Background: Remote monitoring technologies show potential to help health professionals deliver preventative interventions which can avoid hospital admissions and allow patients to remain in a home setting.

Aims: To assess whether an Internet of Things (IoT) driven remote monitoring technology, used in the care pathway of community dementia patients in North Warwickshire improved access to care for patients and cost effectiveness.

Method: Patient level changes to anonymised retrospective healthcare utilisation data were analysed alongside costs.

Results: Urgent care decreased following use of an IoT driven remote monitoring technology; one preventative intervention avoided an average of three urgent interventions. A Chi-Square test showing this change as significant. Estimates show annualised service activity avoidance of £201,583 for the cohort; £8764 per patient.

Conclusions: IoT driven remote monitoring had a positive impact on health utilisation and cost avoidance. Future expansion of the cohort will allow for validation of the results and consider the impact of the technology on patient health outcomes and staff workflows.

Download full-text PDF

Source
http://dx.doi.org/10.12968/bjcn.2024.29.5.224DOI Listing

Publication Analysis

Top Keywords

remote monitoring
16
iot driven
12
driven remote
12
north warwickshire
8
monitoring technology
8
initial evaluation
4
evaluation technologyenabled
4
technologyenabled change
4
change delivery
4
delivery dementia
4

Similar Publications

Remote monitoring of patients with COPD disease using a tablet system: a randomised crossover study of quality-of-life measurements.

ERJ Open Res

January 2025

Department of Respiratory Medicine and Allergology, COPD Center, Sahlgrenska University Hospital and Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.

Background: Remote patient monitoring (RPM) has been evaluated in COPD, but with varying results. We aimed to evaluate whether a tablet system that monitors disease-related parameters in patients with COPD could influence physical and mental health-related quality of life, compared with usual care (UC).

Methods: 70 patients with Global Initiative for Chronic Obstructive Lung Disease (GOLD) group D COPD (61% women, aged 71±8 years, forced expiratory volume in 1 s % predicted 41±13%, COPD Assessment Test (CAT) 19±7 points) were recruited at the COPD centre in Gothenburg, Sweden, and randomised to a tablet-based RPM system or UC for a 26-week period, after which they crossed over to the alternative management for another 26 weeks.

View Article and Find Full Text PDF

Background: The COVID-19 pandemic accelerated a shift to decentralized clinical trials. We present the potential feasibility of this approach from a phase 1 pharmacokinetic (PK) trial.

Methods: Healthy adults (18-55 years) with a body mass index of 19.

View Article and Find Full Text PDF

[Remote monitoring for patients with cardiac implantable electronic devices and heart failure].

Herzschrittmacherther Elektrophysiol

January 2025

Hannover Herzrhythmus Centrum, Klinik für Kardiologie und Angiologie, Medizinische Hochschule Hannover, Carl-Neuberg-Str. 1, 30625, Hannover, Deutschland.

The digitalization in healthcare facilitates continuous monitoring of relevant medical parameters through internal and external sensors. For patients with heart failure and cardiac implantable electronic devices (CIEDs), telemedicine has the potential to improve patient care and reduce use of healthcare resources. Remote monitoring shortens the response time to a clinical event, reduces inappropriate shocks, and increases patient satisfaction.

View Article and Find Full Text PDF

This study highlights the vital role of high-resolution (HR), open-source land cover maps for food security, land use planning, and environmental protection. The scarcity of freely available HR datasets underscores the importance of multi-spectral HR aerial images. We used unmanned aerial vehicle (UAV) to capture images for a centimeter-level orthomosaics, facilitating advanced remote sensing and spatial analysis.

View Article and Find Full Text PDF

XIS-Temperature: A daily spatiotemporal machine-learning model for air temperature in the contiguous United States.

Environ Res

January 2025

Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; The Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva, Israel.

The challenge of reconstructing air temperature for environmental applications is to accurately estimate past exposures even where monitoring is sparse. We present XGBoost-IDW Synthesis for air temperature (XIS-Temperature), a high-resolution machine-learning model for daily minimum, mean, and maximum air temperature, covering the contiguous US from 2003 through 2023. XIS uses remote sensing (land surface temperature and vegetation) along with a parsimonious set of additional predictors to make predictions at arbitrary points, allowing the estimation of address-level exposures.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!