Assessing Equitable Recruitment in a Digital Health Trial for Asthma

Abstract

Objective This study aimed to assess a multipronged strategy using primarily digital methods to equitably recruit asthma patients into a clinical trial of a digital health intervention.

Methods We approached eligible patients using at least one of eight recruitment strategies. We recorded approach dates and the strategy that led to completion of a web-based eligibility questionnaire that was reported during the verbal consent phone call. Study team members conducted monthly sessions using a structured guide to identify recruitment barriers and facilitators. The proportion of participants who reported being recruited by a portal or nonportal strategy was measured as our outcomes. We used Fisher's exact test to compare outcomes by equity variable, and multivariable logistic regression to control for each covariate and adjust effect size estimates. Using grounded theory, we coded and extracted themes regarding recruitment barriers and facilitators.

Results The majority (84.4%) of patients who met study inclusion criteria were patient portal enrollees. Of 6,366 eligible patients who were approached, 627 completed the eligibility questionnaire and were less frequently Hispanic, less frequently Spanish-speaking, and more frequently patient portal enrollees. Of 445 patients who consented to participate, 241 (54.2%) reported completing the eligibility questionnaire after being contacted by a patient portal message. In adjusted analysis, only race (odds ratio [OR]: 0.46, 95% confidence interval [CI]: 0.28–0.77, p = 0.003) and college education (OR: 0.60, 95% CI: 0.39–0.91, p = 0.016) remained significant. Key recruitment barriers included technology issues (e.g., lack of email access) and facilitators included bilingual study staff, Spanish-language recruitment materials, targeted phone calls, and clinician-initiated “1-click” referrals.

Conclusion A primarily digital strategy to recruit patients into a digital health trial is unlikely to achieve equitable participation, even in a population overrepresented by patient portal enrollees. Nondigital recruitment methods that address racial and educational disparities and less active portal enrollees are necessary to ensure equity in clinical trial enrollment.

 

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Keywords

asthma - electronic health records - patient portal - health equity - human

Background and Significance

The recruitment of patients using electronic health records (EHRs) and accompanying digital tools offers a promising approach for targeted recruitment of patients into clinical trials.[1] [2] [3] [4] Investigators conducting human subjects research are increasingly leveraging digital methods for more efficient cohort identification, recruitment, and data collection. For example, early studies found patient portal messaging to be almost twice as effective as traditional methods in recruiting and enrolling study participants.[2] The coronavirus disease 2019 (COVID-19) pandemic has further highlighted the benefits of using digital tools to minimize the need for in-person contact.[5]

While researchers are increasingly utilizing the EHR and digital tools to screen and contact patients for recruitment into clinical trials, a key challenge is ensuring that the sampled cohort accurately represents the eligible population.[6] [7] [8] Equitable representation of the sampled cohort is crucial to ensuring validity and generalizability of the study results. Vulnerable and underserved populations are underrepresented in clinical trials due to numerous factors, including investigator bias, medical mistrust, barriers due to differences in health or research literacy, and lack of access to transportation.[9] [10] [11] [12] In recent years, growing digital divides, including lack of broadband, devices, and digital literacy,[13] have exacerbated recruitment disparities.[14] [15] To date, few studies have prospectively evaluated the use of primarily digital-based strategies to recruit participants from vulnerable and underserved populations.[2] [16]

The AppS for The Home Monitoring of Asthma (ASTHMA) study is a pragmatic randomized clinical trial (RCT) focused on recruiting ambulatory patients with asthma to participate in an app-based remote monitoring intervention. As a clinical trial of a digital health intervention, a priority was to leverage our EHR to identify an inclusive cohort of eligible patients for remote recruitment using a multipronged, primarily digital-based strategy. Our recruitment period started during the second wave of the COVID-19 pandemic in the United States providing a unique opportunity to rigorously assess the impact of this strategy on recruitment equity.

 

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Objectives

The aims of this study were to (1) implement a multipronged recruitment strategy using primarily digital methods to screen, approach, and enroll patients into a clinical trial of an app-based digital health intervention; (2) describe a structured approach to routinely assess enrollment equity during recruitment; and (3) use mixed methods to evaluate recruitment outcomes with regard to “TechQuity,” defined as the strategic development and deployment of technology to advance health equity.[17] Our findings may inform best practices to equitably recruit patients into clinical trials.

