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Differing levels of trust in clinical trials information channels across diverse populations is examined in this research.
The clinical and commercial success of clinical trials depends on having a diverse pool of study participants.1,2,3 But despite considerable effort and investment from industry and encouragement from FDA, we have not yet achieved adequate diversity of trial participants.4,5 This suggests that we do not understand the mechanisms which motivate patients from diverse backgrounds to participate in clinical trials.
A variety of tactics have been suggested to increase diversity in clinical trials, including proactively recruiting diverse participants, addressing language and cultural barriers, creating inclusive eligibility criteria, and employing a diverse research workforce.6,7 All these tactics work by increasing trust in the clinical trial, which increases under-represented patients willingness to participate in clinical trials.8,9 But if we are using trust-based recruiting, and we still don’t have diverse trials, then we should re-consider how we should improve diversity in clinical trials.
To address these questions, this research will examine the role of trust in clinical trials. Specifically, we model how trust in information from different channels is associated with willingness to participate in clinical trials and how this trust varies across ethnicity and race. The results of this study suggest a more modest role for trust than was previously thought, shows how trusted information about clinical trials comes from a variety of channels, and illustrates the differing trust sources work across racial and ethnic boundaries.
To understand how trust works in clinical trial recruitment, we randomly surveyed patients using the Phreesia digital intake software used by medical practices to sign patients in when they arrive for an appointment. The practices were mostly primary care practices but also included a wide variety of specialties. During the digital intake process, patients were asked to participate in a survey. Patients who agreed were presented with a digital questionnaire. Most of the patients responded on a mobile device and the other respondents used home desktop computers or tablets. The data was collected in July 2022.
Since few patients have ever been approached about a clinical trial,10 we asked about their willingness to participate in a clinical trial: “How likely would you be to apply to participate in a clinical trial that is relevant to you?” Trust was assessed by asking how much the respondent trusted various sources of clinical trial information. We included items on how much the respondent trusted doctors, nurses, family, digital intake software, the medical internet, and social media for information about clinical trials. The trust questions were presented as a block and coded on a scale of 1 through 10. As a control, we asked respondents about their familiarity with clinical trials on a 1 to 5 scale. The items for covariates were age, gender, race, and ethnicity, and whether they used a mobile device. Ethnicity was coded as 1 = Hispanic and 0 = non-Hispanic. Race was coded as 1 = Black and 0 = non-black – collapsing multiple racial categories including White, Asian, bi-racial, etc. Only 16 respondents categorized themselves as both Black and Hispanic. The covariates were mostly categorical variables with the exception of age.
Since the goal of this research is to isolate the effects of trust in various trial information channels, we used regression analysis, which parses out the unique effects of each predictor from the effects of other predictors and covariates. All the continuous variables are centered, so coefficients can be interpreted at the average levels of the other predictors and covariates. The Base Model estimates the model described above across the entire sample, accounting for the effects of ethnicity and race in the model. Given the significance of the findings, we re-ran the models estimating the coefficients just in the Hispanic and Black samples, comparing them to the non-Hispanic and non-Black samples.
The regression model included 2,587 respondents, of which 61% were female, 18% were Black, 14% were Hispanic, and 61% responded on a mobile device. The means, standard deviations, and correlations are presented in Table 1. Trust in doctors (μ= 7.5) and nurses (μ= 7.0) was much higher than for social media (μ= 3.3), which we found unsurprising.
Willingness to participate in clinical trials was μ= 3.7 on a 6-point scale and familiarity with clinical trials was 2.6 on a 5-point scale. These middling scores on willingness to participate and familiarity with clinical trials are concerning for the industry. The regression model included 2,587 respondents, of which 61% were female, 18% were Black, 14% were Hispanic, and 61% responded on a mobile device. The means, standard deviations, and correlations are presented in Table 1. Trust in doctors (μ= 7.5) and nurses (μ= 7.0) was much higher than for social media (μ= 3.3), which we found unsurprising. Willingness to participate in clinical trials was μ= 3.7 on a 6-point scale and familiarity with clinical trials was 2.6 on a 5-point scale. These middling scores on willingness to participate and familiarity with clinical trials are concerning for the industry.
The Base Model runs the regression across the entire sample, accounting for the variance due to ethnicity and race. The Base Model predicted a significant (F12, 2,317= 35.91, p< .0001) amount of the respondent’s intent to participate in relevant clinical trials with an R2 that only explained 16% of the variance, as shown in Table 2. The significant predictors of clinical trial participation were Familiarity (b= .30, t= 13.13, p< .0001), Trust in Doctors (b= .08, t= 4.17, p< .0001), and Trust in Social Media (b= .03, t= 2.54, p= .01). It is notable that the Familiarity with Clinical Trials was more than three-times the magnitude to the next most impactful coefficient for Trust in Doctors. That Trust in Social Media was significant was surprising to us, given the low levels of trust we found in the descriptives (μ= 3.3 on a 1-10 scale). Even though people have low levels of trust in social media information, improving that trust will significantly improve clinical trial participation. We also found that Ethnicity (b= -.14, t= -1.80, p= .07), Race (b= -.14, t= -1.83, p= .07) and Trust in Digital Intake (b= .02, t= 1.68, p= .09) were marginally significant. Notice that with the Ethnicity and Race estimates, the magnitude of these coefficients were greater than any of the Trust variables, but noise in the standard error dragged down the significance. We explore the details of the negative impacts of ethnicity and race in the next models.
To examine the role of race and ethnicity in clinical trial recruitment, we ran separate regression models for each of the ethnicity and race categories. The results of the regressions comparing the Non-Hispanic and Hispanic models (i.e. Ethnicity) are shown in Table 3. The Hispanic model is significant (F11,195= 4.40, p< .0001) and the explained variance increased to 20%.
