There’s an overwhelming wealth of information to help improve health care outcomes, with often only primitive means for accessing that information.

The chasm is slowly closing between patients’ rights advocates and health plans: insurers are using their heft to influence measures that proactively improve health care quality and lower costs. Patient groups may agree that things are moving in the right direction, but conflicts still exist. One emerging area of health care policy, however, has generated instant alignment among the two often divided groups — the pursuit and utilization of comparative effectiveness research, according to a recent op-ed piece in the Baltimore Sun,

Comparative effectiveness research will gather information on treatments and related outcomes for chronic conditions to create a database of evidence-based intelligence. Physicians can leverage this intelligence to increase the likelihood of positive outcomes and avoid treatments that don’t work.

Concerns have been raised that this approach will lead to cookie-cutter solutions to complex health issues, but that’s not the case. Sophisticated clinical decision support (CDS) systems exist right now to analyze multiple information sources, such as a patient’s medical history and other clinical data, to suggest highly personalized treatment recommendations. Interoperability with comparative effectiveness data as a rules source can add a new, powerful dimension to the analysis and generation of treatment options.

But there’s one major obstacle. As I’ve often seen, it’s not a dearth of information that prevents the incorporation of evidence-based data into medical decision making, but rather it’s an overwhelming wealth of information with often only primitive means for accessing that information. It’s tougher than you think to standardize this information into a single format accessible enough to seamlessly assist with decision making. It takes a high-performance analytics engine with superior thesaurus-like data translation capabilities.

When considering health-care analytics solutions, it’s important to evaluate interoperability with multiple data sources along with the flexibility to add new data sources, such as comparative effectiveness, as they become available. Only CDS systems that can adapt and grow with the rapidly evolving health information landscape will serve patients – and health care providers — best in the long run.

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Richard Noffsinger is CEO of Anvita Health.