Big Data

Why You Should Integrate Your Clinical and Claims-Based Data

Somya Gulati
Fri 27 May 2016

data integration 2

As health care transitions from volume- to value-based care, physicians, hospitals or ACOs need actionable quality of data and cost information to manage their patients and their populations. They need a global view of their population and they need to move beyond “big data” and look past the EMR to access encounter data from within and outside the health system, as well as claims information from the payer.

Until recently, technology-enabled efforts to improve population health relied heavily on the use of claims data alone. While there is evidence this approach has merit, there is also a new opportunity to take these efforts to the next level. The increased availability of other types of useful data—namely clinical data from electronic medical records and other systems—can help healthcare organizations fine-tune their analytics. As a result, they can better segment populations, improve quality, increase patient engagement – to successfully address population health needs.

Claim data (payers) and clinical data (EMR) naturally complement one another. Claims data has an unmatched value in a complex and quickly evolving healthcare marketplace because it offers a retrospective look at what actually happened. In addition to revealing health facts about individuals and where to focus population health resources, claims data shows whether prescriptions were filled or if recommended lab tests were completed. Therefore, rather than thinking of clinical data and claims data separately, there’s more power in using them together.

Although data sharing between payers and providers is on the increase, there is still tension in the relationship. Many of the roadblocks happen in doing one-to-one clinical data integration because it requires custom coding, having to write new software for each integration point. The reason: many EMRs have been designed as closed or black box systems; one system doesn’t talk to other systems, making data exchange too big to tackle for some health systems.

While collecting clinical data is a natural first step on the road to value-based care, the integration of that data is often complex, costly and time-consuming.

This is where Datashop comes in the picture. What Datashop does is to get all that information together in one place and at the point of care.

It takes the clinical data and matches it with the claims data under a single interface. The hope is that it will drive down unnecessary tests, such as mammograms, if the provider can see that one has been ordered outside the network, which eventually reduces the cost. And they can see whether a patient has actually filled a prescription.

With this information, physicians can address both the acute and longitudinal health needs of a patient – closing gaps in care, easing care transitions and care planning.  Moreover, because interfacing happens seamlessly, it does not disrupt practice operations or patient care. In a nutshell, it is a win-win because the redundant testing can be avoided and quality in care can be improved. Furthermore, Health systems can obtain a holistic view of patients.

Please enter valid .
Please enter valid .
Please enter valid comment.