A review of the impact of utilising electronic medical records for clinical research recruitment

Addressing Statistical Power and Increasing Diversity in Hospice Research: Electronic Medical Record Participant Identification Compared to Nurse Referral Approaches to Recruitment

CONTEXT

Recruitment of targeted samples into hospice clinical trials is often challenging. While electronic medical records (EMR) are commonly used in hospital-based research, it is uncommon in hospice research. The community setting and the variability in hospices and their medical record creates unique challenges.

OBJECTIVES

This paper compares recruitment in two hospice randomized controlled trials, each of which had a group recruited by using the EMR identification and a group recruited by nurse referral. We sought to answer three questions: 1) What is the impact of using the EMR to identify hospice participants for clinical research? 2) How do the referral count and consent rate (referrals that ultimately result in verbal informed consent to participate in research) differ between hospice agencies using an EMR participant identification approach compared to those using a nurse referral approach? and 3) What are the challenges associated with using the EMR to identify potential research participants?

METHOD

Recruitment data from two hospice clinical trials was combined into a new database. Data from hospice nurse referral agencies was compared with data from those agencies who participated in EMR-identified referrals.

RESULTS

The EMR identification process was feasible and efficient, resulting in more referrals and more consented participants than the nurse referral method. Of particular interest is that 8% more black caregivers were recruited using the EMR identification process than the nurse referral.

CONCLUSIONS

The EMR-identified recruitment process is the recommended method in hospice research.

User Requirements for an Electronic Patient Recruitment System: Semistructured Interview Analysis After First Implementation in 3 German University Hospitals

BACKGROUND

Clinical trials are essential for medical research and medical progress. Nevertheless, trials often fail to reach their recruitment goals. Patient recruitment systems aim to support clinical trials by providing an automated search for eligible patients in the databases of health care institutions like university hospitals. To integrate patient recruitment systems into existing workflows, previous works have assessed user requirements for these tools. In this study, we tested patient recruitment systems KAS+ and recruIT as part of the MIRACUM (Medical Informatics in Research and Care in University Medicine) project.

OBJECTIVE

Our goal was to investigate whether and to what extent the 2 different evaluated tools can meet the requirements resulting from the first requirements analysis, which was performed in 2018-2019. A user survey was conducted to determine whether the tools are usable in practice and helpful for the trial staff. Furthermore, we investigated whether the test phase revealed further requirements for recruitment tools that were not considered in the first place.

METHODS

We performed semistructured interviews with 10 participants in 3 German university hospitals who used the patient recruitment tools KAS+ or recruIT for at least 1 month with currently recruiting trials. Thereafter, the interviews were transcribed and analyzed by Meyring method. The identified statements of the interviewees were categorized into 5 groups of requirements and sorted by their frequency.

RESULTS

The evaluated recruIT and KAS+ tools fulfilled 7 and 11 requirements of the 12 previously identified requirements, respectively. The interviewed participants mentioned the need for different notification schedules, integration into their workflow, different patient characteristics, and pseudonymized screening lists. This resulted in a list of new requirements for the implementation or enhancement of patient recruitment systems.

CONCLUSIONS

Trial staff report a huge need of support for the identification of eligible trial participants. Moreover, the workflows in patient recruitment differ across trials. For better suitability of the recruitment systems in the workflow of different kinds of trials, we recommend the implementation of an adjustable notification schedule for screening lists, a detailed workflow analysis, broad patient filtering options, and the display of all information needed to identify the persons on the list. Despite criticisms, all participants confirmed to use the patient recruitment systems again.

Design and implementation of electronic health record-based tools to support a weight management program in primary care

Objectives

This paper reports on a mixed methods formative evaluation to support the design and implementation of information technology (IT) tools for a primary care weight management intervention delivered through the patient portal using primary care staff as coaches.

Methods

We performed a qualitative needs assessment, designed the IT tools to support the weight management program, and developed implementation tracking metrics. Implementation tracking metrics were designed to use real world electronic health record (EHR) data.

Results

The needs assessment revealed IT requirements as well as barriers and facilitators to implementation of EHR-based weight management interventions in primary care. We developed implementation metrics for the IT tools. These metrics were used in weekly project team calls to make sure that project resources were allocated to areas of need.

Conclusion

This study identifies the important role of IT in supporting weight management through patient identification, weight and activity tracking in the patient portal, and the use of the EHR as a population management tool. An intensive multi-level implementation approach is required for successful primary care-based weight management interventions including well-designed IT tools, comprehensive involvement of clinic leadership, and implementation tracking metrics to guide the process of workflow integration. This study helps to bridge the gap between informatics and implementation by using socio-technical formative evaluation methods early in order to support the implementation of IT tools.

Trial registration

clinicaltrials.gov, NCT04420936. Registered June 9, 2020.

recruIT: A cloud-native clinical trial recruitment support system based on Health Level 7 Fast Healthcare Interoperability Resources (HL7 FHIR) and the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM)

BACKGROUND

Clinical trials (CTs) are foundational to the advancement of evidence-based medicine and recruiting a sufficient number of participants is one of the crucial steps to their successful conduct. Yet, poor recruitment remains the most frequent reason for premature discontinuation or costly extension of clinical trials.

METHODS

We designed and implemented a novel, open-source software system to support the recruitment process in clinical trials by generating automatic recruitment recommendations. The development is guided by modern, cloud-native design principles and based on Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) as an interoperability standard with the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) being used as a source of patient data. We evaluated the usability using the system usability scale (SUS) after deploying the application for use by study personnel.

