Healthcare providers have reached a tipping point. As little as ten years ago, they struggled to collect accurate, actionable clinical data. With much of it locked away in siloed electronic health records (EHRs) or paper patient charts, the best most organizations could do was rely on claims data that was optimized for billing purposes—not patient care.
Now that the vast majority of hospitals and large physician practice groups are using EHRs, healthcare provider organizations face an entirely new set of challenges. Access to clinical and operational data is no longer the primary challenge—many of these organizations are drowning in data. Instead, their challenge is turning this data into actionable information that their leaders and clinicians can use to make decisions. As the healthcare system (slowly) shifts from volume-based reimbursement to value-based reimbursement, organizations must be able to leverage their data to prioritize competing clinical and operational opportunities to improve efficiency, reduce unnecessary variation and waste, and identify and address gaps in quality of care.
Clearly, the need for a robust analytics program that can address these challenges has never been greater. Yet many, if not most healthcare providers struggle to develop an effective program. This article examines the analytics programs of four successful organizations that have made the transition to a data-driven culture. What organizational features do these programs have in common, and what lessons have they learned along the way?
The organizations include an academic medical center, two community-based hospital systems, and a regional system with 45 hospitals in four states. Despite these very different organizational dynamics, these four analytics programs have many things in common. Each has strong clinical leadership. While most healthcare systems operate reporting and analytics as a unit within IT, each of the programs reviewed here is a distinct entity with a charter, a clear reporting organization and a robust governance structure. Each system uses Epic (Epic Systems Corporation, Verona, Wisc.) as their primary electronic health record (EHR). Some of them refer to the analytics tools they have built as ‘dashboards’ while others refer to them as ‘apps’—they all have chosen QlikView (QlikTech International, AB) as the data visualization tool for their programs. And each program is relatively small, with staffs ranging from 5-10 FTEs, proving that you don’t need a large staff to make a significant impact.
Pilot Projects and Early Wins
The need for a dedicated analytics program is not always immediately obvious. Demonstrating value with a pilot project or a single, well-defined problem is an important first step in building a more comprehensive program. Dr. Cameron Berg is the director of acute care medicine for the clinical integration program at North Collaborative Care, the ACO for North Memorial Health Care in Minneapolis. His experience in starting his program is fairly typical. He had done some initial work with his colleagues in the ED around workflows. The benefits to both patients and the organization of this early work led to CEO and CMO-level support for a dedicated analytics team.
Dr. Binu Mathew, vice president of medical intelligence and analytics at Mercy Health System headquartered in the St. Louis area, had a similar experience. “We knew there was a significant opportunity around clinical documentation… It started with that use case… But to truly crack it, we needed the whole cycle of people, process, and analytics to all work together.” In explaining the potential benefits of a dedicated analytics, “You can go the traditional route of asking IT to help build a BI solution, but the reason why they liked this was because when you have that cross-domain knowledge you can build faster. But not just faster, you can build contextually a lot deeper and have an organic team that grows over time as opposed to just having consultant services forever.”
At University of Wisconsin Health (UW Health), Dr. Grace Flood is the medical director for clinical analytics and reporting. UW Health’s pilot project was driven by the desire to make Wisconsin Collaborative for Healthcare Quality (WCHQ) data more accessible to providers and organizational leaders. WCHQ publically reports organization and clinic level performance on ambulatory quality measures. UW Health, however, also collects underlying data down to provider and individual patient levels. Since several provider pay-for-performance measures at UW Health are based on WCHQ performance, this project garnered physician support, helping drive early successes. Moreover, the rigor of WCHQ’s standardized data formats and monthly data submissions helped the UW team build a governance structure that provided the basis for subsequent projects. Dr. Flood recommends, “Get some quick wins—show end-users some ‘wows’ that can help build momentum.”
Executive Buy-In and Sponsorship is Essential
While the staffing for each of the analytics programs described here is lean, they all needed financial support in order to build on their early wins. More importantly, the belief and backing of the senior leaders is critical in driving adoption of the analytics tools. As Dave Lehr, executive director for analytics and data strategy at Anne Arundel Medical Center in Annapolis, MD explained, “I’ve definitely seen in the past at other organizations where they are doing so many great things, but nobody knows about it except for the IT folks.” But if the CEO and other senior leaders are championing the analytics program, the front-line managers and clinicians are much more likely to take the time to learn the tools.
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