In the Ethnicity Model, we-ran the model comparing the estimates for the Hispanic sample to the non-Hispanic sample, shown in Table 3. Overall, we see a shift away from trusting doctors and a significant and positive increase in trust in family members for information about clinical trials. Family trust 10-fold from the Non-Hispanic Model (b= .01, t= .41, p= .68) to the Hispanic Model (b= .10, t= 2.26, p= .02) and became significant. At the same time, Trust in Doctors for trial information moved from highly significant in the Non-Hispanic Model (b= .08, t= 3.98, p< .0001) to insignificant in the Hispanic Model (b= .07, t= 1.12, p = .26). Similarly, Trust in Social Media moved from significance in the Non-Hispanic Model (b= .03, t= 2.30, p= .02) to insignificance in the Hispanic Model (b= .03, t= .63, p= .53). Notice that in the Trust in Doctors and Trust in Social Media, the coefficients did not change much but they both moved to insignificance. This means that there is a similar average effect across the sample but wider variation or noise in the Trust in Doctors and Social Media for these Hispanic respondents, suggesting more variation in this sample.
In the Race Model, we compared the estimates with those respondents who identified as Black with the non-Black respondents, shown in Table 4. The model for the Black sample was again significant F11,353= 7.31, p< .0001) and the R2 again increased, this time to 19%. Overall, we saw little effect of trust in the Black sample although the effects of Familiarity remain similar in the Black sample (b= .29, t= 5.20, p< .0001) compared to the Non-Black sample (b= .30, t= 11.98, p< .0001). The most important change was that the magnitude of the Social Media coefficient doubled in the Black sample (b= .06, t 1.84, p= .07) compared to the non-Black sample (b= .03, t= 1.81, p= .07) although both estimates remained marginally significant. This was offset by a decrease in Trust in Doctors in the Non-Black sample (b= .08, t= 4.03, p< .0001) to insignificance in the Black sample (b= .06, t= 1.18, p= .24) without a concomitant increase in Trust in Family (b= .01, t=.16, p= .87) in the Black sample.
In this research, we examined the effectiveness of different information channels in building trust in clinical trials in a way that leads to diverse clinical trials participation. Although trust is widely considered to be the key to clinical trial participation, we found a relatively small effect of trust and greater effects of familiarity in predicting clinical trial participation. We see familiarity as being more than just awareness.9 Familiarity includes awareness but also implies that the prospect has some knowledge of what clinical trials involve (i.e. knowledge) and they also believe that it is a good thing to participate in clinical trials (i.e. affective commitment). The middling levels of familiarity (average of 2.6 on a scale of 1 to 5) suggest an opportunity for more familiarity with clinical trials. Familiarity takes time, so clinical trial recruiting should be thought of as a process to manage over time. In this, it might be useful to think of clinical trial recruiting as analogous to a sales process or a recruit relationship management process instead of a one-time request to participate in a trial.
We found that there was a variety of information channels that built trust and that these channels shifted across ethnic and racial groups. This suggests that clinical trial recruiters need an omnichannel marketing capability to build both trust and familiarity. Rather than focus on one information channel (doctors, social media, or digital intake), recruiters should raise awareness about trials and educate patients across a variety of touchpoints that will echo across channels. Future research can develop the details of these synergies across information channels.
The details of the Hispanic and Black Models offer specific recommendations as to how to approach these groups. First, it is interesting to see that the R2 jumped in both groups by about 5%, suggesting a greater role for trust in these samples. In the Hispanic sample, Trust in Digital Intake, Doctors, and Social Media decreased to non-significance where Trust in Family increased 10-fold to significance. This suggests, for example, that it may be more effective when recruiting to Hispanic segments to target information to younger generations and count on them to communicate to their older grandparents. This was the approach US Surgeon General Jerome Adams used to encourage Hispanic elderly to get COVID vaccines in 2020 (i.e. ‘Tell your abuela to get the shot’).11 We saw a similar shift in the Black sample except the trusted channels shifted to social media.
An important limitation of this research can be seen in our findings from the Ethnicity and Race Models. We found that some coefficients were essentially the same magnitude but lost significance in the Hispanic or Black samples. (e.g. Trust in Doctors and Social Media in the Hispanic sample and Trust in Doctors in the Race Model). The statistical issue here is that there was an increase in the variation or noise in the Hispanic and Black samples, which inflated the standard error, leading to a loss of significance. Since a significance test is a signal-to-noise ratio, increasing the noise in the sample can lead to a loss of significance. The implication in all this is that simple demographic segmentation (identification as Hispanic or Black) is not adequate.Within these ethnic and racial categories, there is diversity which leads to variations in our survey items. These findings suggest that clinical trial recruiters need a more developed segmentation approach than simple ethnic or racial categories.
In this study, we examined how trust in various information channels leads to willingness to participate in clinical trials. We found a variety of information channels that led to willingness to participate in clinical trials (e.g. Doctors, Social Media, Digital Intake) but these channels have complex effects which change across ethnic and racial categories. In our Hispanic and Black samples, for example, patients had less trust in doctors but that Hispanic respondents trusted family more and Black respondents had greater trust in social media. Finally, our findings suggest that clinical trial recruiters should develop more sophisticated segment profiles that go beyond race or ethnic categories.
Michael J Howley, PA-C, MBA, PhD, Clinical Professor, LeBow College of Business, Drexel University, Philadelphia PA, Jai Seth, Senior Research Manager, Phreesia, Stella Sechopoulos, Research Associate, Phreesia, and Peter Malamis, Senior Director, Market Development, Phreesia