SlideShare a Scribd company logo
1 of 8
Navigating NHS Administrative
Data
David A. Ellis
Missed Appointments
• Proxy for low engagement
• As a health harming behaviour
• Proxy for poor health and social vulnerability
Results
Results
Step Back
Process
code appointments
attended = 830,039
DNA = 56,441
appointments.csv
N=892,216
patients.csv
N=73,012
clinical.csv
N=704,828
remove non-appointments
based on time rules
compute number of
appointments attended/missed
for each patient
appointmenthistory dataframe
patient ID
DNA
attended
total
percentage missed
annual DNA rate
Categorise each patient. zero,
low medium, high
appointment History merged with
Patients file
(using patient ID as link)
patientappointments dataset
(N=70,165)
ID
sex
age
distance
Rur8
PracticeRur8
SIMD
PracticeSIMD
Ethnic
attended
DNA
total
percentage missed
category
annual rate (attended)
Ready for analysis and visualization
(N=67,705)
reclassify based on
codes of interest
N=825,784 remaining after (7.4%)
removed
Zero N = 44,685 (63.7%)
Low N = 19,281(27.5%)
Medium N = 5,097 (7.3%)
High N = 1,102 (1.6%)
N = 491 patients (<1%) with no
appointment data removed
remove patients with missing
data
N=2,460
(3.5%)
patients classified as frequent/non
frequent attenders
(10th centile (annual attendance
rate>=8.66))
Yes = 7,283
No = 62,882
subset to remove
remove ethnicity data
add age categories
remove administrative/
secretary appointments
N=891,921 remaining after (<.01%)
removed
remove duplicate
patients
N=2,356
Replication/Transparency?
Key References
Ellis, D. A., McQueenie, R., McConnachie, A., Wilson, P and Williamson, A. E.
(2018). Non-attending patients in general practice. The Lancet Public Health,
3(3), e113
Smits, F. T. and ter Riet, G. (2017). Non-attending patients in general
practice. The Lancet Public Health, 2(12), e538.
Ellis, D. A., McQueenie, R., McConnachie, A., Wilson, P and Williamson, A. E.
(2017). Demographic and practice factors predicting repeated non-
attendance in primary care: A national retrospective cohort analysis. The
Lancet Public Health. 2(12), e551-e559
Williamson, A., Ellis, D. A., Wilson, P., McQueenie, R. and McConnachie, A.
(2017). Understanding repeated non-attendance in health services - pilot
analysis of administrative data and full study protocol. BMJ Open, 7(2),
e01412

More Related Content

Similar to Navigating NHS Administrative Data

Making Healthcare Waste Reduction and Patient Safety Actionable - HAS Session 6
Making Healthcare Waste Reduction and Patient Safety Actionable - HAS Session 6Making Healthcare Waste Reduction and Patient Safety Actionable - HAS Session 6
Making Healthcare Waste Reduction and Patient Safety Actionable - HAS Session 6Health Catalyst
 
Jennifer Horowitz EHR Adoption in Michigan & Nationwide
Jennifer Horowitz EHR Adoption in Michigan & NationwideJennifer Horowitz EHR Adoption in Michigan & Nationwide
Jennifer Horowitz EHR Adoption in Michigan & Nationwidemihinpr
 
Stroke Event 13 Sep - First morning presentations
Stroke Event 13 Sep  - First morning presentationsStroke Event 13 Sep  - First morning presentations
Stroke Event 13 Sep - First morning presentationsCLAHRC-NDL
 
Patient Selection for Primary Prevention Implantable Cardioverter Defibrillat...
Patient Selection for Primary Prevention Implantable Cardioverter Defibrillat...Patient Selection for Primary Prevention Implantable Cardioverter Defibrillat...
Patient Selection for Primary Prevention Implantable Cardioverter Defibrillat...HMO Research Network
 
Patient Selection for Primary Prevention Implantable Cardioverter Defibrillat...
Patient Selection for Primary Prevention Implantable Cardioverter Defibrillat...Patient Selection for Primary Prevention Implantable Cardioverter Defibrillat...
Patient Selection for Primary Prevention Implantable Cardioverter Defibrillat...HMO Research Network
 
Clinical information systems
Clinical information systemsClinical information systems
Clinical information systemsMark Wardle
 
