Electronic medical record-based cohort selection and direct-to-patient, targeted recruitment: early efficacy and lessons learned

Hailey N. Miller, Kelly T. Gleason, Stephen P. Juraschek, Timothy B. Plante, Cassie Lewis-Land, Bonnie Woods, Lawrence J. Appel, Daniel E. Ford, Cheryl R. Dennison Himmelfarb

Research output: Contribution to journalArticle

Abstract

OBJECTIVE: The study sought to characterize institution-wide participation in secure messaging (SM) at a large academic health network, describe our experience with electronic medical record (EMR)-based cohort selection, and discuss the potential roles of SM for research recruitment. MATERIALS AND METHODS: Study teams defined eligibility criteria to create a computable phenotype, structured EMR data, to identify and recruit participants. Patients with SM accounts matching this phenotype received recruitment messages. We compared demographic characteristics across SM users and the overall health system. We also tabulated SM activation and use, characteristics of individual studies, and efficacy of the recruitment methods. RESULTS: Of the 1 308 820 patients in the health network, 40% had active SM accounts. SM users had a greater proportion of white and non-Hispanic patients than nonactive SM users id. Among the studies included (n = 13), 77% recruited participants with a specific disease or condition. All studies used demographic criteria for their phenotype, while 46% (n = 6) used demographic, disease, and healthcare utilization criteria. The average SM response rate was 2.9%, with higher rates among condition-specific (3.4%) vs general health (1.4%) studies. Those studies with a more inclusive comprehensive phenotype had a higher response rate. DISCUSSION: Target population and EMR queries (computable phenotypes) affect recruitment efficacy and should be considered when designing an EMR-based recruitment strategy. CONCLUSIONS: SM guided by EMR-based cohort selection is a promising approach to identify and enroll research participants. Efforts to increase the number of active SM users and response rate should be implemented to enhance the effectiveness of this recruitment strategy.

Original languageEnglish (US)
Pages (from-to)1209-1217
Number of pages9
JournalJournal of the American Medical Informatics Association : JAMIA
Volume26
Issue number11
DOIs
StatePublished - Nov 1 2019

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Electronic Health Records
Patient Selection
Phenotype
Health
Demography
Health Services Needs and Demand
Research
Delivery of Health Care

Keywords

  • cohort selection
  • direct messaging
  • electronic medical records
  • patient portal messaging
  • research recruitment

ASJC Scopus subject areas

  • Health Informatics

Cite this

Electronic medical record-based cohort selection and direct-to-patient, targeted recruitment : early efficacy and lessons learned. / Miller, Hailey N.; Gleason, Kelly T.; Juraschek, Stephen P.; Plante, Timothy B.; Lewis-Land, Cassie; Woods, Bonnie; Appel, Lawrence J.; Ford, Daniel E.; Dennison Himmelfarb, Cheryl R.

In: Journal of the American Medical Informatics Association : JAMIA, Vol. 26, No. 11, 01.11.2019, p. 1209-1217.

Research output: Contribution to journalArticle

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abstract = "OBJECTIVE: The study sought to characterize institution-wide participation in secure messaging (SM) at a large academic health network, describe our experience with electronic medical record (EMR)-based cohort selection, and discuss the potential roles of SM for research recruitment. MATERIALS AND METHODS: Study teams defined eligibility criteria to create a computable phenotype, structured EMR data, to identify and recruit participants. Patients with SM accounts matching this phenotype received recruitment messages. We compared demographic characteristics across SM users and the overall health system. We also tabulated SM activation and use, characteristics of individual studies, and efficacy of the recruitment methods. RESULTS: Of the 1 308 820 patients in the health network, 40{\%} had active SM accounts. SM users had a greater proportion of white and non-Hispanic patients than nonactive SM users id. Among the studies included (n = 13), 77{\%} recruited participants with a specific disease or condition. All studies used demographic criteria for their phenotype, while 46{\%} (n = 6) used demographic, disease, and healthcare utilization criteria. The average SM response rate was 2.9{\%}, with higher rates among condition-specific (3.4{\%}) vs general health (1.4{\%}) studies. Those studies with a more inclusive comprehensive phenotype had a higher response rate. DISCUSSION: Target population and EMR queries (computable phenotypes) affect recruitment efficacy and should be considered when designing an EMR-based recruitment strategy. CONCLUSIONS: SM guided by EMR-based cohort selection is a promising approach to identify and enroll research participants. Efforts to increase the number of active SM users and response rate should be implemented to enhance the effectiveness of this recruitment strategy.",
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AU - Miller, Hailey N.

