Several healthcare leaders from hospitals and health systems across the country share their team’s best practices when implementing a data analytics platform to improve quality and operations initiatives.
The strategies shared by the leaders that yielded the most success at their organizations include encouraging collaboration between data analytics teams and process improvement teams, aligning data insights with established operational initiatives, focusing analytics initiatives on a limited number of improvement areas with the broadest potential impact, telling a story with the data to inspire meaningful change.
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Editor’s Note: This article is based on the Population Health Expert Dialogue Series session at the 2022 Healthcare Analytics Summit. The panel consisted of Julie Watson, MD, MPH, Senior Vice President, and Chief Medical Officer, INTEGRIS Health; Stephanie Jackson, MD, FHM, Senior Vice President, Chief Clinical Officer, HonorHealth; Neal Chawla, MD, FACEP, Chief Medical Information Officer, WakeMed; William Holland, MD, MHA, Senior Vice President of Care Management and Chief Medical Informatics Officer, Banner Health; and Holly Rimmasch, Chief Clinical Officer, Senior Vice President and General Manager, Clinical Quality, Health Catalyst.
These healthcare leaders’ insights underscore the significance of individual understanding and engagement within organizational analysis and application required for improvement initiatives. In sharing their team’s specific challenges, themes and approaches emerged that apply to diverse healthcare organizations confronting their quality and operations initiatives while implementing a data analytics platform.
With the evolution of data analytics platforms, healthcare organizations now have an abundance of data at their disposal. Understanding how best to harness the insights it offers to implement change effectively is a growing challenge. Expert Speaker panelists for Clinical Quality and Operationsat HAS 22 addressed how they have navigated these challenges and provided new approaches that have yielded the most success for their organizations.
The sheer volume of data available often suggests that metrics alone should be the primary determinant of which areas of operation require attention for improvement. The consensus from healthcare leaders with experience implementing data analytics platforms is that the opposite is true. As panelist Neal Chawla succinctly put it, “the point of data is to get out of the data.” Instead, operational initiatives should lead an organization’s development and not be distracted by the volumes of data that can be unrelated to the most impactful opportunities for improvement.
As Julie Watson noted, hospital and organization leaders already know which areas need improvement and have a keen familiarity with industry-wide issues, such as mortality rates, readmissions, patient access, and length of stay. Our healthcare organization leaders demonstrated how metrics are most powerful when aligned with an organization’s established operational initiatives.
Data analytics platforms can augment an organization’s understanding of operational shortcomings by providing a more nuanced context for these circumstances and a deeper understanding of how best to address improving quality. Choosing metrics from the vantage point of leadership initiatives also engages hospital and team leaders, encouraging buy-in for new protocols and a greater sense of ownership and motivation across the organization.
Healthcare leaders emphasized that the key to successful change begins with identifying small, actionable areas that can yield the greatest impact. Instead of focusing on swaths of data that indicate numerous areas for improvement, harnessing the data analytics platform to focus on the areas where the greatest number of people may be positively impacted will build a solid foundation for growth. This strategy helps ensure new protocol adoption throughout an organization and can prevent overwhelming clinicians and care teams already susceptible to burnout.
In addition, focused analytics can be used to identify and measure critical behaviors that impact outcomes most. In her example, Stephanie Jackson emphasized the need for analyzing hand hygiene as an obvious component in measuring surgical site infections.
The start-small approach also applies to developing analytics dashboards within a data analytics platform that reflect the most prioritized areas. Once leadership teams have identified areas for improvement, analytics dashboards are most helpful when they are designed to specifically monitor the behavior changes, adoption, successes, and challenges for new protocols. Keeping extraneous metrics out of the dashboard helps team members stay focused on implementing and monitoring established initiatives.
As an organization grows and new areas for improvement emerge, developers can scale up the complexity of their dashboards.
While a data analytics platform can be a revealing source of truth, always focusing on the numbers makes it easy to lose sight of the human toll these metrics represent. Stephanie Jackson cautioned leaders to remind themselves and their teams that “every number is a person.” The power of patient stories can be a galvanizing force for an organization, from executive leadership and stakeholders down to providers and clinicians responsible for directly implementing change.
While stories can provide powerful motivation, it is equally important to recognize that not all data is relevant to all members of an organization. William Holland offered this insight and encouraged leaders to design relevant and meaningful metrics for the people they intend to engage. Whether it is clinicians, finance, or operations, tailoring your metrics to meet their unique perspectives and understanding will go a long way in optimizing the insights that analytics can provide.
Health organizations are at a pivotal point for optimizing the robust data analytics available, and implementation for quality improvement requires that all team members are on board. As industry experts have demonstrated, fostering collaboration between data analytics teams and process improvement teams is the first step toward identifying and achieving the quality improvement goals unique to their healthcare organization.
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