Automatic Population of the Case Report Forms for an International Multifactorial Adaptive Platform Trial Amid the COVID-19 Pandemic

Andrew J. King; Lisa Higgins; Carly Au; Salim Malakouti; Edvin  Music; Kyle  Kalchthaler; Gilles Clermont; William  Garrard; David T Huang; Bryan J. McVerry; Christopher W. Seymour; Kelsey Linstrum; Amanda  McNamara; Cameron Green; India  Loar; Tracey  Roberts; Oscar C Marroquin; Derek C Angus; Christopher M. Horvat

Automatic Population of the Case Report Forms for an International Multifactorial Adaptive Platform Trial Amid the COVID-19 Pandemic

Andrew J. King, Lisa Higgins, Carly Au, Salim Malakouti, Edvin Music, Kyle Kalchthaler, Gilles Clermont, William Garrard, David T Huang, Bryan J. McVerry, Christopher W. Seymour, Kelsey Linstrum, Amanda McNamara, Cameron Green, India Loar, Tracey Roberts, Oscar C Marroquin, Derek C Angus, Christopher M. Horvat

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

Abstract

Objectives: To automatically populate the case report forms (CRFs) for an international, pragmatic, multifactorial, response-adaptive, Bayesian COVID-19 platform trial. Methods: The locations of focus included 27 hospitals and 2 large electronic health record (EHR) instances (1 Cerner Millennium and 1 Epic) that are part of the same health system in the United States. This paper describes our efforts to use EHR data to automatically populate four of the trial's forms: baseline, daily, discharge, and response-adaptive randomization.
Results: Between April 2020 and May 2022, 417 patients from the UPMC health system were enrolled in the trial. A MySQL-based extract, transform, and load pipeline automatically populated 499 of 526 CRF variables. The populated forms were statistically and manually reviewed and then reported to the trial's international data coordinating center. Conclusions: We accomplished automatic population of CRFs in a large platform trial and made recommendations for improving this process for future trials.

Original language	English
Title of host publication	AMIA Summits on Translational Science Proceedings
Place of Publication	United States
Publisher	American Medical Informatics Association (AMIA)
Pages	276-284
Number of pages	9
Publication status	Published - 31 May 2024
Event	AMIA 2024 Informatics Summit - Hilton Boston Park Plaza, Boston, United States of America Duration: 18 Mar 2024 → 21 Mar 2024 https://amia.org/education-events/amia-2024-informatics-summit

Publication series

Name	AMIA Summits on Translational Science Proceedings
Publisher	American Medical Informatics Association (AMIA)
ISSN (Electronic)	2153-4063

Conference

Conference	AMIA 2024 Informatics Summit
Abbreviated title	IS24
Country/Territory	United States of America
City	Boston
Period	18/03/24 → 21/03/24
Internet address	https://amia.org/education-events/amia-2024-informatics-summit

Access to Document

https://pubmed.ncbi.nlm.nih.gov/38827056/

Cite this

King, A. J., Higgins, L., Au, C., Malakouti, S., Music, E., Kalchthaler, K., Clermont, G., Garrard, W., Huang, D. T., McVerry, B. J., Seymour, C. W., Linstrum, K., McNamara, A., Green, C., Loar, I., Roberts, T., Marroquin, O. C., Angus, D. C., & Horvat, C. M. (2024). Automatic Population of the Case Report Forms for an International Multifactorial Adaptive Platform Trial Amid the COVID-19 Pandemic. In AMIA Summits on Translational Science Proceedings (pp. 276-284). (AMIA Summits on Translational Science Proceedings). American Medical Informatics Association (AMIA). https://pubmed.ncbi.nlm.nih.gov/38827056/

King, Andrew J. ; Higgins, Lisa ; Au, Carly et al. / Automatic Population of the Case Report Forms for an International Multifactorial Adaptive Platform Trial Amid the COVID-19 Pandemic. AMIA Summits on Translational Science Proceedings. United States : American Medical Informatics Association (AMIA), 2024. pp. 276-284 (AMIA Summits on Translational Science Proceedings).

@inproceedings{1330f6f134a44f35ae0fd14efe1dad52,

title = "Automatic Population of the Case Report Forms for an International Multifactorial Adaptive Platform Trial Amid the COVID-19 Pandemic",

abstract = "Objectives: To automatically populate the case report forms (CRFs) for an international, pragmatic, multifactorial, response-adaptive, Bayesian COVID-19 platform trial. Methods: The locations of focus included 27 hospitals and 2 large electronic health record (EHR) instances (1 Cerner Millennium and 1 Epic) that are part of the same health system in the United States. This paper describes our efforts to use EHR data to automatically populate four of the trial's forms: baseline, daily, discharge, and response-adaptive randomization. Results: Between April 2020 and May 2022, 417 patients from the UPMC health system were enrolled in the trial. A MySQL-based extract, transform, and load pipeline automatically populated 499 of 526 CRF variables. The populated forms were statistically and manually reviewed and then reported to the trial's international data coordinating center. Conclusions: We accomplished automatic population of CRFs in a large platform trial and made recommendations for improving this process for future trials.",

