Leveraging clinical data to improve patient care and the bottom line

As accountable care organizations (ACOs) and pay-for-performance reimbursement models take on greater prominence, the ability to leverage clinical data is emerging as a key tool in reducing costs and improving patient outcomes. While both clinical and claims data have valid uses in the industry, robust and complete clinical data can paint a fuller picture of the health of a given patient population.

The challenge lies in accessing and analyzing clinical data. Although it can help to improve patient outcomes — and outcomes-based reimbursement — clinical data is more difficult to obtain. Still, those organizations that take steps to leverage clinical data will be poised for greater success than those that rely on claims data alone.

The strengths and weaknesses of claims data

Claims data will remain a key part of revenue cycle management processes, despite the fact that it cannot provide the same level of detail about a patient’s true health status that clinical data can. Claims data will show practices the kinds of services their patients are receiving throughout the continuum of care and will also reflect the costs of care to payers.  

However, claims data falls short in three important ways. First — and most significantly — is that it does not measure patient outcomes. For example, claims data might reveal that an A1c lab test was ordered for a diabetic patient, but it will not reveal the results of the test. With healthcare’s growing emphasis on pay-for-performance, the ability to monitor outcomes must play a key role in reimbursement.

The second problem is the significant time lags incurred because health insurance claims often take months to complete. In many ways, using claims data is like looking in the rearview mirror while driving a car instead of out the front window to determine how best to proceed. Imagine the “accidents” that could occur.

Finally, claims data is biased. Those who generate claims want to maximize billing, yet often must work within the confines of bundled and non-covered services. As a result, claims data often provides a skewed vision of actual clinical status. Take, for example, a patient with obesity who visits their physician for high blood pressure. Since there is no code for obesity, the claims data will not provide a full view of the patient’s health. The bottom line is that claims data is not designed for clinical purposes, so it cannot serve as an effective driver for healthcare transformation.

Clinical data: A better solution

A more effective solution for driving costs down and improving patient care lies in clinical data from the electronic health record (EHR). This is true for three reasons:

1)    EHR data captures all outcomes from an individual patient’s treatment.

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