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Patient and staff experiences of using technology-enabled and analogue models of remote home monitoring for COVID-19 in England: A mixed-method evaluation.
Herlitz, Lauren; Crellin, Nadia; Vindrola-Padros, Cecilia; Ellins, Jo; Georghiou, Theo; Litchfield, Ian; Massou, Efthalia; Ng, Pei Li; Sherlaw-Johnson, Chris; Sidhu, Manbinder S; Tomini, Sonila M; Walton, Holly; Fulop, Naomi J.
Afiliação
  • 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.
  • Crellin N; Nuffield Trust, 59 New Cavendish St, London W1G 7LP, UK.
  • Vindrola-Padros C; Department of Targeted Intervention, University College London, Charles Bell House, 43-45 Foley Street, London, W1W 7TY, UK.
  • Ellins J; Health Services Management Centre, School of Social Policy, University of Birmingham, 40 Edgbaston Park Road, Birmingham, B15 2RT, UK.
  • Georghiou T; Nuffield Trust, 59 New Cavendish St, London W1G 7LP, UK.
  • Litchfield I; Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, 40 Edgbaston Park Road, Birmingham, B15 2RT, UK.
  • Massou E; Department of Public Health and Primary Care, University of Cambridge, UK.
  • Ng PL; Department of Applied Health Research, University College London, Gower Street, London WC1E 6BT, UK.
  • Sherlaw-Johnson C; Nuffield Trust, 59 New Cavendish St, London W1G 7LP, UK.
  • Sidhu MS; Health Services Management Centre, School of Social Policy, University of Birmingham, 40 Edgbaston Park Road, Birmingham, B15 2RT, UK.
  • Tomini SM; Global Business School for Health, University College London, Gower Street. Bloomsbury London SC1E 6BT, UK.
  • Walton H; Department of Applied Health Research, University College London, Gower Street, London WC1E 6BT, UK.
  • Fulop NJ; Department of Applied Health Research, University College London, Gower Street, London WC1E 6BT, UK.
Int J Med Inform ; 179: 105230, 2023 Nov.
Article em En | MEDLINE | ID: mdl-37774428
ABSTRACT

OBJECTIVE:

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.

METHODS:

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.

RESULTS:

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.

CONCLUSIONS:

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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Qualitative_research Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Qualitative_research Idioma: En Ano de publicação: 2023 Tipo de documento: Article