Utility of primary care-based TIA electronic decision support
Utility of primary care-based TIA electronic decision supportUtility of primary care-based TIA electronic decision support
Utility of primary care-based TIA electronic decision supportHealth Informatics New Zealand
 
Clinical Decision Support Systems
Clinical Decision Support SystemsClinical Decision Support Systems
Clinical Decision Support Systemsrkblacey
 
General_Registry Presentation
General_Registry PresentationGeneral_Registry Presentation
General_Registry PresentationShazia Subhani
 
ANZICS S&Q 2014 - Abstract Presentation: Kyle Brooks on Impact of night time ...
ANZICS S&Q 2014 - Abstract Presentation: Kyle Brooks on Impact of night time ...ANZICS S&Q 2014 - Abstract Presentation: Kyle Brooks on Impact of night time ...
ANZICS S&Q 2014 - Abstract Presentation: Kyle Brooks on Impact of night time ...ANZICS
 
Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...
Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...
Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...Perficient, Inc.
 
Laos: Hospital treatment costs and tertiary care
Laos: Hospital treatment costs and tertiary careLaos: Hospital treatment costs and tertiary care
Laos: Hospital treatment costs and tertiary careHealthSpace.Asia
 
A standards-based approach to development of clinical registries
A standards-based approach to development of clinical registriesA standards-based approach to development of clinical registries
A standards-based approach to development of clinical registriesHealth Informatics New Zealand
 
CEMRC presentation 20091108
CEMRC presentation 20091108CEMRC presentation 20091108
CEMRC presentation 20091108Marion Sills
 
Looking Back on Clinical Decision Support and Data Warehousing
Looking Back on Clinical Decision Support and Data WarehousingLooking Back on Clinical Decision Support and Data Warehousing
Looking Back on Clinical Decision Support and Data WarehousingHealth Catalyst
 
Γιώτα Τουλούμη, 6th Clinical Research Conference
Γιώτα Τουλούμη, 6th Clinical Research ConferenceΓιώτα Τουλούμη, 6th Clinical Research Conference
Γιώτα Τουλούμη, 6th Clinical Research ConferenceStarttech Ventures
 

Similar to Navigating NHS Administrative Data (20)

Making Healthcare Waste Reduction and Patient Safety Actionable - HAS Session 6
Making Healthcare Waste Reduction and Patient Safety Actionable - HAS Session 6Making Healthcare Waste Reduction and Patient Safety Actionable - HAS Session 6
Making Healthcare Waste Reduction and Patient Safety Actionable - HAS Session 6
 
Jennifer Horowitz EHR Adoption in Michigan & Nationwide
Jennifer Horowitz EHR Adoption in Michigan & NationwideJennifer Horowitz EHR Adoption in Michigan & Nationwide
Jennifer Horowitz EHR Adoption in Michigan & Nationwide
 
Stroke Event 13 Sep - First morning presentations
Stroke Event 13 Sep  - First morning presentationsStroke Event 13 Sep  - First morning presentations
Stroke Event 13 Sep - First morning presentations
 
Patient Selection for Primary Prevention Implantable Cardioverter Defibrillat...
Patient Selection for Primary Prevention Implantable Cardioverter Defibrillat...Patient Selection for Primary Prevention Implantable Cardioverter Defibrillat...
Patient Selection for Primary Prevention Implantable Cardioverter Defibrillat...
 
Patient Selection for Primary Prevention Implantable Cardioverter Defibrillat...
Patient Selection for Primary Prevention Implantable Cardioverter Defibrillat...Patient Selection for Primary Prevention Implantable Cardioverter Defibrillat...
Patient Selection for Primary Prevention Implantable Cardioverter Defibrillat...
 
Clinical information systems
Clinical information systemsClinical information systems
Clinical information systems
 
Utility of primary care-based TIA electronic decision support
Utility of primary care-based TIA electronic decision supportUtility of primary care-based TIA electronic decision support
Utility of primary care-based TIA electronic decision support
 
Clinical Decision Support Systems
Clinical Decision Support SystemsClinical Decision Support Systems
Clinical Decision Support Systems
 
MLS13 QI Workshop
MLS13 QI WorkshopMLS13 QI Workshop
MLS13 QI Workshop
 
E-poster09 Iniguez aimradial20170921 Transradial bioresorbable stent
E-poster09 Iniguez aimradial20170921 Transradial bioresorbable stentE-poster09 Iniguez aimradial20170921 Transradial bioresorbable stent
E-poster09 Iniguez aimradial20170921 Transradial bioresorbable stent
 
General_Registry Presentation
General_Registry PresentationGeneral_Registry Presentation
General_Registry Presentation
 
ANZICS S&Q 2014 - Abstract Presentation: Kyle Brooks on Impact of night time ...
ANZICS S&Q 2014 - Abstract Presentation: Kyle Brooks on Impact of night time ...ANZICS S&Q 2014 - Abstract Presentation: Kyle Brooks on Impact of night time ...
ANZICS S&Q 2014 - Abstract Presentation: Kyle Brooks on Impact of night time ...
 
Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...
Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...
Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...
 
Stich trial.pptx
Stich trial.pptxStich trial.pptx
Stich trial.pptx
 
Laos: Hospital treatment costs and tertiary care
Laos: Hospital treatment costs and tertiary careLaos: Hospital treatment costs and tertiary care
Laos: Hospital treatment costs and tertiary care
 
A standards-based approach to development of clinical registries
A standards-based approach to development of clinical registriesA standards-based approach to development of clinical registries
A standards-based approach to development of clinical registries
 
Saito S DRAGON trial
Saito S DRAGON trialSaito S DRAGON trial
Saito S DRAGON trial
 
CEMRC presentation 20091108
CEMRC presentation 20091108CEMRC presentation 20091108
CEMRC presentation 20091108
 
Looking Back on Clinical Decision Support and Data Warehousing
Looking Back on Clinical Decision Support and Data WarehousingLooking Back on Clinical Decision Support and Data Warehousing
Looking Back on Clinical Decision Support and Data Warehousing
 
Γιώτα Τουλούμη, 6th Clinical Research Conference
Γιώτα Τουλούμη, 6th Clinical Research ConferenceΓιώτα Τουλούμη, 6th Clinical Research Conference
Γιώτα Τουλούμη, 6th Clinical Research Conference
 

More from Lancaster University Library

Promoting a culture of Open Research at Lancaster University
Promoting a culture of Open Research at Lancaster UniversityPromoting a culture of Open Research at Lancaster University
Promoting a culture of Open Research at Lancaster UniversityLancaster University Library
 
"We're in the land of poo" - Fertilising your work with knowledge from the field
"We're in the land of poo" - Fertilising your work with knowledge from the field"We're in the land of poo" - Fertilising your work with knowledge from the field
"We're in the land of poo" - Fertilising your work with knowledge from the fieldLancaster University Library
 
Stephen Robinson containers for software preservation
Stephen Robinson containers for software preservationStephen Robinson containers for software preservation
Stephen Robinson containers for software preservationLancaster University Library
 
Kris Geyer retrieving psychological relevant data from smartphones
Kris Geyer retrieving psychological relevant data from smartphonesKris Geyer retrieving psychological relevant data from smartphones
Kris Geyer retrieving psychological relevant data from smartphonesLancaster University Library
 
Running Research as a Service. Implications for Privacy Policies and Ethics
Running Research as a Service. Implications for Privacy Policies and EthicsRunning Research as a Service. Implications for Privacy Policies and Ethics
Running Research as a Service. Implications for Privacy Policies and EthicsLancaster University Library
 
Better data for better justice - Towards data-driven analyses of Family Court...
Better data for better justice - Towards data-driven analyses of Family Court...Better data for better justice - Towards data-driven analyses of Family Court...
Better data for better justice - Towards data-driven analyses of Family Court...Lancaster University Library
 
Sharing Qualitative Data - Challenges and Opportunities
Sharing Qualitative Data - Challenges and OpportunitiesSharing Qualitative Data - Challenges and Opportunities
Sharing Qualitative Data - Challenges and OpportunitiesLancaster University Library
 

More from Lancaster University Library (20)

Open Research exercise using Mission Model Canvas
Open Research exercise using Mission Model CanvasOpen Research exercise using Mission Model Canvas
Open Research exercise using Mission Model Canvas
 
Promoting a culture of Open Research at Lancaster University
Promoting a culture of Open Research at Lancaster UniversityPromoting a culture of Open Research at Lancaster University
Promoting a culture of Open Research at Lancaster University
 
PSC2019 - Community Building: How Does It Work?
PSC2019 - Community Building: How Does It Work?PSC2019 - Community Building: How Does It Work?
PSC2019 - Community Building: How Does It Work?
 
