Service demand prediction based on information from connected devices
Research type
Research Study
Full title
Service demand prediction based on information from connected devices
IRAS ID
276149
Contact name
Thomas Hughes
Contact email
Sponsor organisation
West Essex Clinical Commissioning Group
Duration of Study in the UK
0 years, 11 months, 5 days
Research summary
The NHS faces an unprecedented demand for services. Contributing to this increasing demand is a population who are living longer but not necessarily in good health. There is a rise in patients with long-term conditions requiring regular care from the NHS. As part of the response to this, research is aimed at investigating possibilities for system-wide transformation. The research will focus on testing the theory that information about patient behaviour, conditions and events can be gleaned from wearables, monitors and other smart technologies in care homes and in the community. This information could enable a better understanding of drivers or triggers for the demand of services and therefore inform strategic health commissioning. The theory or hypothesis will be tested in this trial.
West Essex Clinical Commissioning Group will identify NHS sites that are prepared to participate in this study. Patient records will be interrogated to identify patients in these sites who have had multiple hospitalisations in the previous year, have multiple co-morbidities and are over 65 years of age.
The study will be conducted in two phases: Phase 1 will use up to 100 patients in a care home setting; Phase 2 will use up to 500 patients in community settings.
Remote monitoring medical devices appropriate to the patient’s condition will be issued to Health Care Assistants (HCAs) and patients and used to collect data on a regular basis throughout the study phases.
The study will record which patients require unscheduled care during a study phase, they will then be replaced in the study by another patient.
These observational data will be analysed to identify whether it would be possible to have predicted the exacerbation leading to the requirement for unscheduled care.
Service transformation recommendations will be made as appropriate.
Summary of study results:
This study investigated whether older people provided with connected medical devices could self-measure to generate data from which early signals of deterioration could be detected, enabling hospitalisation to be avoided.
We can report that, for this cohort of older people living with significant co-morbidities and frailty, it was possible to identify some exacerbations from the data captured in the ten days before the exacerbation date, in particular infections.
It was also shown that it is possible to obtain data of high quality from this cohort with high compliance to the self-monitoring regime.
Although statistical confidence is necessarily low, as the data are too limited and the cohort too heterogeneous for statistically significant results to be reported, this result is nonetheless of interest as it suggests that daily, simple self-measurements, could provide an early warning system to identify some exacerbations and reduce emergency admissions and would be highly affordable to roll out across a whole population.REC name
East of England - Essex Research Ethics Committee
REC reference
20/EE/0029
Date of REC Opinion
23 Jun 2020
REC opinion
Further Information Favourable Opinion