Q4 2024 Trends Focus: Data-Driven Healthcare Cost Management

This issue of Trends also includes a compliance reminder about the recently released final rules for mental health parity under the Mental Health Parity and Addiction Equity Act (MHPAEA).

Key statistics

Segal’s balanced, three-pronged approach to effective healthcare cost management involves managing plan design/networks, vendors and population health, with data analytics being central to supporting each of those cost-management strategies.

Sample Results of Data Analytics for Each of Three Effective Cost-Management Strategies

Source: Segal’s SHAPE data warehouse, 2024

Using data analytics to improve health plan performance and outcomes

Approximately 25 percent of healthcare spending in the U.S. is considered wasteful, according to several studies, including one published in the American Medical Association’s journal JAMA^®. This waste includes costs from duplicate services, variation in treatment costs, inefficiencies in contracting, overtreatment or use of low-value treatment, costs of fraud and abuse, and other administrative complexities.

Waste and the high rate of health plan cost trends highlight the need for plan sponsors to use data analytics. Because one cannot manage what isn’t measured, plan sponsors use data analytics to understand the true drivers of cost, so they can focus their cost-management strategies more efficiently and align them with the changing environment as well as improve participant outcomes.

Data analytic strategies

These are the most common data analytic strategies:

• Financial cost management. Data analytics are used to evaluate plan expenses, understand trend and monitor claims reserves. Plan sponsors often analyze spending patterns to identify areas where costs can be reduced without compromising quality. This can include use of anomaly detection algorithms to identify and prevent fraudulent claims or transactions.
• Risk assessment and management. Plan sponsors can use predictive modeling to identify high-risk participants within a population, which allows for targeted, clinical interventions that aim to reduce future costs by preventing costly complications, hospitalizations and hospital readmissions for the same illness. By identifying those at high risk for disease, emphasis can be placed on early detection and treatment with less invasive and less costly options. Assessing the severity of participants’ conditions will identify those needing significant care management.
• Benefit design optimization. This data analytics strategy examines how participants use different benefits to identify underutilized or overutilized services. The results can be used to design benefit plans that better meet the needs of different participant groups based on usage patterns and preferences.
• Vendor management. Plan sponsors with data analytics capabilities can better hold their vendors and administrators accountable for meeting service and performance guarantees that deliver value. Data analytics can also be used to evaluate the cost-effectiveness of different vendors and negotiate better terms based on the data insights.
• Health and wellness outcomes. Plan sponsors use data analytics to track key performance indicators to measure the effectiveness of benefit programs. The strategy can be used to assess the impact of specific interventions on participant outcomes or financial well-being.
• Participant engagement. Data analytics can analyze participant behavior to understand engagement levels and identify factors that drive engagement. This information can be used to develop incentive programs that increase engagement in wellness programs.
• Regulatory compliance. Plan sponsors use data analytics to identify the financial impact of new regulations, such as the recently issued final mental health parity rules, and confirm their benefit plans are in compliance with regulatory requirements.

By using these strategies, plan sponsors can make data-driven decisions that enhance the value of their benefits program, improve participant satisfaction and manage costs more effectively.

From theory to practice

Most plan sponsors rely on their healthcare vendors to provide data on their plan costs and utilization. These sponsors need to decide if the data analytics offered by their existing healthcare vendors is sufficient.

While healthcare vendors have sophisticated reporting, most plan sponsors use many administrators and reporting is not integrated. Furthermore, vendor reporting may be unlikely to highlight results that are unfavorable to the vendor. Consequently, outsourcing data analytics to an independent plan advocate may make sense, to get a holistic view.

Compliance reminder: Final mental health parity rules establish new standards

The final rules set forth new standards for imposing nonquantitative treatment limitations (NQTLs) on mental health and substance use disorder conditions and require additional data collection and evaluation requirements for compliant NQTL documented comparative analyses. Plan sponsors should evaluate the final rules and determine the impact on future MHPAEA compliance efforts.

Learn more about the final rules in our September 26, 2024 insight.