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[(27/101, 26.7%) in GDm-Health group versus (47/102, 46.1%) in the control group, P=.005]1
[(5/101, 5.0%) in GDm-Health group versus (13/102, 12.7%; OR 0.36, 95% CI 0.12-1.01) in the control group]1
[5-level EuroQol 5-dimension questionnaire, mean score increased in the EDGE-COPD group by 0.01 and decreased in the standard care arm by 0.08, P=.003]2
Saving per patient using GDm-Health3
Reduction in the number of visits to see practice nurses for COPD patients using the EDGE-COPD app2
Reduction in time spent on clerical and administrative tasks when using GDm-Health3
Reduction in the time to take a set of vital sign observations when using SEND4
GDm-Health
Population served
Patients monitored
SEND
Population served
Patients monitored
BPm-Health
Population served
Patients monitored
Strategic Research
Agreement
Patient records
Data Access
Agreement
Patient records
Healthcare - we work with health systems to supply advanced software that enables better care of patients remotely.
Life sciences - we work with pharmaceutical companies to analyse real world data from electronic patient records to accelerate medical research.
We supply Clinical AI software for remote patient monitoring ~50% of NHS England Trusts and have access to anonymised data on over 8.5 million NHS patients.
Users of GDm-HealthTM app would recommend the app to their family and friends1
[mean 3.80 readings per day, (SD 1.80) in the GDm-Health group and mean 2.63 readings per day, (SD 1.71) in the control group (P=1)
[EDGE-COPD patients submitted symptoms and oxygen saturations 5.9 days per week and completed their diaries within 2 hours of their chosen time.]2
1. Mackillop, L., Hirst, J.E., Bartlett, K.J., Birks, J.S., Clifton, L., Farmer, A.J., Gibson, O., Kenworthy, Y., Levy, J.C., Loerup, L. and Rivero-Arias, O., 2018. Comparing the Efficacy of a Mobile Phone-Based Blood Glucose Management System With Standard Clinic Care in Women With Gestational Diabetes: Randomized Controlled Trial. JMIR mHealth and uHealth, 6(3), p.e71.
2. Farmer, A., Williams, V., Velardo, C., Shah, S.A., Yu, L.M., Rutter, H., Jones, L., Williams, N., Heneghan, C., Price, J. and Hardinge, M., 2017. Self-Management support using a digital health system compared with usual care for chronic obstructive pulmonary disease: randomized controlled trial. Journal of medical Internet research, 19(5).
3. National Institute for Clinical Excellence (2017) Health app: GDm-Health for people with gestational diabetes. Medtech innovation briefing (MIB131).
4. Wong, D., Bonnici, T., Knight, J., Gerry, S., Turton, J. and Watkinson, P., 2017. A ward-based time study of paper and electronic documentation for recording vital sign observations. Journal of the American Medical Informatics Association, 24(4), pp.717-721.
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In support of the ‘Understanding Patient Data’ initiative, Sensyne Health cite that its “work uses data provided by patients and collected by the NHS as part of their care and support”. #datasaveslives