RESULTS

The implementation is based on the OMOP CDM as a repository of patient data that is continuously queried for possible trial candidates based on given clinical trial eligibility criteria. A web-based screening list can be used to display the candidates and email notifications about possible new trial participants can be sent automatically. All interactions between services use HL7 FHIR as the communication standard. The system can be installed using standard container technology and supports more sophisticated deployments on Kubernetes clusters. End-users (n = 19) rated the system with a SUS score of 79.9/100.

CONCLUSION

We contribute a novel, open-source implementation to support the patient recruitment process in clinical trials that can be deployed using state-of-the art technologies. According to the SUS score, the system provides good usability.

Sociotechnical feasibility of natural language processing-driven tools in clinical trial eligibility prescreening for Alzheimer's disease and related dementias

BACKGROUND

Alzheimer's disease and related dementias (ADRD) affect over 55 million globally. Current clinical trials suffer from low recruitment rates, a challenge potentially addressable via natural language processing (NLP) technologies for researchers to effectively identify eligible clinical trial participants.

OBJECTIVE

This study investigates the sociotechnical feasibility of NLP-driven tools for ADRD research prescreening and analyzes the tools' cognitive complexity's effect on usability to identify cognitive support strategies.

METHODS

A randomized experiment was conducted with 60 clinical research staff using three prescreening tools (Criteria2Query, Informatics for Integrating Biology and the Bedside [i2b2], and Leaf). Cognitive task analysis was employed to analyze the usability of each tool using the Health Information Technology Usability Evaluation Scale. Data analysis involved calculating descriptive statistics, interrater agreement via intraclass correlation coefficient, cognitive complexity, and Generalized Estimating Equations models.

RESULTS

Leaf scored highest for usability followed by Criteria2Query and i2b2. Cognitive complexity was found to be affected by age, computer literacy, and number of criteria, but was not significantly associated with usability.

DISCUSSION

Adopting NLP for ADRD prescreening demands careful task delegation, comprehensive training, precise translation of eligibility criteria, and increased research accessibility. The study highlights the relevance of these factors in enhancing NLP-driven tools' usability and efficacy in clinical research prescreening.

CONCLUSION

User-modifiable NLP-driven prescreening tools were favorably received, with system type, evaluation sequence, and user's computer literacy influencing usability more than cognitive complexity. The study emphasizes NLP's potential in improving recruitment for clinical trials, endorsing a mixed-methods approach for future system evaluation and enhancements.

Evidence-based recruitment strategies for clinical research: Study personnel's and research participants' perceptions about successful methods of outreach for a U.S. Autism-Research Cohort

Introduction

Under enrollment of participants in clinical research is costly and delays study completion to impact public health. Given that research personnel make decisions about which strategies to pursue and participants are the recipients of these efforts, we surveyed research staff (n = 52) and participants (n = 4,144) affiliated with SPARK (Simons Foundation Powering Autism for Knowledge) - the largest study of autism in the U.S. - to understand their perceptions of effective recruitment strategies.

Methods

In Study 1, research personnel were asked to report recruitment strategies that they tried for SPARK and to indicate which ones they would and would not repeat/recommend. In Study 2, SPARK participants were asked to indicate all the ways they heard about the study prior to enrollment and which one was most influential in their decisions to enroll.

Results

Staff rated speaking with a SPARK-study-team member (36.5%), speaking with a medical provider (19.2%), word of mouth (11.5%), and a live TV news story (11.5%) as the most successful strategies. Participants most often heard about SPARK via social media (47.0%), speaking with a medical provider (23.1%), and an online search (20.1%). Research personnel's and participants' views on effective recruitment strategies often differed, with the exception of speaking with a medical provider.

Conclusion

Results suggest that a combination of strategies is likely to be most effective in reaching diverse audiences. Findings have implications for the selection of strategies that meet a study's specific needs, as well as recruitment-strategy "combinations" that may enhance the influence of outreach efforts.

Evaluation of Eligibility Criteria Relevance for the Purpose of IT-Supported Trial Recruitment: Descriptive Quantitative Analysis

BACKGROUND

Clinical trials (CTs) are crucial for medical research; however, they frequently fall short of the requisite number of participants who meet all eligibility criteria (EC). A clinical trial recruitment support system (CTRSS) is developed to help identify potential participants by performing a search on a specific data pool. The accuracy of the search results is directly related to the quality of the data used for comparison. Data accessibility can present challenges, making it crucial to identify the necessary data for a CTRSS to query. Prior research has examined the data elements frequently used in CT EC but has not evaluated which criteria are actually used to search for participants. Although all EC must be met to enroll a person in a CT, not all criteria have the same importance when searching for potential participants in an existing data pool, such as an electronic health record, because some of the criteria are only relevant at the time of enrollment.

OBJECTIVE

In this study, we investigated which groups of data elements are relevant in practice for finding suitable participants and whether there are typical elements that are not relevant and can therefore be omitted.

METHODS

We asked trial experts and CTRSS developers to first categorize the EC of their CTs according to data element groups and then to classify them into 1 of 3 categories: necessary, complementary, and irrelevant. In addition, the experts assessed whether a criterion was documented (on paper or digitally) or whether it was information known only to the treating physicians or patients.

RESULTS

We reviewed 82 CTs with 1132 unique EC. Of these 1132 EC, 350 (30.9%) were considered necessary, 224 (19.8%) complementary, and 341 (30.1%) total irrelevant. To identify the most relevant data elements, we introduced the data element relevance index (DERI). This describes the percentage of studies in which the corresponding data element occurs and is also classified as necessary or supplementary. We found that the query of "diagnosis" was relevant for finding participants in 79 (96.3%) of the CTs. This group was followed by "date of birth/age" with a DERI of 85.4% (n=70) and "procedure" with a DERI of 35.4% (n=29).

