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.

iBGStar meter now on sale at Walgreens and Apple

I expect that this meter will be very popular, as it will allow people with diabetes to automatically record their glucose values on their iPhones, eliminating the arduous task of manual entry.  I would love to hear from patients who are planning on using one or have already tried one about their experiences with them.

They will be sold not only at Walgreens but also the Apple store, which is proof about the growing and profound connection between consumer technology and healthcare.  People want their healthcare devices to be designed just as elegantly as they want their smartphone or laptop or speakers designed.  I’m hopeful that the days of unusable, obtuse healthcare devices will soon be behind us.

See here for story from mobihealthnews and here is a review of the meter from a person with diabetes who writes a blog named “DiabeticallyYours.”

Two clinical trials ongoing with diabetes mhealth

There are two ongoing clinical trials to be aware of.

One is at the Univ of Maryland and is using the forementioned Telcare meter.  This study is a 6-month pilot study taking 100 patients with diabetes (both types 1 and 2) and randomizing them to either typical glucose meter or the Telcare meter.  The outcome measures will be to see if connecting the patients via the Telcare meter will improve self-monitoring of blood glucose (SMBG) compliance, to see if A1c is affected, and to see if patient satisfaction is improved.

The other is being sponsored by a company called Diabetech with a link to the trial information here.  This study is using an investigational device designed by Diabetech that attaches a self-contained wireless accessory to a glucose meter, and then transmits data to a centralized data management system.  The system then analyzes the data and either sends educational materials to the patient or alerts or reports to the healthcare team.  The primary outcome measures in this study are glucose control and patient satisfaction.  The secondary outcome measures are HbA1c, self-test frequency of glucoses, and standard deviation of HbA1c and SMBG.

Two quick links: Telemedicine at the Joslin and iPhone app reviews

1) The Joslin Diabetes Center at Harvard is creating a telemedicine platform so that they can start doing long-distance diabetes consultations.

I’ve been doing a lot of “VTel” (VA slang for videoconferencing) at the VA this past year for diabetes consultations.  While they aren’t a perfect substitution for a face-to-face conversation, they do generally seem to get the job done and save our remote patients a lot of travel time.

2) Reviews by Marisa Moore of 10 iPhone diabetes apps.

I have not yet tried most of these applications, but my overall feeling is that the current generation of smartphone apps for diabetes are not going to take us very far.  There are several reasons for this.  The main reason, as I’ve discussed in previous posts about Glooko and the iBGStar, is that asking patients to manually enter blood sugars after doing a fingerstick is an unworkable workflow.  Also, with the way data is currently displayed, the iPhone screen is too small to glean anything useful.  A lot of apps have education modules, which may be useful, but I think to really gain traction, education will have to be personally targeted to the right patient at the right point in time.

iBGStar turns your iPhone into a glucose meter… on sale in UK now

Still not available in US (though finally FDA approved), but now available in the UK, is the iBGStar from Sanofi.  This device is similar in spirit to the Glooko iPhone connector dongle that I recently wrote about here.

This is a very exciting device because it will allow patients to electronically capture their blood sugars without any extra work.  No more transcribing numbers into a logbook, either a paper one or even a digital logbook.  As soon as you check your blood sugar, the number is already captured into a digital logbook with no extra work.  This device clearly has an advantage over the Glooko solution in that the data is collected in real-time, rather than needing a connection and download the way the Glooko does.  The introduction of real-time data upload brings the possibility of shorter and faster feedback loops between patient and clinician.  There is also no extra hardware to potentially lose, since the extra hardware is your glucometer.  Glooko’s advantage is that it works with many existing glucose meters, so you can get the one covered by your insurance, and still easily digitize the data.  So, they each bring a unique new capability to glucose monitoring that should be welcome.

Glooko: A cable to download glucose data from (most) meters

While I haven’t tested this cable or software from Glooko, they seem to be on the right track.  Largely for insurance reasons, patients use a wide variety of glucometers.  Many patients have one glucometer for home, one for the car, one for work, all different brands.  Being able to download all of the data from different devices into one location is very helpful.  It should make many providers happy that the Glooko app appears to let you display the downloaded data in the format of a traditional paper glucose logbook.  While it currently only supports seven meters, they seem to be working on supporting more.
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