Couple being counseled by helpful hospital staff

More and more healthcare systems are transitioning to performance-based, population-focused models of care. As they do so, many are turning to data analytics to support this transition. Unfortunately, healthcare organizations sometimes find their efforts stymied by one or more of these challenges:

  • A weak healthcare analytics team
  • Inadequate support
  • Skepticism about data validity
  • Lack of knowledge about analytics
  • Resistance to increasing workload
  • A volume-focused mindset

In fact, a 2014 Price Waterhouse Cooper survey of healthcare executives found that while 93 percent were attempting to incorporate analytics into their decision-making, many did not feel that they were equipped to do so. In fact, despite a desire to move towards analytics, only 40 percent of executives said that they relied primarily on data to make decisions. For the remaining 60 percent, these were the top reasons for discounting the value of data:

  • 44 percent – a lack of necessary skills or expertise
  • 42 percent – belief that analytics had limited use for their role
  • 29 percent – belief that data was lacking in quality or completeness
  • 26 percent – belief that it is too difficult to assess data usefulness

Overcoming these concerns requires re-energizing the culture of the organization so that data is trusted, respected and utilized effectively.

As we implemented an enterprise healthcare analytics program at Self Regional Healthcare, we faced some of these same challenges. Though we were fortunate to begin with highly skilled analysts and a supportive group of executive-level leaders, transforming the overall culture required starting from the ground up, engaging stakeholders from across the organization throughout the process.

Our experience illustrates the importance of building confidence by investing in six key cornerstones:

Collaboration
Analysts in silos have a limited opportunity to share their expertise with their peers or with the organization as a whole. A united team of data specialists can serve enterprise-wide goals.

Leadership
Executive- and director-level leaders can offer both guidance and support to drive adoption throughout the organization. A well-planned data governance structure aligns healthcare analytics with organizational missions and gives analysts a voice.

Transparency
Skepticism regarding data validity is one of the biggest stumbling blocks on the road to full utilization of enterprise analytics. Including stakeholders in the validation process answers questions and develops trust.

Education
Non-specialists may lack understanding of data sources, effective metrics and analytics methods. Communication between users and analysts help guide the appropriate use of data resources.

Value
Clinicians and staff may already experience data input as a laborious task. Initiatives that reduce duplicated efforts, manual data entry and recordkeeping for regulatory compliance can motivate buy-in.

Focus
Clinicians in particular are accustomed to data sources that emphasize volume and a focus on the provider. The use and presentation of analytics can and should emphasize a focus on patient outcomes and organization-wide goals.

At Self Regional Healthcare, this process took time, but it was well worth it. Users no longer question the data—they know where it comes from, and they know what affects it. Analysts feel like valued resources. Best of all, we've seen positive changes across the board. Nearly every dashboard or scorecard we've presented has yielded some measurable improvement.


To find out more about the steps Self Regional Healthcare took to build a culture of data confidence, download our webinar, Best Practices to Building a Culture of Data Confidence, Recorded Sept. 30th. Also download our case study, Building a Culture of Data Confidence: Best Practices for Implementing Enterprise Analytics.