Payers have routinely looked at data from a non-clinical perspective, for intelligence and financial analytics. It's now time for payers to focus on leveraging next-generation analytics solutions. To reap the benefits that “Big Data” promises, analytic solutions must act as the core platform.

In a recent survey, the need to manage new risk levels resulting from the ACA was the number one concern from respondents. The main solution that focus group participants identified for better management and understanding of issues related to the post enrollment risk environment, risk modeling and exchange risk structures: the leveraging of robust analytic solutions, like predictive and comparative analytics tools.

Two national quality initiatives - Star Ratings program from CMS and the Healthcare Effectiveness Data and Information Set (HEDIS) from the National Committee for Quality Assurance (NCQA) are of particular concern in reporting and compliance, and payer analytics can play a significant role.

The comparison of Prescription Drug Plans and Medicare Advantage Plans can be done using the CMS Star Ratings, while payers get help to benchmark and evaluate the quality of their services through HEDIS measures. As CMS increasingly relies on patient outcomes in establishing Medicare reimbursements to payers, they are using payers’ Star Ratings, as a key factor. For payers achieving the highest ratings of 4 or 5 stars, in 2012 they became eligible for bonus payments from CMS.

Payers must also strive to work with providers to get members engaged in managing and improving their own health status. However, to be successful the top three analytics challenges were identified by a panel of experts:

1. Data Integration
2. Data Governance
3. Access to clinical data

Payer Analytics: The Top Three Challenges

Poor data integration is the first of the major challenges. It is of vital importance for both provider and payer organizations to integrate different IT systems and applications that are operating on various, typically proprietary technology platforms. As the payer market continues to consolidate at a faster than expected pace, this active M&A environment makes data integration even more challenging as organizations struggle to make their newly adopted IT systems play together.

Secondly, in terms of ensuring analytics success, how data quality is monitored and maintained along with data governance may eventually present bigger challenges than IT integration. For most payer organizations, their business units are extremely silo-ed, making an enterprise wide, strategic approach to analytics difficult. Effective data governance requires payers to view analytics from a more top down, broad strategic perspective. Better data governance means dismantling the operational and technical siloes - where the issues of data ownership, who gets access to the data, how the data is shared, and how data is leveraged become entangled. This access to data is the third challenge.

Providers and payers often don't know how to work effectively with each other to share clinical data. The involvement of a third-party entity like an HIE becomes a viable option. They can offer extraction of clinical data from both the providers and payers, and provide support for population health and other initiatives by sifting through the data and performing predictive modeling and other analytic methodologies.

The Analytics Challenge that Payers will Face

Traditionally, payers have viewed analytics functions as mere overhead, where the focus has been on basic compliance activities. Payers have yet to embrace robust analytics as a core asset.

It’s becoming critical for payers to improve their reporting capabilities by targeting appropriate measures, and analyzing the data to create actionable insights, not just for their own operations, but for their providers at the point of care as well. As payers adopt the new role as the hub of the healthcare information for diverse constituents throughout the healthcare continuum, it becomes imperative that they expand, and embrace, a more vigorous approach to analytics.