 

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Methods

Overview and Study Design

The ASTHMA study (clinicaltrials.gov identifier: NCT04401332) is an RCT of a clinically integrated digital health intervention that uses electronic patient reported outcome (ePRO) questionnaires to monitor asthma symptoms in adult English- and Spanish-speaking patients between primary care clinic visits. The intervention was initially designed and tested in subspecialty pulmonary care, and expanded to the primary care setting for this study.[18] [19] We used a user-centered approach to design the application, and an implementation framework (nonadoption, abandonment, scale-up, spread, sustainability [NASSS]) to maximize scalability.[20] The patient-facing components of the intervention included an app that prompts patients to complete weekly ePRO questionnaires and offers patients the option to request a callback from a clinic nurse when symptoms worsen. The clinician practice model included an EHR-integrated dashboard showing a summary of the ePRO trends for clinicians, and previsit reminders to prompt both patients and clinicians to discuss reported asthma symptoms during clinic appointments.[18] The intervention was designed to be usable and beneficial for any patient with asthma of varying severity.[21]

We conducted a prospective study of the recruitment methods used to screen, approach, and enroll patients into our RCT which will evaluate the impact of the digital intervention on asthma-related quality of life measured by the Mini Asthma Quality of Life Questionnaire (mini-AQLQ), a validated, patient-reported measure.[22] [23] We used specific EHR criteria (see below) to identify and categorize potentially eligible patients into tiers of varying asthma activity. We approached potential participants using eight different strategies implemented over the course of our RCT's recruitment period ([Fig. 1]). The study team met monthly to assess and implement changes to the recruitment strategy.

Fig. 1 Recruitment period strategy implementation.

 

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Study Setting, Sites, and Participants

The study was conducted during a 20-month recruitment period between July 2020 and March 2022 at seven primary care clinics affiliated with Brigham Health, an academic medical center affiliated with Mass General Brigham (MGB) in Boston, Massachusetts, Unites States. All clinics used a commercial EHR system (Epic Systems, Inc.) and were a part of Brigham Health's Primary Care Practice-Based Research Network. All patients can enroll in MGB's patient portal, Patient Gateway, which is powered by MyChart (Epic Systems, Inc.) and is available in Spanish as well as other languages. The institutional review boards (IRBs) of MGB and the RAND Corporation approved all study procedures.

Potentially eligible adult patients (18 years or older) from these clinics were identified by querying MGB's electronic data warehouse (EDW) at any time during the 24 months prior to study initiation (July 2020), and from subsequent data refreshes during the recruitment period (July 2020 to March 2022) if they were assigned to a primary care provider (PCP) affiliated with one of the seven primary care clinics and had either one of the following criteria: (1) a prior diagnosis of asthma (ICD-10 code defined as J45.xx) either on their EHR problem list or specified during a subspecialty, inpatient, or emergency department encounter; or (2) a diagnosis of asthma and a referral to an Allergy or Pulmonary subspecialist. Potentially eligible patients who were not considered appropriate (e.g., complex mental health or social issues) for the study per their PCP or clinic medical director were excluded. Of note, patient portal enrollment, defined by an “activated” status in the EHR, was not used to identify this initial cohort.

Potentially eligible patients were approached (detailed below) and further screened using a web-based eligibility questionnaire to confirm that the patient had an asthma diagnosis, were English- or Spanish-speaking, had a PCP from one of the seven study clinics, and owned and used a smartphone. The web-based eligibility questionnaire was accessed by patients either electronically through a hyperlink from a patient portal message, a link provided in the mailed letter, a QR code provided on a flyer or letter, or by phone with a research assistant (RA) during the initial recruitment phone call.

 

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Disease Activity Tiers and Recruitment Approach

We defined tiers of varying levels of disease activity based on a targeted review of the literature and consultation with two asthma clinicians (D.F. and W.C.—see Acknowledgments).[24] [25] Disease activity tiers were constructed based on encounter data retrieved from the EDW using the following criteria (one or more unless otherwise specified) in order of decreasing disease activity: (1) hospital visit, (2) emergency department visit, (3) prednisone prescription, (4) urgent care or walk-in visit, (5) specialist visit, (6) two or more visits to any provider, and (7) visit related to asthma in past 2 years and asthma on the problem list. Patients who met the criteria for more than one recruitment tier were placed in the higher activity tier. After initiation of recruitment, we refreshed the cohort at regular 2-month intervals using the above criteria to identify additional patients. Of note, the seventh tier (lowest disease activity) was added after initiating recruitment of patients from the first six tiers (higher disease activity).