CONCLUSIONS

The distribution of data element groups in CTs has been heterogeneously described in previous works. Therefore, we recommend identifying the percentage of CTs in which data element groups can be found as a more reliable way to determine the relevance of EC. Only necessary and complementary criteria should be included in this DERI.

The Roles of Electronic Health Records for Clinical Trials in Low- and Middle-Income Countries: Scoping Review

BACKGROUND

Clinical trials are a crucial element in advancing medical knowledge and developing new treatments by establishing the evidence base for safety and therapeutic efficacy. However, the success of these trials depends on various factors, including trial design, project planning, research staff training, and adequate sample size. It is also crucial to recruit participants efficiently and retain them throughout the trial to ensure timely completion.

OBJECTIVE

There is an increasing interest in using electronic health records (EHRs)-a widely adopted tool in clinical practice-for clinical trials. This scoping review aims to understand the use of EHR in supporting the conduct of clinical trials in low- and middle-income countries (LMICs) and to identify its strengths and limitations.

METHODS

A comprehensive search was performed using 5 databases: MEDLINE, Embase, Scopus, Cochrane Library, and the Cumulative Index to Nursing and Allied Health Literature. We followed the latest version of the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guideline to conduct this review. We included clinical trials that used EHR at any step, conducted a narrative synthesis of the included studies, and mapped the roles of EHRs into the life cycle of a clinical trial.

RESULTS

A total of 30 studies met the inclusion criteria: 13 were randomized controlled trials, 3 were cluster randomized controlled trials, 12 were quasi-experimental studies, and 2 were feasibility pilot studies. Most of the studies addressed infectious diseases (15/30, 50%), with 80% (12/15) of them about HIV or AIDS and another 40% (12/30) focused on noncommunicable diseases. Our synthesis divided the roles of EHRs into 7 major categories: participant identification and recruitment (12/30, 40%), baseline information collection (6/30, 20%), intervention (8/30, 27%), fidelity assessment (2/30, 7%), primary outcome assessment (24/30, 80%), nonprimary outcome assessment (13/30, 43%), and extended follow-up (2/30, 7%). None of the studies used EHR for participant consent and randomization.

CONCLUSIONS

Despite the enormous potential of EHRs to increase the effectiveness and efficiency of conducting clinical trials in LMICs, challenges remain. Continued exploration of the appropriate uses of EHRs by navigating their strengths and limitations to ensure fitness for use is necessary to better understand the most optimal uses of EHRs for conducting clinical trials in LMICs.

A Centralized EHR-Based Model for the Recruitment of Rural and Lower Socioeconomic Participants in Pragmatic Trials: A Secondary Analysis of the Diuretic Comparison Project

Importance

Participant diversity is important for reducing study bias and increasing generalizability of comparative effectiveness research.

Objective

Demonstrate the operational efficiency of a centralized electronic health record (EHR)-based model for recruiting difficult-to-reach participants in a pragmatic trial.

Design, Setting, and Participants

This comparative effectiveness study was a secondary analysis of Diuretic Comparison Project, a randomized clinical trial conducted between 2016 and 2022 (mean [SD] follow-up, 2.4 [1.4] years) comparing 2 commonly prescribed antihypertensives, which used an EHR-based recruitment model. Electronic study workflows, in tandem with routine clinical practice, were adapted by 72 Veteran Affairs (VA) primary care networks. Data were analyzed from August to December 2022.

Main Outcomes and Measures

Measures reflecting recruitment capacity (monthly rate), operational efficiency (median time for completion of electronic procedures), and geographic reach (percentage of patients recruited from rural areas) were examined.

Results

A total of 13 523 patients with hypertension (mean [SD] age, 72 [5.4] years; 13 092 male [96.8%]) were recruited from 537 outpatient clinics. Approximately 205 patients were randomized per month and a median of 35 days (Q1-Q3, 23-80 days) was needed to complete electronic recruitment. The annual income was below the national median for 69% of the cohort. Patients from all 50 states, Puerto Rico, and the District of Columbia were included and 45% resided in rural areas.

Conclusions and Relevance

In this secondary analysis of a multicenter pragmatic trial, a centralized EHR-based recruitment model was associated with improved participation from underrepresented groups. These participants often are difficult to reach, with their exclusion potentially biasing trial results; eliminating in-person study visits and local site involvement can minimize barriers for the recruitment of patients from rural and lower socioeconomic areas.

Trial Registration

The Diuretic Comparison Project (DCP) was registered on ClinicalTrials.gov Identifier: NCT02185417.

Clinical research staff perceptions on a natural language processing-driven tool for eligibility prescreening: An iterative usability assessment

BACKGROUND

Participant recruitment is a barrier to successful clinical research. One strategy to improve recruitment is to conduct eligibility prescreening, a resource-intensive process where clinical research staff manually reviews electronic health records data to identify potentially eligible patients. Criteria2Query (C2Q) was developed to address this problem by capitalizing on natural language processing to generate queries to identify eligible participants from clinical databases semi-autonomously.

OBJECTIVE

We examined the clinical research staff's perceived usability of C2Q for clinical research eligibility prescreening.

