Understanding repeated non-attendance in health services: a pilot analysis of administrative data and full study protocol for a national retrospective cohort

Publikation: Bidrag til tidsskriftTidsskriftartikelfagfællebedømt

Standard

Understanding repeated non-attendance in health services : a pilot analysis of administrative data and full study protocol for a national retrospective cohort. / Williamson, Andrea E; Ellis, David A; Wilson, Philip; McQueenie, Ross; McConnachie, Alex.

I: BMJ Open, Bind 7, Nr. 2, e014120, 2017.

Publikation: Bidrag til tidsskriftTidsskriftartikelfagfællebedømt

Harvard

Williamson, AE, Ellis, DA, Wilson, P, McQueenie, R & McConnachie, A 2017, 'Understanding repeated non-attendance in health services: a pilot analysis of administrative data and full study protocol for a national retrospective cohort', BMJ Open, bind 7, nr. 2, e014120. https://doi.org/10.1136/bmjopen-2016-014120

APA

Williamson, A. E., Ellis, D. A., Wilson, P., McQueenie, R., & McConnachie, A. (2017). Understanding repeated non-attendance in health services: a pilot analysis of administrative data and full study protocol for a national retrospective cohort. BMJ Open, 7(2), [e014120]. https://doi.org/10.1136/bmjopen-2016-014120

Vancouver

Williamson AE, Ellis DA, Wilson P, McQueenie R, McConnachie A. Understanding repeated non-attendance in health services: a pilot analysis of administrative data and full study protocol for a national retrospective cohort. BMJ Open. 2017;7(2). e014120. https://doi.org/10.1136/bmjopen-2016-014120

Author

Williamson, Andrea E ; Ellis, David A ; Wilson, Philip ; McQueenie, Ross ; McConnachie, Alex. / Understanding repeated non-attendance in health services : a pilot analysis of administrative data and full study protocol for a national retrospective cohort. I: BMJ Open. 2017 ; Bind 7, Nr. 2.

Bibtex

@article{15078b5f0f5649f8a2a2ca37ac6e5aef,
title = "Understanding repeated non-attendance in health services: a pilot analysis of administrative data and full study protocol for a national retrospective cohort",
abstract = "INTRODUCTION: Understanding the causes of low engagement in healthcare is a pre-requisite for improving health services' contribution to tackling health inequalities. Low engagement includes missing healthcare appointments. Serially (having a pattern of) missing general practice (GP) appointments may provide a risk marker for vulnerability and poorer health outcomes.METHODS AND ANALYSIS: A proof of concept pilot using GP appointment data and a focus group with GPs informed the development of missed appointment categories: patients can be classified based on the number of appointments missed each year. The full study, using a retrospective cohort design, will link routine health service and education data to determine the relationship between GP appointment attendance, health outcomes, healthcare usage, preventive health activity and social circumstances taking a life course approach and using data from the whole journey in the National Health Service (NHS) healthcare. 172 practices will be recruited (∼900 000 patients) across Scotland. The statistical analysis will focus on 2 key areas: factors that predict patients who serially miss appointments, and serial missed appointments as a predictor of future patient outcomes. Regression models will help understand how missed appointment patterns are associated with patient and practice characteristics. We shall identify key factors associated with serial missed appointments and potential interactions that might predict them.ETHICS AND DISSEMINATION: The results of the project will inform debates concerning how best to reduce non-attendance and increase patient engagement within healthcare systems. Significant non-academic beneficiaries include governments, policymakers and medical practitioners. Results will be disseminated via a combination of academic outputs (papers, conferences), social media and through collaborative public health/policy fora.",
keywords = "Appointments and Schedules, Focus Groups, General Practice/statistics & numerical data, Health Services/statistics & numerical data, Humans, Medical Record Linkage, No-Show Patients/psychology, Pilot Projects, Proof of Concept Study, Research Design, Retrospective Studies, Scotland, Vulnerable Populations/statistics & numerical data",
author = "Williamson, {Andrea E} and Ellis, {David A} and Philip Wilson and Ross McQueenie and Alex McConnachie",
note = "Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.",
year = "2017",
doi = "10.1136/bmjopen-2016-014120",
language = "English",
volume = "7",
journal = "BMJ Open",
issn = "2044-6055",
publisher = "BMJ Publishing Group",
number = "2",