author = "King, \{Andrew J.\} and Lisa Higgins and Carly Au and Salim Malakouti and Edvin Music and Kyle Kalchthaler and Gilles Clermont and William Garrard and Huang, \{David T\} and McVerry, \{Bryan J.\} and Seymour, \{Christopher W.\} and Kelsey Linstrum and Amanda McNamara and Cameron Green and India Loar and Tracey Roberts and Marroquin, \{Oscar C\} and Angus, \{Derek C\} and Horvat, \{Christopher M.\}",

year = "2024",

month = may,

day = "31",

language = "English",

series = "AMIA Summits on Translational Science Proceedings",

publisher = "American Medical Informatics Association (AMIA)",

pages = "276--284",

booktitle = "AMIA Summits on Translational Science Proceedings",

address = "United States of America",

note = "AMIA 2024 Informatics Summit<br/>, IS24 ; Conference date: 18-03-2024 Through 21-03-2024",

url = "https://amia.org/education-events/amia-2024-informatics-summit",

}

King, AJ, Higgins, L, Au, C, Malakouti, S, Music, E, Kalchthaler, K, Clermont, G, Garrard, W, Huang, DT, McVerry, BJ, Seymour, CW, Linstrum, K, McNamara, A, Green, C, Loar, I, Roberts, T, Marroquin, OC, Angus, DC & Horvat, CM 2024, Automatic Population of the Case Report Forms for an International Multifactorial Adaptive Platform Trial Amid the COVID-19 Pandemic. in AMIA Summits on Translational Science Proceedings. AMIA Summits on Translational Science Proceedings, American Medical Informatics Association (AMIA), United States, pp. 276-284, AMIA 2024 Informatics Summit
, Boston, Massachusetts, United States of America, 18/03/24. <https://pubmed.ncbi.nlm.nih.gov/38827056/>

Automatic Population of the Case Report Forms for an International Multifactorial Adaptive Platform Trial Amid the COVID-19 Pandemic. / King, Andrew J.; Higgins, Lisa; Au, Carly et al.
AMIA Summits on Translational Science Proceedings. United States: American Medical Informatics Association (AMIA), 2024. p. 276-284 (AMIA Summits on Translational Science Proceedings).

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

TY - GEN

T1 - Automatic Population of the Case Report Forms for an International Multifactorial Adaptive Platform Trial Amid the COVID-19 Pandemic

AU - King, Andrew J.

AU - Higgins, Lisa

AU - Au, Carly

AU - Malakouti, Salim

AU - Music, Edvin

AU - Kalchthaler, Kyle

AU - Clermont, Gilles

AU - Garrard, William

AU - Huang, David T

AU - McVerry, Bryan J.

AU - Seymour, Christopher W.

AU - Linstrum, Kelsey

AU - McNamara, Amanda

AU - Green, Cameron

AU - Loar, India

AU - Roberts, Tracey

AU - Marroquin, Oscar C

AU - Angus, Derek C

AU - Horvat, Christopher M.

PY - 2024/5/31

Y1 - 2024/5/31

N2 - Objectives: To automatically populate the case report forms (CRFs) for an international, pragmatic, multifactorial, response-adaptive, Bayesian COVID-19 platform trial. Methods: The locations of focus included 27 hospitals and 2 large electronic health record (EHR) instances (1 Cerner Millennium and 1 Epic) that are part of the same health system in the United States. This paper describes our efforts to use EHR data to automatically populate four of the trial's forms: baseline, daily, discharge, and response-adaptive randomization. Results: Between April 2020 and May 2022, 417 patients from the UPMC health system were enrolled in the trial. A MySQL-based extract, transform, and load pipeline automatically populated 499 of 526 CRF variables. The populated forms were statistically and manually reviewed and then reported to the trial's international data coordinating center. Conclusions: We accomplished automatic population of CRFs in a large platform trial and made recommendations for improving this process for future trials.

AB - Objectives: To automatically populate the case report forms (CRFs) for an international, pragmatic, multifactorial, response-adaptive, Bayesian COVID-19 platform trial. Methods: The locations of focus included 27 hospitals and 2 large electronic health record (EHR) instances (1 Cerner Millennium and 1 Epic) that are part of the same health system in the United States. This paper describes our efforts to use EHR data to automatically populate four of the trial's forms: baseline, daily, discharge, and response-adaptive randomization. Results: Between April 2020 and May 2022, 417 patients from the UPMC health system were enrolled in the trial. A MySQL-based extract, transform, and load pipeline automatically populated 499 of 526 CRF variables. The populated forms were statistically and manually reviewed and then reported to the trial's international data coordinating center. Conclusions: We accomplished automatic population of CRFs in a large platform trial and made recommendations for improving this process for future trials.

M3 - Conference Paper

C2 - 38827056

T3 - AMIA Summits on Translational Science Proceedings

SP - 276

EP - 284

BT - AMIA Summits on Translational Science Proceedings

PB - American Medical Informatics Association (AMIA)

CY - United States

T2 - AMIA 2024 Informatics Summit<br/>

Y2 - 18 March 2024 through 21 March 2024

ER -

King AJ, Higgins L, Au C, Malakouti S, Music E, Kalchthaler K et al. Automatic Population of the Case Report Forms for an International Multifactorial Adaptive Platform Trial Amid the COVID-19 Pandemic. In AMIA Summits on Translational Science Proceedings. United States: American Medical Informatics Association (AMIA). 2024. p. 276-284. (AMIA Summits on Translational Science Proceedings).