Feasibility and Acceptability of Automated Texts to Offer, Screen, and Enroll Patients in a Cancer Clinical Trial Financial Reimbursement Program: A Mixed-Methods Study (Preprint)
Bibliographic record
Abstract
Background: Out-of-pocket (OOP) costs pose a significant barrier to participating in cancer clinical trials (CCTs). Financial reimbursement programs (FRPs) that reduce the burden of OOP costs can support participation in CCTs if the information is readily available to participants at the time of enrollment. Prior studies have shown the importance and impact of FRPs, but despite improvements, significant barriers still remain. Objective: This study was designed to explore the feasibility and acceptability of automated texts designed to offer, screen, and enroll CCT participants in an FRP for OOP travel and lodging-related clinical trial costs. Methods: This study used a mixed methods approach. Eligible participants were those who consented to participate in a breast, leukemia, or chimeric antigen receptor T cell (CAR-T) trial at the Abramson Cancer Center of the University of Pennsylvania, a National Cancer Institute comprehensive cancer center. Quantitative data were collected through engagement metrics, including text response rates and enrollment rates, as well as patient-reported satisfaction scores. Qualitative data were derived from semistructured interviews. Program enrollment rates were used to determine feasibility, whereas the engagement metrics were used to measure the acceptability of the program. Semistructured interviews were conducted with a subsample of patients who responded to at least one of the FRP texts and agreed to be interviewed to determine the barriers to and facilitators of enrolling in the Improving Patient Access to Cancer Clinical Trials (IMPACT) program via text, perceived advantages and disadvantages of the text messaging program compared to a phone call, and overall feedback on the acceptability of the automated text messaging program. Results: Quantitative data, including engagement with texts, FRP eligibility screening, and enrollment rates, were collected from all participants who successfully received a text (n=51), and qualitative data were collected from a subsample of participants who agreed to participate in a semistructured interview (n=28) about the text-based program. Participants' mean age was 58 (SD 12) years, approximately 65% (n=33) of participants were female, 21% (n=11) of participants were Black, and 4% (n=2) of participants were Hispanic or Latino. There was high engagement with texts (n=49, 96.1%) and a high screening rate for FRP eligibility (n=33, 64.7%). Of those who successfully screened, 26 (51%) screened via text. We also saw high overall FRP enrollment rates of those who completed the texts (n=16 of 24 eligible, 66.7%) and high satisfaction (Net Promoter Score=51). The text-based platform streamlined the enrollment process, allowing one-third of patients to complete enrollment independently, without assistance from the FRP coordinator. Reported facilitators for completion of the text conversation included support from the coordinator and introduction of the FRP by CCT teams. Barriers were a lack of communication from CCT teams, patient skepticism about the legitimacy of the texts, and limited program information via text. Conclusions: Despite the small sample size and single study site, these findings suggest that automated text messaging can be an effective, low-cost, and scalable strategy to increase awareness and streamline enrollment in FRPs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.068 | 0.277 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".