METHODS

Twenty clinical research staff evaluated the usability of C2Q using a cognitive walkthrough with a think-aloud protocol and a Post-Study System Usability Questionnaire. On-screen activity and audio were recorded and transcribed. After every-five evaluators completed an evaluation, usability problems were rated by informatics experts and prioritized for system refinement. There were four iterations of system refinement based on the evaluation feedback. Guided by the Organizational Framework for Intuitive Human-computer Interaction, we performed a directed deductive content analysis of the verbatim transcriptions.

RESULTS

Evaluators aged from 24 to 46 years old (33.8; SD: 7.32) demonstrated high computer literacy (6.36; SD:0.17); female (75 %), White (35 %), and clinical research coordinators (45 %). C2Q demonstrated high usability during the final cycle (2.26 out of 7 [lower scores are better], SD: 0.74). The number of unique usability issues decreased after each refinement. Fourteen subthemes emerged from three themes: seeking user goals, performing well-learned tasks, and determining what to do next.

CONCLUSIONS

The cognitive walkthrough with a think-aloud protocol informed iterative system refinement and demonstrated the usability of C2Q by clinical research staff. Key recommendations for system development and implementation include improving system intuitiveness and overall user experience through comprehensive consideration of user needs and requirements for task completion.

Utilization of a Best Practice Alert (BPA) at Point-of-Care for Recruitment into a US-Based Autism Research Study

Provider referral is one of the most influential factors in research recruitment. To ease referral burden on providers, we adapted the Best Practice Alert (BPA) in the EPIC Electronic Health Record and assessed its utility in recruiting pediatric patients with autism spectrum disorder for the national SPARK study. During a year-long surveillance, 1203 (64.0%) patients were Interested in SPARK and 223 enrolled. Another 754 participants not recruited via the BPA also enrolled; 35.5% of these participants completed their participation compared to 58.3% of BPA-referred participants. Results suggest that (a) a BPA can successfully engage providers in the study-referral process and (b) families who learn about research through their providers may be more engaged and effectively retained.

Use of a Best Practice Alert (BPA) to Increase Diversity Within a US-Based Autism Research Cohort

We evaluated the success of a best practice alert (BPA) in recruiting underrepresented families into an autism spectrum disorder research cohort by comparing BPA-response outcomes (Interested, Declined, Enrolled, Dismissed) in pediatric primary care practices (TCPs) serving diverse communities with those of subspecialty clinics. Compared to subspecialty clinics, TCPs had higher proportions of Interested responses for patients with private insurance (60.9% vs. 46.2%), Dismissed responses for patients with public insurance (30.1% vs. 20.0%), and Interested responses for non-white patients (47.7% vs. 33.3%). A targeted BPA can help researchers access more diverse groups and improve equitable representation. However, select groups more often had their alert dismissed, suggesting possible selection bias among some pediatricians regarding who should receive information about study opportunities.

Leveraging electronic health record query to streamline adverse event reporting and protocol compliance

Electronic medical records are increasingly being leveraged to improve the efficiency and effectiveness of clinical trials. Reporting safety data and adhering to follow-up schedules are two challenges faced by study centers conducting a large number of clinical trials led by a single principal investigator. The Lenox Hill Electrophysiology Research Department collaborated with Northwell Health's informatics department to develop a live query accessing both inpatient and outpatient data. To demonstrate the efficacy of this approach we compared the compliance rate of adverse event reporting and patient follow-up visits between a clinical trial run using this approach and a clinical trial conducted prior to use. We compared the number of out of window visits, missed visits, missed assessments, subject drop out and number of late reported adverse events between both studies. The trial run using the described query method had a marked reduction in these categories. Leveraging available informatics resources have allowed for improved efficiency, accurate adverse even reporting and improved follow-up scheduling.

A multicenter program for electronic health record screening for patients with heart failure with preserved ejection fraction: Lessons from the DELIVER-EHR initiative

Efficiency in clinical trial recruitment and enrollment remains a major challenge in many areas of clinical medicine. In particular, despite the prevalence of heart failure with preserved ejection fraction (HFpEF), identifying patients with HFpEF for clinical trials has proven to be especially challenging. In this manuscript, we review strategies for contemporary clinical trial recruitment and present insights from the results of the DELIVER Electronic Health Record (EHR) Screening Initiative. The DELIVER trial was designed to evaluate the effects of dapagliflozin on clinical outcomes in patients with HFpEF. Within this trial, the multicenter DELIVER EHR Screening Initiative utilized EHR-based techniques in order to improve recruitment at selected sites in the United States. For this initiative, we developed and deployed a computable phenotype from the trial's eligibility criteria along with additional EHR tools at interested sites. Sites were then surveyed at the end of the program regarding lessons learned. Six sites were recruited, trained, and supported to utilize the EHR methodology and computable phenotype. Sites found the initiative to be helpful in identifying eligible patients and cited the individualized expert technical support as a critical factor in utilizing the program effectively. We found that the major challenge of implementation was the process of converting traditional inclusion/exclusion criteria into a computable phenotype within an established and ongoing trial. Other significant challenges noted by sites were the following: impact of the COVID-19 pandemic, engagement/support by local institutions, and limited availability of internal EHR experts/resources to execute programming. The study represents a proof-of-concept in the ability to utilize EHR-based tools in clinical trial recruitment for patients with HFpEF and provides important lessons for future initiatives. ClinicalTrials.gov Identifier: NCT03619213.

