Designing Electronic Data Capture Systems for Sustainability in Low-Resource Settings: Viewpoint With Lessons Learned From Ethiopia and Myanmar

Electronic data capture (EDC) is a crucial component in the design, evaluation, and sustainment of population health interventions. Low-resource settings, however, present unique challenges for developing a robust EDC system due to limited financial capital, differences in technological infrastructure, and insufficient involvement of those who understand the local context. Current literature focuses on the evaluation of health interventions using EDC but does not provide an in-depth description of the systems used or how they are developed. In this viewpoint, we present case descriptions from 2 low- and middle-income countries: Ethiopia and Myanmar. We address a gap in evidence by describing each EDC system in detail and discussing the pros and cons of different approaches. We then present common lessons learned from the 2 case descriptions as recommendations for considerations in developing and implementing EDC in low-resource settings, using a sociotechnical framework for studying health information technology in complex adaptive health care systems. Our recommendations highlight the importance of selecting hardware compatible with local infrastructure, using flexible software systems that facilitate communication across different languages and levels of literacy, and conducting iterative, participatory design with individuals with deep knowledge of local clinical and cultural norms.

An Electronic Teen Questionnaire, the eTeenQ, for Risk Behavior Screening During Adolescent Well Visits in an Integrated Health System: Development and Pilot Implementation

Background

Screening for risk behaviors is a routine and essential component of adolescent preventive health visits. Early identification of risks can inform targeted counseling and care. If stored in discrete fields in the electronic health record (EHR), adolescent screening data can also be used to understand risk behaviors across a clinic or health system or to support quality improvement projects.

Objective

Goals of this pilot study were to adapt and implement an existing paper adolescent risk behavior screening tool for use as an electronic data capture tool (the eTeenQ), to evaluate acceptance of the eTeenQ, and to describe the prevalence of the selected risk behaviors reported through the eTeenQ.

Methods

The multidisciplinary project team applied an iterative process to develop the 29-item eTeenQ. Two unique data entry forms were created with attention to (1) user interface and user experience, (2) the need to maintain patient privacy, and (3) the potential to transmit and store data for future use in clinical care and research. Three primary care clinics within a large health system piloted the eTeenQ from August 17, 2020, to August 27, 2021. During preventive health visits for adolescents aged 12 to 18 years, the eTeenQ was completed on tablets and responses were converted to a provider display for teens and providers to review together. Responses to the eTeenQ were stored in a REDCap (Research Electronic Data Capture; Vanderbilt University) database, and for patients who agreed, responses were transferred to an EHR flowsheet. Responses to selected eTeenQ questions are reported for those consenting to research. At the conclusion of the pilot, the study team conducted semistructured interviews with providers and staff regarding their experience using the eTeenQ.

Results

Among 2816 adolescents with well visits, 2098 (74.5%) completed the eTeenQ. Of these, 1811 (86.3%) agreed to store responses in the EHR. Of 1632 adolescents (77.8% of those completing the eTeenQ) who consented for research and remained eligible, 1472 (90.2%) reported having an adult they can really talk to and 1510 (92.5%) reported feeling safe in their community, yet 401 (24.6%) reported someone they lived with had a gun and 172 (10.5%) reported having had a stressful or scary event that still bothered them. In addition, 157 (9.6%) adolescents reported they were or wondered if they were gay, lesbian, bisexual, pansexual, asexual, or other, and 43 (2.6%) reported they were or wondered if they were transgender or gender diverse. Of 11 staff and 7 providers completing interviews, all felt that the eTeenQ improved confidentiality and willingness among adolescents to answer sensitive questions. All 7 providers preferred the eTeenQ over the paper screening tool.

Conclusions

Electronic capture of adolescent risk behaviors is feasible in a busy clinic setting and well accepted among staff and clinicians. Most adolescents agreed for their responses to risk behavior screening to be stored in the EHR.

Harnessing the potential of data-driven strategies to optimise transfusion practice

No one doubts the significant variation in the practice of transfusion medicine. Common examples are the variability in transfusion thresholds and the use of tranexamic acid for surgery with likely high blood loss despite evidence-based standards. There is a long history of applying different strategies to address this variation, including education, clinical guidelines, audit and feedback, but the effectiveness and cost-effectiveness of these initiatives remains unclear. Advances in computerised decision support systems and the application of novel electronic capabilities offer alternative approaches to improving transfusion practice. In England, the National Institute for Health and Care Research funded a Blood and Transplant Research Unit (BTRU) programme focussing on 'A data-enabled programme of research to improve transfusion practices'. The overarching aim of the BTRU is to accelerate the development of data-driven methods to optimise the use of blood and transfusion alternatives, and to integrate them within routine practice to improve patient outcomes. One particular area of focus is implementation science to address variation in practice.

Impact of Clinical Study Implementation on Data Quality Assessments - Using Contradictions within Interdependent Health Data Items as a Pilot Indicator

INTRODUCTION

Contradiction is a relevant data quality indicator to evaluate the plausibility of interdependent health data items. However, while contradiction assessment is achieved using domain-established contradictory dependencies, recent studies have shown the necessity for additional requirements to reach conclusive contradiction findings. For example, the oral or rectal methods used in measuring the body temperature will influence the thresholds of fever definition. The availability of this required information as explicit data items must be guaranteed during study design. In this work, we investigate the impact of activities related to study database implementation on contradiction assessment from two perspectives including: 1) additionally required metadata and 2) implementation of checks within electronic case report forms to prevent contradictory data entries.

METHODS

Relevant information (timestamps, measurement methods, units, and interdependency rules) required for contradiction checks are identified. Scores are assigned to these parameters and two different studies are evaluated based on the fulfillment of the requirements by two selected interdependent data item sets.

RESULTS

None of the studies have fulfilled all requirements. While timestamps and measurement units are found, missing information about measurement methods may impede conclusive contradiction assessment. Implemented checks are only found if data are directly entered.

DISCUSSION

Conclusive contradiction assessment typically requires metadata in the context of captured data items. Consideration during study design and implementation of data capture systems may support better data quality in studies and could be further adopted in primary health information systems to enhance clinical anamnestic documentation.

Automated Electronic Health Record to Electronic Data Capture Transfer in Clinical Studies in the German Health Care System: Feasibility Study and Gap Analysis

BACKGROUND

Data transfer between electronic health records (EHRs) at the point of care and electronic data capture (EDC) systems for clinical research is still mainly carried out manually, which is error-prone as well as cost- and time-intensive. Automated digital transfer from EHRs to EDC systems (EHR2EDC) would enable more accurate and efficient data capture but has so far encountered technological barriers primarily related to data format and the technological environment: in Germany, health care data are collected at the point of care in a variety of often individualized practice management systems (PMSs), most of them not interoperable. Data quality for research purposes within EDC systems must meet the requirements of regulatory authorities for standardized submission of clinical trial data and safety reports.

OBJECTIVE

We aimed to develop a model for automated data transfer as part of an observational study that allows data of sufficient quality to be captured at the point of care, extracted from various PMSs, and automatically transferred to electronic case report forms in EDC systems. This required addressing aspects of data security, as well as the lack of compatibility between EHR health care data and the data quality required in EDC systems for clinical research.

METHODS

The SaniQ software platform (Qurasoft GmbH) is already used to extract and harmonize predefined variables from electronic medical records of different Compu Group Medical-hosted PMSs. From there, data are automatically transferred to the validated AlcedisTRIAL EDC system (Alcedis GmbH) for data collection and management. EHR2EDC synchronization occurs automatically overnight, and real-time updates can be initiated manually following each data entry in the EHR. The electronic case report form (eCRF) contains 13 forms with 274 variables. Of these, 5 forms with 185 variables contain 67 automatically transferable variables (67/274, 24% of all variables and 67/185, 36% of eligible variables).

RESULTS

This model for automated data transfer bridges the current gap between clinical practice data capture at the point of care and the data sets required by regulatory agencies; it also enables automated EHR2EDC data transfer in compliance with the General Data Protection Regulation (GDPR). It addresses feasibility, connectivity, and system compatibility of currently used PMSs in health care and clinical research and is therefore directly applicable.

CONCLUSIONS

This use case demonstrates that secure, consistent, and automated end-to-end data transmission from the treating physician to the regulatory authority is feasible. Automated data transmission can be expected to reduce effort and save resources and costs while ensuring high data quality. This may facilitate the conduct of studies for both study sites and sponsors, thereby accelerating the development of new drugs. Nevertheless, the industry-wide implementation of EHR2EDC requires policy decisions that set the framework for the use of research data based on routine PMS data.