 

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Multipronged Recruitment Strategy

Eligible patients were approached using one or more of eight recruitment strategies ([Table 1]) until they consented or declined to participate in the study. Initially, patients were sent a mailed letter and a patient portal message. For patients who did not respond to the letter or patient portal message, RAs conducted follow-up phone calls and targeted phone calls prior to an upcoming appointment at their primary care clinic. Clinician-centered strategies included “1-click” referrals, a simple digital workflow initiated by referring clinicians from within the EHR, and entries in electronic “huddle” notes in the EHR to remind clinicians to recruit specific eligible patients scheduled for a clinic appointment that day ([Fig. 2]). Targeted, in-person recruitment was conducted by a bilingual RA on days where four or more patients had an appointment at a given clinic or if a patient opted to be recruited in person when approached using one of the other strategies. At the outset of our study, we obtained IRB approval for our consent form and protocol, which included a list of initial recruitment strategies. Subsequently, we submitted amendments to our IRB protocol to add new recruitment strategies to the original list, all of which were approved before being implemented.

Fig. 2 Clinician-centered recruitment strategies: “1-click” referral, a button to initiate a referral accessible from the patient's chart in the EHR (blurred border, Epic Systems, Inc.), and “huddle” notes, entries in “pre-visit planning” notes in the EHR to remind clinicians to recruit specific patients scheduled for a clinic visit that day. EHR, electronic health record.

Table 1 Recruitment strategies

Recruitment strategy	Description
1. Mailed letters	• RA mailed letters to patients through U.S. postal service • Initially, all patients were mailed a hard copy of the study recruitment letter.
2. Patient portal message	• RA sent a minimum of two patient portal messages to patients with an activated patient portal status.
3. Clinic-centered strategies	• Participating clinics were provided flyers with information for participating in the study and instructed to give them to potentially eligible patients • Study investigators presented an overview of study to primary care providers at participating clinics during clinic staff meetings
4. Follow-up phone call	• RA made follow-up phone calls to patients who were sent a mailed letter and or patient portal message.
5. Text messages	• RA sent patients a text message inviting them to participate in the study.
6. Clinician-centered strategies	• EHR-based “1-click” referrals[a] (2022 Epic Systems Corporation—see [Fig. 2])  – Study team designed a “1-click” referral button made available from within the EHR for clinicians to refer a patient to the study.  – The “1-click” referral generates a message containing the patient medical record number (MRN) that is automatically sent to the study team via email.  – The RAs followed up with referred patients via follow-up phone call and patient portal messaging if available. • “Huddle notes”[b]  – RA added a recruitment note to patients' charts before their upcoming appointments to remind provider to mention the study. • RA emailed clinicians with multiple upcoming appointments in a week, reminding them to mention the study to eligible patients
7. Targeted phone calls	• RAs called patients before and or after appointments scheduled with a provider in an ambulatory setting to discuss the study
8. Targeted, in-person	• A bilingual RA recruited patients before or after clinic visit  – RAs recruited in-person if there was a cluster of upcoming appointments, or if a patient (English- or Spanish-speaking) opted to be consented in person.

Abbreviations: EHR, electronic health record; RA, research assistant.

^a “1-click” referrals are automatically generated emails initiated by the clinician to the study team using a button available in the patient's chart in the EHR. The email contains patient identifying information for the study team to recruit the patient.

^b “Huddle notes” are electronic notes within the eligible patient's chart in the EHR that are reviewed by medical staff during scheduled clinic encounters.

 

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Structured Recruitment Debrief Sessions

During each month of the recruitment period, we conducted a 20-minute session with research team members using a structured “recruitment debrief” guide ([Supplementary Appendix A1], available in the online version) and recorded all input and feedback. The recruitment debrief guide was constructed based on review of the literature and consultation with an expert on health equity (J.R.).[16] [26] The guide included the following topics: monthly recruitment goals, consented patient demographics, barriers and facilitators to equitable recruitment, overall recruitment experience during the preceding month, and possible improvements to the recruitment process. During each session we tracked and reviewed key demographics based on our equity variables (described below). Based on our analysis of findings from these discussions (see below), RAs implemented changes to our approach during subsequent months of the recruitment period.

 

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Data Collection

RAs collected and tracked all recruitment activities in Microsoft Excel. Collected data included dates and strategies by which potentially eligible patients were approached, the recruitment strategy reported to be successful by eligible patients during the consent phone call, and free text field notes of any barriers or facilitators to recruitment reported by patients. We retrieved demographic data, patient portal enrollment status (defined above), Charlson comorbidity indices, structured social determinant of health (food, housing, transportation, and others), and disease activity (defined above) from the EHR for eligible patients. For all consented patient participants, RAs asked patients how they were recruited to the study and recorded the specific strategy by which the patient was recruited.

