TY - CHAP

T1 - Missed appointments in healthcare systems

T2 - A national retrospective data linkage project

AU - Ellis, David

AU - McQueenie, Ross

AU - Williamson , Andrea

AU - Wilson, Philip

PY - 2020

Y1 - 2020

N2 - Healthcare systems across the world generate large volumes of data about patients including information about their age, sex, and medical history. It also captures information on how patients interact across multiple points of care (e.g., hospitals, dentists and general practice). Advances in data availability and computational power now means that much of this data can be leveraged for social good. This ranges from the use of behavioural analytics to better predict service demand through to understanding the impact of behaviour change interventions. In this project, we used patient data to explore the causes of low engagement in healthcare and the impact this has on patients and services. This also involved linking data sets from different organisations (e.g., health, death and education). We observed that serially missing general practice (GP) appointments provided a risk marker for vulnerability and poorer health outcomes. While the project was administratively and methodologically challenging, the interdisciplinary background of the team ensured that the project was ultimately successful. This was particularly important when navigating a variety of different systems used to manage and distribute sensitive patient data. Our results have already started to inform debates concerning how best to reduce non-attendance and increase patient engagement within healthcare systems. Following a series of high-profile publications and associated impact events, non-academic beneficiaries have included governments, policymakers and medical practitioners.

AB - Healthcare systems across the world generate large volumes of data about patients including information about their age, sex, and medical history. It also captures information on how patients interact across multiple points of care (e.g., hospitals, dentists and general practice). Advances in data availability and computational power now means that much of this data can be leveraged for social good. This ranges from the use of behavioural analytics to better predict service demand through to understanding the impact of behaviour change interventions. In this project, we used patient data to explore the causes of low engagement in healthcare and the impact this has on patients and services. This also involved linking data sets from different organisations (e.g., health, death and education). We observed that serially missing general practice (GP) appointments provided a risk marker for vulnerability and poorer health outcomes. While the project was administratively and methodologically challenging, the interdisciplinary background of the team ensured that the project was ultimately successful. This was particularly important when navigating a variety of different systems used to manage and distribute sensitive patient data. Our results have already started to inform debates concerning how best to reduce non-attendance and increase patient engagement within healthcare systems. Following a series of high-profile publications and associated impact events, non-academic beneficiaries have included governments, policymakers and medical practitioners.

U2 - 10.4135/9781529723014

DO - 10.4135/9781529723014

M3 - Chapter

T3 - SAGE Research Methods

BT - SAGE Research Methods Cases

PB - Sage

ER -