Remote and semi-automated methods to conduct a decentralized randomized clinical trial

Introduction

Designing and conducting clinical trials is challenging for some institutions and researchers due to associated time and personnel requirements. We conducted recruitment, screening, informed consent, study product distribution, and data collection remotely. Our objective is to describe how to conduct a randomized clinical trial using remote and automated methods.

Methods

A randomized clinical trial in healthcare workers is used as a model. A random group of workers were invited to participate in the study through email. Following an automated process, interested individuals scheduled consent/screening interviews. Enrollees received study product by mail and surveys via email. Adherence to study product and safety were monitored with survey data review and via real-time safety alerts to study staff.

Results

A staff of 10 remotely screened 406 subjects and enrolled 299 over a 3-month period. Adherence to study product was 87%, and survey data completeness was 98.5% over 9 months. Participants and study staff scored the System Usability Scale 93.8% and 90%, respectively. The automated and remote methods allowed the study maintenance period to be managed by a small study team of two members, while safety monitoring was conducted by three to four team members. Conception of the trial to study completion was 21 months.

Conclusions

The remote and automated methods produced efficient subject recruitment with excellent study product adherence and data completeness. These methods can improve efficiency without sacrificing safety or quality. We share our XML file for researchers to use as a template for learning purposes or designing their own clinical trials.

Electronic Data Capture System (REDCap) for Health Care Research and Training in a Resource-Constrained Environment: Technology Adoption Case Study

Background

Electronic data capture (EDC) in academic health care organizations provides an opportunity for the management, aggregation, and secondary use of research and clinical data. It is especially important in resource-constrained environments such as the South African public health care sector, where paper records are still the main form of clinical record keeping.

Objective

The aim of this study was to describe the strategies followed by the University of the Witwatersrand Faculty of Health Sciences (Wits FHS) during the period from 2013 to 2021 to overcome resistance to, and encourage the adoption of, the REDCap (Research Electronic Data Capture; Vanderbilt University) system by academic and clinical staff. REDCap has found wide use in varying domains, including clinical studies and research projects as well as administrative, financial, and human resource applications. Given REDCap’s global footprint in >5000 institutions worldwide and potential for future growth, the strategies followed by the Wits FHS to support users and encourage adoption may be of importance to others using the system, particularly in resource-constrained settings.

Methods

The strategies to support users and encourage adoption included top-down organizational support; secure and reliable application, hosting infrastructure, and systems administration; an enabling and accessible REDCap support team; regular hands-on training workshops covering REDCap project setup and data collection instrument design techniques; annual local symposia to promote networking and awareness of all the latest software features and best practices for using them; participation in REDCap Consortium activities; and regular and ongoing mentorship from members of the Vanderbilt University Medical Center.

Results

During the period from 2013 to 2021, the use of the REDCap EDC system by individuals at the Wits FHS increased, respectively, from 129 active user accounts to 3447 active user accounts. The number of REDCap projects increased from 149 in 2013 to 12,865 in 2021. REDCap at Wits also supported various publications and research outputs, including journal articles and postgraduate monographs. As of 2020, a total of 233 journal articles and 87 postgraduate monographs acknowledged the use of the Wits REDCap system.

Conclusions

By providing reliable infrastructure and accessible support resources, we were able to successfully implement and grow the REDCap EDC system at the Wits FHS and its associated academic medical centers. We believe that the increase in the use of REDCap was driven by offering a dependable, secure service with a strong end-user training and support model. This model may be applied by other academic and health care organizations in resource-constrained environments planning to implement EDC technology.

ICT Tools for Registry Research: A Market Survey

Registries in health research are complex systems requiring a diverse infrastructure with information and communications technology tools (ICT tools) for manifold tasks. Those tools should support not only data management but also several core and accompanying processes. Recent trends in registry research also need to be taken into account. Thirty-five vendors, suppliers, and experts were included in a survey on ICT tools for registries and cohorts. Information from 28 tools was available for a preliminary analysis. In comparison to 2015 and 2018, coverage of core processes such as registry development or data analysis and utilization increased from below 40% to 39% and higher. Recording patient-reported information and linkage to other data collections was well covered. However, near-patient trends were less supported. The market offers a rich selection of commercial and non-commercial ICT tools for registry research. Due to the manifold offers available from the market, in-house developed software should be an absolute exception.

Re-engineering a Clinical Trial Management System Using Blockchain Technology: System Design, Development, and Case Studies

Background

A clinical trial management system (CTMS) is a suite of specialized productivity tools that manage clinical trial processes from study planning to closeout. Using CTMSs has shown remarkable benefits in delivering efficient, auditable, and visualizable clinical trials. However, the current CTMS market is fragmented, and most CTMSs fail to meet expectations because of their inability to support key functions, such as inconsistencies in data captured across multiple sites. Blockchain technology, an emerging distributed ledger technology, is considered to potentially provide a holistic solution to current CTMS challenges by using its unique features, such as transparency, traceability, immutability, and security.

Objective

This study aimed to re-engineer the traditional CTMS by leveraging the unique properties of blockchain technology to create a secure, auditable, efficient, and generalizable CTMS.

Methods

A comprehensive, blockchain-based CTMS that spans all stages of clinical trials, including a sharable trial master file system; a fast recruitment and simplified enrollment system; a timely, secure, and consistent electronic data capture system; a reproducible data analytics system; and an efficient, traceable payment and reimbursement system, was designed and implemented using the Quorum blockchain. Compared with traditional blockchain technologies, such as Ethereum, Quorum blockchain offers higher transaction throughput and lowers transaction latency. Case studies on each application of the CTMS were conducted to assess the feasibility, scalability, stability, and efficiency of the proposed blockchain-based CTMS.

Results

A total of 21.6 million electronic data capture transactions were generated and successfully processed through blockchain, with an average of 335.4 transactions per second. Of the 6000 patients, 1145 were matched in 1.39 seconds using 10 recruitment criteria with an automated matching mechanism implemented by the smart contract. Key features, such as immutability, traceability, and stability, were also tested and empirically proven through case studies.

Conclusions

This study proposed a comprehensive blockchain-based CTMS that covers all stages of the clinical trial process. Compared with our previous research, the proposed system showed an overall better performance. Our system design, implementation, and case studies demonstrated the potential of blockchain technology as a potential solution to CTMS challenges and its ability to perform more health care tasks.

An Interoperable Resuscitation Registry for the University Hospital of Bern

During resuscitation, the patient is the primary focus with the documentation of actions and outcomes being secondary. In most cases, a cardiac event leads to further treatment or hospitalization, in which complex patient pathways, independent documentation systems and information loss represent the key challenges for successful quality management. Hence, the need for a system that takes all these aspects into account. Market research, system analysis and requirements engineering for such a solution were performed and a prototype was created. A complete reference architecture for a web-based electronic data capture system was developed and implemented that enables healthcare professionals to enter resuscitation-relevant data uniformly and store it centrally in compliance with human research legislation. A qualitative evaluation concerning the process flows of the as-is and the to-be situation suggests that there is potential to achieve benefits in the form of improved data quality and quantity.

Automated provision of clinical routine data for a complex clinical follow-up study: A data warehouse solution

A deep integration of routine care and research remains challenging in many respects. We aimed to show the feasibility of an automated transformation and transfer process feeding deeply structured data with a high level of granularity collected for a clinical prospective cohort study from our hospital information system to the study's electronic data capture system, while accounting for study-specific data and visits. We developed a system integrating all necessary software and organizational processes then used in the study. The process and key system components are described together with descriptive statistics to show its feasibility in general and to identify individual challenges in particular. Data of 2051 patients enrolled between 2014 and 2020 was transferred. We were able to automate the transfer of approximately 11 million individual data values, representing 95% of all entered study data. These were recorded in n = 314 variables (28% of all variables), with some variables being used multiple times for follow-up visits. Our validation approach allowed for constant good data quality over the course of the study. In conclusion, the automated transfer of multi-dimensional routine medical data from HIS to study databases using specific study data and visit structures is complex, yet viable.

An Open-Source, Standard-Compliant, and Mobile Electronic Data Capture System for Medical Research (OpenEDC): Design and Evaluation Study

BACKGROUND

Medical research and machine learning for health care depend on high-quality data. Electronic data capture (EDC) systems have been widely adopted for metadata-driven digital data collection. However, many systems use proprietary and incompatible formats that inhibit clinical data exchange and metadata reuse. In addition, the configuration and financial requirements of typical EDC systems frequently prevent small-scale studies from benefiting from their inherent advantages.

OBJECTIVE

The aim of this study is to develop and publish an open-source EDC system that addresses these issues. We aim to plan a system that is applicable to a wide range of research projects.

METHODS

We conducted a literature-based requirements analysis to identify the academic and regulatory demands for digital data collection. After designing and implementing OpenEDC, we performed a usability evaluation to obtain feedback from users.