}

RIS

TY - JOUR

T1 - Understanding repeated non-attendance in health services

T2 - a pilot analysis of administrative data and full study protocol for a national retrospective cohort

AU - Williamson, Andrea E

AU - Ellis, David A

AU - Wilson, Philip

AU - McQueenie, Ross

AU - McConnachie, Alex

N1 - Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

PY - 2017

Y1 - 2017

N2 - INTRODUCTION: Understanding the causes of low engagement in healthcare is a pre-requisite for improving health services' contribution to tackling health inequalities. Low engagement includes missing healthcare appointments. Serially (having a pattern of) missing general practice (GP) appointments may provide a risk marker for vulnerability and poorer health outcomes.METHODS AND ANALYSIS: A proof of concept pilot using GP appointment data and a focus group with GPs informed the development of missed appointment categories: patients can be classified based on the number of appointments missed each year. The full study, using a retrospective cohort design, will link routine health service and education data to determine the relationship between GP appointment attendance, health outcomes, healthcare usage, preventive health activity and social circumstances taking a life course approach and using data from the whole journey in the National Health Service (NHS) healthcare. 172 practices will be recruited (∼900 000 patients) across Scotland. The statistical analysis will focus on 2 key areas: factors that predict patients who serially miss appointments, and serial missed appointments as a predictor of future patient outcomes. Regression models will help understand how missed appointment patterns are associated with patient and practice characteristics. We shall identify key factors associated with serial missed appointments and potential interactions that might predict them.ETHICS AND DISSEMINATION: The results of the project will inform debates concerning how best to reduce non-attendance and increase patient engagement within healthcare systems. Significant non-academic beneficiaries include governments, policymakers and medical practitioners. Results will be disseminated via a combination of academic outputs (papers, conferences), social media and through collaborative public health/policy fora.

AB - INTRODUCTION: Understanding the causes of low engagement in healthcare is a pre-requisite for improving health services' contribution to tackling health inequalities. Low engagement includes missing healthcare appointments. Serially (having a pattern of) missing general practice (GP) appointments may provide a risk marker for vulnerability and poorer health outcomes.METHODS AND ANALYSIS: A proof of concept pilot using GP appointment data and a focus group with GPs informed the development of missed appointment categories: patients can be classified based on the number of appointments missed each year. The full study, using a retrospective cohort design, will link routine health service and education data to determine the relationship between GP appointment attendance, health outcomes, healthcare usage, preventive health activity and social circumstances taking a life course approach and using data from the whole journey in the National Health Service (NHS) healthcare. 172 practices will be recruited (∼900 000 patients) across Scotland. The statistical analysis will focus on 2 key areas: factors that predict patients who serially miss appointments, and serial missed appointments as a predictor of future patient outcomes. Regression models will help understand how missed appointment patterns are associated with patient and practice characteristics. We shall identify key factors associated with serial missed appointments and potential interactions that might predict them.ETHICS AND DISSEMINATION: The results of the project will inform debates concerning how best to reduce non-attendance and increase patient engagement within healthcare systems. Significant non-academic beneficiaries include governments, policymakers and medical practitioners. Results will be disseminated via a combination of academic outputs (papers, conferences), social media and through collaborative public health/policy fora.

KW - Appointments and Schedules

KW - Focus Groups

KW - General Practice/statistics & numerical data

KW - Health Services/statistics & numerical data

KW - Humans

KW - Medical Record Linkage

KW - No-Show Patients/psychology

KW - Pilot Projects

KW - Proof of Concept Study

KW - Research Design

KW - Retrospective Studies

KW - Scotland

KW - Vulnerable Populations/statistics & numerical data

U2 - 10.1136/bmjopen-2016-014120

DO - 10.1136/bmjopen-2016-014120

M3 - Journal article

C2 - 28196951

VL - 7

JO - BMJ Open

JF - BMJ Open

SN - 2044-6055

IS - 2

M1 - e014120

ER -

ID: 217945469