Identifying long COVID patients using general practice data: challenges, classification and long COVID patterns

Mirela Prgomet; Abbish Kamalakkannan; Judith Thomas; Christopher Pearce; Adam McLeod; Karina Gardner; Andrew Georgiou

doi:10.3233/SHTI250477

Identifying long COVID patients using general practice data: challenges, classification and long COVID patterns

Mirela Prgomet, Abbish Kamalakkannan, Judith Thomas, Christopher Pearce, Adam McLeod, Karina Gardner, Andrew Georgiou

Australian Institute of Health Innovation

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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Abstract

General practice data, extracted from electronic medical records, holds immense potential to generate a wealth of public health knowledge. But it is not without challenges. Our aim in this study was to identify long COVID patients within a large general practice dataset through data classification. We discuss the classification and its validation, and present initial data patterns for the identified long COVID cohort. We found significant variation in how general practitioners document and describe long COVID presentations. Less than half of the identified long COVID patients had a documented acute COVID infection. The highest proportion of long COVID patients were female and those 40-49 years of age. Overall, this study highlights key lessons for researchers utilizing general practice data, particularly in the context of long COVID, and underscores the vital importance of collaboration between researchers, general practitioners, and data custodians to ensure the robustness of data underpinning knowledge translation.

Original language	English
Title of host publication	Intelligent health systems
Subtitle of host publication	from technology to data and knowledge. Proceedings of MIE 2025
Editors	Elisavet Andrikopoulou, Parisis Gallos, Theodoros N. Arvanitis, Rosalynn Austin, Arriel Benis, Ronald Cornet, Panagiotis Chatzistergos, Alexander Dejaco, Linda Dusseljee-Peute, Alaa Mohasseb, Pantelis Natsiavas, Haythem Nakkas, Philip Scott
Place of Publication	Amsterdam
Publisher	IOS Press
Pages	838-842
Number of pages	5
ISBN (Electronic)	9781643685960
DOIs	https://doi.org/10.3233/SHTI250477
Publication status	Published - 15 May 2025
Event	35th Medical Informatics in Europe Conference, MIE 2025 - Glasgow, United Kingdom Duration: 19 May 2025 → 21 May 2025

Publication series

Name	Studies in Health Technology and Informatics
Publisher	IOS Press
Volume	327
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	35th Medical Informatics in Europe Conference, MIE 2025
Country/Territory	United Kingdom
City	Glasgow
Period	19/05/25 → 21/05/25

Bibliographical note

Copyright the Author(s) 2025. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

data analytics
electronic health records
general practice
long COVID

Access to Document

10.3233/SHTI250477Licence: CC BY-NC

Publisher version (open access)Final published version, 388 KBLicence: CC BY-NC

Cite this

Prgomet, M., Kamalakkannan, A., Thomas, J., Pearce, C., McLeod, A., Gardner, K., & Georgiou, A. (2025). Identifying long COVID patients using general practice data: challenges, classification and long COVID patterns. In E. Andrikopoulou, P. Gallos, T. N. Arvanitis, R. Austin, A. Benis, R. Cornet, P. Chatzistergos, A. Dejaco, L. Dusseljee-Peute, A. Mohasseb, P. Natsiavas, H. Nakkas, & P. Scott (Eds.), Intelligent health systems: from technology to data and knowledge. Proceedings of MIE 2025 (pp. 838-842). (Studies in Health Technology and Informatics; Vol. 327). IOS Press. https://doi.org/10.3233/SHTI250477

Prgomet, Mirela ; Kamalakkannan, Abbish ; Thomas, Judith et al. / Identifying long COVID patients using general practice data : challenges, classification and long COVID patterns. Intelligent health systems: from technology to data and knowledge. Proceedings of MIE 2025. editor / Elisavet Andrikopoulou ; Parisis Gallos ; Theodoros N. Arvanitis ; Rosalynn Austin ; Arriel Benis ; Ronald Cornet ; Panagiotis Chatzistergos ; Alexander Dejaco ; Linda Dusseljee-Peute ; Alaa Mohasseb ; Pantelis Natsiavas ; Haythem Nakkas ; Philip Scott. Amsterdam : IOS Press, 2025. pp. 838-842 (Studies in Health Technology and Informatics).

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title = "Identifying long COVID patients using general practice data: challenges, classification and long COVID patterns",

abstract = "General practice data, extracted from electronic medical records, holds immense potential to generate a wealth of public health knowledge. But it is not without challenges. Our aim in this study was to identify long COVID patients within a large general practice dataset through data classification. We discuss the classification and its validation, and present initial data patterns for the identified long COVID cohort. We found significant variation in how general practitioners document and describe long COVID presentations. Less than half of the identified long COVID patients had a documented acute COVID infection. The highest proportion of long COVID patients were female and those 40-49 years of age. Overall, this study highlights key lessons for researchers utilizing general practice data, particularly in the context of long COVID, and underscores the vital importance of collaboration between researchers, general practitioners, and data custodians to ensure the robustness of data underpinning knowledge translation.",

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note = "Copyright the Author(s) 2025. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.; 35th Medical Informatics in Europe Conference, MIE 2025 ; Conference date: 19-05-2025 Through 21-05-2025",

