How To Leverage Your Claims Data To Better Understand Your Medical Costs
November 6, 2023David Stoddard
For most employers who offer health insurance to their employees, medical and prescription drug spending is one of the largest expenses. And it continues to outpace wage and other inflation, which means it has continued to become a larger overall percentage of an employer’s budget. While ever-increasing medical costs signify a potential dilemma for employers, they also represent a real opportunity. To take advantage, employers must work with an advisor who truly understands claims analytics.
Claims analytics uses healthcare data to identify trends and patterns that can be used to improve the cost, quality, and efficiency of care. It can also be used to assess the overall performance of health plans, providers, and other stakeholders within the healthcare system. To leverage this information, employers should work with an advisor to analyze the following:
Overall Plan Performance
Plan performance can be defined as how actual claims compare to expected (or budgeted), both in total and on a per employee or per member basis. Additionally, per employee or per member year-over-year changes (trends) can measure plan performance. If claims and trends are significantly different than what is expected, this should be the “canary in the coal mine.” It then becomes imperative to drill down further and identify the causes of this deviation. If, on the other hand, claims and trends are within a reasonable range of what is expected, it means claims behaved as expected; however, it does not mean there are no opportunities to dissect the data to determine if there are inefficiencies that can be addressed.
Identifying Actual And Expected High-cost Plan Participants
High-cost claimants are typically the largest driver of plan spend. For an average plan, various rules of thumb say approximately 80% of claims will be incurred by 20% of the plan population, or 50% of claims will be incurred by 5% of the plan population. To understand these high-cost drivers, one can isolate the top 5% or 10% of plan participants (based on the highest cost in a given year) and drill down on disease/diagnosis, site of service (i.e., hospital system), or geographical area to determine if and how these participants are similar to each other. This analysis can be performed on actual high-cost claimants from the previous 12 months (retrospectively) and on expected future high-cost claimants (prospectively), assuming the employer is working with an advisor or claims analytics engine that provides such a forecast. Once the different variables are analyzed, one can begin to isolate specific issues underlying the plan to provide the high-cost claimants with additional support and resources to improve health outcomes and reduce future costs.
Identifying Chronic Conditions
Chronic conditions can also be a significant driver of plan spending. A chronic condition is a diagnosis or disease a plan participant will have for multiple years. Costs associated with chronic conditions tend to be somewhat predictable in the aggregate, though certain events can trigger higher than expected claims. Similar to how a plan can analyze its high-cost claimants, a group can also identify the most common chronic conditions among participants (in terms of total plan spend) and then implement disease management programs or other point solutions to help those members manage their conditions effectively, which can reduce cost and improve population health. It is typically worthwhile to analyze disease states by specific variables (e.g., age, gender, area, etc.) to most effectively manage these conditions, as there is no one-size-fits-all approach.
Monitoring Specific Plan Cohorts
Once high-cost claimants and members with chronic conditions are identified, the final piece of the puzzle is to sort the claims data by other, less obvious cohorts. There is no specific variable to sort cohorts; the groupings will vary depending on the specific plan population. There are various statistical methods one can employ, including cluster analysis. Clustering will create cohorts based on variables (e.g., age, gender, area, site of service, etc.), with each participant being assigned a specific cohort based on a similarity score to other members within a cohort. While the statistical process behind this type of analysis is much more complex than the other topics discussed in this article, the results are not, with the goal being to better understand and identify underlying inefficiencies and opportunities within a plan to reduce costs and improve outcomes.
Claims data can be a valuable tool for understanding an employer group’s medical costs and making informed decisions about health benefits. By reviewing, analyzing, and understanding medical claims data, employers can identify savings opportunities, improve overall health, and avoid unnecessary medical expenses.