 

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Outcomes and Measures

We defined four groups of patients: (1) potentially eligible patients who met inclusion criteria (identified in the EHR using disease activity tier criteria); (2) approached patients recruited using one or more strategies; (3) screened patients who completed the web-based eligibility questionnaire and provided contact information including a phone number and/or email; and (4) consented patients who provided written informed consent.

For consented patients, we defined two main outcomes: patient portal recruit, or participants who reported completing the web-based eligibility questionnaire on their own after receiving a patient portal message; and nonpatient portal recruit, or participants who reported being contacted using any nonpatient portal recruitment strategy prior to completing the web-based eligibility questionnaire. We defined dichotomous equity variables for age (greater than 65 years of age vs. less than or equal to 65 years of age), self-identified sex (female vs. male), race (non-White vs. White), ethnicity (Hispanic vs. non-Hispanic), primary language (Spanish vs. English), median income by zip code (≤$63,000 vs. >$63,000), education (no college vs. some college or graduate education), and clinic location (urban vs. suburban). We defined two process outcomes, the mean number of approaches per patient, and mean number of unique recruitment strategies used per patient.

 

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Mixed Methods Analysis

We linked data queried from the EHR to corresponding data from our patient recruitment tracker by medical record number. We used descriptive statistics to report demographics of patients who were screened (i.e., met EHR inclusion criteria), approached via the multipronged recruitment strategy, completed the web-based eligibility questionnaire, and consented; and to report the number and percentages of patient portal recruits and nonpatient portal recruits. A two-sample t-test was used to compare the mean number of approaches per patient for consented patients and approached patients who were not consented to examine any recruitment effort disparities between patients who consented and those who did not consent to participate in the study. We used Fisher's exact test to compare our main outcomes, patient portal and nonpatient portal recruits, by each dichotomized equity variable (above). Multivariable logistic regression was used to adjust effect size estimates and control for all covariates. All quantitative analyses were performed using R Studio (Version 2022.02.0 + 443 for Windows).

For our qualitative analysis, two members of the research team trained in grounded theory (S.P. and J.S.F.) independently reviewed and coded all feedback, notes, and responses from monthly recruitment debrief sessions, extracted representative quotes, and identified codes and preliminary themes for key recruitment facilitators and barriers from the research team perspective.[27] Using a similar process, we compiled and analyzed free-text entries and notes recorded by RAs in our patient recruitment tracker to identify key facilitators and barriers from the patient perspective. Preliminary themes were reviewed and reconciled during a final group consensus meeting. All qualitative analyses were conducted using Microsoft Excel.

 

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Results

We identified a total of 6,853 patients ([Fig. 3]) from the EDW who met study inclusion criteria; notably, 5,783 (84.4%) were patient portal enrollees. Of these 6,853 potentially eligible patients, 6,366 (92.9%) were approached, 627 patients (9.1%) were screened using the web-based eligibility questionnaire, and 445 patients (6.5%) consented using our multipronged strategy. The demographics of the 627 patients who were approached and completed the web-based eligibility questionnaire were similar to the 5,739 patients who were approached but did not complete the eligibility questionnaire ([Table 2]; see also [Supplementary Appendices A2] and [A3], available in the online version) with several notable exceptions: in absolute percentages, those who did not complete the eligibility questionnaire were more frequently Hispanic (+6.7%), more frequently Spanish-speaking (+6.3%), and less frequently patient portal enrollees (−7.4%). The demographics of the 445 patients who completed the web-based eligibility questionnaire and consented to participate in the main trial were similar to the 667 patients who completed the web-based questionnaire. The frequencies of missing social determinants data([Supplementary Appendix A2], available in the online version) from the EHR were similarly high across all four groups.

Fig. 3 Patient enrollment flowchart.

