Leveraging clinical data to succeed in value-based care | Geneia

Leveraging Clinical Data to Succeed in Value-Based Care

October 08, 2019
Fred Rahmanian, Chief Analytics and Technology Officer

Each type of data - clinical and claims - tells part of a story - the story of patient care

If you work in healthcare, you’ve heard the term “value-based care.” And you probably interact regularly with claims and clinical data. Each type of data tells part of a story – the story of patient care. Value-based care has brought about a turning point in which health plans and providers are shifting their focus from the volume of services provided to the quality and outcomes of the care itself

Historically, claims data was the perfect means for measuring volume in the fee-for-service world: what was done (procedure), why it was done (diagnosis), when it was done (date of service), who provided the service (specific provider), and where the service was provided (site of service).

Claims data, however, does not tell you what happened beyond a medication being paid for, nor the results of a laboratory test. This is where clinical data becomes critical. Clinical data includes a broader range of sources, such as information captured in electronic health records (EHRs), socioeconomic data, and patient-reported responses to clinical assessments and surveys. Clinical data also provides critical insights into the outcomes of services rendered – such as laboratory results, the findings of diagnostic imaging, and the success or failure of medical treatments. In short, clinical data provides the color to the otherwise black-and-white picture drawn by claims data. 

Value-based care means we need to find ways to objectively measure care to determine the quality of care provided. The structured nature of claims data makes it the ideal candidate for analysis. It’s easy to identify subsets of the population based on age, gender and medical conditions. These subsets are the basis for the vast array of quality measures used in nearly every value-based care model. Claims data can also be leveraged to measure efficiency – the care patterns and performance of providers – where providers are referring their patients for specialty care, and whether they are prescribing generic medications whenever possible.

There are, however, limitations with using claims data to evaluate population health and succeed in value-based care arrangements. The most glaring limitation is the inherent lag time in claims processing – generally 30-90 days from the time a service was provided. This means never getting real-time insight into your population. Another major limitation is that claims data likely lacks a great deal of historical information about patients – information about care they have received in the past, before they were covered by their current health plan. While claims data presents a realistic depiction of care, it is likely incomplete.

Clinical data, on the other hand, sheds light on the past, present and future of a patient’s care and overall health – demographics, patient-reported medical and family history, current conditions and medications, as well as the agreed-upon plan of care for the future.

However, clinical data tends to be far less discrete than claims data. Information is captured in EHRs in a wide variety of ways, and may or may not be structured in such a way that it can be easily analyzed or extracted. It also may not be completely indicative of the care a patient receives – medications may be ordered but never filled, lab tests may be ordered but never performed, follow-up appointments may be scheduled but never kept. While clinical data certainly provides a wide depth and breadth of information, it doesn’t tell the entire story.  

So what are some ways claims data and clinical data can be used together to achieve the goals of value-based care?

Ways claims data and clinical data can be used together to achieve the goals of value-based care

  • Identify and close gaps in care – laboratory results and vital signs can be used to close outcomes-based quality measures, such as high blood pressure screening or diabetes testing
  • Identify and stratify populations by risk – certain clinical markers, such as a positive family history of cancer, can be used to stratify the population into risk categories for targeted care management
  • Reduce network leakage – understanding provider referral patterns can help steer services within network
  • Engage and improve patient experience – incorporating administrative data, such as scheduling information, into your population health management strategy can drive more efficient and effective patient engagement

Health plans and provider organizations can leverage clinical and claims data to be successful in value-based care arrangements.

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