Notes from the Field: 7 Questions with Ben Sparks, Senior Director of Data Analytics at Bryan Health

“Notes from the Field” is a special newsroom feature highlighting industry professionals working to transform healthcare. In this edition, we spoke with Ben Sparks, Senior Director of Data Analytics at Bryan Health.

1. Tell us about your role.  
   
   I’m the Sr. Director of Analytics for Bryan Health, a five-hospital health system based in Lincoln, NE. I oversee the organization’s Data and Analytics Center of Excellence, which includes everything from data governance and architecture to data science to internal consulting and everything in between. I also spend a lot of time supporting our managed care and market planning teams with business cases and pro forma development.
   
   
2. What inspired you to pursue a career in healthcare? 
   
   I think what draws me, like many others, to healthcare is the feeling that what I’m doing is making a real difference to the patients and communities we serve. It’s easy to stay motivated and engaged when our work has such a clear connection to improving such a major and critical part of the human experience. I really love what I do. I certainly consider myself blessed to work in healthcare, and I’m glad I can be part of a team and a professional community that spends each day trying to make the world a better place in our own small way.
   
   
3. What do you see as the biggest opportunity to improve healthcare?  
   
   Reducing the cost and quality-of-life burden of chronic disease is our single greatest opportunity. If we succeed in lowering the incidence and severity of chronic illness, we will drastically reduce cost and waste while improving the quality and accessibility of care. The opportunity here is, quite literally, an order of magnitude or more greater than many of the challenges we see on a day-to-day basis.
   
   
4. What is the greatest challenge facing healthcare? 
   
   Healthcare is too expensive in the US, and that is the core of so many other critical challenges. Cost causes inequity in access, outcomes, quality, and in many other key areas. Healthcare is too expensive for most people, and the rate of cost increase is unsustainable. There are a lot of reasons for and causes of that, but one major contributor is the burden of chronic disease. Depending on the source, somewhere between 70 to 90 percent of healthcare spending is directly attributable to chronic disease. The downstream impact on the economy is even worse.
   
   The modern healthcare industry in the U.S. is built on the treatment and management of illness, but we, as a society, need to find a way to focus on prevention and early detection. Over the past few decades, we’ve tried to throw resources at better treatments and management, and honestly, we’ve made major progress across a wide spectrum of conditions. However, the result was a massive increase in spending, and we’re losing that battle.
   
   If we start to reduce the prevalence and severity of chronic illness in this country, that, by itself, can make our system sustainable. Without that focus, we’re only going to see a continued deterioration in affordability, quality, and equity as healthcare becomes less accessible to larger portions of the population.
   
   
5. How do you envision technology playing a role in addressing this challenge in healthcare?  
   
   We have so much data that shows us who is at risk for which conditions, how likely those conditions are to develop or become more severe, how costly those patients will be to the system, and what the short- and long-term prognosis is for those patients. Technology plays a critical role  in helping us be proactive with all of this information. The healthcare system is excellent at processing if/then types of problems. We’re still largely amateurs at managing the complex economics surrounding more probabilistic, less binary scenarios.
   
   The ongoing revolutions in data science and AI are opening door after door. We’re making rapid strides in our ability to find patients and vulnerable populations earlier and with greater accuracy.
   
   Data integration and synthesis is another critical area where we’re breaking down traditional barriers. We’re able to share clinical, claims, and predictive data among clinicians and population health teams, getting us much closer to something like a 360-degree view of our patients. And, we’re able to combine all of that with operational data, allowing us to make the whole system more efficient and effective for patients and providers.
   
   
6. What best practices have your team employed over the past year that resulted in meaningful improvement or outcomes (i.e., financial, operational, or clinical)?  
   
   We’ve really begun to focus on trying to prioritize our work based on the expected ROI to the organization. This isn’t strictly financial by any means. We’re focused on trying to understand how our work improves outcomes generally. This has helped us sort through the massive volume of requests we receive and allowed us to achieve bigger wins faster. We bring the improvement conversation up early in our discussions with our partners. It helps them and us better focus on the “why” while also allowing us to better target our work to generate maximum impact.
   
   One part of our team, mostly focused on supporting our inpatient nurses, has had quarters where our average measurable ROI has been 18x on our discretionary projects, mostly gained by reducing process waste for our nursing teams. Another great recent example is related to our data science team. We recently developed an enhanced deterioration detection model that improved our ability to identify high-risk patients by up to 50 percent with up to 30 percent less effort. Focusing on sizing projects earlier and measuring them once complete allows us and our stakeholders to better articulate and celebrate the achievement.
   
   
7. How are you leveraging new technologies, such as augmented intelligence, to enhance productivity/efficiency, quality, and outcomes? 
   
   We’re still early in our AI journey, but we’re trying to move quickly. We are either currently utilizing or building capabilities in patient safety, revenue cycle, and operations, and I’m sure there are a few other areas that I’m missing. A lot of our current efforts are focused directly or indirectly on productivity. As everyone already knows, the healthcare staffing shortage is impacting critical positions across the industry. A lot of our work is designed to assist current staff productivity, to reduce process waste, or to more efficiently source and allocate staff.