Health care costs are rising these days. In fact, it seems everything is more expensive, impacting your bottom line. But there are actions you can take to cut those costs and maintain the plan’s maximum benefit to your employees.
Take data mining, for instance. Done effectively, says Tim Sullivan, director of medical informatics and performance metrics for Care Choices in Farmington Hills, this process can help companies implement fact-based solutions that can become cost-effective bottom-line management tools. That includes cutting health care costs and suggesting more appropriate medical treatments.
Smart Business spoke to Sullivan about data mining and the numerous benefits it can provide.
What exactly is data mining and how is it useful to a company?
Data mining is the process of analyzing data and summarizing it into useful information to identify patterns or relationships. It gives an employer and/or health plan the ability to summarize data in order to create predictive models that can assist an employer or plan sponsor in implementing fact-based solutions that, when implemented, become cost-effective bottom-line management tools.
Data mining allows a user to analyze data from many different dimensions and angles, and identify correlations and patterns among dozens of data fields. It results in useful information that can be used to cut health care costs, suggest more appropriate medical treatments, predict medical outcomes, and assist the plan administrator in designing more effective benefits among many users.
For example, an employer may want to use data mining to respond to human resource/benefit questions like how much emergency room visits cost the plan. By evaluating data, the employer may find that the benefit design in place encourages ER visits through lower co-pays than physician office visits or the urgent care center. In this case, data mining can be used to restructure the benefit to direct high-cost care to a lower-cost setting.
How long has data mining been around?
Data mining has been around since the 1980s but has become more popular in the 1990s and into the present due to several factors mainly, the ability of personal computers to better handle large amounts of data. The creation of more advanced software applications such as SAS has also aided in the popularity surge.
Increases in health care costs and complexity in health care as a result of advances in medicine, pharmacy and technology that were experienced in the late 1990s and early 2000s forced employers to begin understanding how their health care dollars were being spent, how they could maximize their budgets, and how they could improve employees’ productivity and design benefits that would maximize those benefit dollars.
Finally, health plans are providing more integrated care management programs such as disease management and health coaching than ever before. Data mining tools are utilized by many health plans to identify at-risk patients and intervene with care management programs prior to the member becoming a high-cost one.
Is there one better way of conducting data mining over another?
The basics of data mining are that you have a database and a tool to query the database and a way to present the data in a useful format. The database is where you collect and store data from various sources (health plans, pharmacy benefit managers, workers’ compensation carriers, disease management firms, wellness programs). There are many tools that can either build the database, query the database, present the data, or do all of these tasks, such as SAS, SPSS, ACCESS or Excel.
Data mining can be accomplished by either looking at all of the data or by taking a subset of it. One can take a random sample of the population to create a smaller subset of the entire population in which the results can then be extrapolated to the overall population. The random-sampling methodology can be beneficial if your entire population is so large that it would impact the analysis run-time negatively.
Can a company conduct data mining itself, or should it rely on an outside party? What are the advantages/disadvantages to this?
Skills required for data mining and obtaining useful information from the mountains of data available can be compared to the same skills required of a detective. To conduct data mining, you need a database that has significant and detailed historical data as well as a tool to query the database. Also, it’s necessary to understand the relationship of the data, to have the ability to understand and manipulate it, and to have some knowledge of statistical methodologies if you intend to compile statistical analyses. Most businesses don’t have in-house expertise to do this.
TIM SULLIVAN is director of medical informatics and performance metrics for Care Choices. Reach him at (248) 848-2140.