Recruiting Complex Medical Patients: Lessons From a Study of Pain in Alzheimer’s Disease and Cancer

Abstract We are conducting a study on pain differences among people with Alzheimer’s disease (AD), cancer, and both AD and cancer. We encountered difficulties recruiting a hard to identify group—those with AD and cancer. We initially recruited through newspaper, social media, and public transportation advertisements as well as community health fairs and organizations with low yield. We partnered with clinicians in oncology and dementia-specialty practices but experienced challenges obtaining referrals even with our staff embedded in the clinic. Developing relationships with community centers and retirement communities with memory care facilities supported recruitment of individuals with solely AD or cancer. To reach the AD and cancer group, we finally partnered with cancer clinical trials office recruitment coordinators. With trained recruiters systematically reviewing clinic schedules and proactively approaching individuals meeting screening criteria, our rates of study participants with both AD and cancer increased (0.24/month to 2.26/month). These recruiters are trained for empathy and compassion in recruitment and informed consent. This group also has access to artificial intelligence (AI) chart search tools that are being developed; these tools have not yet been utilized as we explore the ethical and regulatory challenges of AI in recruitment. While our approach included a specialty recruitment service, this proactive clinic screening approach could be modified and replicated by teams in the future to reach difficult to recruit medical populations. We have also theorized reasons for this recruitment difficulty such as concern for comfort and caregiver burden that can be considered as part of future recruitment planning.

Authors

Institutions

Publication Details

Journal
Innovation in Aging
Published
2025-12-01
DOI
https://doi.org/10.1093/geroni/igaf122.586
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Recruiting Complex Medical Patients: Lessons From a Study of Pain in Alzheimer’s Disease and Cancer

Ronald L. Cowan, Jeffrey T. Boon, Todd B. Monroe, Michelle Crum et al.
Innovation in Aging
Artificial Intelligence in Healthcare and Education
article

Recruiting Complex Medical Patients: Lessons From a Study of Pain in Alzheimer’s Disease and Cancer

Ronald L. Cowan, Jeffrey T. Boon, Todd B. Monroe, Michelle Crum, Jennifer Moon
article en

Abstract

Abstract We are conducting a study on pain differences among people with Alzheimer’s disease (AD), cancer, and both AD and cancer. We encountered difficulties recruiting a hard to identify group—those with AD and cancer. We initially recruited through newspaper, social media, and public transportation advertisements as well as community health fairs and organizations with low yield. We partnered with clinicians in oncology and dementia-specialty practices but experienced challenges obtaining referrals even with our staff embedded in the clinic. Developing relationships with community centers and retirement communities with memory care facilities supported recruitment of individuals with solely AD or cancer. To reach the AD and cancer group, we finally partnered with cancer clinical trials office recruitment coordinators. With trained recruiters systematically reviewing clinic schedules and proactively approaching individuals meeting screening criteria, our rates of study participants with both AD and cancer increased (0.24/month to 2.26/month). These recruiters are trained for empathy and compassion in recruitment and informed consent. This group also has access to artificial intelligence (AI) chart search tools that are being developed; these tools have not yet been utilized as we explore the ethical and regulatory challenges of AI in recruitment. While our approach included a specialty recruitment service, this proactive clinic screening approach could be modified and replicated by teams in the future to reach difficult to recruit medical populations. We have also theorized reasons for this recruitment difficulty such as concern for comfort and caregiver burden that can be considered as part of future recruitment planning.

Innovation in AgingVol. 9(Supplement_2)
University of Tennessee Health Science Center (US), The Ohio State University (US)
Partnerships for the goals
Openalex Percentile: Top 36%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.