A Novel Patient Recruitment Strategy: Patient Selection Directly from the Community through Linkage to Clinical Data

Lindsay P. Zimmerman, Satyender Goel, Shazia Sathar, Charon E. Gladfelter, Alejandra Onate, Lindsey L. Kane, Shelly Sital, Jasmin Phua, Paris Davis, Helen Margellos-Anast, David O. Meltzer, Tamar S. Polonsky, Raj C. Shah, William E. Trick, Faraz S. Ahmad, Abel N. Kho

Research output: Contribution to journalArticle

Abstract

Objective This article presents and describes our methods in developing a novel strategy for recruitment of underrepresented, community-based participants, for pragmatic research studies leveraging routinely collected electronic health record (EHR) data. Methods We designed a new approach for recruiting eligible patients from the community, while also leveraging affiliated health systems to extract clinical data for community participants. The strategy involves methods for data collection, linkage, and tracking. In this workflow, potential participants are identified in the community and surveyed regarding eligibility. These data are then encrypted and deidentified via a hashing algorithm for linkage of the community participant back to a record at a clinical site. The linkage allows for eligibility verification and automated follow-up. Longitudinal data are collected by querying the EHR data and surveying the community participant directly. We discuss this strategy within the context of two national research projects, a clinical trial and an observational cohort study. Conclusion The community-based recruitment strategy is a novel, low-touch, clinical trial enrollment method to engage a diverse set of participants. Direct outreach to community participants, while utilizing EHR data for clinical information and follow-up, allows for efficient recruitment and follow-up strategies. This new strategy for recruitment links data reported from community participants to clinical data in the EHR and allows for eligibility verification and automated follow-up. The workflow has the potential to improve recruitment efficiency and engage traditionally underrepresented individuals in research.

Original languageEnglish (US)
Article number170176ra
Pages (from-to)114-121
Number of pages8
JournalApplied Clinical Informatics
Volume9
Issue number1
DOIs
StatePublished - Jan 1 2018

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Patient Selection
Health
Electronic Health Records
Workflow
Surveying
Research
Clinical Trials
Information Storage and Retrieval
Touch
Observational Studies
Cohort Studies

Keywords

  • clinical trials
  • cohort studies
  • data linkage
  • patient outcomes
  • patient recruitment

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications
  • Health Information Management

Cite this

Zimmerman, Lindsay P. ; Goel, Satyender ; Sathar, Shazia ; Gladfelter, Charon E. ; Onate, Alejandra ; Kane, Lindsey L. ; Sital, Shelly ; Phua, Jasmin ; Davis, Paris ; Margellos-Anast, Helen ; Meltzer, David O. ; Polonsky, Tamar S. ; Shah, Raj C. ; Trick, William E. ; Ahmad, Faraz S. ; Kho, Abel N. / A Novel Patient Recruitment Strategy : Patient Selection Directly from the Community through Linkage to Clinical Data. In: Applied Clinical Informatics. 2018 ; Vol. 9, No. 1. pp. 114-121.
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Zimmerman, LP, Goel, S, Sathar, S, Gladfelter, CE, Onate, A, Kane, LL, Sital, S, Phua, J, Davis, P, Margellos-Anast, H, Meltzer, DO, Polonsky, TS, Shah, RC, Trick, WE, Ahmad, FS & Kho, AN 2018, 'A Novel Patient Recruitment Strategy: Patient Selection Directly from the Community through Linkage to Clinical Data' Applied Clinical Informatics, vol. 9, no. 1, 170176ra, pp. 114-121. https://doi.org/10.1055/s-0038-1625964

A Novel Patient Recruitment Strategy : Patient Selection Directly from the Community through Linkage to Clinical Data. / Zimmerman, Lindsay P.; Goel, Satyender; Sathar, Shazia; Gladfelter, Charon E.; Onate, Alejandra; Kane, Lindsey L.; Sital, Shelly; Phua, Jasmin; Davis, Paris; Margellos-Anast, Helen; Meltzer, David O.; Polonsky, Tamar S.; Shah, Raj C.; Trick, William E.; Ahmad, Faraz S.; Kho, Abel N.

In: Applied Clinical Informatics, Vol. 9, No. 1, 170176ra, 01.01.2018, p. 114-121.

Research output: Contribution to journalArticle

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T2 - Applied Clinical Informatics

AU - Zimmerman, Lindsay P.

AU - Goel, Satyender

AU - Sathar, Shazia

AU - Gladfelter, Charon E.

AU - Onate, Alejandra

AU - Kane, Lindsey L.

AU - Sital, Shelly

AU - Phua, Jasmin

AU - Davis, Paris

AU - Margellos-Anast, Helen

AU - Meltzer, David O.

AU - Polonsky, Tamar S.

AU - Shah, Raj C.

AU - Trick, William E.

AU - Ahmad, Faraz S.

AU - Kho, Abel N.

PY - 2018/1/1

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N2 - Objective This article presents and describes our methods in developing a novel strategy for recruitment of underrepresented, community-based participants, for pragmatic research studies leveraging routinely collected electronic health record (EHR) data. Methods We designed a new approach for recruiting eligible patients from the community, while also leveraging affiliated health systems to extract clinical data for community participants. The strategy involves methods for data collection, linkage, and tracking. In this workflow, potential participants are identified in the community and surveyed regarding eligibility. These data are then encrypted and deidentified via a hashing algorithm for linkage of the community participant back to a record at a clinical site. The linkage allows for eligibility verification and automated follow-up. Longitudinal data are collected by querying the EHR data and surveying the community participant directly. We discuss this strategy within the context of two national research projects, a clinical trial and an observational cohort study. Conclusion The community-based recruitment strategy is a novel, low-touch, clinical trial enrollment method to engage a diverse set of participants. Direct outreach to community participants, while utilizing EHR data for clinical information and follow-up, allows for efficient recruitment and follow-up strategies. This new strategy for recruitment links data reported from community participants to clinical data in the EHR and allows for eligibility verification and automated follow-up. The workflow has the potential to improve recruitment efficiency and engage traditionally underrepresented individuals in research.

AB - Objective This article presents and describes our methods in developing a novel strategy for recruitment of underrepresented, community-based participants, for pragmatic research studies leveraging routinely collected electronic health record (EHR) data. Methods We designed a new approach for recruiting eligible patients from the community, while also leveraging affiliated health systems to extract clinical data for community participants. The strategy involves methods for data collection, linkage, and tracking. In this workflow, potential participants are identified in the community and surveyed regarding eligibility. These data are then encrypted and deidentified via a hashing algorithm for linkage of the community participant back to a record at a clinical site. The linkage allows for eligibility verification and automated follow-up. Longitudinal data are collected by querying the EHR data and surveying the community participant directly. We discuss this strategy within the context of two national research projects, a clinical trial and an observational cohort study. Conclusion The community-based recruitment strategy is a novel, low-touch, clinical trial enrollment method to engage a diverse set of participants. Direct outreach to community participants, while utilizing EHR data for clinical information and follow-up, allows for efficient recruitment and follow-up strategies. This new strategy for recruitment links data reported from community participants to clinical data in the EHR and allows for eligibility verification and automated follow-up. The workflow has the potential to improve recruitment efficiency and engage traditionally underrepresented individuals in research.

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