Table 2 Patient cohort demographics

Characteristics	Met inclusion Criteria, n = 6,853	Approached, n = 6,366	Completed web-based eligibility questionnaire		Consented, n = 445	p-Value[a]
			No, n = 5,739	Yes, n = 627
Age in years, mean (SD)	53.8 (17.1)	53.9 (17.1)	55.1 (17.3)	52.5 (15.5)	52.0 (15.5)	0.452
Female sex, no. (%)	5,158 (75.3)	4,793 (75.3)	4,293 (74.8)	499 (79.6)	346 (77.8)	0.065
Race/ethnicity, no. (%)
American Indian or Alaska Native	9 (0.1)	10 (0.2)	8 (0.1)	2 (0.3)	2 (0.4)	0.005
Asian	188 (2.7)	183 (2.9)	166 (2.9)	18 (2.9)	12 (2.7)
White non-Hispanic	3,394 (49.5)	3,245 (51.0)	2,915 (50.8)	331 (52.7)	236 (53.0)
Black non-Hispanic	1,164 (17.0)	1,049 (16.5)	916 (16.0)	130 (20.7)	92 (20.7)
Hispanic	1,837 (26.8)	1,642 (25.8)	1,519 (26.5)	124 (19.8)	89 (20.0)
Native Hawaiian or other Pacific Islander	2 (0.03)	3 (0.05)	2 (0.03)	1 (0.2)	1 (0.2)
Other	123 (1.8)	110 (1.7)	98 (1.7)	13 (2.1)	6 (1.3)
Declined/unavailable/missing	136 (2.0)	124 (1.9)	115 (2.0)	8 (1.3)	7 (1.6)
Marital status, no. (%)
Partnered	2,996 (43.7)	2,822 (44.3)	2,563 (44.7)	261 (41.6)	189 (42.5)	0.693
Nonpartnered/single	3,775 (55.1)	3,465 (54.4)	3,102 (54.1)	361 (57.6)	252 (56.6)
Unknown/missing	82 (1.2)	79 (1.2)	74 (1.3)	5 (0.8)	4 (0.9)
Primary language Spanish, no. (%)	829 (12.1)	705 (11.1)	670 (11.7)	34 (5.4)	20 (4.5)	<0.001
Education, no. (%)
Less than high school	659 (9.6)	554 (8.7)	505 (8.8)	47 (7.5)	30 (6.7)	0.142
Graduated high school	2,086 (30.4)	1,858 (29.2)	1,669 (29.1)	188 (30.0)	132 (29.7)
Graduated college	2,512 (36.7)	2,425 (38.1)	2,185 (38.1)	241 (38.4)	169 (38.0)
Graduated higher education	741 (10.8)	727 (11.4)	648 (11.3)	80 (12.8)	61 (13.7)
Unknown/missing	855 (12.5)	802 (12.6)	732 (12.8)	71 (11.3)	53 (11.9)
Socioeconomic status (median income by zip code), no. (%)
Less than or equal to $47,000	648 (9.5)	568 (8.9)	510 (8.9)	57 (9.1)	44 (9.9)	0.449
$47,001–$63,000	776 (11.3)	708 (11.1)	650 (11.3)	58 (9.3)	37 (8.3)
Greater than $63,000	5,399 (78.8)	5,060 (79.5)	4,554 (79.4)	507 (80.9)	362 (81.3)
Missing	30 (0.4)	30 (0.5)	25 (0.4)	5 (0.8)	2 (0.4)
Insurance status–no. (%)
Commercial	3,835 (56.0)	3,656 (57.4)	3,298 (57.5)	365 (58.2)	257 (57.8)	0.719
Medicaid	1,299 (19.0)	1,129 (17.7)	1,012 (17.6)	116 (18.5)	82 (18.4)
Medicare	1,635 (23.9)	1,496 (23.5)	1,349 (23.5)	141 (22.5)	104 (23.4)
Self-pay	79 (1.2)	80 (1.3)	75 (1.3)	5 (0.8)	2 (0.4)
Other government	5 (0.1)	5 (0.1)	5 (0.1)	0 (0.0)	0 (0.0)
Patient portal enrollees,[b] no. (%)	5,783 (84.4)	5,575 (87.6)	4,992 (87.0)	592 (94.4)	423 (95.1)	<0.001
Charlson comorbidity index, mean (SD)	1.8 (1.6)	1.7 (1.6)	1.7 (1.6)	1.7 (1.4)	1.7 (1.5)	0.217

Abbreviation: SD, standard deviation.

^a Compared met inclusion criteria, approached, completed web-based eligibility questionnaire “yes,” and consented.

^b Patient portal enrollees = defined as having an “activated” status in the EHR.

