Organizations that manage population health are only as good as their data. And as more of the healthcare industry moves to an ACO model, the limits of clinical data are becoming more apparent-it’s not standardized, it doesn’t go through a rigorous review for accuracy, and it’s not easily sharable across disparate systems. It’s also limited in terms of how comprehensive it is-for example, while it might list the medications prescribed, it won’t necessarily be able to tell you whether those prescriptions were ever filled or refilled.
At the same time, the value of claims data and the healthcare information it contains is more evident than ever-especially for managing population health care and cost.
Claims data’s strengths almost perfectly counter clinical data’s limits. Diagnosis and procedure codes are standardized nationwide-and with ICD-10 set to take effect in October 2014, claims are poised to become the most complete, specific form of health records available. There are also multiple layers of scrutiny baked into coding and claims processing. Coders, providers, and sophisticated clearinghouses that provide automated claims scrubbing all have a stake in ensuring that claims are accurate and comprehensive, and each goes to great lengths to continually improve accuracy before claims are even submitted.
This front-end scrutiny-the careful translation of clinical documentation, the claims passing through scrubs and edits so that any errors can be immediately corrected-results in comprehensive, accurate data that supports making clinical decisions about managing or tracking a particular population’s health, as opposed to data that first has to be manually reviewed, corrected, and standardized.
Claims data is also easy to share across an entire organization, no matter how large the organization is or will become in the future. This is partly due to claims data being standardized, and partly because there has always been a powerful incentive to efficiently transmit and exchange this information: it’s how providers get paid.
For ACOs, claims data delivers additional benefits: it includes the specific dollar amounts billed and paid for each visit or procedure, which is invaluable insight if you’re trying to figure out how much to budget for individual patients or segments of your patient population. Claims data can also be used to determine whether patients are getting the tests needed to manage certain diseases or therapy, and whether they’re following up with providers about preventative or ongoing care. Knowing the most common diagnoses or procedures is obviously important-but claims data is the only way to see how those realities translate into financial obligation, rewards, and risks.
This is not to say that clinical data has no value for ACOs-far from it. But its value lies in using it to bolster claims data, rather than using it as the primary data source for population-health analysis. EHR data can reveal additional patient health information. For example, EHRs may include “lifestyle” data points such as tobacco and alcohol use, as well as existing conditions or past occurrences that can affect patients’ health or quality of life now or in the future, but which aren’t necessarily treated or addressed in detail on every office visit. Patients’ lifestyle information can add an actionable layer of insight to the already highly specific data embedded in their healthcare claims.
Clinical data also provides “health context”-information that enhances the picture provided by claims data. Claims data won’t include information such as vital signs, weight, or non-prescription medications that patients take. That’s valuable information-and provides additional insight into patient (and population) health patterns and trends-but it’s best used to supplement the standardized claims data that tracks patients’ diagnoses and procedures across all providers.
Claims data will always be the broadest in scope. It will always be the most standardized. And because it includes DME and home healthcare claims, the visibility and insight it provides extends beyond office visits and encounters.
With these considerations in mind, it’s not surprising that organizations are now seeking out new ways to access and integrate claims data into their population health research and management efforts-and giving it greater prominence. For example, the Mayo Clinic recently teamed up with Optum Labs to form a new research alliance. Mayo has clinical data on five million patients-Optum has claims data on 149 million.
ACOs continue to be at the forefront of the shift toward pay-for-quality. Understanding what’s being billed (and paid) and augmenting that with additional clinical data on outcomes is the most natural way to connect the disparate dots along the entire continuum of care. Merely knowing the outcomes is far less valuable without a comprehensive, standardized record of what led to them-and positive outcomes are far more difficult to replicate when you aren’t aware of all contributing factors.
The healthcare information embedded in claims data is more than just a solid foundation that ACOs can build on to track and drive improvements in population health-it’s the most accurate, standardized, and comprehensive source of patient health information available nationwide. To leverage it, you’ll need sophisticated, solid analytical capabilities, and gaining those capabilities doesn’t have to be financially painful or operationally disruptive. Today’s cloud-based analytics tools continue to become ever more robust-vendor-agnostic, user-friendly, cost-effective, and fully interoperable across disparate systems.
Doug Fielding is the vice president of product strategy at ZirMed.