The widespread adoption of electronic health records (EHRs), and more recently cloud-based servers, is helping to make big data analytics a reality for more healthcare organizations.
Too often, though, organizations want to leverage the power of analytics to improve care and reduce costs across the enterprise all at once. Confronting this type of project can seem overwhelming, especially since Gartner suggests healthcare has the most complex data of any industry.1
Complexity and size causes many organizations to either postpone or not succeed with their adoption of analytics. However, a strategic three-phase approach to enterprise-wide analytics adoption can help organizations realize a much larger return on investment over the long run in terms of improving care quality and controlling costs. These phases, described in greater detail below, are often the missing link in business analytics success and include:
1. Start with understanding and defining what key metrics can provide the most value
2. Add evidence-based tools to build a foundation and support decision making
3. Incorporate predictive analytics to forecast and proactively intervene
Each phase is a stepping stone for the next and prevents an organization from becoming overloaded with data and reports that are unreliable and unusable. Adoption will accelerate as trust and reliance in analytical tools grows and value is proven, resulting in superior clinical and financial performance and increased patient satisfaction.
Start with identifying and tracking key metrics
Before implementing analytics technology, an organization needs to identify key performance indicators (KPIs) it wants to improve. Selecting KPIs can be daunting considering the numerous improvement initiatives that are occurring at most hospitals and health systems. A best practice is to start with easily defined and monitored KPIs that are associated with higher costs and lower value-based care reimbursement. A few examples include: inpatient readmission rates and emergency department and billing throughput.
Once initial metrics are selected, organizations may decide that they want to advance to more complex analyses, such as:
• Chronic condition management performance;
• Resource utilization studies to maximize the capacity of physicians and facilities; and
• Identification and tracking for conditions and characteristics of recently discharged patients to prevent avoidable readmissions.
Identifying KPIs first is essential because an organization's data analytics plan and implementation will be driving toward improving those metrics. Focusing on KPIs and offering tools, such as simple dashboards, to track those metrics also offers clarity to providers and administrators and puts everyone on the same page around the organization's goals.
Evidence-based tools drive performance improvement
As organizations begin to understand and monitor key metrics, evidence-based decision support tools should be the next phase. This evidence is built from a healthcare organization's own data that has been cleansed and verified through the data analytics platform. Using an organization's reliable data as evidence promotes trust among providers that they are delivering safe, effective interventions that will result in better outcomes.
Efficiency is also a priority to time-pressed providers. Evidence-based tools can help reduce ineffective and wasted care, which according to a recent estimate, accounts for about 14 percent of healthcare spending.2 Delivered at or near the point-of-care, evidence-based tools offer physicians and other caregivers easily accessible data to support their decisions more quickly, streamline workflows, and have more productive conversations with patients, increasing their satisfaction.
Once evidence-based tools are adopted, the analytics dashboards become a reflection of the technology's effectiveness and help identify adherence gaps among physicians. Organizations can then respond by offering physicians additional metrics training or tools to support their success.
Predictive analytics brings it all together
The first two phases alone—tracking performance through dashboards and evidence-based clinical decision making—should offer noticeable improvements on KPIs. However, the final phase of the data analytics journey is to be able to combine those metrics, historical data, as well as advanced algorithms, to predict patient outcomes.
Predictive analytics allows organizations to identify when high-risk patients, such as those with multiple chronic conditions, need clinical interventions. This level of deeper insight and proactive outreach will drive organizations to next-level clinical and financial performance. Simply decreasing avoidable readmissions alone can reduce costs by millions of dollars per year, greatly improve clinical outcomes and increase patient satisfaction.
Key considerations for success
To encourage adoption of data analytic technology, the tools must be simple to use and understand. Dashboard generation needs to be automated, but so do more in-depth reporting to minimize steps before physicians can view insight. Easily customizable reporting is also crucial.
Working with a qualified IT partner with deep healthcare experience can overcome IT staffing limitations and accelerate adoption among key stakeholders. This partner should be vendor agnostic so that integration and capturing complete data from disparate health IT systems is feasible without replacing large legacy platforms. Likewise, the analytics platform should be turnkey, cloud-based and its infrastructure scalable to grow as the ongoing progress of analytics increases.
As providers and administrators build competency with the analytical tools, it will build trust and reliance, becoming an indispensable step in their workflows. The drive to improve performance is a common characteristic among nearly all physicians and providers. When focused on a common set of KPIs and given the tools to succeed, physicians and administrators will be united on a clear pathway to foster transparency among care team members to improve outcomes, lower costs and increase patient satisfaction.
By Jim Deren, Senior Healthcare IT Strategist, CareTech Solutions, an HTC Global Services company1 Gartner. "Top Actions for Healthcare Delivery Organization CIOs, 2014: Avoid 25 Years of Mistakes in Enterprise Data Warehousing." Feb. 10, 2014. https://www.gartner.com/doc/2664433/top-actions-healthcare-delivery-organization
2 Nikhil Sahni, Anuraag Chigurupati, Bob Kocher, MD, David M. Cutler. "How the U.S. Can Reduce Waste in Health Care Spending by $1 Trillion." Harvard Business Review. October 13, 2015. https://hbr.org/2015/10/how-the-u-s-can-reduce-waste-in-health-care-spending-by-1-trillion
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