Providers and consumers of behavioral health services hailed the passage of the Mental Health Parity and Addiction Equity Act (MHPAEA) in 2008 and its full application to private insurance through the Affordable Care Act. While parity requirements are now in place, there is much debate about the extent to which health insurance companies have fully implemented changes required by the MHPAEA.
Specifically, how does one use data analytics to measure the impact of parity changes that have been implemented?
There are three phases to measuring the impact of compliance with MHPAEA:
- Define the metrics to be used;
- Establish your baseline; and
- Monitor change in the metrics over time.
Defining the Metrics – It is critical to clearly define what metrics will be used to measure parity compliance. Since parity is focused on providing access to behavioral health services comparable to that for medical/surgical services, there are obvious metrics such as per member per month claim costs and utilization rates per 1,000 members (e.g., inpatient admissions/days, routine outpatient visits, residential treatment admissions, etc.) that readily lend themselves to measurement. In addition to these standard cost and utilization measures, denial rates (as a percent of medical necessity reviews), appeal rates (as a percent of denials) and reversal rates (as a percent of appeals) can provide valuable insight into the impact of changes to utilization review policies and procedures for behavioral healthcare.
Setting the Baseline – Once what is to be measured is defined, the baseline experience prior to implementing parity compliance changes must be developed for each metric. Factors requiring consideration in setting the baseline include: (1) the population(s) or lines of business affected by parity compliance changes; (2) the timing of the changes made; and (3) the amount of historical data to be included in the baseline (i.e., one year, 18 months, etc.). Because parity compliance changes will not likely be implemented all at the same time, the baseline period for different metrics may not be the same.
Monitoring Change – In order to monitor change, the data used to develop the metrics must be refreshed periodically. Annual updates are insufficient for most metrics since such frequency does not provide timely feedback to support change management. Monthly updates provide timely feedback but may be too resource intensive to be cost-effective. Based on our experience, quarterly updates represent a reasonable compromise between timeliness and resource intensity. Another factor to consider is changes not related to parity compliance (e.g., the addition or elimination of a population cohort or significant changes in plan design) may impact some/all of your selected metrics. When these types of changes are introduced, care must be given to how the data should be interpreted.
Measurement is critical to assess the impact of any change management process, and proper measurement requires careful preparation and ongoing management to ensure results are meaningful.
This Issue Brief was written by Alex Hutchinson. Alex is a key player of SAE’s Independent Compliance Administrator team and lead for Data Reporting. Alex has more than 25 years of healthcare experience that includes: performance measurement and management; healthcare program assessment and implementation; and financial analysis and risk assessment. Alex has worked with a broad base of clients including health plans, fortune 500 corporations, public sector agencies, hospitals, and physician organizations.