In healthcare, the phrase “data-driven decision making” is a popular one, meant to describe how organizations integrate objective information to inform and improve all sorts of decisions. However, it’s an admittedly abstract concept.

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Exactly how can healthcare organizations use data to shape better decision-making? How do they sort through the deluge of healthcare data to find the insights that can truly effect change? Let’s see what practical steps we can learn from a Maryland healthcare system that used data-driven decisions to catapult itself from “worst to first” in quality.

Motivation and ingenuity are key to deriving value from data

Western Maryland Health System (WMHS) presents an inspiring case of how data-driven decisions actually happen on the front lines of healthcare. It’s also an example of how motivation and ingenuity are important ingredients in deciphering the meaning and extracting the value of data. And if that motivation involves the very survival of an organization, the ingenuity has a tendency to kick into high gear.

WMHS, a Dimensional Insight customer since 2012, is in the Appalachian Mountains where Maryland, Pennsylvania and West Virginia converge. The health system serves one of the most economically challenged populations in the United States. Not surprisingly, providing effective care while remaining financially viable proved to be a struggle. But this adversity helped drive a reinvention that has ensured WMHS’s continued ability to serve its community.

Maryland has an unusual approach to healthcare because the federal government has granted it a waiver from many of Medicare’s stipulations. Under this arrangement, Maryland healthcare organizations have considerable latitude to find better, more cost-effective ways to deliver care. However, to retain the waiver, together they must perform better than the national average on cost and quality measures.

Data-driven decisions result in catapult to first

To encourage experimentation, Maryland established the Total Patient Revenue (TPR) program, which ties reimbursements, regardless of payer, to a global budget based on patient population. Reimbursement is in turn adjusted, upward or downward, based on an organization’s performance on standardized quality measures. Realizing that survival would only be possible through the disruptive change that comes with a “bet the house” decision, WHMS signed up for the then-voluntary program.

The initial experience was challenging. During 2012, its first year in the TPR program, WMHS lost over $1 million and ranked 46th in quality measure performance – last in the state. However, through inspired leadership, buy-in across the organization and plenty of hard work, WMHS completely turned the situation around within two years, resulting in a $1 million surplus and rising to #1 in the state on quality measure performance and improvement in 2014. It’s certainly an incredible story. At its core, it’s about the integral role that information and data-driven decisions play in transformational change. But even more significantly, WMHS’s experience highlights the power of meaningful information in the hands of those who are in a position to drive change as a result of their observations.

Data-driven decisions as a driver for change

As part of its long-term strategy, WMHS had simultaneously implemented both a new electronic health record (EHR) system and analytics infrastructure to help leverage data for better informed decisions. This is where the ingenuity comes into play. Succeeding in the TPR program hinged on improving performance on quality measures, such as hospitalization rates, readmissions, emergency department utilization, and preventable conditions. However, the feedback loop for these consisted of reports from the state that only became available on a monthly basis, rather than as needed.

WMHS realized that it needed to replicate these measurements in a way that made the results available in time to react. So essentially, immediately. It used the analytics system to approximate the measures as closely as possible using EHR data, and integrate the results on a daily basis with patient encounter-level detail. Having access to this enriched data set was the equivalent to turning on the lights in a pitch-dark room. Opportunities for improvement became clear. Intriguingly, the most important insights didn’t come from sophisticated predictive algorithms. They came from front-line care providers who understood the fundamental processes and goals of healthcare at the most intimate level. Armed with meaningful information, they were quick to pinpoint the greatest opportunities.

Data-driven decisions through the provider’s lens

Tracey Davidson, a WMHS registered nurse and quality manager, embodies what data-driven decision making in healthcare is all about.  Though she isn’t a technologist by training, Tracey is adept at connecting data with the possibilities of making things better. Her observations led the way on a variety of improvement projects, which were largely responsible for WMHS’s impressive ascent to the top of Maryland’s quality measure charts.

One such initiative focused on improving care for discharged patients. Data from analytical models suggested that if patients received follow-up care within a few days of discharge, the probability of readmission decreased significantly. However, access to primary care providers wasn’t always available in that timeframe. WMHS formed a new team to work proactively to ensure patients had access to the appropriate outpatient care. As a result, readmissions rates have stayed below the state average while patients receive care in a more convenient community setting.

As a fitting conclusion to the story, WMHS has defied the “regression to the mean” phenomenon by not only sustaining performance levels, but also continuing to improve year-after-year. Another testament to the power of current, accessible, and relevant data.


So what are the practical takeaways around making data-driven decisions work? Judging from WMHS’s experience, here are a few that rise to the top:

  • Motivation from aggressive — and likely uncomfortable — goals that everyone can rally around helps to inspire transformational change.
  • Supportive leadership is critical to encouraging new approaches and guiding progress.
  • Ingenuity and curiosity to look at things from different perspectives using new data exposes possibilities that weren’t previously apparent.
  • It doesn’t need to be complicated. In fact, distilling information down to a form that’s intuitive and accessible broadens opportunities for more people to contribute to the change.

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