Hospitals and health systems often struggle to establish a data-driven culture due to challenges that include fragmented data sources, outdated infrastructure, the complexity of healthcare regulations, and the need for significant investment in analytics capabilities.
But there’s also a bigger problem: Most health systems don’t use the data they already have.
THE CHALLENGE
McGill University Health Centre has set the creation of a data-driven culture as its strategy to improve the quality of patient care and drive increased access, while at the same time lowering costs.
The health system must identify 5% budget cuts, while maintaining service volumes and quality.
“To achieve this challenge, we need to put in place the capacity to diminish low-value care,” said Dr. Alan Forster, director of innovation, transformation and clinical performance at McGill University Health Centre.
“This can only be achieved through careful analysis of our data combined with engaged staff to implement changes in a disciplined and evaluative approach. A cornerstone of this goal is our data strategy.”
Previously, McGill’s data strategy was to “centralize functions and services,” Forster added. “We developed a data warehouse that integrated key clinical and administrative data using a consistent knowledge structure. We also develop a centralized team to administer and support access to data.”
This team supported several functions including the creation of automated reports and scorecards, an ad hoc reporting service, a data service to support the creation of analytic data sets, a consultation service for those interested in data but uncertain of their specific needs, and a data science team to support complex analytic requirements. This team also was responsible for building and maintaining data.
“The main challenge created by this strategy was the roadblock created by our central team and governance,” Forster explained. “The demand on this central team was far greater than the team could offer. By creating access to data, the demand for data increased, which was desirable but put pressure on the staff in our team.
“Also, the data team could not know all the operational pressures faced by the clinical teams, so when people asked for data, the results were not always what the user needed, nor was there an ability to provide all the assessments of data quality required before distribution,” he continued. “This further put pressure on the team and diminished trust between data requesters and the data team.”
While the healthcare organization had significantly more knowledge about its data, it was not having the broad-based cultural impact it needed to improve the value of healthcare.
PROPOSAL
McGill University Health Centre spends more than $1.2 billion to treat half a million patients annually. Its goal is to maximize the impact of every single one of those dollars so that it has the biggest health impact on even more patients. For this to occur, it knew it must transform how it cares for patients in many ways.
“Our underlying premise is that our health system has emerging leaders capable of transforming care if they have the tools to do so,” Forster noted.
“One of the tools is access to data and this is where self-service data analytics come in. We have literally hundreds of examples of doctors, nurses, pharmacists, dietitians, respiratory therapists, physiotherapists, business leaders, and other professionals and staff, who have created meaningful change in our system.
“When such people make a difference, they often are filled with a sense of accomplishment and energy, and they always inspire others,” he continued. “This gives us hope. Unfortunately, we have thousands of other professionals and staff unable to make these impacts, which at times is discouraging. What is it that explains the difference in how people contribute?”
There are many factors that drive the difference – some of which are hard to change, he added.
“However, one consistent difference is access to the ‘key ingredients’ to motivate change, including data,” he said. “With respect to data, if someone can, on their own, use data to translate an observed problem into quantifiable impacts of cost and quality, then that person is in a much better position to help others see the benefit of change.
“Further, the improvement idea is much more precise and the expected impact definable,” he continued. “Finally, if someone has access to data, then it is possible to see if the problem improves with efforts.”
MEETING THE CHALLENGE
The organization has two technologies that complement each other. First, it developed a series of cascading performance scorecards using Microsoft Power BI. These scorecards provide leaders access to performance data using pre-built metrics against the organization’s standard dimensions.
For metric selection, staff used a balanced scorecard methodology, promoting parsimony feasibility and benchmarking capability. Their view was that fewer metrics reflecting critical performance domains was more important than too many.
They developed a performance framework to accompany the scorecards with annual objectives and work plans. Objectives were defined using key results, which were measurable using the data.
“Second, we have implemented MDClone’s ADAMS (Ask, Discover, Act, Measure, Share) Center approach for self-service data analytics,” Forster reported.
“This allows our stakeholders to access data on their own, define and test specific hypotheses related to causes of care quality, and include variables not available in our scorecards. This allows us to push our teams to find their own solutions to their performance gaps, as identified in their scorecards.”
RESULTS
McGill University Health Centre still is very early in its journey to self-service data use. It has loaded three data events into its ADAMS Center data lake. The biggest win staff have so far is the number of users. Within 60 days of go-live, it has more than 90 users trained to use the ADAMS Center data platform.
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Email him: bsiwicki@himss.org
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