Bringing the Obvious to the Surface Through Analytics

Posted on May 26, 2015 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site (http://oreilly.com/) and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

Analytics can play many roles, big and small, in streamlining health care. Data crunching may uncover headline-making revelations such as the role smoking plays in cancer. Or it may save a small clinic a few thousand dollars. In either case, it’s the hidden weapon of modern science.

The experience of Dr. Jordan Shlain (@drshlain) is a success story in health care analytics, one that he taking big time with a company called HealthLoop. The new venture dazzles customers with fancy tools for tracking and measuring their customer interactions–but it all started with an orthopedic clinic and a simple question Shlain asked the staff: how many phone calls do you get each week?

Asking the right question is usually the start to a positive experience with analytics. In the clinic’s case, it wasn’t hard to find the right question because Shlain could hear the phones ringing off the hook all day. The staff told him they get some 200 calls each week and it was weighing them down.

OK, the next step was to write down who called and the purpose of every call. The staff kept journals for two weeks. Shlain and his colleagues then reviewed the data and found out what was generating the bulk of the calls.

Sometimes, analytics turns up an answer so simple, you feel you should have known it all along. That’s what happened in this case.

The clinic found that most calls came from post-operative patients who were encountering routine symptoms during recovery. After certain surgeries, for instance, certain things tend to happen 6 to 9 days afterward. As if they had received instructions to do, patients were calling during that 6-to-9-day period to ask whether they symptoms were OK and what they should do. Another set of conditions might turn up 11 to 14 days after the surgery.

Armed with this information, the clinic proceeded to eliminate most of their phone calls and free up their time for better work. Shlain calls the clinic’s response to patient needs “health loops,” a play on the idea of feedback loops. Around day 5 after a surgery, staff would contact the patient to warn her to look for certain symptoms during the 6-to-9-day period. They did this for every condition that tended to generate phone calls.

HealthLoop builds on this insight and attaches modern digital tools for tracking and communications. Patients are contacted through secure messaging on the device of their choice. They are provided with checklists of procedures to perform at home. There’s even a simple rating system, like the surveys you receive after taking your car in to be fixed or flying on an airline.

Patient engagement–probably the most popular application of health IT right now–is also part of HealthLoop. A dashboard warns the clinician which patients to perform each day, surfacing the results of risk stratification at a glance. There’s also an activity feed for each patient that summarizes what a doctor needs to know.

Analytics doesn’t have to be rocket science. But you have to know what you’re looking for, collect the data that tells you the answer, and embody the resulting insights into workflow changes and supporting technologies. With his first experiment in phone call tracking, Shlain just took the time to look. So look around your own environment and ask what obvious efficiencies analytics could turn up for you.