Electronic medical records and biobanking

Biosample preservation for future research is a fundamental component of translational medicine. At the same time, the value of stored biosamples is largely determined by the presence of related clinical data and other information. Electronic medical records are a unique source of a large amount of information received over a long period of time. In this regard, genetic and other types of data obtained from the biosample analysis can be associated with phenotypic and other types of information stored in electronic medical records, which pushes the boundaries in large-scale genetic research and improves healthcare. The aim of this review was to analyze the literature on the potential of combining electronic medical records and biobank databases in research and clinical practice.

Electronic Health Record-Based Recruitment and Retention and Mobile Health App Usage: Multisite Cohort Study

BACKGROUND

To address the obesity epidemic, there is a need for novel paradigms, including those that address the timing of eating and sleep in relation to circadian rhythms. Electronic health records (EHRs) are an efficient way to identify potentially eligible participants for health research studies. Mobile health (mHealth) apps offer available and convenient data collection of health behaviors, such as timing of eating and sleep.

OBJECTIVE

The aim of this descriptive analysis was to report on recruitment, retention, and app use from a 6-month cohort study using a mobile app called Daily24.

METHODS

Using an EHR query, adult patients from three health care systems in the PaTH clinical research network were identified as potentially eligible, invited electronically to participate, and instructed to download and use the Daily24 mobile app, which focuses on eating and sleep timing. Online surveys were completed at baseline and 4 months. We described app use and identified predictors of app use, defined as 1 or more days of use, versus nonuse and usage categories (ie, immediate, consistent, and sustained) using multivariate regression analyses.

RESULTS

Of 70,661 patients who were sent research invitations, 1021 (1.44%) completed electronic consent forms and online baseline surveys; 4 withdrew, leaving a total of 1017 participants in the analytic sample. A total of 53.79% (n=547) of the participants were app users and, of those, 75.3% (n=412), 50.1% (n=274), and 25.4% (n=139) were immediate, consistent, and sustained users, respectively. Median app use was 28 (IQR 7-75) days over 6 months. Younger age, White race, higher educational level, higher income, having no children younger than 18 years, and having used 1 to 5 health apps significantly predicted app use (vs nonuse) in adjusted models. Older age and lower BMI predicted early, consistent, and sustained use. About half (532/1017, 52.31%) of the participants completed the 4-month online surveys. A total of 33.5% (183/547), 29.3% (157/536), and 27.1% (143/527) of app users were still using the app for at least 2 days per month during months 4, 5, and 6 of the study, respectively.

CONCLUSIONS

EHR recruitment offers an efficient (ie, high reach, low touch, and minimal participant burden) approach to recruiting participants from health care settings into mHealth research. Efforts to recruit and retain less engaged subgroups are needed to collect more generalizable data. Additionally, future app iterations should include more evidence-based features to increase participant use.

Selecting EHR-driven recruitment strategies: An evidence-based decision guide

Participant recruitment for research is a persistent bottleneck that can be improved by leveraging electronic health records (EHRs). Despite emerging evidence for various EHR-driven approaches, guidance for those attempting to select and use such approaches is limited. The national Recruitment Innovation Center established the EHR Recruitment Consult Resource (ERCR) service line to support multisite studies through implementation of EHR-driven recruitment strategies. As the ERCR, we evolved a guide through 17 consultations over 3 years with multisite studies recruiting in diverse biomedical research domains. We assessed literature and engaged domain experts to identify five key EHR-driven recruitment strategies: direct to patient messages, candidate lists for mailings/calls, direct to research alerts, point of care alerts, and participant registries. Differentiating factors were grouped into factors of study population, study protocol and recruitment workflows, and recruitment site capabilities. The decision matrix indicates acceptable or preferred strategies based on the differentiating factors. Across the ERCR consultations, candidate lists for mailing or calls were most common, participant registries were least frequently recommended, and for some studies no EHR-driven recruitment was recommended. Comparative effectiveness research is needed to refine further evidence for these and potentially new strategies to come.

Using a 3 stage process to create a consumer research contact list in a paediatric health setting: the PARTICIPATE project

The impact of child health research can be far reaching; affecting children's immediate health, their adult health, the health of future generations and the economic wellbeing of countries. Consumer and community involvement is increasingly recognised as key to successful research recruitment. Systematic approaches to research recruitment include research registries or research contact lists.

OBJECTIVE

Develop a process of creating a consumer research contact list for participating in future research opportunities at a children's health service.

METHODS

A healthcare improvement approach using a 3 stage framework; 1) evidence review and consultation 2) co-production of a research communications plan with stakeholders (including consumers), including a draft research information brochure 3) prototyping involved iteratively testing the brochure, surveying parents or carers who attended outpatient clinics or the hospital Emergency Department, and conducting follow up telephone calls.

RESULTS

There was overall support for the creation of a research contact list, but some unknowns remain. 367 parents or carers completed the survey and 36 participated in a follow up telephone call. Over half would be willing to join a research contact list and more than 90% of the children of parents or carers surveyed were not currently participating in research. Several potential barriers identified by health service staff were dispelled. Research communications and a future contact list should be available in electronic form.

CONCLUSIONS

There was strong support for creating a research contact list. The approach will inform our future directions including creation of an electronic research contact list easily accessible by consumers of the children's health service. Recruiting enough children to participate in research studies can be challenging. Establishing a registry or list of young people willing to be contacted to participate in research is one way of addressing this problem. At our children's health service, we wanted to explore the idea of developing a research contact list and we were particularly keen to involve consumers and community members in this process, which involved: 1.Reviewing other examples of research contact lists and consulting with a range of people, including consumers and community members, 2. Co-producing a research communications plan with parents, young people, health service staff and research staff, including a draft research information brochure for families, and 3. Testing the acceptability of the brochure by surveying parents or carers who attended outpatient clinics or the hospital Emergency Department, and conducting follow up telephone calls with them. 367 parents or carers completed a survey and 36 participated in a follow up telephone call. Over half were willing to join a research contact list and more than 90% of the children of parents or carers surveyed were not currently participating in research. Several potential barriers raised by consumers and health professionals in the first stage of the project were not found to be a concern for the parents or carers surveyed. Responses showed research communications and a future contact list should be available in electronic form. These findings will inform the future creation of an electronic research contact list, easily accessible by consumers of the children's health service.