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Prgomet, M, Kamalakkannan, A, Thomas, J, Pearce, C, McLeod, A, Gardner, K & Georgiou, A 2025, Identifying long COVID patients using general practice data: challenges, classification and long COVID patterns. in E Andrikopoulou, P Gallos, TN Arvanitis, R Austin, A Benis, R Cornet, P Chatzistergos, A Dejaco, L Dusseljee-Peute, A Mohasseb, P Natsiavas, H Nakkas & P Scott (eds), Intelligent health systems: from technology to data and knowledge. Proceedings of MIE 2025. Studies in Health Technology and Informatics, vol. 327, IOS Press, Amsterdam, pp. 838-842, 35th Medical Informatics in Europe Conference, MIE 2025, Glasgow, United Kingdom, 19/05/25. https://doi.org/10.3233/SHTI250477

Identifying long COVID patients using general practice data: challenges, classification and long COVID patterns. / Prgomet, Mirela; Kamalakkannan, Abbish; Thomas, Judith et al.
Intelligent health systems: from technology to data and knowledge. Proceedings of MIE 2025. ed. / Elisavet Andrikopoulou; Parisis Gallos; Theodoros N. Arvanitis; Rosalynn Austin; Arriel Benis; Ronald Cornet; Panagiotis Chatzistergos; Alexander Dejaco; Linda Dusseljee-Peute; Alaa Mohasseb; Pantelis Natsiavas; Haythem Nakkas; Philip Scott. Amsterdam: IOS Press, 2025. p. 838-842 (Studies in Health Technology and Informatics; Vol. 327).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - Identifying long COVID patients using general practice data

T2 - 35th Medical Informatics in Europe Conference, MIE 2025

AU - Prgomet, Mirela

AU - Kamalakkannan, Abbish

AU - Thomas, Judith

AU - Pearce, Christopher

AU - McLeod, Adam

AU - Gardner, Karina

AU - Georgiou, Andrew

N1 - Copyright the Author(s) 2025. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

PY - 2025/5/15

Y1 - 2025/5/15

N2 - General practice data, extracted from electronic medical records, holds immense potential to generate a wealth of public health knowledge. But it is not without challenges. Our aim in this study was to identify long COVID patients within a large general practice dataset through data classification. We discuss the classification and its validation, and present initial data patterns for the identified long COVID cohort. We found significant variation in how general practitioners document and describe long COVID presentations. Less than half of the identified long COVID patients had a documented acute COVID infection. The highest proportion of long COVID patients were female and those 40-49 years of age. Overall, this study highlights key lessons for researchers utilizing general practice data, particularly in the context of long COVID, and underscores the vital importance of collaboration between researchers, general practitioners, and data custodians to ensure the robustness of data underpinning knowledge translation.

AB - General practice data, extracted from electronic medical records, holds immense potential to generate a wealth of public health knowledge. But it is not without challenges. Our aim in this study was to identify long COVID patients within a large general practice dataset through data classification. We discuss the classification and its validation, and present initial data patterns for the identified long COVID cohort. We found significant variation in how general practitioners document and describe long COVID presentations. Less than half of the identified long COVID patients had a documented acute COVID infection. The highest proportion of long COVID patients were female and those 40-49 years of age. Overall, this study highlights key lessons for researchers utilizing general practice data, particularly in the context of long COVID, and underscores the vital importance of collaboration between researchers, general practitioners, and data custodians to ensure the robustness of data underpinning knowledge translation.

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KW - electronic health records

KW - general practice

KW - long COVID

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DO - 10.3233/SHTI250477

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C2 - 40380585

T3 - Studies in Health Technology and Informatics

SP - 838

EP - 842

BT - Intelligent health systems

A2 - Andrikopoulou, Elisavet

A2 - Gallos, Parisis

A2 - Arvanitis, Theodoros N.

A2 - Austin, Rosalynn

A2 - Benis, Arriel

A2 - Cornet, Ronald

A2 - Chatzistergos, Panagiotis

A2 - Dejaco, Alexander

A2 - Dusseljee-Peute, Linda

A2 - Mohasseb, Alaa

A2 - Natsiavas, Pantelis

A2 - Nakkas, Haythem

A2 - Scott, Philip

PB - IOS Press

CY - Amsterdam

Y2 - 19 May 2025 through 21 May 2025

ER -

Prgomet M, Kamalakkannan A, Thomas J, Pearce C, McLeod A, Gardner K et al. Identifying long COVID patients using general practice data: challenges, classification and long COVID patterns. In Andrikopoulou E, Gallos P, Arvanitis TN, Austin R, Benis A, Cornet R, Chatzistergos P, Dejaco A, Dusseljee-Peute L, Mohasseb A, Natsiavas P, Nakkas H, Scott P, editors, Intelligent health systems: from technology to data and knowledge. Proceedings of MIE 2025. Amsterdam: IOS Press. 2025. p. 838-842. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI250477

Identifying long COVID patients using general practice data: challenges, classification and long COVID patterns

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Phase 2 - COVID-19: utilising near real-time electronic general practice data to establish effective care and best-practice policy

Cite this

Identifying long COVID patients using general practice data: challenges, classification and long COVID patterns

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Projects

Phase 2 - COVID-19: utilising near real-time electronic general practice data to establish effective care and best-practice policy

Cite this