Posted by: Jenny Laurello
BI, business intelligence, Data analytics, Health care analytics, Health care quality, QI, Quality improvement, Quality measures
Guest post by: Trevor Strome MSc, PMP, Informatics Lead, Winnipeg Regional Health Authority; Assistant Professor, Department of Emergency Medicine, Faculty of Medicine, University of Manitoba
The use of healthcare analytics is rapidly transforming how health care organizations (HCOs) improve clinical quality, operational efficiency and patient safety. Healthcare analytics is the application of statistical, qualitative and predictive analysis to data in the quest to address the many financial, regulatory and quality issues facing healthcare. The use of analytics in healthcare is made possible by the rapidly increasing adoption of computerized clinical systems (such as EMRs) and robust business intelligence (BI) infrastructures and HCOs are becoming more evidence-based and data-driven as a result.
Healthcare has a strong tradition of research and of seeking evidence to guide best practices. Yet until recently, health management and quality improvement (QI) practices have not followed suit. Whereas data was usually found in financial systems (and reported on through reams of tables), management of processes and work-flows was largely top-down ill-informed, and based on what was done in the past. Clearly the “old ways” of managing healthcare quality are no longer acceptable; customer-focused and data-driven methods such as Lean and Six Sigma are now being used to drive health care transformation initiatives.
With the increasing volumes of data available, HCOs, almost paradoxically, do not require more reports — they require better insight into operations and accountability for their performance. This insight is best gained when analytic and BI tools (such as dashboards, scorecards, other informative applications) are applied in a strategic combination with quality improvement initiatives (via approaches such as Lean and Six Sigma).
Analytics developers and quality improvement practitioners do not typically work closely together to achieve the goals of healthcare transformation initiatives. Such teamwork, however, is exactly necessary for the full value of analytics to be realized on such projects. The following are some lessons learned as HCOs have sought to leverage analytics on QI initiatives.
- Consider analytics from the start. Avoid analytics becoming an “afterthought,” which can result in underutilized data, rushed development requests and poorly designed information tools. The need to quantify baseline performance, monitor progress and evaluate outcomes is necessary on nearly every QI project, so consider the information and analysis needs for an initiative from the time planning of the initiative starts, not when it is well underway.
- Include analytics resources directly on QI projects. Most QI project team members are not aware of the data available from source systems, nor are they experts in the appropriate analysis, visualization and communication of such data. Including an analytics expert (with solid knowledge of the business) on QI projects can help ensure that the available data is maximally and appropriately utilized.
- Focus on information needs of end-users. QI initiatives typically have broad information sharing requirements, from high-level status reporting for senior management to detailed, process-related information for front-line teams to evaluate progress and outcomes. When developing analytical applications and BI tools, know which stakeholders require what information, at what frequency, and in what format.
- Innovate. Analytics and BI offer so much more than reporting, but most people just think of reports when thinking about the information they require. QI initiatives offer the opportunity to use available tools to their maximum potential. This can range from making information tools more intelligent by highlighting where and alerting when certain defined conditions occur, through to the use of simulation modeling to test new workflows and processes prior to implementation. QI teams are attempting to transform health care through innovation; the analytics should match these efforts.
- Evaluate. Although the impact of QI projects is always being evaluated, the use of analytics should be evaluated and “lessons learned” compiled. Organizational learning about what tools were developed, how information was used and which analytics applications proved the most helpful is often lost when projects and team members move on to other projects. Ongoing evaluation, summarizing, and documentation of outcomes will help ensure that mistakes of the past are not repeated, and successes are built-upon and continued.
Don’t leave QI teams begging for data and reports. When integrated into healthcare quality improvement initiatives “from the ground up,” analytics methods, tools and practitioners can provide more value and offer more opportunities for true transformation than the traditional report and data request approach followed by most HCOs.
About the author:
Trevor Strome MSc, PMP, is the Informatics Lead for the Winnipeg Regional Health Authority, and is Assistant Professor at the Department of Emergency Medicine, Faculty of Medicine, University of Manitoba. You can visit Trevor’s blog at http://healthcareanalytics.info, or contact him by email at email@example.com.