RESULTS

We identified 20 frequently stated requirements for EDC. According to the International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 25010 norm, we categorized the requirements into functional suitability, availability, compatibility, usability, and security. We developed OpenEDC based on the regulatory-compliant Clinical Data Interchange Standards Consortium Operational Data Model (CDISC ODM) standard. Mobile device support enables the collection of patient-reported outcomes. OpenEDC is publicly available and released under the MIT open-source license.

CONCLUSIONS

Adopting an established standard without modifications supports metadata reuse and clinical data exchange, but it limits item layouts. OpenEDC is a stand-alone web app that can be used without a setup or configuration. This should foster compatibility between medical research and open science. OpenEDC is targeted at observational and translational research studies by clinicians.

Randomization of Clinical Trial Participants via an Integrated Web Service

Randomization is an inherent part of Randomized Clinical Trials (RCTs), typically requiring the split of participants in intervention and control groups. We present a web service supporting randomized patient distribution, developed in the context of the MyPal project RCT. The randomization process is based on a block permutation approach to mitigate the risk of various kind of biases. The presented service can be used via its web user interface to produce randomized lists of patients distributed in the various study groups, with a variant block size. Alternatively, the presented service can be integrated as part of wider IT systems supporting clinical trials via a REST interface following a micro-service architectural pattern.

Sociodemographic Representativeness in a Nationwide Web-Based Survey of the View of Men on Involvement in Health Care Decision-Making: Cross-Sectional Questionnaire Study

Background

Being able to generalize research findings to a broader population outside of the study sample is an important goal in surveys on the internet. We conducted a nationwide, cross-sectional, web-based survey with vignettes illustrating different levels of patient involvement to investigate men’s preferences regarding participation in health care decision-making. Following randomization into vignette variants, we distributed the survey among men aged 45 to 70 years through the state-authorized digital mailbox provided by the Danish authorities for secure communication with citizens.

Objective

This study aimed to investigate the sociodemographic representativeness of our sample of men obtained in a nationwide web-based survey using the digital mailbox.

Methods

Response rate estimates were established, and comparisons were made between responders and nonresponders in terms of age profiles (eg, average age) and municipality-level information on sociodemographic characteristics.

Results

Among 22,288 men invited during two waves, a total of 6756 (30.31%) participants responded to the survey. In adjusted analyses, responders’ characteristics mostly resembled those of nonresponders. Response rates, however, were significantly higher in older men (odds ratio [OR] 2.83 for responses among those aged 65-70 years compared with those aged 45-49 years, 95% CI 2.58-3.11; P<.001) and in rural areas (OR 1.10 compared with urban areas, 95% CI 1.03-1.18; P=.005). Furthermore, response rates appeared lower in areas with a higher tax base (OR 0.89 in the highest tertile, 95% CI 0.81-0.98; P=.02).

Conclusions

Overall, the general population of men aged 45 to 70 years was represented very well by the responders to our web-based survey. However, the imbalances identified highlight the importance of supplementing survey findings with studies of the representativeness of other characteristics of the sample like trait and preference features, so that proper statistical corrections can be made in upcoming analyses of survey responses whenever needed.

Process Coverage and Use Case Support of Health Registry Software in Germany

Registries usually operate an IT-infrastructure supporting at least data management as one of the business processes. Several activities in Germany between 2007 and 2018 surveyed the market of respective software products. Combining a survey with representatives of software products with a workshop protocol of software demonstrations, a detailed insight into the market of IT-components arose. A comparison between 2015 and 2018 revealed little progress. The focus is still electronic data capture functionality. With the presented activities, rich material is available to assist registry developers in the planning of their IT-infrastructure and the selection of software products.

Development of a Process and Infrastructure to Outreach Stakeholders for Capturing Healthcare System Stress in Emergency Response Situations

Real time data provided by frontline clinicians could be used to direct immediate resources during a public health emergency and inform increased preparedness for future events. The United States Critical Illness and Injury Trials Group Program for Emergency Preparedness (USCIIT-PREP), a group of expert critical care and emergency medicine physicians at various academic medical centers across the US, aims to enhance the national capability of rapid electronic data collection, along with analysis and dissemination of findings. To achieve these aims, USCIIT-PREP created a process for real-time data capture that relies on a curated and engaged network of clinical providers from various geographical regions to respond to short online "Pulse" queries about healthcare system stress. During a period of three years, five queries were created and distributed. The first two queries were used to develop and validate the data collection infrastructure. Results are reported for the last three queries between June 2015 and March 2016. Response rates consistently ranged from 39% to 42%. Our team demonstrated that our system and processes were ready for creation and rapid dissemination of episodic queries for rapid data collection, transmittal, and analysis through a curated national network of clinician responders during a public health emergency. USCIIT-PREP aims to further increase the response rate through additional engagement efforts within the network, to continue to grow the clinician responder database, and to optimize additional query content.

Improving the Efficacy of the Data Entry Process for Clinical Research With a Natural Language Processing-Driven Medical Information Extraction System: Quantitative Field Research

BACKGROUND

The growing interest in observational trials using patient data from electronic medical records poses challenges to both efficiency and quality of clinical data collection and management. Even with the help of electronic data capture systems and electronic case report forms (eCRFs), the manual data entry process followed by chart review is still time consuming.

OBJECTIVE

To facilitate the data entry process, we developed a natural language processing-driven medical information extraction system (NLP-MIES) based on the i2b2 reference standard. We aimed to evaluate whether the NLP-MIES-based eCRF application could improve the accuracy and efficiency of the data entry process.

METHODS

We conducted a randomized and controlled field experiment, and 24 eligible participants were recruited (12 for the manual group and 12 for NLP-MIES-supported group). We simulated the real-world eCRF completion process using our system and compared the performance of data entry on two research topics, pediatric congenital heart disease and pneumonia.

RESULTS

For the congenital heart disease condition, the NLP-MIES-supported group increased accuracy by 15% (95% CI 4%-120%, P=.03) and reduced elapsed time by 33% (95% CI 22%-42%, P<.001) compared with the manual group. For the pneumonia condition, the NLP-MIES-supported group increased accuracy by 18% (95% CI 6%-32%, P=.008) and reduced elapsed time by 31% (95% CI 19%-41%, P<.001).

CONCLUSIONS

Our system could improve both the accuracy and efficiency of the data entry process.

Automation and Simplification: Drivers of Innovative Collection and Use of Patient-Reported Outcomes Data

The aim was to develop an electronic data capture (EDC) system to capture patient-reported outcome (PRO) measures successfully by automating processes identified as barriers to implementation. Clinical success, research impact, and patient acceptance of this system were evaluated during a pilot and a follow-up period 2 years later. During the pilot, there were 44,831 eligible visits. Capture rate was 99.0% (44,374 visits) and completion rate was 99.4% (44,108 visits). Capture rate was 99.4% and completion rate was 95.2% during the follow-up period. Zero help desk tickets were put in for the EDC system during either time period. Patients accepted the EDC system both during the pilot (1.4% refusal rate) and follow-up period (1.2%). An automated Structured Query Language server feed provided data used to produce numerous abstracts and manuscripts. Automation was crucial to overcoming implementation barriers and delivering PRO scores to the electronic health record in real time with minimal impact on clinical workflow. Automation also has supported PRO research.

Development of an Electronic Data Collection System to Support a Large-Scale HIV Behavioral Intervention Trial: Protocol for an Electronic Data Collection System

Background

Advancing technology has increased functionality and permitted more complex study designs for behavioral interventions. Investigators need to keep pace with these technological advances for electronic data capture (EDC) systems to be appropriately executed and utilized at full capacity in research settings. Mobile technology allows EDC systems to collect near real-time data from study participants, deliver intervention directly to participants’ mobile devices, monitor staff activity, and facilitate near real-time decision making during study implementation.

Objective

This paper presents the infrastructure of an EDC system designed to support a multisite HIV biobehavioral intervention trial in Los Angeles and New Orleans: the Adolescent Medicine Trials Network “Comprehensive Adolescent Research & Engagement Studies” (ATN CARES). We provide an overview of how multiple EDC functions can be integrated into a single EDC system to support large-scale intervention trials.

Methods

The CARES EDC system is designed to monitor and document multiple study functions, including, screening, recruitment, retention, intervention delivery, and outcome assessment. Text messaging (short message service, SMS) and nearly all data collection are supported by the EDC system. The system functions on mobile phones, tablets, and Web browsers.