We observed no significant difference in the mean (standard deviation) number of approaches per patient between consented patients (3.2 (2.1)) and approached patients who were not consented (3.1 (1.7)), t =  − 0.1, 95% confidence interval (CI): −0.20 to 0.18, p = 0.92. Of the 445 patient enrollees (of whom, 423 [95.1%] were patient portal enrollees), 241 (54.2%) reported being recruited via the patient portal (i.e., completed the eligibility questionnaire after receiving a patient portal message). In unadjusted analyses, patient portal recruits were significantly more likely to be White, non-Hispanic, higher income, and have some college education compared with nonpatient portal recruits ([Table 3]). There were no significant discrepancies for age, sex, or language among patient portal recruits compared with nonpatient portal recruits. Patients affiliated with urban clinics were significantly less likely to be recruited via the patient portal. In adjusted analyses, non-White participants (odds ratio [OR]: 0.46, 95% CI: 0.28–0.77, p = 0.003) and participants with no college education (OR: 0.60, 95% CI: 0.39–0.91, p = 0.016) were significantly less likely to be recruited via the patient portal. The demographics of consented participants recruited by patient portal message and specific nonpatient portal recruitment strategies are available in [Supplementary Appendix A4] (available in the online version).

Table 3 Patient portal recruits versus nonpatient portal recruits by equity variable

	Patient portal recruits, n = 241	Nonpatient portal recruits, n = 204	Un adjusted OR	95% CI	p-Value	Adjusted OR	95% CI	p-Value
Age, no. (%)
Greater than 65	56 (23.3)	41 (20.3)	1.20	(0.75–1.95)	0.490	0.80	(0.48–1.32)	0.385
Less than or equal to 65	185 (76.7)	163 (79.7)
Sex, no. (%)
Female	182 (75.8)	164 (80.2)	0.74	(0.46–1.19)	0.211	0.84	(0.52–1.36)	0.484
Male	60 (24.2)	40 (19.8)
Race, no. (%)
Non-White	84 (34.6)	125 (60.9)	0.34	(0.23–0.51)	<0.001	0.46	(0.28–0.77)	0.003[a]
White	157 (65.4)	79 (39.1)
Ethnicity, no. (%)
Hispanic	33 (13.3)	56 (26.7)	0.42	(0.25–0.69)	<0.001	0.82	(0.44–1.52)	0.535
Non-Hispanic	208 (86.7)	148 (73.3)
Primary language, no. (%)
Non-English	7 (2.9)	14 (6.9)	0.41	(0.14–1.10)	0.071	0.91	(0.31–2.51)	0.856
English	234 (97.1)	190 (93.1)
Median income by zip code, no. (%)
Low income (≤$63,000)	33 (13.8)	50 (24.3)	0.50	(0.29–0.82)	0.005	0.91	(0.52–1.59)	0.736
High income (>63,000)	208 (86.3)	154 (75.7)
Education, no. (%)
No college	95 (39.2)	120 (58.4)	0.46	(0.31–0.68)	<0.001	0.60	(0.39–0.91)	0.016[a]
Some college and above	146 (60.8)	84 (41.6)
Clinic location,[b] no. (%)
Urban	133 (55.0)	173 (84.7)	0.22	(0.13–0.36)	<0.001	0.68	(0.43–1.04)	0.079
Suburban	108 (45.0)	31 (15.3)

Abbreviations: CI, confidence interval; OR, odds ratio.

^a Hommel-corrected values for race and education were p = 0.026 and 0.115, respectively.

^b Urban clinics are those located within Boston city limits.

Table 4 Monthly recruitment debrief findings: key themes and examples

Theme	Descriptions and examples
Barriers
Technological issues	• Lack of access to email and smartphones • Lack of confidence using a smartphone • Limited data plan availability
Caregiver availability	• Caregivers unavailable to help patient with recruitment documentation and history.
Small pool of Spanish-speaking patients	• Diminishing number of Spanish-speaking patients to approach for recruitment toward the end of the recruitment period.
Facilitators
Large pool of eligible patients	• Data refreshes were necessary to ensure a large pool of eligible patients and corresponded to an increase in patients who were successfully recruited. • Addition of two clinics later in the recruitment period increased eligible patient pool.
Bilingual study staff	• Bilingual RA facilitated the recruitment of English- and Spanish-speaking patients. • RA was able to offer in-person recruitment to all patients who requested to be recruited in person. All patients who were ultimately recruited in-person were Spanish speakers. • Recruitment of Spanish-speaking patients via phone calls around scheduled clinic appointments was a successful strategy for enrollment.
English and Spanish recruitment materials	• Sending patient portal messages, letters, and text messages with improved readability for health literacy in both English and Spanish. • Few patients listed as “Spanish-speaking” and “does not need an interpreter” in the EHR preferred Spanish materials or speaking in Spanish.
Targeted recruitment at upcoming patient appointments	• Focusing recruitment efforts (patient portal messages, phone calls, huddle notes) around scheduled patient appointments improved patient engagement. • Higher success rate of patients answering the phone when calling patients within a few days of a scheduled appointments.
Clinician-initiated referrals	• Clinicians sent study referrals using the “1-click” referral button available in the EHR. • Patients reported receiving recommendations from their PCP regarding study enrollment.

