Delivery Model, By Application, And Segment Forecasts, 2022 – 2030. Grand View Research. https://www.grandviewresearch.com/industry-analysis/us-healthcare-payer-analytics-market