AU - Gleason, Kelly T.

AU - Juraschek, Stephen P.

AU - Plante, Timothy B.

AU - Lewis-Land, Cassie

AU - Woods, Bonnie

AU - Appel, Lawrence J.

AU - Ford, Daniel E.

AU - Dennison Himmelfarb, Cheryl R.

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N2 - OBJECTIVE: The study sought to characterize institution-wide participation in secure messaging (SM) at a large academic health network, describe our experience with electronic medical record (EMR)-based cohort selection, and discuss the potential roles of SM for research recruitment. MATERIALS AND METHODS: Study teams defined eligibility criteria to create a computable phenotype, structured EMR data, to identify and recruit participants. Patients with SM accounts matching this phenotype received recruitment messages. We compared demographic characteristics across SM users and the overall health system. We also tabulated SM activation and use, characteristics of individual studies, and efficacy of the recruitment methods. RESULTS: Of the 1 308 820 patients in the health network, 40% had active SM accounts. SM users had a greater proportion of white and non-Hispanic patients than nonactive SM users id. Among the studies included (n = 13), 77% recruited participants with a specific disease or condition. All studies used demographic criteria for their phenotype, while 46% (n = 6) used demographic, disease, and healthcare utilization criteria. The average SM response rate was 2.9%, with higher rates among condition-specific (3.4%) vs general health (1.4%) studies. Those studies with a more inclusive comprehensive phenotype had a higher response rate. DISCUSSION: Target population and EMR queries (computable phenotypes) affect recruitment efficacy and should be considered when designing an EMR-based recruitment strategy. CONCLUSIONS: SM guided by EMR-based cohort selection is a promising approach to identify and enroll research participants. Efforts to increase the number of active SM users and response rate should be implemented to enhance the effectiveness of this recruitment strategy.

AB - OBJECTIVE: The study sought to characterize institution-wide participation in secure messaging (SM) at a large academic health network, describe our experience with electronic medical record (EMR)-based cohort selection, and discuss the potential roles of SM for research recruitment. MATERIALS AND METHODS: Study teams defined eligibility criteria to create a computable phenotype, structured EMR data, to identify and recruit participants. Patients with SM accounts matching this phenotype received recruitment messages. We compared demographic characteristics across SM users and the overall health system. We also tabulated SM activation and use, characteristics of individual studies, and efficacy of the recruitment methods. RESULTS: Of the 1 308 820 patients in the health network, 40% had active SM accounts. SM users had a greater proportion of white and non-Hispanic patients than nonactive SM users id. Among the studies included (n = 13), 77% recruited participants with a specific disease or condition. All studies used demographic criteria for their phenotype, while 46% (n = 6) used demographic, disease, and healthcare utilization criteria. The average SM response rate was 2.9%, with higher rates among condition-specific (3.4%) vs general health (1.4%) studies. Those studies with a more inclusive comprehensive phenotype had a higher response rate. DISCUSSION: Target population and EMR queries (computable phenotypes) affect recruitment efficacy and should be considered when designing an EMR-based recruitment strategy. CONCLUSIONS: SM guided by EMR-based cohort selection is a promising approach to identify and enroll research participants. Efforts to increase the number of active SM users and response rate should be implemented to enhance the effectiveness of this recruitment strategy.

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KW - research recruitment

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