TY - BOOK

T1 - Utilising and Developing Methods for Routinely Collected Data in Health Research

AU - Garner, Alex

PY - 2024

Y1 - 2024

N2 - The UK’s National Health Service (NHS) is at a turning point, the organisation isstill recovering from the ongoing impact of the COVID-19 pandemic and healthcareworker strikes have further increased pressures on healthcare delivery. Technological advancements and improvements in data usage both provide significant opportunities and challenges for the NHS’s near future.Data is collected for almost every patient interaction with the NHS, this isroutinely collected data (RCD). There are vast amounts of RCD held within NHSsystems, with massive potential for health research. Enabling large scale usageof this data requires complex data infrastructure along with streamlining of data access procedures, while ensuring patient data remains anonymous and confidential. Developing this infrastructure is a technological undertaking in itself.Presented in this thesis are three projects conducted using RCD demonstratingopportunities of using this data in research while providing findings to impacthealthcare provision. The first project uses linked NHS data in a State SequenceAnalysis to investigate patterns of healthcare usage of care home residents around COVID-19 testing events, demonstrating that vulnerable residents received high impact inpatient stays despite known risks. The second project evaluates a digital technology intervention in care homes using a Generalised Linear Mixed Model framework, finding a reduction in unplanned secondary care usage for residents registered on the technology. The third project uses administrative emergency department data in a survival analysis framework, finding improvements in patient flow on strike days are likely due to increased inpatient capacity made available.Improved access to NHS routine data is crucial to ensuring that researchers canundertake responsive analysis to current pressures, such as those presented in this thesis, providing evidence to support optimised patient care throughout the NHS.

AB - The UK’s National Health Service (NHS) is at a turning point, the organisation isstill recovering from the ongoing impact of the COVID-19 pandemic and healthcareworker strikes have further increased pressures on healthcare delivery. Technological advancements and improvements in data usage both provide significant opportunities and challenges for the NHS’s near future.Data is collected for almost every patient interaction with the NHS, this isroutinely collected data (RCD). There are vast amounts of RCD held within NHSsystems, with massive potential for health research. Enabling large scale usageof this data requires complex data infrastructure along with streamlining of data access procedures, while ensuring patient data remains anonymous and confidential. Developing this infrastructure is a technological undertaking in itself.Presented in this thesis are three projects conducted using RCD demonstratingopportunities of using this data in research while providing findings to impacthealthcare provision. The first project uses linked NHS data in a State SequenceAnalysis to investigate patterns of healthcare usage of care home residents around COVID-19 testing events, demonstrating that vulnerable residents received high impact inpatient stays despite known risks. The second project evaluates a digital technology intervention in care homes using a Generalised Linear Mixed Model framework, finding a reduction in unplanned secondary care usage for residents registered on the technology. The third project uses administrative emergency department data in a survival analysis framework, finding improvements in patient flow on strike days are likely due to increased inpatient capacity made available.Improved access to NHS routine data is crucial to ensuring that researchers canundertake responsive analysis to current pressures, such as those presented in this thesis, providing evidence to support optimised patient care throughout the NHS.

U2 - 10.17635/lancaster/thesis/2524

DO - 10.17635/lancaster/thesis/2524

M3 - Doctoral Thesis

PB - Lancaster University

ER -