TY - JOUR
T1 - Technological Adjuncts to Streamline Patient Recruitment, Informed Consent, and Data Management Processes in Clinical Research
T2 - Observational Study
AU - Koh, Jodie
AU - Caron, Stacey
AU - Watters, Amber N.
AU - Vaidyanathan, Mahesh
AU - Melnick, David
AU - Santi, Alyssa
AU - Hudson, Kenneth
AU - Arguelles, Catherine
AU - Mathur, Priyanka
AU - Etemadi, Mozziyar
N1 - Publisher Copyright:
©Jodie Koh, Stacey Caron, Amber N Watters, Mahesh Vaidyanathan, David Melnick, Alyssa Santi, Kenneth Hudson, Catherine Arguelles, Priyanka Mathur, Mozziyar Etemadi.
PY - 2025
Y1 - 2025
N2 - Background: Patient recruitment and data management are laborious, resource-intensive aspects of clinical research that often dictate whether the successful completion of studies is possible. Technological advances present opportunities for streamlining these processes, thus improving completion rates for clinical research studies. Objective: This paper aims to demonstrate how technological adjuncts can enhance clinical research processes via automation and digital integration. Methods: Using one clinical research study as an example, we highlighted the use of technological adjuncts to automate and streamline research processes across various digital platforms, including a centralized database of electronic medical records (enterprise data warehouse [EDW]); a clinical research data management tool (REDCap [Research Electronic Data Capture]); and a locally managed, Health Insurance Portability and Accountability Act–compliant server. Eligible participants were identified through automated queries in the EDW, after which they received personalized email invitations with digital consent forms. After digital consent, patient data were transferred to a single Health Insurance Portability and Accountability Act–compliant server where each participant was assigned a unique QR code to facilitate data collection and integration. After the research study visit, data obtained were associated with existing electronic medical record data for each participant via a QR code system that collated participant consent, imaging data, and associated clinical data according to a unique examination ID. Results: Over a 19-month period, automated EDW queries identified 20,988 eligible patients, and 10,582 patients received personalized email invitations. In total, 1000 (9.45%) patients signed consents to participate in the study. Of the consented patients, 549 unique patients completed 779 study visits; some patients consented to the study at more than 1 time period during their pregnancy. Conclusions: Technological adjuncts in clinical research decrease human labor while increasing participant reach and minimizing disruptions to clinic operations. Automating portions of the clinical research process benefits clinical research efforts by expanding and optimizing participant reach while reducing the limitations of labor and time in completing research studies.
AB - Background: Patient recruitment and data management are laborious, resource-intensive aspects of clinical research that often dictate whether the successful completion of studies is possible. Technological advances present opportunities for streamlining these processes, thus improving completion rates for clinical research studies. Objective: This paper aims to demonstrate how technological adjuncts can enhance clinical research processes via automation and digital integration. Methods: Using one clinical research study as an example, we highlighted the use of technological adjuncts to automate and streamline research processes across various digital platforms, including a centralized database of electronic medical records (enterprise data warehouse [EDW]); a clinical research data management tool (REDCap [Research Electronic Data Capture]); and a locally managed, Health Insurance Portability and Accountability Act–compliant server. Eligible participants were identified through automated queries in the EDW, after which they received personalized email invitations with digital consent forms. After digital consent, patient data were transferred to a single Health Insurance Portability and Accountability Act–compliant server where each participant was assigned a unique QR code to facilitate data collection and integration. After the research study visit, data obtained were associated with existing electronic medical record data for each participant via a QR code system that collated participant consent, imaging data, and associated clinical data according to a unique examination ID. Results: Over a 19-month period, automated EDW queries identified 20,988 eligible patients, and 10,582 patients received personalized email invitations. In total, 1000 (9.45%) patients signed consents to participate in the study. Of the consented patients, 549 unique patients completed 779 study visits; some patients consented to the study at more than 1 time period during their pregnancy. Conclusions: Technological adjuncts in clinical research decrease human labor while increasing participant reach and minimizing disruptions to clinic operations. Automating portions of the clinical research process benefits clinical research efforts by expanding and optimizing participant reach while reducing the limitations of labor and time in completing research studies.
KW - automation
KW - clinical research methods
KW - clinical research processes
KW - consent
KW - data management
KW - data warehouse
KW - digital health
KW - digital platforms
KW - imaging data
KW - patient data
KW - patient recruitment
KW - pregnancy
KW - technological adjuncts
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U2 - 10.2196/58628
DO - 10.2196/58628
M3 - Article
C2 - 39879093
AN - SCOPUS:85217119814
SN - 2561-326X
VL - 9
JO - JMIR Formative Research
JF - JMIR Formative Research
M1 - e58628
ER -