To help its providers know what laboratory tests to order and when, the Mayo Clinic is leveraging analytics to reduce test overutilization and unnecessary healthcare costs.
Inefficient clinical laboratory test utilization can not only increase costs but can negatively impact patient safety and quality of care, according to Daniel Boettcher, senior programmer and analyst at the Mayo Clinic’s laboratory in Rochester, Minnesota.
However, the Mayo Clinic has embraced clinical laboratory test utilization management to track provider ordering patterns to identify areas where performance can be improved and to prevent providers from ordering tests that don’t benefit patient outcomes.
That’s where analytics come into play—helping to reduce unnecessary testing and improve utilization management by recommending approaches for diagnoses of specific diseases, guiding selection of the correct tests, and assisting in the treatment and monitoring of patient care.
“Over the last four years or so, we’ve been working to transform the way we provide analytics services to the laboratories here,” says Boettcher.
Rather than building a laboratory-focused analytics platform in-house, the Mayo Clinic decided to partner with an outside vendor, because of the work demands that such a project would place on its in-house IT team of analysts, developers and programmers, as well as the complexities involved in such a project. Mayo Medical Laboratories and Mayo’s Department of Laboratory Medicine and Pathology (DLMP), one of the world’s largest clinical laboratories conducting more than 27 million tests annually, selected healthcare analytics company Viewics to serve as their analytics platform.
Among other capabilities, the Viewics Utilization Management solution analyzes physician peer-to-peer test ordering patterns; provides peer-to-peer utilization reports to physicians; and identifies the most expensive tests and those providers with the highest send-out rates.
“With self-service reporting, we are actually putting the data in the hands of the users,” observes Boettcher. “They have direct access to it. Nobody is in their way.”
In addition, the software helps manage laboratory test utilization problem areas, such as eliminating clinically obsolete tests, reducing clinically duplicative testing, and identifying and changing tests with confusing or similar codes.
For those hospitals and health systems looking to implement and use analytics to drive organizational initiatives, Boettcher contends that creating an analytics-driven culture is critical in which the data flowing into an organization is transformed into information that can be used to make actionable decisions.
Boettcher notes that “getting information into the system needs to be managed just as carefully—if not more carefully—than getting information out of the system.” He makes the case that “if we don’t get good quality information put into the system, we can’t get good quality information out of the system.”
“At the Mayo Clinic, it meant taking a strategic approach to creating a culture of analytics,” he concludes, adding that it’s about “ingraining into the laboratory staff and their processes the value of analytics so that it’s part of their everyday, regular workflow and operations.”
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