Business intelligence helps Maryland facility thrive with value-based care

Business intelligence helps Maryland facility thrive with value-based care

Business intelligence helps Maryland facility thrive with value-based care
As the healthcare industry looks to adopt alternative payment models based on quality rather than quantity, Western Maryland Health System in Cumberland, Md., has shifted from a fee-for-service to a value-based care model using business intelligence technology.

In the process, the healthcare organization has received $1.3 million in quality-based reimbursements from Maryland’s Health Services Cost Review Commission (HSCRC), which reviews and approves “reasonable” hospital rates and publicly discloses information on the costs and financial performance of hospitals in the state.

At the same time, the health system went from dead last out of 46 Maryland hospitals in terms of in quality-based reimbursement measures to first in the state. Maryland was the first state to be granted a waiver from Medicare rules, giving it flexibility to implement its own quality-based program under the HSCRC.

Colby Lutz, business intelligence analyst for Western Maryland Health System, credits the Diver platform from Dimensional Insight with helping the 205-bed hospital identify ways to reduce costs while improving health outcomes for patients.

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“Previously, we had different pieces of health data in different systems, and couldn’t bring it together to show patient care across the entire healthcare spectrum,” says Lutz. “What having Dimensional Insight has allowed us to do is create a central repository where we can utilize data from each of their own databases, bring it together, perform calculations and output whatever we need on a real-time basis to make real-time changes.”

The data platform enables the health system to make crucial adjustments through analytics that reflect care delivery trends and optimize quality-based reimbursement as well as the overall quality of patient care.

“It’s put us into a position where we can focus more on population health,” observes Lutz. “From the ground up, it was fairly easy to implement. It took us approximately four months to go live from the start of the implementation. It required a little bit of database background so that we could then pull what we needed from the right tables and right systems.

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