EHRs allowing more analytics review for physicians

Chris Landon, MD, head of pediatrics at California-based Ventura County Medical Center, wanted to ensure the center’s 83 pediatric practitioners were following new guidelines for treating patients with respiratory syncytial virus (RSV) bronchiolitis.

So he analyzed the electronic health records (EHRs), searching for data that pointed to patients with the infection as well as information about their treatments.

Landon found that 80% of the pediatricians weren’t following the guidelines the first year the medical center analyzed the data, prompting an education campaign that helped bring more clinicians in line.

EHRs and the corresponding push to digitize records have created new opportunities to move analytics into doctors’ offices where they can use analytics technologies to gain insights that had once been too cumbersome, if not impossible, to tease out.

“There is a return that exists on that information if we can get it standardized and consolidated,” said Vince Vickers, a principal at the professional services firm KPMG and a recognized authority and thought leader in health IT.

Next: Challenges

Existing and emerging analytics software expands the abilities that doctors have to query the data captured by their EHRs, said Douglas B. Fridsma, MD, PhD, president and CEO of the American Medical Informatics Association. A clinical practice could, for example, use an analytics program to generate reports on how the practices’ hypertensive patients or diabetic patients compare against national quality measures then drill down into possible factors for why that group’s patients deviate from the standards.

“Trying to do that in the past with paper records was extremely difficult. Now we have EHRs so it’s going to be increasingly popular to do that,” Fridsma said.

But healthcare organizations face multiple challenges in moving forward, experts said.

One of the biggest challenges is the poor state of data quality. Although EHRs are collecting huge volumes of information on individual patients, the information generally can’t be easily or cleanly combined and stored in ways amendable to electronic analysis. That’s because the information includes both structured data (such as blood pressure readings and reimbursement codes) as well as unstructured data (physician notes), and there are usually huge variances in how clinicians input both kinds of data.

Then there’s the cost of acquiring and maintaining the technology needed to support an analytics program as well as the skills required to effectively run it, Vickers said.

Experts also noted that the technologies themselves present challenges, as they’re far from user friendly and certainly not plug-and-play.

Moving forward, Fridsma said organizations will need to create and enforce more standards around the data going into EHRs and other computer programs. They’ll have to prioritize the need to invest in the analytics tools, and they’ll have to add analytics expertise to their practices by either hiring or training clinicians or medical informatics specialists.

Next: Where do we go from here?

He also said such investments will become increasingly worthwhile as shared-risk and alternative-pay reimbursement models take hold and delivering proven outcomes becomes more critical.

“An institution’s ability to good analytics will impact the bottom line, so I think more institutions are going to start paying more attention to this,” Fridsma said