Investigating Primary Care Indications to Improve the Quality of Electronic Health Record Data in Target Trial Emulation for Dementia

Max I Sunog, Colin Magdamo, Marie-Laure Charpignon, Mark W. Albers
Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:610-648, 2025.

Abstract

Missing data, inaccuracies in medication lists, and recording delays in electronic health records (EHR) are major limitations for target trial emulation (TTE), which uses EHR data to retrospectively emulate a clinical trial. EHR-based TTE relies on recorded data that proxy actual drug exposures and outcomes. While prior work has proposed various methods to improve EHR data quality, here we investigate the under-utilized consideration that encounters with a primary care provider (PCP) may result in more accurate data in the EHR. Patients with a PCP within the EHR network being studied tend to have more encounters overall and a greater proportion of the types of encounters that yield comprehensive and up-to-date records. By contrasting data for patients with and without a PCP in the considered EHR network, we demonstrate how PCP status affects EHR data quality. Through a case study, we then empirically examine the impact on TTE of including a PCP status feature either in the propensity score and outcome models or as an eligibility criterion for cohort selection, versus ignoring it. Specifically, we compare the estimated effects of two first-line antidiabetic drug classes on the onset of Alzheimer’s Disease and Related Dementias. We find that the estimated treatment effect is sensitive to the consideration of PCP status, particularly when used as an eligibility criterion. Our work suggests that further researching the role of PCP status may improve the design of pragmatic trials.

Cite this Paper

BibTeX
@InProceedings{pmlr-v287-sunog25a, title = {Investigating Primary Care Indications to Improve the Quality of Electronic Health Record Data in Target Trial Emulation for Dementia}, author = {Sunog, Max I and Magdamo, Colin and Charpignon, Marie-Laure and Albers, Mark W.}, booktitle = {Proceedings of the sixth Conference on Health, Inference, and Learning}, pages = {610--648}, year = {2025}, editor = {Xu, Xuhai Orson and Choi, Edward and Singhal, Pankhuri and Gerych, Walter and Tang, Shengpu and Agrawal, Monica and Subbaswamy, Adarsh and Sizikova, Elena and Dunn, Jessilyn and Daneshjou, Roxana and Sarker, Tasmie and McDermott, Matthew and Chen, Irene}, volume = {287}, series = {Proceedings of Machine Learning Research}, month = {25--27 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v287/main/assets/sunog25a/sunog25a.pdf}, url = {https://proceedings.mlr.press/v287/sunog25a.html}, abstract = {Missing data, inaccuracies in medication lists, and recording delays in electronic health records (EHR) are major limitations for target trial emulation (TTE), which uses EHR data to retrospectively emulate a clinical trial. EHR-based TTE relies on recorded data that proxy actual drug exposures and outcomes. While prior work has proposed various methods to improve EHR data quality, here we investigate the under-utilized consideration that encounters with a primary care provider (PCP) may result in more accurate data in the EHR. Patients with a PCP within the EHR network being studied tend to have more encounters overall and a greater proportion of the types of encounters that yield comprehensive and up-to-date records. By contrasting data for patients with and without a PCP in the considered EHR network, we demonstrate how PCP status affects EHR data quality. Through a case study, we then empirically examine the impact on TTE of including a PCP status feature either in the propensity score and outcome models or as an eligibility criterion for cohort selection, versus ignoring it. Specifically, we compare the estimated effects of two first-line antidiabetic drug classes on the onset of Alzheimer’s Disease and Related Dementias. We find that the estimated treatment effect is sensitive to the consideration of PCP status, particularly when used as an eligibility criterion. Our work suggests that further researching the role of PCP status may improve the design of pragmatic trials.} }
Endnote
%0 Conference Paper %T Investigating Primary Care Indications to Improve the Quality of Electronic Health Record Data in Target Trial Emulation for Dementia %A Max I Sunog %A Colin Magdamo %A Marie-Laure Charpignon %A Mark W. Albers %B Proceedings of the sixth Conference on Health, Inference, and Learning %C Proceedings of Machine Learning Research %D 2025 %E Xuhai Orson Xu %E Edward Choi %E Pankhuri Singhal %E Walter Gerych %E Shengpu Tang %E Monica Agrawal %E Adarsh Subbaswamy %E Elena Sizikova %E Jessilyn Dunn %E Roxana Daneshjou %E Tasmie Sarker %E Matthew McDermott %E Irene Chen %F pmlr-v287-sunog25a %I PMLR %P 610--648 %U https://proceedings.mlr.press/v287/sunog25a.html %V 287 %X Missing data, inaccuracies in medication lists, and recording delays in electronic health records (EHR) are major limitations for target trial emulation (TTE), which uses EHR data to retrospectively emulate a clinical trial. EHR-based TTE relies on recorded data that proxy actual drug exposures and outcomes. While prior work has proposed various methods to improve EHR data quality, here we investigate the under-utilized consideration that encounters with a primary care provider (PCP) may result in more accurate data in the EHR. Patients with a PCP within the EHR network being studied tend to have more encounters overall and a greater proportion of the types of encounters that yield comprehensive and up-to-date records. By contrasting data for patients with and without a PCP in the considered EHR network, we demonstrate how PCP status affects EHR data quality. Through a case study, we then empirically examine the impact on TTE of including a PCP status feature either in the propensity score and outcome models or as an eligibility criterion for cohort selection, versus ignoring it. Specifically, we compare the estimated effects of two first-line antidiabetic drug classes on the onset of Alzheimer’s Disease and Related Dementias. We find that the estimated treatment effect is sensitive to the consideration of PCP status, particularly when used as an eligibility criterion. Our work suggests that further researching the role of PCP status may improve the design of pragmatic trials.
APA
Sunog, M.I., Magdamo, C., Charpignon, M. & Albers, M.W.. (2025). Investigating Primary Care Indications to Improve the Quality of Electronic Health Record Data in Target Trial Emulation for Dementia. Proceedings of the sixth Conference on Health, Inference, and Learning, in Proceedings of Machine Learning Research 287:610-648 Available from https://proceedings.mlr.press/v287/sunog25a.html.

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