Using Electronic Medical Record Data for Research in a Healthcare Information and Management Systems Society (HIMSS) Analytics Electronic Medical Record Adoption Model (EMRAM) Stage 7 Hospital in Beijing: Cross-sectional Study

BACKGROUND

With the proliferation of electronic medical record (EMR) systems, there is an increasing interest in utilizing EMR data for medical research; yet, there is no quantitative research on EMR data utilization for medical research purposes in China.

OBJECTIVE

This study aimed to understand how and to what extent EMR data are utilized for medical research purposes in a Healthcare Information and Management Systems Society (HIMSS) Analytics Electronic Medical Record Adoption Model (EMRAM) Stage 7 hospital in Beijing, China. Obstacles and issues in the utilization of EMR data were also explored to provide a foundation for the improved utilization of such data.

METHODS

For this descriptive cross-sectional study, cluster sampling from Xuanwu Hospital, one of two Stage 7 hospitals in Beijing, was conducted from 2016 to 2019. The utilization of EMR data was described as the number of requests, the proportion of requesters, and the frequency of requests per capita. Comparisons by year, professional title, and age were conducted by double-sided chi-square tests.

RESULTS

From 2016 to 2019, EMR data utilization was poor, as the proportion of requesters was 5.8% and the frequency was 0.1 times per person per year. The frequency per capita gradually slowed and older senior-level staff more frequently used EMR data compared with younger staff.

CONCLUSIONS

The value of using EMR data for research purposes is not well studied in China. More research is needed to quantify to what extent EMR data are utilized across all hospitals in Beijing and how these systems can enhance future studies. The results of this study also suggest that young doctors may be less exposed or have less reason to access such research methods.

Using a health information technology survey to explore the availability of addiction treatment data in the electronic health records: A National Drug Abuse Treatment Clinical Trials Network study

BACKGROUND

Healthcare data from electronic health records (EHRs) and related health information technology (IT) tools are critical data sources for pragmatic clinical trials and observational studies aimed at producing real-world evidence. To unlock the full potential of such data to advance science, the data must be complete and in structured formats to facilitate research use.

METHODS

A Health IT survey was conducted within the National Drug Abuse Treatment Clinical Trials Network (CTN) to explore information related to data completeness and presence of unstructured data (e.g., clinical notes, free text) for conducting the EHR-based research for substance use disorders (SUDs). The analysis was based on 36 participants from 36 facilities located in 14 states and affiliated with the CTN.

RESULTS

The mean age of the participants (n = 34) was 48.0 years (SD = 9.8). Of the participants enrolled, 50.0% were female and 82.4% were white. Participants' facilities were from four census-defined regions (South 35.3%, Northeast 29.4%, West 20.6%, Midwest 11.8%, Missing 2.9%) and represented diverse settings. The EHR was used by all surveyed facilities including 17 different kinds of EHR platforms or vendors, and 17.6% (n = 6) of surveyed facilities also used a separate EHR for behavioral health care (e.g., SUD care). Paper records were also used by 76.5% of surveyed facilities for clinical care (e.g., for health risk appraisal questionnaires, substance use screening or assessment, check-in screening, substance use specific intervention/treatment or referral, or labs/testing). The prevalence of using a patient portal, practice management system, and mHealth for patient care was 76.5%, 50.0%, and 29.4%, respectively.

CONCLUSION

While results are descriptive in nature, they reveal the heterogeneity in the existing EHRs and frequent use of paper records to document patient care tasks, especially for SUD care. The use of a separate EHR for behavioral healthcare also suggests the challenge of obtaining complete EHR data to support research for SUDs. Much EHR development, integration, and standardization needs to be done especially in regard to SUD treatment to facilitate research across disparate healthcare systems.

Realising the full potential of data-enabled trials in the UK: a call for action

RATIONALE

Clinical trials are the gold standard for testing interventions. COVID-19 has further raised their public profile and emphasised the need to deliver better, faster, more efficient trials for patient benefit. Considerable overlap exists between data required for trials and data already collected routinely in electronic healthcare records (EHRs). Opportunities exist to use these in innovative ways to decrease duplication of effort and speed trial recruitment, conduct and follow-up.

APPROACH

The National Institute of Health Research (NIHR), Health Data Research UK and Clinical Practice Research Datalink co-organised a national workshop to accelerate the agenda for 'data-enabled clinical trials'. Showcasing successful examples and imagining future possibilities, the plenary talks, panel discussions, group discussions and case studies covered: design/feasibility; recruitment; conduct/follow-up; collecting benefits/harms; and analysis/interpretation.

REFLECTION

Some notable studies have successfully accessed and used EHR to identify potential recruits, support randomised trials, deliver interventions and supplement/replace trial-specific follow-up. Some outcome measures are already reliably collected; others, like safety, need detailed work to meet regulatory reporting requirements. There is a clear need for system interoperability and a 'route map' to identify and access the necessary datasets. Researchers running regulatory-facing trials must carefully consider how data quality and integrity would be assessed. An experience-sharing forum could stimulate wider adoption of EHR-based methods in trial design and execution.