Abbreviations: EHR, electronic health record; PCP, primary care provider; RA, research assistant.

Eight major themes (three barriers, five facilitators) emerged from the 13 recruitment debrief sessions. Key barriers included: technological issues related to smartphone or email access, caregiver availability for patients who expressed needing support with recruitment procedures, and a small pool of Spanish-speaking patients to recruit ([Table 4]). Key recruitment facilitators included: availability of bilingual study staff, recruitment from a large pool of eligible patients, use of Spanish-language recruitment materials, conducting targeted recruitment prior to upcoming patient appointments, and clinician-initiated referrals.

Of the 6,366 patients approached, 934 (14.7%) patients reported one or more barriers to participation in the study. The 943 barriers reported by patients were due to the following reasons: mild or well-controlled asthma (331, 35.1%); not interested in participating (161, 17.1%); technology barriers such as not having a smartphone or email access (148, 15.7%); unable to make time commitment (141, 15.0%); health issues (54, 5.7%); spoke a language other than English or Spanish and required an interpreter (35, 3.7%); out of network or had a change in PCP (23, 2.4%); skepticism about participating (22, 2.3%); had cognitive issues or dementia (12, 1.3%); not enough study compensation (7, 0.7%); ineligible due to age (5, 0.5%); or had asthma complications (4, 0.4%).

 

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Discussion

We conducted a mixed methods study to assess equity in recruitment for a clinical trial of a digital health intervention aimed at remotely monitoring asthma. Our recruitment efforts coincided with the start of the second wave of the COVID-19 pandemic in the United States, which provided an opportunity to rigorously evaluate our digital recruitment methods during a time of mostly remote health care. Most of the patients who were potentially eligible for our study were patient portal enrollees, according to our EHR data. Of those approached, 9.1% completed the electronic screening process, and ultimately, 6.5% were consented using our multipronged approach. We did observe a few disparities in potentially eligible patients who did not complete the web-based eligibility questionnaire. These disparities were more frequently seen in Hispanic and Spanish-speaking patients, and less frequently in patient portal enrollees, but these were at most 6 to 8% on an absolute basis. While the majority of participants who completed the electronic eligibility questionnaire and consented were patient portal enrollees (95%), only 54% reported being recruited via the patient portal. In adjusted analysis, we observed significant disparities in race and education for patient portal recruits compared with nonpatient portal recruits. From our thematic analysis of monthly structured recruitment debrief sessions, targeted strategies, such as calling eligible patients prior to scheduled clinic appointments, offering in-person recruitment, in addition to patient portal messaging and mailed letters were effective in achieving diversity. Both the research team and patients identified technological gaps, such as lack of access to email or a smartphone and limited digital literacy, were identified as major barriers to remote recruitment of Spanish-speaking individuals.

Our research team utilized monthly recruitment debriefs to identify key barriers (e.g., lack of email access) and facilitators (e.g., targeted recruitment strategies) for equitable recruitment. The debriefs followed a structured approach, allowing us to assess our recruitment approach in real-time and adopt a flexible strategy to optimize recruitment from underrepresented groups. Comparing the barriers and facilitators identified by the research team and patients offered insights into the mechanisms likely required to ensure successful recruitment equity (i.e., targeted, in-person recruitment using a bilingual RA). For example, both the research team and patients identified language as a key barrier. Consequently, we prioritized assignment of a full-time bilingual RA to actively conduct in-person recruitment for Spanish-speaking patients. Language concordance between prospective patients and a bilingual RA was subsequently identified as a critical facilitator for recruiting Spanish-speaking patients and may explain why we did not observe a significant disparity in the language category in both unadjusted and adjusted outcomes analyses.