"We're in the land of poo" - Fertilising your work with knowledge from the field
"We're in the land of poo" - Fertilising your work with knowledge from the field"We're in the land of poo" - Fertilising your work with knowledge from the field
"We're in the land of poo" - Fertilising your work with knowledge from the field
 
Working with police recorded data
Working with police recorded dataWorking with police recorded data
Working with police recorded data
 
Lancaster 2018-open data
Lancaster 2018-open dataLancaster 2018-open data
Lancaster 2018-open data
 
Data bites
Data bitesData bites
Data bites
 
Documenting Flood Experience
Documenting Flood ExperienceDocumenting Flood Experience
Documenting Flood Experience
 
Stephen Robinson containers for software preservation
Stephen Robinson containers for software preservationStephen Robinson containers for software preservation
Stephen Robinson containers for software preservation
 
Kris Geyer retrieving psychological relevant data from smartphones
Kris Geyer retrieving psychological relevant data from smartphonesKris Geyer retrieving psychological relevant data from smartphones
Kris Geyer retrieving psychological relevant data from smartphones
 
20171003 lancaster data conversations Chue-Hong
20171003 lancaster data conversations Chue-Hong20171003 lancaster data conversations Chue-Hong
20171003 lancaster data conversations Chue-Hong
 
Andrew Moore past-present-potential
Andrew Moore past-present-potentialAndrew Moore past-present-potential
Andrew Moore past-present-potential
 
Barry Rowlingson CHICAS use of git lab
Barry Rowlingson CHICAS use of git labBarry Rowlingson CHICAS use of git lab
Barry Rowlingson CHICAS use of git lab
 
The sensor cloud around us
The sensor cloud around usThe sensor cloud around us
The sensor cloud around us
 
Running Research as a Service. Implications for Privacy Policies and Ethics
Running Research as a Service. Implications for Privacy Policies and EthicsRunning Research as a Service. Implications for Privacy Policies and Ethics
Running Research as a Service. Implications for Privacy Policies and Ethics
 
Security overview at Lancaster University
Security overview at Lancaster UniversitySecurity overview at Lancaster University
Security overview at Lancaster University
 
Better data for better justice - Towards data-driven analyses of Family Court...
Better data for better justice - Towards data-driven analyses of Family Court...Better data for better justice - Towards data-driven analyses of Family Court...
Better data for better justice - Towards data-driven analyses of Family Court...
 
Cloud computing - When is Deletion Deletion?
Cloud computing - When is Deletion Deletion?Cloud computing - When is Deletion Deletion?
Cloud computing - When is Deletion Deletion?
 
Sharing Qualitative Data - Challenges and Opportunities
Sharing Qualitative Data - Challenges and OpportunitiesSharing Qualitative Data - Challenges and Opportunities
Sharing Qualitative Data - Challenges and Opportunities
 
Mining and mapping places with multiple names
Mining and mapping places with multiple names Mining and mapping places with multiple names
Mining and mapping places with multiple names
 

Recently uploaded

wepik-insightful-infographics-a-data-visualization-overview-20240401133220kwr...
wepik-insightful-infographics-a-data-visualization-overview-20240401133220kwr...wepik-insightful-infographics-a-data-visualization-overview-20240401133220kwr...
wepik-insightful-infographics-a-data-visualization-overview-20240401133220kwr...KarteekMane1
 
Advanced Machine Learning for Business Professionals
Advanced Machine Learning for Business ProfessionalsAdvanced Machine Learning for Business Professionals
Advanced Machine Learning for Business ProfessionalsVICTOR MAESTRE RAMIREZ
 
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...Boston Institute of Analytics
 
Data Factory in Microsoft Fabric (MsBIP #82)
Data Factory in Microsoft Fabric (MsBIP #82)Data Factory in Microsoft Fabric (MsBIP #82)
Data Factory in Microsoft Fabric (MsBIP #82)Cathrine Wilhelmsen
 
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdfEnglish-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdfblazblazml
 
Learn How Data Science Changes Our World
Learn How Data Science Changes Our WorldLearn How Data Science Changes Our World
Learn How Data Science Changes Our WorldEduminds Learning
 
Unveiling the Role of Social Media Suspect Investigators in Preventing Online...
Unveiling the Role of Social Media Suspect Investigators in Preventing Online...Unveiling the Role of Social Media Suspect Investigators in Preventing Online...
Unveiling the Role of Social Media Suspect Investigators in Preventing Online...Milind Agarwal
 