DISCUSSION

EHR offer opportunities to better plan clinical trials, assess patients and capture data more efficiently, reducing research waste and increasing focus on each trial's specific challenges. The short-term emphasis should be on facilitating patient recruitment and for postmarketing authorisation trials where research-relevant outcome measures are readily collectable. Sharing of case studies is encouraged. The workshop directly informed NIHR's funding call for ambitious data-enabled trials at scale. There is the opportunity for the UK to build upon existing data science capabilities to identify, recruit and monitor patients in trials at scale.

Patient Recruitment Into a Multicenter Clinical Cohort Linking Electronic Health Records From 5 Health Systems: Cross-sectional Analysis

Background

There is growing interest in identifying and recruiting research participants from health systems using electronic health records (EHRs). However, few studies have described the practical aspects of the recruitment process or compared electronic recruitment methods to in-person recruitment, particularly across health systems.

Objective

The objective of this study was to describe the steps and efficiency of the recruitment process and participant characteristics by recruitment strategy.

Methods

EHR-based eligibility criteria included being an adult patient engaged in outpatient primary or bariatric surgery care at one of 5 health systems in the PaTH Clinical Research Network and having ≥2 weight measurements and 1 height measurement recorded in their EHR within the last 5 years. Recruitment strategies varied by site and included one or more of the following methods: (1) in-person recruitment by study staff from clinical sites, (2) US postal mail recruitment letters, (3) secure email, and (4) direct EHR recruitment through secure patient web portals. We used descriptive statistics to evaluate participant characteristics and proportion of patients recruited (ie, efficiency) by modality.

Results

The total number of eligible patients from the 5 health systems was 5,051,187. Of these, 40,048 (0.8%) were invited to enter an EHR-based cohort study and 1085 were enrolled. Recruitment efficiency was highest for in-person recruitment (33.5%), followed by electronic messaging (2.9%), including email (2.9%) and EHR patient portal messages (2.9%). Overall, 779 (65.7%) patients were enrolled through electronic messaging, which also showed greater rates of recruitment of Black patients compared with the other strategies.

Conclusions

We recruited a total of 1085 patients from primary care and bariatric surgery settings using 4 recruitment strategies. The recruitment efficiency was 2.9% for email and EHR patient portals, with the majority of participants recruited electronically. This study can inform the design of future research studies using EHR-based recruitment.

Shared-Task Worklists Improve Clinical Trial Recruitment Workflow in an Academic Emergency Department

BACKGROUND

Clinical trials performed in our emergency department at Barnes-Jewish Hospital utilize a centralized infrastructure for alerting, screening, and enrollment with rule-based alerts sent to clinical research coordinators. Previously, all alerts were delivered as text messages via dedicated cellular phones. As the number of ongoing clinical trials increased, the volume of alerts grew to an unmanageable level. Therefore, we have changed our primary notification delivery method to study-specific, shared-task worklists integrated with our pre-existing web-based screening documentation system.

OBJECTIVE

To evaluate the effects on screening and recruitment workflow of replacing text-message delivery of clinical trial alerts with study-specific shared-task worklists in a high-volume academic emergency department supporting multiple concurrent clinical trials.

METHODS

We analyzed retrospective data on alerting, screening, and enrollment for 10 active clinical trials pre- and postimplementation of shared-task worklists.

RESULTS

Notifications signaling the presence of potentially eligible subjects for clinical trials were more likely to result in a screen (p < 0.001) with the implementation of shared-task worklists compared with notifications delivered as text messages for 8/10 clinical trials. The change in workflow did not alter the likelihood of a notification resulting in an enrollment (p = 0.473). The Director of Research reported a substantial reduction in the amount of time spent redirecting clinical research coordinator screening activities.

CONCLUSION

Shared-task worklists, with the functionalities we have described, offer a viable alternative to delivery of clinical trial alerts via text message directly to clinical research coordinators recruiting for multiple concurrent clinical trials in a high-volume academic emergency department.

Assessing an Electronic Health Record research platform for identification of clinical trial participants

Electronic health records (EHR) are a potential resource for identification of clinical trial participants. We evaluated how accurately a commercially available EHR Research Platform, InSite, is able to identify potential trial participants from the EHR system of a large tertiary care hospital. Patient counts were compared with results obtained in a conventional manual search performed for a reference study that investigated the associations of atrial fibrillation (AF) and cerebrovascular incidents. The Clinical Data Warehouse (CDW) of Turku University Hospital was used to verify the capabilities of the EHR Research Platform.

The EHR query resulted in a larger patient count than the manual query (EHR Research Platform 5859 patients, manual selection 2166 patients). This was due to the different search logic and some exclusion criteria that were not addressable in structured digital format. The EHR Research Platform (5859 patients) and the CDW search (5840 patients) employed the same search logic. The temporal relationship between the two diagnoses could be identified when they were available in structured format and the time difference was longer than a single hospital visit.

Searching for patients with the EHR Research Platform can help to identify potential trial participants from a hospital's EHR system by limiting the number of records to be manually reviewed. EHR query tools can best be utilized in trials where the selection criteria are expressed in structured digital format.

