The Most Important Digital Health App of 2013: Now THIS is a Learning Healthcare System

The Most Important Digital Health App of 2013: Bugs and Drugs

In a year that saw consumer-facing digital health app after consumer-facing digital health app, the app that impressed me most was actually clinician-facing, not consumer-facing.  In 2012, the digital health apps that stood out to me most were Kinsa, a smartphone-connected thermometer enabling real-time community maps of infectious disease, and GeckoCap, a wireless sensor-in-a-smartcap for asthma inhalers enabling parents to track their kids’ asthma.  (Of course, for fairness sake, I’m leaving out Tidepool, the open platform for type 1 diabetes for which I’m medical advisor, and about which I am incredibly enthusiastic.)

When seeing new digital health devices and apps, I usually have one of three reactions, either: a) “Nope, next!”; b) “This has potential, I want to hear more about it”; or c) “I need to immediately call everyone I know and tell them about what I just saw”.  This year, reaction C came from the AthenaHealth/ePocrates Bugs and Drugs app.  This app makes me feel optimistic about real progress happening in healthcare.  This app makes me feel like the promise of the Learning Healthcare System is either upon us, or truly just around the corner.

If you’ve not seen this app yet, stop reading this article for a moment (come back to finish it, of course!) and go download it from the App Store.  The Bugs and Drugs app is a real-time, aggregated, cloud antibiogram.

What’s an antibiogram?  

Here is an example of the 2011 UCSF adult antibiogram.  First, a quick explanation for the non-clinician.  To test a patient for urine or bloodstream infections, clinicians order cultures to see if bacteria will grow (literally) out of the respective collection sites from a patient.  If bacteria grows from a culture and the patient is thus deemed infected, tests are done to see which bacteria is the specific cause.  Additional tests are then done to see which antibiotics will be effective at killing this particular bacteria strain.  This is known as sensitivity or susceptibility data.  This information can make the difference between giving a patient an ineffective antibiotic and an effective one.  Without it, we as clinicians are guessing about which bacterial strain we think the patient might have and which antibiotic to use.  We base this on our knowledge about which bacteria are most commonly pathogenic and which antibiotics are designed to kill which bacteria.  We also use available past data about cultured bacteria and antibiotic susceptibilities.  This last piece of data comes from antibiograms.  Many hospitals regularly publish an antibiogram, a handout that aggregates all of the culture and susceptibility data from each culture site (e.g. blood or urine) from the past year.  It shows the relative frequency of the occurrence of each bacterial strain and the frequency of each particular bacteria being sensitive or resistant to each common antibiotic.  For example, in the example UCSF antibiogram linked to above, there were 810 E. coli isolates (the most common bacteria isolated), and 85% of these were susceptible to ceftriaxone, a common antibiotic.  You might find that in another hospital in another region of the country, say North Carolina, that the sensitivity rate of E. coli to ceftriaxone is 35%.  Thus in the first hospital, the treating doctor would be likely to use ceftriaxone to treat the next patient with an E coli urinary tract infection, whereas in Texas, the doctor would certainly want to choose something else, knowing that ceftriaxone is unlikely to be effective.

So, this information can truly be life or death information.  It also contributes greatly to the concept of antibiotic stewardship and appropriate use of antibiotics to maintain their effectiveness for future use.  Traditionally, antibiograms are published regularly with an aggregation of the previous year’s data for each particular hospital.  But, that is static data, a collection of one year at a time.  It is also data bound within the physical or virtual walls of each healthcare organization or medical center.

Bugs and Drugs: An Antibiogram for the Learning Healthcare System

The Bugs and Drugs app has taken this concept and moved it into the cloud era.  The app capitalizes on the fact that AthenaHealth, as a cloud EHR provider, is able to aggregate all of the clinical data from their EHR, in real-time.  They have aggregated together all of the bacterial culture and antibiotic susceptibility data from all of their users and display it in real time in this app.  You are a doctor in Wichita and your patient has a urinary tract infection?  Pull open the Bugs and Drugs app and you can actually see what the most common bacteria are in the Wichita area right now that are causing urinary tract infections.  You can see which antibiotics are effective against those bacteria in the Wichita area right now.  This data is not from last year, it is from the last few weeks.  This data is not just from your hospital’s lab, it is from all of the hospitals’ labs in the area.

The catch of course is that this still lacks true health information exchange.  While the data does cross boundaries between health systems, it does not cross EHR vendor boundaries, coming only from AthenaHealth locations.  So, in the example above, you would not be getting data from every location in Wichita, just those that use AthenaHealth.

However, the really important thing about this app is that it shows on a nuts-and-bolts clinical level what we can do with aggregated real-time clinical data when it is put into a useful format in the hands of a clinician.  This information can influence care right now, for the patient sitting right in front of you.  This is the realization of the possibilities of the Learning Healthcare System, moving valuable information much more efficiently into the hands of the treating physician.  I predict (and hope) that we’ll see many more innovations like this in the coming year.

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About Aaron Neinstein, MD
I'm an Assistant Professor in Endocrinology and Director of Clinical Informatics at the University of California, San Francisco.

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