Results

ATN CARES is enrolling study participants and collecting baseline and follow-up data through the EDC system. Besides data collection, the EDC system is being used to generate multiple reports that inform recruitment planning, budgeting, intervention quality, and field staff supervision. The system is supporting both incoming and outgoing text messages (SMS) and offers high-level data security. Intervention design details are also influenced by EDC system platform capabilities and constraints. Challenges of using EDC systems are addressed through programming updates and training on how to improve data quality.

Conclusions

There are three key considerations in the development of an EDC system for an intervention trial. First, it needs to be decided whether the flexibility provided by the development of a study-specific, in-house EDC system is needed relative to the utilization of an existing commercial platform that requires less in-house programming expertise. Second, a single EDC system may not provide all functionality. ATN CARES is using a main EDC system for data collection, text messaging (SMS) interventions, and case management and a separate Web-based platform to support an online peer support intervention. Decisions need to be made regarding the functionality that is crucial for the EDC system to handle and what functionality can be handled by other systems. Third, data security is a priority but needs to be balanced with the need for flexible intervention delivery. For example, ATN CARES is delivering text messages (SMS) to study participants’ mobile phones. EDC data security protocols should be developed under guidance from security experts and with formative consulting with the target study population as to their perceptions and needs.

International Registered Report Identifier (IRRID)

DERR1-10.2196/10777

Experiences in running a complex electronic data capture system using mobile phones in a large-scale population trial in southern Nepal

The increasing availability and capabilities of mobile phones make them a feasible means of data collection. Electronic Data Capture (EDC) systems have been used widely for public health monitoring and surveillance activities, but documentation of their use in complicated research studies requiring multiple systems is limited. This paper shares our experiences of designing and implementing a complex multi-component EDC system for a community-based four-armed cluster-Randomised Controlled Trial in the rural plains of Nepal, to help other researchers planning to use EDC for complex studies in low-income settings. We designed and implemented three interrelated mobile phone data collection systems to enrol and follow-up pregnant women (trial participants), and to support the implementation of trial interventions (women's groups, food and cash transfers). 720 field staff used basic phones to send simple coded text messages, 539 women's group facilitators used Android smartphones with Open Data Kit Collect, and 112 Interviewers, Coordinators and Supervisors used smartphones with CommCare. Barcoded photo ID cards encoded with participant information were generated for each enrolled woman. Automated systems were developed to download, recode and merge data for nearly real-time access by researchers. The systems were successfully rolled out and used by 1371 staff. A total of 25,089 pregnant women were enrolled, and 17,839 follow-up forms completed. Women's group facilitators recorded 5717 women's groups and the distribution of 14,647 food and 13,482 cash transfers. Using EDC sped up data collection and processing, although time needed for programming and set-up delayed the study inception. EDC using three interlinked mobile data management systems (FrontlineSMS, ODK and CommCare) was a feasible and effective method of data capture in a complex large-scale trial in the plains of Nepal. Despite challenges including prolonged set-up times, the systems met multiple data collection needs for users with varying levels of literacy and experience.

Brain-CODE: A Secure Neuroinformatics Platform for Management, Federation, Sharing and Analysis of Multi-Dimensional Neuroscience Data

Historically, research databases have existed in isolation with no practical avenue for sharing or pooling medical data into high dimensional datasets that can be efficiently compared across databases. To address this challenge, the Ontario Brain Institute’s “Brain-CODE” is a large-scale neuroinformatics platform designed to support the collection, storage, federation, sharing and analysis of different data types across several brain disorders, as a means to understand common underlying causes of brain dysfunction and develop novel approaches to treatment. By providing researchers access to aggregated datasets that they otherwise could not obtain independently, Brain-CODE incentivizes data sharing and collaboration and facilitates analyses both within and across disorders and across a wide array of data types, including clinical, neuroimaging and molecular. The Brain-CODE system architecture provides the technical capabilities to support (1) consolidated data management to securely capture, monitor and curate data, (2) privacy and security best-practices, and (3) interoperable and extensible systems that support harmonization, integration, and query across diverse data modalities and linkages to external data sources. Brain-CODE currently supports collaborative research networks focused on various brain conditions, including neurodevelopmental disorders, cerebral palsy, neurodegenerative diseases, epilepsy and mood disorders. These programs are generating large volumes of data that are integrated within Brain-CODE to support scientific inquiry and analytics across multiple brain disorders and modalities. By providing access to very large datasets on patients with different brain disorders and enabling linkages to provincial, national and international databases, Brain-CODE will help to generate new hypotheses about the biological bases of brain disorders, and ultimately promote new discoveries to improve patient care.

Web-Based Recruitment and Survey Methodology to Maximize Response Rates from Followers of Popular Diets: the Adhering to Dietary Approaches for Personal Taste (ADAPT) Feasibility Survey

BACKGROUND

Although there is interest in popular diets such as vegan and vegetarian, Paleo, and other "whole food" diets, existing cohort studies lack data for these subgroups. The use of electronic data capture and Web-based surveys in nutrition research may be valuable for future studies by allowing targeting of specific dietary subgroups.

OBJECTIVE

The aim was to perform a Feasibility Survey (FS) to assess the practicality of Web-based research methods to gather data and to maximize response rates among followers of popular diets.

METHODS

The FS was an open, voluntary, 15-min survey conducted over 8 wk in the summer of 2015. Recruitment targeted self-identified followers of popular diets from a convenience sample, offering no incentives, via social media and e-newsletters shared by recruitment partners. Feasibility was assessed by number of responses, survey completion rate, distribution of diets, geographic location, and willingness to participate in future research.

RESULTS

A total of 14,003 surveys were initiated; 13,787 individuals consented, and 9726 completed the survey (71% of consented). The numbers of unique visitors to the questionnaire site, view rate, and participation rate were not captured. Among respondents with complete demographic data, 83% were female and 93% were white. Diet designations were collapsed into the following groups: whole-food, plant-based (25%); vegan and raw vegan (19%); Paleo (14%); try to eat healthy (11%); vegetarian and pescatarian (9%); whole food (8%); Weston A Price (5%); and low-carbohydrate (low-carb) (4%). Forced-response, multiple-choice questions produced the highest response rates (0-2% selected "prefer not to answer"). The percentage who were willing to complete future online questionnaires was 86%, diet recall was 93%, and food diary was 75%; the percentages willing to provide a finger-stick blood sample, venipuncture blood sample, urine sample, and stool sample were 60%, 44%, 58%, and 42%, respectively.

CONCLUSIONS

This survey suggests that recruiting followers of popular diets is feasible with the use of Web-based methods. The unbalanced sample with respect to sex and race/ethnicity could be corrected with specific recruitment strategies using targeted online marketing techniques.

Baseline Assessment of the Evolving 2017 eClinical Landscape

The volume and diversity of data collected to support each clinical study has increased dramatically in response to the rising scope and complexity of global drug development programs. The Tufts Center for the Study of Drug Development conducted an online survey of 257 unique global companies-77% drug development sponsors and 23% contract service providers-to assess clinical data management practices and experiences. Study results indicate that companies are using an average of 6 different applications to support each clinical study and that companies are collecting a range of data types including that from case report forms, lab procedures, pharmacokinetics, biomarker, outcomes assessment, mobile health, and social media. Companies report that the primary electronic data capture (EDC) is capturing traditional data types but not many of the newer ones. Respondents report spending an average of 68.3 days to build and release a study database, 8.1 days between the patient visit and when that patient's data are entered into the EDC system, and 36.3 days on average to lock the database following the last patient last visit. Average cycle time durations are longer and more variable than those observed ten years ago. Subgroup differences (eg, by company size and company type) and factors contributing to data management cycle time and experience are discussed.

Data Management and Data Quality in PERCH, a Large International Case-Control Study of Severe Childhood Pneumonia

The Pneumonia Etiology Research for Child Health (PERCH) study is the largest multicountry etiology study of pediatric pneumonia undertaken in the past 3 decades. The study enrolled 4232 hospitalized cases and 5325 controls over 2 years across 9 research sites in 7 countries in Africa and Asia. The volume and complexity of data collection in PERCH presented considerable logistical and technical challenges. The project chose an internet-based data entry system to allow real-time access to the data, enabling the project to monitor and clean incoming data and perform preliminary analyses throughout the study. To ensure high-quality data, the project developed comprehensive quality indicator, data query, and monitoring reports. Among the approximately 9000 cases and controls, analyzable laboratory results were available for ≥96% of core specimens collected. Selected approaches to data management in PERCH may be extended to the planning and organization of international studies of similar scope and complexity.