Conf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
Conf42-LLM_Adding Generative AI to Real-Time Streaming PipelinesConf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
Conf42-LLM_Adding Generative AI to Real-Time Streaming PipelinesTimothy Spann
 
Real-Time AI Streaming - AI Max Princeton
Real-Time AI  Streaming - AI Max PrincetonReal-Time AI  Streaming - AI Max Princeton
Real-Time AI Streaming - AI Max PrincetonTimothy Spann
 
What To Do For World Nature Conservation Day by Slidesgo.pptx
What To Do For World Nature Conservation Day by Slidesgo.pptxWhat To Do For World Nature Conservation Day by Slidesgo.pptx
What To Do For World Nature Conservation Day by Slidesgo.pptxSimranPal17
 
Defining Constituents, Data Vizzes and Telling a Data Story
Defining Constituents, Data Vizzes and Telling a Data StoryDefining Constituents, Data Vizzes and Telling a Data Story
Defining Constituents, Data Vizzes and Telling a Data StoryJeremy Anderson
 
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default  Presentation : Data Analysis Project PPTPredictive Analysis for Loan Default  Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPTBoston Institute of Analytics
 
The Power of Data-Driven Storytelling_ Unveiling the Layers of Insight.pptx
The Power of Data-Driven Storytelling_ Unveiling the Layers of Insight.pptxThe Power of Data-Driven Storytelling_ Unveiling the Layers of Insight.pptx
The Power of Data-Driven Storytelling_ Unveiling the Layers of Insight.pptxTasha Penwell
 
6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...
6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...
6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...Dr Arash Najmaei ( Phd., MBA, BSc)
 
convolutional neural network and its applications.pdf
convolutional neural network and its applications.pdfconvolutional neural network and its applications.pdf
convolutional neural network and its applications.pdfSubhamKumar3239
 
INTRODUCTION TO Natural language processing
INTRODUCTION TO Natural language processingINTRODUCTION TO Natural language processing
INTRODUCTION TO Natural language processingsocarem879
 
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...Boston Institute of Analytics
 
Decoding Patterns: Customer Churn Prediction Data Analysis Project
Decoding Patterns: Customer Churn Prediction Data Analysis ProjectDecoding Patterns: Customer Churn Prediction Data Analysis Project
Decoding Patterns: Customer Churn Prediction Data Analysis ProjectBoston Institute of Analytics
 
SMOTE and K-Fold Cross Validation-Presentation.pptx
SMOTE and K-Fold Cross Validation-Presentation.pptxSMOTE and K-Fold Cross Validation-Presentation.pptx
SMOTE and K-Fold Cross Validation-Presentation.pptxHaritikaChhatwal1
 

Recently uploaded (20)

wepik-insightful-infographics-a-data-visualization-overview-20240401133220kwr...
wepik-insightful-infographics-a-data-visualization-overview-20240401133220kwr...wepik-insightful-infographics-a-data-visualization-overview-20240401133220kwr...
wepik-insightful-infographics-a-data-visualization-overview-20240401133220kwr...
 
Advanced Machine Learning for Business Professionals
Advanced Machine Learning for Business ProfessionalsAdvanced Machine Learning for Business Professionals
Advanced Machine Learning for Business Professionals
 
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
 
Data Factory in Microsoft Fabric (MsBIP #82)
Data Factory in Microsoft Fabric (MsBIP #82)Data Factory in Microsoft Fabric (MsBIP #82)
Data Factory in Microsoft Fabric (MsBIP #82)
 
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdfEnglish-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
 
Learn How Data Science Changes Our World
Learn How Data Science Changes Our WorldLearn How Data Science Changes Our World
Learn How Data Science Changes Our World
 
Unveiling the Role of Social Media Suspect Investigators in Preventing Online...
Unveiling the Role of Social Media Suspect Investigators in Preventing Online...Unveiling the Role of Social Media Suspect Investigators in Preventing Online...
Unveiling the Role of Social Media Suspect Investigators in Preventing Online...
 
