Example of monitoring measurements in a virtual eye clinic using 'big data'.

AIM: To assess the equivalence of measurement outcomes between patients attending a standard glaucoma care service, where patients see an ophthalmologist in a face-to-face setting, and a glaucoma monitoring service (GMS).
METHODS: The average mean deviation (MD) measurement on the visual field (VF) test for 250 patients attending a GMS were compared with a 'big data' repository of patients attending a standard glaucoma care service (reference database). In addition, the speed of VF progression between GMS patients and reference database patients was compared. Reference database patients were used to create expected outcomes that GMS patients could be compared with. For GMS patients falling outside of the expected limits, further analysis was carried out on the clinical management decisions for these patients.
RESULTS: The average MD of patients in the GMS ranged from +1.6dB to -18.9dB between two consecutive appointments at the clinic. In the first analysis, 12 (4.8%; 95% CI 2.5% to 8.2%) GMS patients scored outside the 90% expected values based on the reference database. In the second analysis, 1.9% (95% CI 0.4% to 5.4%) GMS patients had VF changes outside of the expected 90% limits.
CONCLUSIONS: Using 'big data' collected in the standard glaucoma care service, we found that patients attending a GMS have equivalent outcomes on the VF test. Our findings provide support for the implementation of virtual healthcare delivery in the hospital eye service. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.