Sankofa pediatric HIV disclosure intervention cyber data management: building capacity in a resource-limited setting and ensuring data quality

Prevalence of pediatric HIV disclosure is low in resource-limited settings. Innovative, culturally sensitive, and patient-centered disclosure approaches are needed. Conducting such studies in resource-limited settings is not trivial considering the challenges of capturing, cleaning, and storing clinical research data. To overcome some of these challenges, the Sankofa pediatric disclosure intervention adopted an interactive cyber infrastructure for data capture and analysis. The Sankofa Project database system is built on the HUBzero cyber infrastructure (https://hubzero.org), an open source software platform. The hub database components support: (1) data management – the “databases” component creates, configures, and manages database access, backup, repositories, applications, and access control; (2) data collection – the “forms” component is used to build customized web case report forms that incorporate common data elements and include tailored form submit processing to handle error checking, data validation, and data linkage as the data are stored to the database; and (3) data exploration – the “dataviewer” component provides powerful methods for users to view, search, sort, navigate, explore, map, graph, visualize, aggregate, drill-down, compute, and export data from the database. The Sankofa cyber data management tool supports a user-friendly, secure, and systematic collection of all data. We have screened more than 400 child–caregiver dyads and enrolled nearly 300 dyads, with tens of thousands of data elements. The dataviews have successfully supported all data exploration and analysis needs of the Sankofa Project. Moreover, the ability of the sites to query and view data summaries has proven to be an incentive for collecting complete and accurate data. The data system has all the desirable attributes of an electronic data capture tool. It also provides an added advantage of building data management capacity in resource-limited settings due to its innovative data query and summary views and availability of real-time support by the data management team.

Designing and Developing a Multi-Center/Multi-Device National Registry for Implantable Medical Devices

Designing, developing, and establishing the multi-device/multi-center Comprehensive Implantable Medical Device Registry (CIMDR) for Saudi Arabia is a strategic objective of the Saudi Food and Drug Administration (SFDA). The goal of the CIMDR is to capture all related clinical data along with device related information for implantable medical devices and study population-related outcomes. There is an immediate need in Saudi Arabia to establish the CIMDR to carryout device surveillance, gauge the efficiency and efficacy of various implantable medical devices, and track and recall implantable medical devices.In this work, we report on the development of the SFDA's CIMDR. We specifically focus on the project organization, five primary modules of the CIMDR, and development of the CIMDR through dynamic forms. We anticipate that the collected information in the CIMDR will be used by hospitals and the SFDA to improve patient safety relating to implantable medical devices in Saudi Arabia. Future development of the CIMDR will include a wide range of reporting and embedded analytical tools that will help researchers improve clinical standards and contribute to the research and development of implantable medical device technology.

IT Infrastructure for Registries in Health Services Research: A Market Study in Germany

Registries are increasingly implemented to record the practice of health care. Within a national funding scheme for registries, an accompanying project was launched to support the design of the registries' IT infrastructure amongst other tasks for 16 projects. A challenge of data management systems was organized by the accompanying project in order to enable the projects to define realistic expectations towards IT support in their research protocols. Twelve vendors participated in the challenge. They presented their solutions for selected use cases. In advance, the projects considered a sufficient authorization concept and the possibility to export data to be of highest importance. However, the systems covered mainly core processes of electronic data capture. The accompanying project will continue its support for the next stage of the funding scheme, which will be the implementation of the registries that win a competitive review of their research protocols prepared in the concept development stage.

Implementation and Feasibility of Electronic Patient-Reported Outcome (ePRO) Data Entry in the PRAEGNANT Real-Time Advanced and Metastatic Breast Cancer Registry

PURPOSE

Patient-reported outcomes (PROs) have been incorporated into clinical trials for many symptoms and medical conditions. A transition from paper-based capture of PROs to electronic PROs (ePROs) has recently started. This study reports on the feasibility of ePRO assessment in a prospective registry including molecular data for patients with advanced breast cancer.

METHODS

As part of the PRAEGNANT network, patients were invited by clinical trial staff, physicians, and nurses to complete three standardized Internet-based questionnaires (EQ 5D 5 L, CES-D and IPAQ). Feasibility was assessed by the staff members who assigned the user accounts by the patients. The completeness of the questionnaires was also assessed.

RESULTS

Fifteen of 17 patients who were asked agreed to participate to complete the PRO questionnaires (EQ-5D-5L and CES-D). However, the IPAQ (physical activity) questionnaire was only validly completed by 9 patients. Feasibility was ranked better by the physicians and dedicated clinical trial staff than by the nursing staff.

CONCLUSIONS

Incorporating ePRO questionnaires into an advanced breast cancer registry is feasible, and no major hurdles were reported. Involving stakeholders from the start, the application is tailored to the capacities and abilities of both patients and clinical staff. The patients' compliance was better with some questionnaires, but others may present difficulties.

Electronic case report forms and electronic data capture within clinical trials and pharmacoepidemiology

AIMS

Researchers in clinical and pharmacoepidemiology fields have adopted information technology (IT) and electronic data capture, but these remain underused despite the benefits. This review discusses electronic case report forms and electronic data capture, specifically within pharmacoepidemiology and clinical research.

METHODS

The review used PubMed and the Institute of Electrical and Electronic Engineers library. Search terms used were agreed by the authors and documented. PubMed is medical and health based, whereas Institute of Electrical and Electronic Engineers is technology based. The review focuses on electronic case report forms and electronic data capture, but briefly considers other relevant topics; consent, ethics and security.

RESULTS

There were 1126 papers found using the search terms. Manual filtering and reviewing of abstracts further condensed this number to 136 relevant manuscripts. The papers were further categorized: 17 contained study data; 40 observational data; 27 anecdotal data; 47 covering methodology or design of systems; one case study; one literature review; two feasibility studies; and one cost analysis.

CONCLUSION

Electronic case report forms, electronic data capture and IT in general are viewed with enthusiasm and are seen as a cost-effective means of improving research efficiency, educating participants and improving trial recruitment, provided concerns about how data will be protected from misuse can be addressed. Clear operational guidelines and best practises are key for healthcare providers, and researchers adopting IT, and further work is needed on improving integration of new technologies with current systems. A robust method of evaluation for technical innovation is required.

Mobile Device-Based Electronic Data Capture System Used in a Clinical Randomized Controlled Trial: Advantages and Challenges

Background

Electronic data capture (EDC) systems have been widely used in clinical research, but mobile device–based electronic data capture (mEDC) system has not been well evaluated.

Objective

The aim of our study was to evaluate the feasibility, advantages, and challenges of mEDC in data collection, project management, and telemonitoring in a randomized controlled trial (RCT).

Methods

We developed an mEDC to support an RCT called “Telmisartan and Hydrochlorothiazide Antihypertensive Treatment (THAT)” study, which was a multicenter, double-blinded, RCT, with the purpose of comparing the efficacy of telmisartan and hydrochlorothiazide (HCTZ) monotherapy in high-sodium-intake patients with mild to moderate hypertension during a 60 days follow-up. Semistructured interviews were conducted during and after the trial to evaluate the feasibility, advantage, and challenge of mEDC. Nvivo version 9.0 (QSR International) was used to analyze records of interviews, and a thematic framework method was used to obtain outcomes.

Results

The mEDC was successfully used to support the data collection and project management in all the 14 study hospitals. A total of 1333 patients were recruited with support of mEDC, of whom 1037 successfully completed all 4 visits. Across all visits, the average time needed for 141 questions per patient was 53 min, which were acceptable to both doctors and patients. All the interviewees, including 24 doctors, 53 patients, 1 clinical research associate (CRA), 1 project manager (PM), and 1 data manager (DM), expressed their satisfaction to nearly all the functions of the innovative mEDC in randomization, data collection, project management, quality control, and remote monitoring in real time. The average satisfaction score was 9.2 (scale, 0-10). The biggest challenge came from the stability of the mobile or Wi-Fi signal although it was not a problem in THAT study.

Conclusions

The innovative mEDC has many merits and is well acceptable in supporting data collection and project management in a timely manner in clinical trial.

A scalable approach to measuring the impact of nonignorable nonresponse with an EMA application

There is often a need to assess the dependence of standard analyses on the strong untestable assumption of ignorable missingness. To tackle this problem, past research developed simple sensitivity index measures assuming a linear impact of nonignorability and missingness in outcomes only. These restrictions limit their applicability for studies with missingness in both outcome and covariates. Nonignorable missingness in this setting poses significant new analytic challenges and calls for more general and flexible methods that are also computationally tractable even for large datasets. In this paper, we relax the restrictions of extant linear sensitivity index methods and develop nonlinear sensitivity indices that maintain computational simplicity and perform equally well when the impact of nonignorability is locally linear. On the other hand, they can substantially improve the effectiveness of local sensitivity analysis when regression outcomes and covariates are subject to concurrent missingness. In this situation, the local linear sensitivity analysis fails to detect the impact of nonignorability while the proposed nonlinear sensitivity measures can. Because the new sensitivity indices avoid fitting complicated nonignorable models, they are computationally tractable (i.e., scalable) for use in large datasets. We develop general formula for nonlinear sensitivity index measures, and evaluate the new measures in simulated data and a real dataset collected using the ecological momentary assessment method. Copyright © 2016 John Wiley & Sons, Ltd.

