Patient and staff experiences of using technology-enabled and analogue models of remote home monitoring for COVID-19 in England: A mixed-method evaluation.

COVID-19 Home monitoring Patient experience Remote monitoring Staff experience Telehealth

Journal

International journal of medical informatics
ISSN: 1872-8243
Titre abrégé: Int J Med Inform
Pays: Ireland
ID NLM: 9711057

Informations de publication

Date de publication:
Nov 2023
Historique:
received: 02 06 2023
revised: 24 08 2023
accepted: 21 09 2023
pubmed: 29 9 2023
medline: 29 9 2023
entrez: 29 9 2023
Statut: ppublish

Résumé

To evaluate patient and staff experiences of using technology-enabled ('tech-enabled') and analogue remote home monitoring models for COVID-19, implemented in England during the pandemic. Twenty-eight sites were selected for diversity in a range of criteria (e.g. pre-hospital or early discharge service, mode of patient data submission). Between February and May 2021, we conducted quantitative surveys with patients, carers and staff delivering the service, and interviewed patients, carers, and staff from 17 of the 28 services. Quantitative data were analysed using descriptive statistics and both univariate and multivariate analyses. Qualitative data were interpreted using thematic analysis. Twenty-one sites adopted mixed models whereby patients could submit their symptoms using either tech-enabled (app, weblink, or automated phone calls) or analogue (phone calls with a health professional) options; seven sites offered analogue-only data submission (phone calls or face-to-face visits with a health professional). Sixty-two patients and carers were interviewed, and 1069 survey responses were received (18 % response rate). Fifty-eight staff were interviewed, and 292 survey responses were received (39 % response rate). Patients who used tech-enabled modes tended to be younger (p = 0.005), have a higher level of education (p = 0.011), and more likely to identify as White British (p = 0.043). Most patients found relaying symptoms easy, regardless of modality, though many received assistance from family or friends. Staff considered the adoption of mixed delivery models beneficial, enabling them to manage large patient numbers and contact patients for further assessment as needed; however, they suggested improvements to the functionality of systems to better fit clinical and operational needs. Human contact was important in all remote home monitoring options. Organisations implementing tech-enabled remote home monitoring at scale should consider adopting mixed models which can accommodate patients with different needs; focus on the usability and interoperability of tech-enabled platforms; and encourage digital inclusivity for patients.

Identifiants

pubmed: 37774428
pii: S1386-5056(23)00248-4
doi: 10.1016/j.ijmedinf.2023.105230
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

105230

Informations de copyright

Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Lauren Herlitz (L)

NIHR Children and Families Policy Research Unit, Great Ormond Street Institute of Child Health, 30 Guilford Street, London WC1N 1EH, UK. Electronic address: l.herlitz@ucl.ac.uk.

Nadia Crellin (N)

Nuffield Trust, 59 New Cavendish St, London W1G 7LP, UK.

Cecilia Vindrola-Padros (C)

Department of Targeted Intervention, University College London, Charles Bell House, 43-45 Foley Street, London, W1W 7TY, UK.

Jo Ellins (J)

Health Services Management Centre, School of Social Policy, University of Birmingham, 40 Edgbaston Park Road, Birmingham, B15 2RT, UK.

Theo Georghiou (T)

Nuffield Trust, 59 New Cavendish St, London W1G 7LP, UK.

Ian Litchfield (I)

Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, 40 Edgbaston Park Road, Birmingham, B15 2RT, UK.

Efthalia Massou (E)

Department of Public Health and Primary Care, University of Cambridge, UK.

Pei Li Ng (PL)

Department of Applied Health Research, University College London, Gower Street, London WC1E 6BT, UK.

Chris Sherlaw-Johnson (C)

Nuffield Trust, 59 New Cavendish St, London W1G 7LP, UK.

Manbinder S Sidhu (MS)

Health Services Management Centre, School of Social Policy, University of Birmingham, 40 Edgbaston Park Road, Birmingham, B15 2RT, UK.

Sonila M Tomini (SM)

Global Business School for Health, University College London, Gower Street. Bloomsbury London SC1E 6BT, UK.

Holly Walton (H)

Department of Applied Health Research, University College London, Gower Street, London WC1E 6BT, UK.

Naomi J Fulop (NJ)

Department of Applied Health Research, University College London, Gower Street, London WC1E 6BT, UK.

Classifications MeSH