Why claims data?
Healthcare in Australia — like the U.S. — is fragmented across many different siloes, including general practice (primary care), specialist care, hospital care, geriatric care and others. This fragmentation of care also makes it extremely difficult to access the type of comprehensive and cohesive health data from patient records that is necessary to underpin strong research.
While other researchers are already doing work with electronic health records (EHRs) and clinical data, we believe that paid claims data represent a different and unique — and more importantly, underutilized — data set.
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For example, one patient may have multiple records scattered across different EHR systems, such as her primary care provider, any specialist she sees and her hospital. In contrast, a paid claims data set aggregates a patient’s entire medical claims history — physician, specialist, pharmacy, hospital, even dental and vision — in one place.
The paid claims data sets we work with are longitudinal and span many years, which is critical for developing predictive models. Further, the fact that paid claims are in standard electronic formats allows us to run the same algorithms across multiple data sets to compare results and determine what may be regional versus universal.
Identifying risk factors for opioid misuse
The first initiative of the research project will be led by Stanford University and will focus on identifying potential risk factors for opioid misuse. Specifically, Stanford researchers will endeavor to develop predictive models that identify three things: 1) opioid misuse disorder risk factors for patients who have no previous abuse history; 2) providers who have risky opioid prescribing patterns; and 3) patients at risk of relapsing into opioid misuse.
We anticipate having initial research results to share from this portion of the opioid project as early as the end of this August.
The second research initiative will be led by Southern Methodist University and will center on developing a predictive analytics model to reduce the rate of hospital readmissions. Hospital readmissions sometimes result from suboptimal patient care, and represent an opportunity to lower costs, improve quality, and increase patient satisfaction all at once.
The Southern Methodist University-led initiative will develop a predictive model to help clinicians identify patients who are more likely to be re-admitted, enabling clinicians to engage with these patients with the objective of preventing future readmissions.