Data Linkage from Clinical to Study Databases via an R Data Warehouse User Interface. Experiences from a Large Clinical Follow-up Study

BACKGROUND

Data that needs to be documented for clinical studies has often been acquired and documented in clinical routine. Usually this data is manually transferred to Case Report Forms (CRF) and/or directly into an electronic data capture (EDC) system.

OBJECTIVES

To enhance the documentation process of a large clinical follow-up study targeting patients admitted for acutely decompensated heart failure by accessing the data created during routine and study visits from a hospital information system (HIS) and by transferring it via a data warehouse (DWH) into the study's EDC system.

METHODS

This project is based on the clinical DWH developed at the University of Würzburg. The DWH was extended by several new data domains including data created by the study team itself. An R user interface was developed for the DWH that allows to access its source data in all its detail, to transform data as comprehensively as possible by R into study-specific variables and to support the creation of data and catalog tables.

RESULTS

A data flow was established that starts with labeling patients as study patients within the HIS and proceeds with updating the DWH with this label and further data domains at a daily rate. Several study-specific variables were defined using the implemented R user interface of the DWH. This system was then used to export these variables as data tables ready for import into our EDC system. The data tables were then used to initialize the first 296 patients within the EDC system by pseudonym, visit and data values. Afterwards, these records were filled with clinical data on heart failure, vital parameters and time spent on selected wards.

CONCLUSIONS

This solution focuses on the comprehensive access and transformation of data for a DWH-EDC system linkage. Using this system in a large clinical study has demonstrated the feasibility of this approach for a study with a complex visit schedule.

Electronic data collection in epidemiological research. The use of REDCap in the Pelotas birth cohorts

OBJECTIVES

This paper describes the use of Research Electronic Data Capture (REDCap) to conduct one of the follow-up waves of the 2004 Pelotas birth cohort. The aim is to point out the advantages and limitations of using this electronic data capture environment to collect data and control every step of a longitudinal epidemiological research, specially in terms of time savings and data quality.

METHODS

We used REDCap as the main tool to support the conduction of a birth cohort follow-up. By exploiting several REDCap features, we managed to schedule assessments, collect data, and control the study workflow. To enhance data quality, we developed specific reports and field validations to depict inconsistencies in real time.

RESULTS

Using REDCap it was possible to investigate more variables without significant increases on the data collection time, when comparing to a previous birth cohort follow-up. In addition, better data quality was achieved since negligible out of range errors and no validation or missing inconsistencies were identified after applying over 7,000 interviews.

CONCLUSIONS

Adopting electronic data capture solutions, such as REDCap, in epidemiological research can bring several advantages over traditional paper-based data collection methods. In favor of improving their features, more research groups should migrate from paper to electronic-based epidemiological research.

Medical Registry Data Collection Efficiency: A Crossover Study Comparing Web-Based Electronic Data Capture and a Standard Spreadsheet

Background

Electronic medical records and electronic data capture (EDC) have changed data collection in clinical and translational research. However, spreadsheet programs, such as Microsoft Excel, are still used as data repository to record and organize patient data for research.

Objective

The objective of this study is to assess the efficiency of EDC as against a standard spreadsheet in regards to time to collect data and data accuracy, measured in number of errors after adjudication.

Methods

This was a crossover study comparing the time to collect data in minutes between EDC and a spreadsheet. The EDC tool used was Research Electronic Data Capture (REDCap), whereas the spreadsheet was Microsoft Excel. The data collected was part of a registry of patients who underwent coronary computed tomography angiography in the emergency setting. Two data collectors with the same experience went over the same patients and collected relevant data on a case report form identical to the one used in our Emergency Department (ED) registry. Data collection tool was switched after the patient that represented half the cohort. For this, the patient cohort was exactly 30 days of our ED coronary Computed Tomography Angiography registry and the point of crossover was determined beforehand to be 15 days. We measured the number of patients admitted, and time to collect data. Accuracy was defined as absence of blank fields and errors, and was assessed by comparing data between data collectors and counting every time the data differed. Statistical analysis was made using paired t -test.

Results

The study included 61 patients (122 observations) and 55 variables. The crossover occurred after the 30th patient. Mean time to collect data using EDC in minutes was 6.2±2.3, whereas using Excel was 8.0±2.0 (P <.001), a difference of 1.8 minutes between both means (22%). The cohort was evenly distributed with 3 admissions in the first half of the crossover and 4 in the second half. We saw 2 (<0.1%) continuous variable typos in the spreadsheet that a single data collector made. There were no blank fields. The data collection tools showed no differences in accuracy of data on comparison.

Conclusions

Data collection for our registry with an EDC tool was faster than using a spreadsheet, which in turn allowed more efficient follow-up of cases.

Review of Developments in Electronic, Clinical Data Collection, and Documentation Systems over the Last Decade - Are We Ready for Big Data in Routine Health Care?

Recently, information availability has become more elaborate and widespread, and treatment decisions are based on a multitude of factors, including imaging, molecular or pathological markers, surgical results, and patient’s preference. In this context, the term “Big Data” evolved also in health care. The “hype” is heavily discussed in literature. In interdisciplinary medical specialties, such as radiation oncology, not only heterogeneous and voluminous amount of data must be evaluated but also spread in different styles across various information systems. Exactly this problem is also referred to in many ongoing discussions about Big Data – the “three V’s”: volume, velocity, and variety. We reviewed 895 articles extracted from the NCBI databases about current developments in electronic clinical data management systems and their further analysis or postprocessing procedures. Few articles show first ideas and ways to immediately make use of collected data, particularly imaging data. Many developments can be noticed in the field of clinical trial or analysis documentation, mobile devices for documentation, and genomics research. Using Big Data to advance medical research is definitely on the rise. Health care is perhaps the most comprehensive, important, and economically viable field of application.

Integrating patient voices into health information for self-care and patient-clinician partnerships: Veterans Affairs design recommendations for patient-generated data applications

Electronic health record content is created by clinicians and is driven largely by intermittent and brief encounters with patients. Collecting data directly from patients in the form of patient-generated data (PGD) provides an unprecedented opportunity to capture personal, contextual patient information that can supplement clinical data and enhance patients' self-care. The US Department of Veterans Affairs (VA) is striving to implement the enterprise-wide capability to collect and use PGD in order to partner with patients in their care, improve the patient healthcare experience, and promote shared decision making. Through knowledge gained from Veterans' and healthcare teams' perspectives, VA created a taxonomy and an evolving framework on which to design and develop applications that capture and help physicians utilize PGD. Ten recommendations for effectively collecting and integrating PGD into patient care are discussed, addressing health system culture, data value, architecture, policy, data standards, clinical workflow, data visualization, and analytics and population reach.

"Bring Your Own Device" (BYOD): The Future of Field-Based Patient-Reported Outcome Data Collection in Clinical Trials?

Field-based patient-reported outcome (PRO) assessments, including measures of signs, symptoms, and events that are administered outside of the research clinic, can be critical in evaluating the efficacy and safety of new medical treatments. Collection of this type of data commonly involves providing subjects with stand-alone electronic devices, such as smartphones, that they can use to respond to assessments in their home or work environment. Although this approach has proven useful, it is also limited in several ways: For example, provisioning stand-alone devices can be costly for sponsors, and requiring subjects to carry a device that is exclusively dedicated to the study can be burdensome. The "Bring Your Own Device" (BYOD) approach, in which subjects use their own smartphone or Internet-enabled device to complete field-based PRO assessments, addresses many of these concerns. However, the BYOD model has its own limitations that should be considered. In this article, representatives of the ePRO Consortium review operational, privacy/security, and scientific/regulatory considerations regarding BYOD. We hope that this review will allow researchers to make informed decisions when choosing methods to collect field-based PRO data in future clinical trials. Additionally, we hope that the discussion in this article will establish a research agenda for further examination of BYOD approaches.

A generic solution for web-based management of pseudonymized data

BACKGROUND

Collaborative collection and sharing of data have become a core element of biomedical research. Typical applications are multi-site registries which collect sensitive person-related data prospectively, often together with biospecimens. To secure these sensitive data, national and international data protection laws and regulations demand the separation of identifying data from biomedical data and to introduce pseudonyms. Neither the formulation in laws and regulations nor existing pseudonymization concepts, however, are precise enough to directly provide an implementation guideline. We therefore describe core requirements as well as implementation options for registries and study databases with sensitive biomedical data.

