How BI and Analytics Improve Clinical Care, Reduce Costs, and More
It may seem intuitive that business intelligence (BI) and analytics can help to improve clinical care, reduce costs and more. What may not be as intuitive is developing a strategy to achieve this in an efficient, scalable and sustainable way.
According to HIMSS, “Clinical and business intelligence efforts empower members with practical guidance and unbiased information on how to harness data to inform accountable, high-quality patient care and fuel unparalleled business insight – all the while improving an organization’s fiscal performance.” The phrase “unbiased information” means an organization has done the work of bringing together all key data sources and matched, merged, cleansed, enriched, mastered, organized and made them (in the form of BI and analytics) easily accessible to every part of the business. Once completed, harnessing data is achieved by keeping three essential strategies in mind: performance improvement, benchmarking and monitoring.
Organizations must use benchmarks and goals to guide clinical and financial improvement efforts. As healthcare has been struggling over the last decade to shift its business model from fee-for-service to valued-based care, efforts around clinical quality and operational performance improvement have reached a fever pitch. If not carefully coordinated, duplicate improvement efforts – especially those that impact clinical workflow – can be more disruptive than beneficial.
Benchmarks help to reduce duplication of efforts while supporting the coordination of programs, enabling workers to focus only on the areas that are most in need. For example, if only 72% of patients in a local Dallas hospital reported that staff always explained medications before administering, that would sound troubling. In reality, the average in Texas is 68% and nationally is 66%. So, the score of 72% is not dire and may possibly convince administrators to redirect their quality improvement resources toward a different area.
Readily incorporate benchmarks into BI and analytics by using sources such as Hospital Compare, Healthgrades, US News & World Report, and the NCQA. Once the integration is complete, you need an additional step to strategically align improvement efforts and goals. Leadership across the board must review how they rank, measure for measure, against benchmarks and set realistic goals for each metric so that everyone can see, at an enterprise level, where they are and where they need to be.
It may help if these goals are generally consistent. For example, top quartile or top decile may be an ideal starting point for all metrics. Once goals are established, review those areas with the greatest gaps and target the top two to five based on the number of performance improvement resources available. As you achieve each of the top priority initiatives, target the next area with the greatest gap and continue to improve.
As performance improvement activities proceed for the top targets, it is important to monitor all other metrics to ensure no changes occur dramatically during day-to-day or monthly operations. A designated team should regularly review all metrics against previous performance as well as all established benchmarks and goals. Source this team from quality assurance, operations, or other areas and report their findings and recommendations on a regular basis through data governance or a similar leadership structure. It may also be helpful to empower members of this group to reach out directly to responsible areas so that they have an opportunity to correct course before the detected variance becomes significant.
These guidelines can help establish an efficient and orthodox way to harness BI and analytics, improve clinical care and reduce costs. But what else can they help with? Most BI and analytics are generally descriptive in nature and characterize what has happened or is happening using past or near real time data. At some point, analytics need to be more predictive in nature so organizations can be proactive, increasing their efficiency and effectiveness. With modern analytics technology, developing predictive models is not as daunting as it once was. Use cases should be oriented around the best clinical and business ROI, which will vary based on each hospital system or patient. As a starting point, some areas to consider in healthcare are predictive staffing, readmissions, general and ICU LOS, appointment no-shows, fraud detection and prevention, denials management and supply chain.
BI and analytics are essential for organizations to thrive in the midst of the complexities of modern healthcare. As value-based care creates financial pressure and required regulatory reporting becomes essential, building these technologies upon a foundation of high-quality, benchmarked data coupled with careful coordination of performance improvement and monitoring activities is a must. Following these approaches will create consistent, sustainable, organizational improvement in clinical outcomes and cost reductions, and maximize the utilization and ROI of your analytics program.