Real-World Data for Planning Eligibility Criteria and Enhancing Recruitment: Recommendations from the Clinical Trials Transformation Initiative

The growing availability of real-world data (RWD) creates opportunities for new evidence generation and improved efficiency across the research enterprise. To varying degrees, sponsors now regularly use RWD to make data-driven decisions about trial feasibility, based on assessment of eligibility criteria for planned clinical trials. Increasingly, RWD are being used to support targeted, timely, and personalized outreach to potential trial participants that may improve the efficiency and effectiveness of the recruitment process. This paper highlights recommendations and resources, including specific case studies, developed by the Clinical Trials Transformation Initiative (CTTI) for applying RWD to planning eligibility criteria and recruiting for clinical trials. Developed through a multi-stakeholder, consensus- and evidence-driven process, these actionable tools support researchers in (1) determining whether RWD are fit for purpose with respect to study planning and recruitment, (2) engaging cross-functional teams in the use of RWD for study planning and recruitment, and (3) understanding patient and site needs to develop successful and patient-centric approaches to RWD-supported recruitment. Future considerations for the use of RWD are explored, including ensuring full patient understanding of data use and developing global datasets.

Applications of artificial intelligence in drug development using real-world data

The US Food and Drug Administration (FDA) has been actively promoting the use of real-world data (RWD) in drug development. RWD can generate important real-world evidence reflecting the real-world clinical environment where the treatments are used. Meanwhile, artificial intelligence (AI), especially machine- and deep-learning (ML/DL) methods, have been increasingly used across many stages of the drug development process. Advancements in AI have also provided new strategies to analyze large, multidimensional RWD. Thus, we conducted a rapid review of articles from the past 20 years, to provide an overview of the drug development studies that use both AI and RWD. We found that the most popular applications were adverse event detection, trial recruitment, and drug repurposing. Here, we also discuss current research gaps and future opportunities.

Text-mining in electronic healthcare records can be used as efficient tool for screening and data collection in cardiovascular trials: a multicenter validation study

OBJECTIVE

This study aimed to validate trial patient eligibility screening and baseline data collection using text-mining in electronic healthcare records (EHRs), comparing the results to those of an international trial.

STUDY DESIGN AND SETTING

In three medical centers with different EHR vendors, EHR-based text-mining was used to automatically screen patients for trial eligibility and extract baseline data on nineteen characteristics. First, the yield of screening with automated EHR text-mining search was compared with manual screening by research personnel. Second, the accuracy of extracted baseline data by EHR text mining was compared to manual data entry by research personnel.

RESULTS

Of the 92,466 patients visiting the out-patient cardiology departments, 568 (0.6%) were enrolled in the trial during its recruitment period using manual screening methods. Automated EHR data screening of all patients showed that the number of patients needed to screen could be reduced by 73,863 (79.9%). The remaining 18,603 (20.1%) contained 458 of the actual participants (82.4% of participants). In trial participants, automated EHR text-mining missed a median of 2.8% (Interquartile range [IQR] across all variables 0.4-8.5%) of all data points compared to manually collected data. The overall accuracy of automatically extracted data was 88.0% (IQR 84.7-92.8%).

CONCLUSION

Automatically extracting data from EHRs using text-mining can be used to identify trial participants and to collect baseline information.

Five analytic challenges in working with electronic health records data to support clinical trials with some solutions

Electronic health records data are becoming a key data resource in clinical research. Owing to issues of data efficiency, electronic health records data are being used for clinical trials. This includes both large-scale pragmatic trails and smaller—more focused—point-of-care trials. While electronic health records data open up a number of scientific opportunities, they also present a number of analytic challenges. This article discusses five particular challenges related to organizing electronic health records data for analytic purposes. These are as follows: (1) data are not organized for research purposes, (2) data are both densely and irregularly observed, (3) we don’t have all data elements we may want or need, (4) data are both cross-sectional and longitudinal, and (5) data may be informatively observed. While laying out these challenges, the article notes how many of these challenges can be addressed by careful and thoughtful study design as well as by integration of clinicians and informaticians into the analytic team.

Clinical Workflow and Substance Use Screening, Brief Intervention, and Referral to Treatment Data in the Electronic Health Records: A National Drug Abuse Treatment Clinical Trials Network Study

Introduction:

The use of electronic health records (EHR) data in research to inform recruitment and outcomes is considered a critical element for pragmatic studies. However, there is a lack of research on the availability of substance use disorder (SUD) treatment data in the EHR to inform research.

Methods:

This study recruited providers who used an EHR for patient care and whose facilities were affiliated with the National Institute on Drug Abuse’s National Drug Abuse Treatment Clinical Trials Network (NIDA CTN). Data about providers’ use of an EHR and other methods to support and document clinical tasks for Substance use screening, Brief Intervention, and Referral to Treatment (SBIRT) were collected.

Results:

Participants (n = 26) were from facilities across the country (South 46.2%, West 23.1%, Midwest 19.2 percent, Northeast 11.5 percent), representing 26 different health systems/facilities at various settings: primary care (30.8 percent), ambulatory other/specialty (26.9 percent), mixed setting (11.5 percent), hospital outpatient (11.5 percent), emergency department (7.7 percent), inpatient (3.8 percent), and other (7.7 percent). Validated tools were rarely used for substance use screen and SUD assessment. Structured and unstructured EHR fields were commonly used to document SBIRT. The following tasks had high proportions of using unstructured EHR fields: substance use screen, treatment exploration, brief intervention, referral, and follow-up.

Conclusion:

This study is the first of its kind to investigate the documentation of SBIRT in the EHR outside of unique settings (e.g., Veterans Health Administration). While results are descriptive, they emphasize the importance of developing EHR features to collect structured data for SBIRT to improve health care quality evaluation and SUD research.