METHODS

We first analyze existing concepts and compile a set of fundamental requirements for pseudonymized data management. Then we derive a system architecture that fulfills these requirements. Next, we provide a comprehensive overview and a comparison of different technical options for an implementation. Finally, we develop a generic software solution for managing pseudonymized data and show its feasibility by describing how we have used it to realize two research networks.

RESULTS

We have found that pseudonymization models are highly heterogeneous, already on a conceptual level. We have compiled a set of requirements from different pseudonymization schemes. We propose an architecture and present an overview of technical options. Based on a selection of technical elements, we suggest a generic solution. It supports the multi-site collection and management of biomedical data. Security measures are multi-tier pseudonymity and physical separation of data over independent backend servers. Integrated views are provided by a web-based user interface. Our approach has been successfully used to implement a national and an international rare disease network.

CONCLUSIONS

We were able to identify a set of core requirements out of several pseudonymization models. Considering various implementation options, we realized a generic solution which was implemented and deployed in research networks. Still, further conceptual work on pseudonymity is needed. Specifically, it remains unclear how exactly data is to be separated into distributed subsets. Moreover, a thorough risk and threat analysis is needed.

Paperless clinical trials: Myth or reality?

There is an urgent need to expedite the time-to-market for new drugs and to make the approval process simpler. But clinical trials are a complex process and the increased complexity leads to decreased efficiency. Hence, pharmaceutical organizations want to move toward a more technology-driven clinical trial process for recording, analyzing, reporting, archiving, etc., In recent times, the progress has certainly been made in developing paperless systems that improve data capture and management. The adaptation of paperless processes may require major changes to existing procedures. But this is in the best interests of these organizations to remain competitive because a paperless clinical trial would lead to a consistent and streamlined framework. Moreover, all major regulatory authorities also advocate adoption of paperless trial. But challenges still remain toward implementation of paperless clinical trial process.

Effectiveness of Using Mobile Phone Image Capture for Collecting Secondary Data: A Case Study on Immunization History Data Among Children in Remote Areas of Thailand

Background

Entering data onto paper-based forms, then digitizing them, is a traditional data-management method that might result in poor data quality, especially when the secondary data are incomplete, illegible, or missing. Transcription errors from source documents to case report forms (CRFs) are common, and subsequently the errors pass from the CRFs to the electronic database.

Objective

This study aimed to demonstrate the usefulness and to evaluate the effectiveness of mobile phone camera applications in capturing health-related data, aiming for data quality and completeness as compared to current routine practices exercised by government officials.

Methods

In this study, the concept of “data entry via phone image capture” (DEPIC) was introduced and developed to capture data directly from source documents. This case study was based on immunization history data recorded in a mother and child health (MCH) logbook. The MCH logbooks (kept by parents) were updated whenever parents brought their children to health care facilities for immunization. Traditionally, health providers are supposed to key in duplicate information of the immunization history of each child; both on the MCH logbook, which is returned to the parents, and on the individual immunization history card, which is kept at the health care unit to be subsequently entered into the electronic health care information system (HCIS). In this study, DEPIC utilized the photographic functionality of mobile phones to capture images of all immunization-history records on logbook pages and to transcribe these records directly into the database using a data-entry screen corresponding to logbook data records. DEPIC data were then compared with HCIS data-points for quality, completeness, and consistency.

Results

As a proof-of-concept, DEPIC captured immunization history records of 363 ethnic children living in remote areas from their MCH logbooks. Comparison of the 2 databases, DEPIC versus HCIS, revealed differences in the percentage of completeness and consistency of immunization history records. Comparing the records of each logbook in the DEPIC and HCIS databases, 17.3% (63/363) of children had complete immunization history records in the DEPIC database, but no complete records were reported in the HCIS database. Regarding the individual’s actual vaccination dates, comparison of records taken from MCH logbook and those in the HCIS found that 24.2% (88/363) of the children’s records were absolutely inconsistent. In addition, statistics derived from the DEPIC records showed a higher immunization coverage and much more compliance to immunization schedule by age group when compared to records derived from the HCIS database.

Conclusions

DEPIC, or the concept of collecting data via image capture directly from their primary sources, has proven to be a useful data collection method in terms of completeness and consistency. In this study, DEPIC was implemented in data collection of a single survey. The DEPIC concept, however, can be easily applied in other types of survey research, for example, collecting data on changes or trends based on image evidence over time. With its image evidence and audit trail features, DEPIC has the potential for being used even in clinical studies since it could generate improved data integrity and more reliable statistics for use in both health care and research settings.

The feasibility of using electronic clinical outcome assessments in people with schizophrenia and their informal caregivers

Many clinical outcome assessments (COAs) were originally developed for completion via pen and paper. However, in recent years there have been movements toward electronic capture of such data in an effort to reduce missing data, provide time-stamped records, minimize administrative burden, and avoid secondary data entry errors. Although established in many patient populations, the implications of using electronic COAs in schizophrenia are unknown. In accordance with International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Task Force recommendations, in-depth cognitive debriefing and usability interviews were conducted with people with schizophrenia (n=12), their informal (unpaid) caregivers (n=12), and research support staff (n=6) to assess the suitability of administration of various electronic COA measures using an electronic tablet device. Minimal issues were encountered by participants when completing or administering the COAs in electronic format, with many finding it easier to complete instruments in this mode than by pen and paper. The majority of issues reported were specific to the device functionality rather than the electronic mode of administration. Findings support data collection via electronic tablet in people with schizophrenia and their caregivers. The appropriateness of other forms of electronic data capture (eg, smartphones, interactive voice response systems, etc) is a topic for future investigation.

The process of installing REDCap, a web based database supporting biomedical research: the first year

BACKGROUND

Clinical and research data are essential for patient care, research and healthcare system planning. REDCapTM is a web-based tool for research data curatorship developed at Vanderbilt University in Nashville, USA. The Faculty of Health Sciences at the University of the Witwatersrand, Johannesburg South Africa identified the need for a cost effective data management instrument. REDCap was installed as per the user agreement with Vanderbilt University in August 2012.

OBJECTIVES

In order to assist other institutions that may lack the in-house Information Technology capacity, this paper describes the installation and support of REDCap and incorporates an analysis of user uptake over the first year of use.

METHODS

We reviewed the staffing requirements, costs of installation, process of installation and necessary infrastructure and end-user requests following the introduction of REDCap at Wits. The University Legal Office and Human Research Ethics Committee were consulted regarding the REDCap end-user agreement. Bi-monthly user meetings resulted in a training workshop in August 2013. We compared our REDCap software user numbers and records before and after the first training workshop.

RESULTS

Human resources were recruited from existing staff. Installation costs were limited to servers and security certificates. The total costs to provide a functional REDCap platform was less than $9000. Eighty-one (81) users were registered in the first year. After the first training workshop the user numbers increased by 59 in one month and the total number of active users to 140 by the end of August 2013. Custom software applications for REDCap were created by collaboration between clinicians and software developers.

CONCLUSION

REDCap was installed and maintained at limited cost. A small number of people with defined skills can support multiple REDCap users in two to four hours a week. End user training increased in the number of users, number of projects created and the number of projects moved to production.

Electronic data capture in a rural African setting: evaluating experiences with different systems in Malawi

Background

As hardware for electronic data capture (EDC), such as smartphones or tablets, becomes cheaper and more widely available, the potential for using such hardware as data capture tools in routine healthcare and research is increasing.

Objective

We aim to highlight the advantages and disadvantages of four EDC systems being used simultaneously in rural Malawi: two for Android devices (CommCare and ODK Collect), one for PALM and Windows OS (Pendragon), and a custom-built application for Android (Mobile InterVA – MIVA).

Design

We report on the personal field and development experience of fieldworkers, project managers, and EDC system developers.

Results

Fieldworkers preferred using EDC to paper-based systems, although some struggled with the technology at first. Highlighted features include in-built skip patterns for all systems, and specifically the ‘case’ function that CommCare offers. MIVA as a standalone app required considerably more time and expertise than the other systems to create and could not be customised for our specific research needs; however, it facilitates standardised routine data collection. CommCare and ODK Collect both have user-friendly web-interfaces for form development and good technical support. CommCare requires Internet to build an application and download it to a device, whereas all steps can be done offline with ODK Collect, a desirable feature in low connectivity settings. Pendragon required more complex programming of logic, using a Microsoft Access application, and generally had less technical support. Start-up costs varied between systems, and all were considered more expensive than setting up a paper-based system; however running costs were generally low and therefore thought to be cost-effective over the course of our projects.

Conclusions

EDC offers many opportunities for efficient data collection, but brings some issues requiring consideration when designing a study; the decision of which hardware and software to use should be informed by the aim of data collection, budget, and local circumstances.

