“We get data by the truckload,” Tim Putnam, president and CEO of Margaret Mary Community Hospital, says of the organization’s participation in the Medicare National Rural Accountable Care Organization. The critical access hospital, located in Batesville, Ind., struggles with what to do with all of the data sets. “A big eye-opener for us is that the data is often incorrect,” Putnam says. “What do we do with that?”

That sentiment is echoed by Bart Rodier, M.D., chief quality officer of Health Central Hospital in Ocoee, Fla. “We are always looking for better information,” he says. “The foundation of data information and data is attribution and we frequently see problems with attribution.”

A common challenge

A group of hospital executives recently convened in Chicago at the H&HN Executive Forum to discuss the use of data analytics to enhance clinical quality and reduce costs. The organizations represented, which included two critical access hospitals, a mid-sized urban hospital, a specialty hospital and a mid-sized community hospital, all reported the same major challenge: getting data to the point of care in real-time. The discussion was moderated by Suzanna Hoppszallern, senior editor for Hospitals & Health Networks, and sponsored by GE Healthcare.

“We need greater ability to interface across our organizations if we are to fully support caregiving in 2016 and beyond,” says Terika Richardson, president of Chicago’s Advocate Trinity Hospital. Richardson cites the challenge of transferring data between the hospital and the organization’s physician practices, which operate on a different electronic health record platform. “It’s difficult to cull the data and find meaningful information,” she says, adding, “I’m often handicapped because the data is not very clean and it’s often several months old.” That makes it difficult to engage clinicians in quality discussions. “Try having a conversation with a doctor based on data that’s six months old,” she says. “That’s a lifetime ago.”

Marc Augsburger, R.N., president and CEO of Caro (Mich.) Community Hospital agrees. The critical access hospital often has to rely on faxes to exchange lab reports with independent physicians in the community. “What drives me crazy is that the health information exchange was supposed to be quick and work for everybody,” he says. “Now they’re saying it may take another 5 to 10 years until information is free-flowing.”

Start little

One solution is to start small. “We seldom hear about little data,” says Travis Frosch, director of analytics and cybersecurity for GE Healthcare. “Little data is the low-hanging fruit. It’s cheaper, more valuable and can have a greater impact on the organization.” Key questions for organizations to ask are: What problem are we trying to solve? Does the organization really need an enterprise-wide data warehouse or big data platform?

Frosch says that even large health systems, staffed with dozens of data analysts, aren’t solving the small data issues that are impactful on a daily basis. “Having a clear use case and problem that you are trying to solve for is extremely important when making strategic decisions,” Frosch says. Providing small bits of accurate, actionable data is key. “Little data is often more impactful in delivering a quicker, more measureable return on investment, than large data,” he says.

That approach is working at Woman’s Hospital in Baton Rouge, La. “We’re working mostly at the department-level right now,” says Teri Fontenot, the president and CEO. “We’ve been working on throughput in the labor and delivery, our baby rooms and surgical care units,” says Fontenot. “It’s really helped with efficiency, outcomes and patient satisfaction.”

Augsburger of Caro Community Hospital says use of little data has helped improve the organization’s perception in the community. By focusing on test utilization, the organization has been able to reduce overutilization and shorten patient visits. “We’ve made significant changes and enhanced our perception within the community by focusing on appropriate imaging and blood utilization, among other things,” he says. It’s also improved the bottom line, Augsburger notes.

What would Amazon do?

Despite the challenges that organizations face, the promise of data holds strong. The next key step, says Richardson, is creating a push in the distribution of analytics and data, versus a pull environment. “Amazon has it down to a complete science,” she says. “We need to create that capacity in health care,” Richardson adds. Frosch further suggests that the “democratization of data” that is seen in consumer-facing applications means assuring the “right information in the right hands of the right users at the right time” in ways that are easy to consume, use and act.