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Collection of Patient-Reported Outcome Measures in Rural and Underserved Populations.

Publication ,  Journal Article
Cheville, A; Patil, CL; Boyd, AD; Crofford, LJ; Dailey, D; Martelly, VD; Fiol, GD; Ezenwa, MO; Faurot, KR; Knisely, M; McLeod, KR; Morone, NE ...
Published in: Appl Clin Inform
March 2025

BACKGROUND:  The NIH Pragmatic Trials Collaboratory supports the design and conduct of 31 embedded pragmatic clinical trials, and many of these trials use patient-reported outcome measures (PROMs) to provide valuable information about the patients' health and wellness. Often these trials enroll medically underserved patients, including people with incomes below the federal poverty threshold, racial or ethnic minority groups, or rural or frontier communities. OBJECTIVES:  In this series of trial case reports, we provide lessons learned about collecting PROMs in these populations. Unbiased collection of PROM data is critical to increase the generalizability of trial outcomes and to address health inequities. Use of electronic health records (EHRs) and other digital modes of PROM administration has gained traction. However, engagement with these modes is often low among populations prone to disparity due to lower digital proficiency, device access, and uptake of EHR portals and web interfaces. METHODS:  To maximize the completeness and representativeness of their trial outcome data, study teams tested a range of strategies to improve PROM response rates with emphasis on disparities prone and underserved patient groups. This manuscript describes the approaches, their implementation, and the targeted populations. CONCLUSION:  Optimized PROM collection required hybrid approaches with multiple outreach modes, high-touch methods, creativity in promoting digital uptake, multimodal participant engagement, and text messaging.

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Published In

Appl Clin Inform

DOI

EISSN

1869-0327

Publication Date

March 2025

Volume

16

Issue

2

Start / End Page

259 / 266

Location

Germany

Related Subject Headings

  • Vulnerable Populations
  • Rural Population
  • Patient Reported Outcome Measures
  • Medically Underserved Area
  • Humans
  • Electronic Health Records
  • Data Collection
  • 4203 Health services and systems
  • 1103 Clinical Sciences
  • 0806 Information Systems
 

Citation

APA
Chicago
ICMJE
MLA
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Cheville, A., Patil, C. L., Boyd, A. D., Crofford, L. J., Dailey, D., Martelly, V. D., … Schlaeger, J. M. (2025). Collection of Patient-Reported Outcome Measures in Rural and Underserved Populations. Appl Clin Inform, 16(2), 259–266. https://doi.org/10.1055/a-2462-8699
Cheville, Andrea, Crystal L. Patil, Andrew D. Boyd, Leslie J. Crofford, Dana Dailey, Victoria de Martelly, Guilherme Del Fiol, et al. “Collection of Patient-Reported Outcome Measures in Rural and Underserved Populations.Appl Clin Inform 16, no. 2 (March 2025): 259–66. https://doi.org/10.1055/a-2462-8699.
Cheville A, Patil CL, Boyd AD, Crofford LJ, Dailey D, Martelly VD, et al. Collection of Patient-Reported Outcome Measures in Rural and Underserved Populations. Appl Clin Inform. 2025 Mar;16(2):259–66.
Cheville, Andrea, et al. “Collection of Patient-Reported Outcome Measures in Rural and Underserved Populations.Appl Clin Inform, vol. 16, no. 2, Mar. 2025, pp. 259–66. Pubmed, doi:10.1055/a-2462-8699.
Cheville A, Patil CL, Boyd AD, Crofford LJ, Dailey D, Martelly VD, Fiol GD, Ezenwa MO, Faurot KR, Knisely M, McLeod KR, Morone NE, O’Brien E, Gonzalez-Guarda RM, Sluka KA, Staman K, Thackeray A, Zigler CK, Schlaeger JM. Collection of Patient-Reported Outcome Measures in Rural and Underserved Populations. Appl Clin Inform. 2025 Mar;16(2):259–266.
Journal cover image

Published In

Appl Clin Inform

DOI

EISSN

1869-0327

Publication Date

March 2025

Volume

16

Issue

2

Start / End Page

259 / 266

Location

Germany

Related Subject Headings

  • Vulnerable Populations
  • Rural Population
  • Patient Reported Outcome Measures
  • Medically Underserved Area
  • Humans
  • Electronic Health Records
  • Data Collection
  • 4203 Health services and systems
  • 1103 Clinical Sciences
  • 0806 Information Systems