Few studies have utilized a structured process to evaluate and modify recruitment strategies using multiple digital and nondigital strategies to achieve equity.[16] Additionally, few studies have examined recruitment in the context of a population with a high patient portal enrollment rate (which is higher than the current rate [∼70%] reported across the MGB enterprise). Our study presented a unique opportunity to rigorously evaluate a multipronged recruitment strategy to remotely screen, approach, and enroll patients into a clinical trial with regard to “TechEquity.” Despite being one of the earliest investments in telehealth technology, patient portal adoption has remained low among underserved populations due to unevenly distributed access and affordability to internet access and devices, as well as limited digital literacy.[28] [29] [30] [31] These factors are likely to exacerbate disparities in equitable recruitment into research trials and explain why we had a small number of Spanish speaking patients who ultimately consented to participate. However, the targeted strategies we describe in this study could help to address these gaps. Our preliminary data ([Supplementary Appendix A4], available in the online version) suggest that specific nonpatient portal strategies were more frequently successful at recruiting Hispanic and Spanish-speaking patients, as well as those with lower educational backgrounds.

The COVID-19 pandemic has accelerated digitization and the transition to remote interactions in the health care industry, but has also exacerbated existing inequalities.[32] It is important to note that while patient portal enrollment was high among our cohort, it did not necessarily translate to actual patient portal use, including acting upon the patient portal recruitment message. Our findings are consistent with previous research which suggests that patient portal enrollment does not equate to continued patient portal use across various demographics,[33] and particularly among underserved patients who were less likely to use digital tools during the pandemic.[34] [35] For instance, all three patients who opted for in-person recruitment by a bilingual RA were all Spanish speakers, Hispanic, and elderly (mean age of 66 years), even though two of these patients were enrolled in the patient portal which offered multilanguage support. Thus, recruitment through electronic screening and patient portal messaging alone may not be sufficient to achieve recruitment equity in clinical trials. In the future, ensuring equity in clinical trial recruitment will require assessing digital literacy and providing support to patients, such as digital navigators.[36] [37] [38] [39] These findings underscore the importance of digital inclusion as an important focus of health care policies and administration.[40]

Our study has several limitations. First, our study was conducted at an institution with high patient portal enrollment and utilized a web-based screening process which may have introduced recruitment disparities for specific demographics. Second, relying on patient portal enrollment status in the EHR to identify patients to recruit via patient portal message may not accurately reflect patient portal use, which requires adequate digital literacy. Third, despite our intention to prioritize patients based on disease activity ([Supplementary Appendix A3], available in the online version) tiers, we had to recruit all patients due to low consent rates during the pandemic. To include additional eligible patients, we added disease activity tier 7 after commencing recruitment. Fourth, we did not engage patient-based community groups to participate in the recruitment process, though we involved clinic PCPs and administrative staff as study advocates. Lastly, we did not collect data to confirm which strategies reached patients, such as how many patient portal messages were read or phone calls answered by patients.

 

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Conclusion

Our study highlights the importance of intentional and targeted efforts to achieve diversity in clinical trial recruitment. To improve recruitment equity, researchers should plan and budget for such efforts in their study design. A practical approach that researchers can adopt is a multipronged recruitment strategy, including regular debrief sessions, to identify barriers to equitable recruitment. Future studies could maximize recruitment equity by staffing projects with multiple bilingual RAs, principal investigators, and study team members.

 

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Clinical Relevance Statement

With the trend toward digitization of recruitment into clinical trials, strategies that utilize both digital and nondigital methods will continue to be necessary to ensure clinical trial equity.

 

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Multiple-Choice Questions

1. When recruiting patients for a digital health clinical trial intervention, which modality is most likely to contribute to equitable patient enrollment?

   • Patient portal messages

   • Bilingual research assistant

   • Phone calls

   • Text messages

   Correct Answer: The correct answer is option b. A bilingual research assistant was the modality that successfully recruited Spanish-speaking patients in this study.

2. Which of the following are barriers to equitable enrollment?

   • Lack of smartphone access

   • Patient interest

   • Lack of email access

   • Options a and c

   Correct Answer: The correct answer is option d. Lack of smartphone access and email access were common technical barriers identified in our study. Other types of barriers include lack of caregiver support and language discrepancy.

 

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Conflict of Interest

No authors have conflict of interests to declare.

Acknowledgments

The authors would like to thank William Crawford, MD, for input on the disease activity tiers.

Protection of Human and Animal Subjects

The study was reviewed and performed in compliance with the Massachusetts General Brigham Institutional Review Board.

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Publikationsverlauf

Eingereicht: 16. November 2022

Angenommen: 06. Mai 2023

Accepted Manuscript online:
10. Mai 2023

Artikel online veröffentlicht:
09. August 2023