How can a healthcare provider reduce the amount of resources used to treat cardiovascular patients while simultaneously increasing the quality of care? Tom Rohleder, PhD, and other researchers at the Mayo Clinic’s Center for the Science of Health Care Delivery in Rochester, Minn., tackled that challenge using computer models and statistical analysis.
Working with the clinic’s cardiovascular surgery group, Dr. Rohleder â the associate scientific director of Mayo’s Health Systems Engineering Program â and his colleagues leveraged the fact that groups of patients with common characteristics could be managed more efficiently using standardized care protocols
As they developed a discrete-event computer simulation model to predict minimum bed needs to achieve high-level care, the researchers found incorporating surgery growth and new recovery protocols, smoothing surgery schedules and transferring long-stay patients out of the intensive care unit could reduce bed needs by 30 percent for cardiovascular surgical patients in the ICU and progressive care unit at Mayo, according to the March 2013 case study.
“That kind of general approach is the kind of thing that’s being implemented and identified across Mayo Clinic,” Dr. Rohleder says of the protocols. “You create what we call âfocused factoriesâ that deal with groups of patients with common clinical characteristics, identified by data analysis. These focused factories efficiently deliver high quality patient care. That frees up the time for dealing with the more complex cases.â
That case study serves as just one example of how hospitals and health systems have begun to use data analytics and health systems engineering to identify ways to reduce costs and improve care, a growing concern as healthcare reform and economic pressures push providers to spend less and deliver better services.
As hospital and health system CFOs face big questions about the industry’s future due to the unknowns of healthcare reform, data analytics can help them strengthen cost management efforts, up productivity and otherwise prepare for and adapt to the shifting healthcare landscape, says Phil Gaughan, senior director of operational improvement at Truven Health Analytics.
“Most CFOs are still asking themselves, ‘How much cost reduction and process improvement is going to be enough if healthcare reform really matures in the direction that most analysts believe it will?'” Mr. Gaughan says.
However, hospital CFOs and other executives diving into data analytics can also bump into questions and obstacles to successful implementation, ranging from resistance to suggested changes in practice to not knowing how small or big to start. Mr. Gaughan and Dr. Rohleder offer some best practices for making the most out of data analysis.
1. Emphasize patient safety and quality as top priorities. People can get the wrong idea about a data analytics initiative launched to improve operations, Dr. Rohleder says.
“Sometimes you get pigeonholed as an efficiency expert, which can sometimes mean downsizing,” he says of being a systems engineer. “I think that one of the things that we knew we had to do was make it clear that whenever we’re doing an analysis, we’re factoring in patient quality [and] patient safety. When we’re doing our systems engineering work, even though we are talking about becoming more efficient, we’re not doing it at the expense of the patient.”
At Mayo, the systems engineers and analysts simply present reports concerning what the different tradeoffs are for potential strategies, he says. Clinical professionals make the final decision.
2. Find the right talent, and make sure they understand each other. According to Dr. Rohleder, the most important factor for successful data analysis is having people who are experts in the field on board, while also ensuring they understand the medical side of operations.
“I think the reason it’s worked pretty well at Mayo Clinic is we’ve partnered the systems engineering people with the clinical people,” he says.
Mayo keeps its systems engineering staff informed about the constraints of the medical environment, he says.
3. Pay attention to both internal and external data. An internal productivity monitoring system can provide close to real-time data on resource requirements and is a fundamental part of using analytics to improve performance and reduce costs, Mr. Gaughan says. However, providers must look outward as well as inward to assess themselves. He says hospitals and health systems should compare themselves to peers and competitors alike.
“It’s critical to have external data to determine how effective we are in managing our resources relative to the industry,” he says, speaking to how a healthcare provider would see the situation. “I need to keep a very close eye on how am I doing from a cost management and productivity management perspective relative to my competitors.â
4. Start small. Healthcare providers commonly make the mistake off “biting off too big of a chunk” in the beginning when it comes to data analytics, Mr. Gaughan says.
As an example of how to do things right, he recalls the work conducted by Newton-Wellesley Hospital in Newton, Mass. Newton-Wellesley decided to initially focus only on reducing its housekeeping department spending using benchmarks provided by Truven. The hospital tasked its experts with that project and realized about $300,000 in savings in that department.
“They had their proof of concept, and they could share that same process with other departments,” Mr. Gaughan says. “The real principle here is to start smaller. You can’t turn a hospital or health system or an iceberg around overnight. Test the theory, get those early gains and use that as a launching point to convince people that there’s something here worth looking at.”
5. Communicate your objectives clearly and educate your staff. Making data analytics pay off for a hospital or health system takes more than just finding the right experts and giving them a manageable task. In order for the initiative to work, Mr. Gaughan says hospital executives have to be visibly involved and support the project in order for their staff to follow suit. Administrators should also clearly communicate their objectives to everyone in the organization.
Dr. Rohleder stressed the role of education in carrying out systems engineering and data analytics projects. He says executives and data experts should give everyone in the organization a chance to learn about the analytics tools and how those tools can help the health system.
Overall, hospitals that embrace data analysis as a healthcare reform tool will likely stay strong despite declining reimbursement levels and other economic pressures, says Dr. Rohleder.
“The data analysis and the kinds of methods that we apply â like the example I gave with cardiovascular surgery where we actually enhanced the patient quality and safety while at the same time reduced the resources we apply to them â is exactly where healthcare is going to go in the future,” he says. “Using our data, using an evidence-based approach, it gives us that edge to be able to achieve those ends.”
Note: This article originally appeared in Becker Hospital Review. Click for link here.
Originally Posted at: 5 Best Practices for Hospital Executives Using Data Analytics: How to Improve Outcomes, Cut Costs by analyticsweekpick