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. 2017 Mar;10(3):e003326.
doi: 10.1161/CIRCOUTCOMES.116.003326.

Smartphone-Based Geofencing to Ascertain Hospitalizations

Kaylin T Nguyen  1 , Jeffrey E Olgin  1 , Mark J Pletcher  1 , Madelena Ng  1 , Leanne Kaye  1 , Sai Moturu  1 , Rachel A Gladstone  1 , Chaitanya Malladi  1 , Amy H Fann  1 , Carol Maguire  1 , Laura Bettencourt  1 , Matthew A Christensen  1 , Gregory M Marcus  2
Affiliations

Smartphone-Based Geofencing to Ascertain Hospitalizations

Kaylin T Nguyen et al. Circ Cardiovasc Qual Outcomes. 2017 Mar.

Abstract

Background: Ascertainment of hospitalizations is critical to assess quality of care and the effectiveness and adverse effects of various therapies. Smartphones, mobile geolocators that are ubiquitous, have not been leveraged to ascertain hospitalizations. Therefore, we evaluated the use of smartphone-based geofencing to track hospitalizations.

Methods and results: Participants aged ≥18 years installed a mobile application programmed to geofence all hospitals using global positioning systems and cell phone tower triangulation and to trigger a smartphone-based questionnaire when located in a hospital for ≥4 hours. An in-person study included consecutive consenting patients scheduled for electrophysiology and cardiac catheterization procedures. A remote arm invited Health eHeart Study participants who consented and engaged with the study via the internet only. The accuracy of application-detected hospitalizations was confirmed by medical record review as the reference standard. Of 22 eligible in-person patients, 17 hospitalizations were detected (sensitivity 77%; 95% confidence interval, 55%-92%). The length of stay according to the application was positively correlated with the length of stay ascertained via the electronic medical record (r=0.53; P=0.03). In the remote arm, the application was downloaded by 3443 participants residing in all 50 US states; 243 hospital visits at 119 different hospitals were detected through the application. The positive predictive value for an application-reported hospitalization was 65% (95% confidence interval, 57%-72%).

Conclusions: Mobile application-based ascertainment of hospitalizations can be achieved with modest accuracy. This first proof of concept may ultimately be applicable to geofencing other types of prespecified locations to facilitate healthcare research and patient care.

Keywords: fast food; hospitalization; internet; pharmacies; smartphone.

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Conflict of interest statement

Disclosures: Drs. Moturu and Kaye are employees of Ginger.io. None of the other authors report any potential conflict of interest.

Figures

Figure 1
Figure 1. Enrollment and study process
Figure 2
Figure 2
Geographical distribution of participants (panel A) and hospitals (panel B). Location based on zip code. The number of records represented by relative size. Created with Tableau Software (www.tableau.com) and U.S. map provided under a CC BY-SA license from OpenStreetMap (www.openstreetmap.org/copyright), © OpenStreetMap contributors.
Figure 3
Figure 3
Comparison between application-based and actual duration of hospital stay. Actual length of hospital stay based on the electronic medical record. A. Correlation between two methods. B. Bland-Alman plots showing the difference between application (app)-based and actual duration of hospital stay. Solid line represents mean difference between the app-based and actual length of stay. Dashed line depicts the upper and lower bounds of the 95% confidence interval.
Figure 4
Figure 4
Accuracy of medical visits confirmed through the application in “remote arm.” *Three participants were included in both groups (multiple visits detected with participants verifying via e-mail at least one visit for medical care and denied at least one visit for medical care). HIPAA = Health Insurance Portability and Accountability Act.
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