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An evidence-based approach to quality improvement for COVIDoximetry@Home

An evidence-based approach to quality improvement for COVIDoximetry@Home
An evidence-based approach to quality improvement for COVIDoximetry@Home
Robust data-driven insights are critical for the design, adaptation, and improvement of clinical and operational management policies governing care pathways and resource models. However, understanding the requirements for data and analysis can be challenging when faced with disruptive innovations that offer new or reconfigured services such as COVIDOximetry@Home(NHS England and NHS Improvement, 2020), and when such change impacts multiple providers in an Integrated Care System (ICS). In this report we outline measurement, monitoring and analysis of COVIDOximetry@Homeusing evidence-based practice as the underpinning foundation for PDSA quality improvement[1]. Many operational and clinical decisions should be considered, and it is the purpose of the data and analytics to offer decision makers with insights necessary to design, assessment and implement of policies for better care. ▪Clinical: predict patient outcomes; understand the efficacy of interventions at different COVID patient disease stages and associated clinical care settings▪Operational: understand how clinical services respond to workload and resources for planning, optimisation, and reconfiguration; identification and validation of processes▪Compliance: understand the degree to which services are operating according to procedures and practices▪Programme Evaluation: deliver evidence as part of programme evaluation and for sustainability investment decisionsWhilst the COVID-19 Virtual Wards Data Provision Notice (NHS Digital 2020-1) mandates the “data to be collected for the evaluation of the Virtual Wards pilot”, our work puts data into the context of digital systems, and ongoing clinical and operational quality improvement. We describe the COVID19 Virtual Ward concept and clinical setting, and then elaborate the clinical, operation, compliance, and evaluation requirements. Finally, we summarise a system view from an exemplar ICS, outlining the relation between structure and data.
COVID-19, virtual wards, remote monitoring, data analytics, quality improvement
Boniface, Michael
f30bfd7d-20ed-451b-b405-34e3e22fdfba
Zlatev, Zlatko
8f2e3635-d76c-46e2-85b9-53cc223fee01
Guerrero-Luduena, Richard
db129ad5-e7f4-427c-b834-34fd22d67537
Armitage, Htwe
f20a9e2d-1b62-4f17-8e9c-bfd6a0e0589f
Boniface, Michael
f30bfd7d-20ed-451b-b405-34e3e22fdfba
Zlatev, Zlatko
8f2e3635-d76c-46e2-85b9-53cc223fee01
Guerrero-Luduena, Richard
db129ad5-e7f4-427c-b834-34fd22d67537
Armitage, Htwe
f20a9e2d-1b62-4f17-8e9c-bfd6a0e0589f

Boniface, Michael, Zlatev, Zlatko, Guerrero-Luduena, Richard and Armitage, Htwe (2020) An evidence-based approach to quality improvement for COVIDoximetry@Home 14pp.

Record type: Monograph (Project Report)

Abstract

Robust data-driven insights are critical for the design, adaptation, and improvement of clinical and operational management policies governing care pathways and resource models. However, understanding the requirements for data and analysis can be challenging when faced with disruptive innovations that offer new or reconfigured services such as COVIDOximetry@Home(NHS England and NHS Improvement, 2020), and when such change impacts multiple providers in an Integrated Care System (ICS). In this report we outline measurement, monitoring and analysis of COVIDOximetry@Homeusing evidence-based practice as the underpinning foundation for PDSA quality improvement[1]. Many operational and clinical decisions should be considered, and it is the purpose of the data and analytics to offer decision makers with insights necessary to design, assessment and implement of policies for better care. ▪Clinical: predict patient outcomes; understand the efficacy of interventions at different COVID patient disease stages and associated clinical care settings▪Operational: understand how clinical services respond to workload and resources for planning, optimisation, and reconfiguration; identification and validation of processes▪Compliance: understand the degree to which services are operating according to procedures and practices▪Programme Evaluation: deliver evidence as part of programme evaluation and for sustainability investment decisionsWhilst the COVID-19 Virtual Wards Data Provision Notice (NHS Digital 2020-1) mandates the “data to be collected for the evaluation of the Virtual Wards pilot”, our work puts data into the context of digital systems, and ongoing clinical and operational quality improvement. We describe the COVID19 Virtual Ward concept and clinical setting, and then elaborate the clinical, operation, compliance, and evaluation requirements. Finally, we summarise a system view from an exemplar ICS, outlining the relation between structure and data.

Text
RECOxCARE-Evidence-Based-QI-CWD-v1.0-final - Author's Original
Available under License Creative Commons Attribution.
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More information

Published date: 27 November 2020
Keywords: COVID-19, virtual wards, remote monitoring, data analytics, quality improvement

Identifiers

Local EPrints ID: 445388
URI: http://eprints.soton.ac.uk/id/eprint/445388
PURE UUID: e22ba117-c9f4-4ae7-9ac8-45558b2e13b3
ORCID for Michael Boniface: ORCID iD orcid.org/0000-0002-9281-6095
ORCID for Richard Guerrero-Luduena: ORCID iD orcid.org/0000-0002-1217-015X

Catalogue record

Date deposited: 07 Dec 2020 17:31
Last modified: 18 Feb 2021 17:25

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Contributors

Author: Zlatko Zlatev
Author: Htwe Armitage

University divisions

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