Conf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
Conf42-LLM_Adding Generative AI to Real-Time Streaming PipelinesConf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
Conf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
 
Real-Time AI Streaming - AI Max Princeton
Real-Time AI  Streaming - AI Max PrincetonReal-Time AI  Streaming - AI Max Princeton
Real-Time AI Streaming - AI Max Princeton
 
What To Do For World Nature Conservation Day by Slidesgo.pptx
What To Do For World Nature Conservation Day by Slidesgo.pptxWhat To Do For World Nature Conservation Day by Slidesgo.pptx
What To Do For World Nature Conservation Day by Slidesgo.pptx
 
Defining Constituents, Data Vizzes and Telling a Data Story
Defining Constituents, Data Vizzes and Telling a Data StoryDefining Constituents, Data Vizzes and Telling a Data Story
Defining Constituents, Data Vizzes and Telling a Data Story
 
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default  Presentation : Data Analysis Project PPTPredictive Analysis for Loan Default  Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPT
 
The Power of Data-Driven Storytelling_ Unveiling the Layers of Insight.pptx
The Power of Data-Driven Storytelling_ Unveiling the Layers of Insight.pptxThe Power of Data-Driven Storytelling_ Unveiling the Layers of Insight.pptx
The Power of Data-Driven Storytelling_ Unveiling the Layers of Insight.pptx
 
6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...
6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...
6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...
 
Data Analysis Project: Stroke Prediction
Data Analysis Project: Stroke PredictionData Analysis Project: Stroke Prediction
Data Analysis Project: Stroke Prediction
 
convolutional neural network and its applications.pdf
convolutional neural network and its applications.pdfconvolutional neural network and its applications.pdf
convolutional neural network and its applications.pdf
 
INTRODUCTION TO Natural language processing
INTRODUCTION TO Natural language processingINTRODUCTION TO Natural language processing
INTRODUCTION TO Natural language processing
 
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...
 
Decoding Patterns: Customer Churn Prediction Data Analysis Project
Decoding Patterns: Customer Churn Prediction Data Analysis ProjectDecoding Patterns: Customer Churn Prediction Data Analysis Project
Decoding Patterns: Customer Churn Prediction Data Analysis Project
 
SMOTE and K-Fold Cross Validation-Presentation.pptx
SMOTE and K-Fold Cross Validation-Presentation.pptxSMOTE and K-Fold Cross Validation-Presentation.pptx
SMOTE and K-Fold Cross Validation-Presentation.pptx
 

Navigating NHS Administrative Data

  • 2. Missed Appointments • Proxy for low engagement • As a health harming behaviour • Proxy for poor health and social vulnerability
  • 6. Process code appointments attended = 830,039 DNA = 56,441 appointments.csv N=892,216 patients.csv N=73,012 clinical.csv N=704,828 remove non-appointments based on time rules compute number of appointments attended/missed for each patient appointmenthistory dataframe patient ID DNA attended total percentage missed annual DNA rate Categorise each patient. zero, low medium, high appointment History merged with Patients file (using patient ID as link) patientappointments dataset (N=70,165) ID sex age distance Rur8 PracticeRur8 SIMD PracticeSIMD Ethnic attended DNA total percentage missed category annual rate (attended) Ready for analysis and visualization (N=67,705) reclassify based on codes of interest N=825,784 remaining after (7.4%) removed Zero N = 44,685 (63.7%) Low N = 19,281(27.5%) Medium N = 5,097 (7.3%) High N = 1,102 (1.6%) N = 491 patients (<1%) with no appointment data removed remove patients with missing data N=2,460 (3.5%) patients classified as frequent/non frequent attenders (10th centile (annual attendance rate>=8.66)) Yes = 7,283 No = 62,882 subset to remove remove ethnicity data add age categories remove administrative/ secretary appointments N=891,921 remaining after (<.01%) removed remove duplicate patients N=2,356
  • 8. Key References Ellis, D. A., McQueenie, R., McConnachie, A., Wilson, P and Williamson, A. E. (2018). Non-attending patients in general practice. The Lancet Public Health, 3(3), e113 Smits, F. T. and ter Riet, G. (2017). Non-attending patients in general practice. The Lancet Public Health, 2(12), e538. Ellis, D. A., McQueenie, R., McConnachie, A., Wilson, P and Williamson, A. E. (2017). Demographic and practice factors predicting repeated non- attendance in primary care: A national retrospective cohort analysis. The Lancet Public Health. 2(12), e551-e559 Williamson, A., Ellis, D. A., Wilson, P., McQueenie, R. and McConnachie, A. (2017). Understanding repeated non-attendance in health services - pilot analysis of administrative data and full study protocol. BMJ Open, 7(2), e01412