Breaking down the barriers: challenges with development and implementation of an industry-sponsored antimicrobial stewardship data collection and analysis tool

Partnership between clinicians and the pharmaceutical industry with a focus on antimicrobial stewardship research initiatives is a necessary step toward meeting the shared goals of combating inappropriate antimicrobial use, improving patient outcomes, and minimizing resistance development. Achieving these goals requires outcomes-focused data collection and monitoring tools for antimicrobial stewardship programs (ASP) that consider real-world data about how antimicrobials are used to treat patients. Here we highlight the experiences and challenges associated with the development and implementation of an industry-sponsored electronic antimicrobial stewardship data collection and analysis tool (AS-DCAT). The benefits and risks of the industry-sponsored AS-DCAT from the perspectives of the sponsoring company and participating sites are discussed. Barriers encountered as well as general considerations and recommendations for preventing or overcoming those barriers for future studies and tool development are provided.

principles and the realization with RANDI2 and OpenClinica

BACKGROUND

Clinical trials (CT) are in a wider sense experiments to prove and establish clinical benefit of treatments. Nowadays electronic data capture systems (EDCS) are used more often bringing a better data management and higher data quality into clinical practice. Also electronic systems for the randomization are used to assign the patients to the treatments.

OBJECTIVES

If the mentioned randomization system (RS) and EDCS are used, possibly identical data are collected in both, especially by stratified randomization. This separated data storage may lead to data inconsistency and in general data samples have to be aligned. The article discusses solutions to combine RS and EDCS. In detail one approach is realized and introduced.

METHODS

Different possible settings of combination of EDCS and RS are determined and the pros and cons for each solution are worked out. For the combination of two independent applications the necessary interfaces for the communication are defined. Thereby, existing standards are considered. An example realization is implemented with the help of open-source applications and state-of-the-art software development procedures.

RESULTS

Three possibilities of separate usage or combination of EDCS and RS are presented and assessed: i) the complete independent usage of both systems; ii) realization of one system with both functions; and iii) two separate systems, which communicate via defined interfaces. In addition a realization of our preferred approach, the combination of both systems, is introduced using the open source tools RANDI2 and OpenClinica.

CONCLUSION

The advantage of a flexible independent development of EDCS and RS is shown based on the fact that these tool are very different featured. In our opinion the combination of both systems via defined interfaces fulfills the requirements of randomization and electronic data capture and is feasible in practice. In addition, the use of such a setting can reduce the training costs and the error-prone duplicated data entry.

Integrating patient reported outcomes with clinical cancer registry data: a feasibility study of the electronic Patient-Reported Outcomes From Cancer Survivors (ePOCS) system

Background

Routine measurement of Patient Reported Outcomes (PROs) linked with clinical data across the patient pathway is increasingly important for informing future care planning. The innovative electronic Patient-reported Outcomes from Cancer Survivors (ePOCS) system was developed to integrate PROs, collected online at specified post-diagnostic time-points, with clinical and treatment data in cancer registries.

Objective

This study tested the technical and clinical feasibility of ePOCS by running the system with a sample of potentially curable breast, colorectal, and prostate cancer patients in their first 15 months post diagnosis.

Methods

Patients completed questionnaires comprising multiple Patient Reported Outcome Measures (PROMs) via ePOCS within 6 months (T1), and at 9 (T2) and 15 (T3) months, post diagnosis. Feasibility outcomes included system informatics performance, patient recruitment, retention, representativeness and questionnaire completion (response rate), patient feedback, and administration burden involved in running the system.

Results

ePOCS ran efficiently with few technical problems. Patient participation was 55.21% (636/1152) overall, although varied by approach mode, and was considerably higher among patients approached face-to-face (61.4%, 490/798) than by telephone (48.8%, 21/43) or letter (41.0%, 125/305). Older and less affluent patients were less likely to join (both P<.001). Most non-consenters (71.1%, 234/329) cited information technology reasons (ie, difficulty using a computer). Questionnaires were fully or partially completed by 85.1% (541/636) of invited participants at T1 (80 questions total), 70.0% (442/631) at T2 (102-108 questions), and 66.3% (414/624) at T3 (148-154 questions), and fully completed at all three time-points by 57.6% (344/597) of participants. Reminders (mainly via email) effectively prompted responses. The PROs were successfully linked with cancer registry data for 100% of patients (N=636). Participant feedback was encouraging and positive, with most patients reporting that they found ePOCS easy to use and that, if asked, they would continue using the system long-term (86.2%, 361/419). ePOCS was not administratively burdensome to run day-to-day, and patient-initiated inquiries averaged just 11 inquiries per month.

Conclusions

The informatics underlying the ePOCS system demonstrated successful proof-of-concept – the system successfully linked PROs with registry data for 100% of the patients. The majority of patients were keen to engage. Participation rates are likely to improve as the Internet becomes more universally adopted. ePOCS can help overcome the challenges of routinely collecting PROs and linking with clinical data, which is integral for treatment and supportive care planning and for targeting service provision.

Usability of an innovative and interactive electronic system for collection of patient-reported data in axial spondyloarthritis: comparison with the traditional paper-administered format

OBJECTIVE

To evaluate the validity, in terms of the patients' acceptance, preference, feasibility and reliability of an innovative, interactive computerized system for collection of patient-reported outcome (PRO) data on axial SpA against the paper-and-pencil version.

METHODS

Fifty-five patients with axial SpA completed both the touch screen and the paper-and-pencil set of questionnaires. A computerized touch-screen system, SPEAMonitor, was developed to capture PRO data. Variables recorded included demographic data, patient's assessment of general health status, BASDAI, BASFI, BASMI and acute-phase reactant levels. In order to assess the patient's acceptance of, preference for and feasibility of computer-based questionnaires, the participants filled in an additional questionnaire. The time taken to complete both formats was measured. In a further test-retest study, 25 patients were re-evaluated.

RESULTS

The agreement between the paper-administered and computer touch-screen format of the BASFI, BASDAI questionnaires and the Ankylosing Spondylitis Disease Activity Scores was excellent. Intraclass correlation coefficients (ICCs) between data ranged from 0.90 to 0.96. Additionally the test-retest study showed a very good agreement between the scores for the two administrations (ICC ≥ 0.90). Age, computer experience and education level had no significant impact on the results. The computerized questionnaires were reported to be easier to use. The mean time spent completing the questionnaires on a touch screen was 5.1 min and on paper 7.9 min.

CONCLUSION

Our newly developed computer-assisted touch-screen questionnaires for PRO in axial SpA were well accepted by patients, with good data quality, reliability and score agreement.

Development of a computer-adaptive version of the forgotten joint score

Patient-reported outcomes (PROs) are an important endpoint in orthopedics providing comprehensive information about patients' perspectives on treatment outcome. Computer-adaptive test (CAT) measures are an advanced method for assessing PROs using item sets that are tailored to the individual patient. This provides increased measurement precision and reduces the number of items. We developed a CAT version of the Forgotten Joint Score (FJS), a measure of joint awareness in everyday life. CAT development was based on FJS data from 580 patients after THA or TKA (808 assessments). The CAT version reduced the number of items by half at comparable measurement precision. In a feasibility study we administered the newly developed CAT measure on tablet PCs and found that patients actually preferred electronic questionnaires over paper-pencil questionnaires.

Evaluation of partial and total splenectomy in children with sickle cell disease using an Internet-based registry

BACKGROUND

Clinical outcomes of children with sickle cell disease (SCD) who undergo total or partial splenectomy (PS) are poorly defined. The purpose of this retrospective study was to initiate an Internet-based registry to facilitate analysis of clinical outcomes for these children. We hypothesized that both surgical procedures would be well tolerated and would eliminate risk of splenic sequestration.

METHODS

We developed a web-based registry using the Research Electronic Data Capture (REDCap) platform. Children were included if they had SCD and underwent total splenectomy (TS) or PS between 2003 and 2010. Clinical outcomes were compared between cohorts, with follow-up to 1 year.

RESULTS

Twenty-four children were included, TS (n = 15) and PS (n = 9). There were no differences in surgical time or intraoperative blood loss. The length of stay was longer after PS (4.1 ± 1.7 days) compared to TS, (2.4 ± 1.2 days, P = 0.02). Within 30 days of surgery, 2 (20%) patients had acute chest syndrome (ACS) following TS and 2 (15%) patients had ACS after PS. During 1-year follow-up, no patient in either cohort had recurrent splenic sequestration, venous thrombosis or overwhelming postsplenectomy sepsis. All children who were transfused preoperatively to prevent recurrent splenic sequestration successfully discontinued transfusions.

CONCLUSIONS

Both TS and PS result in favorable hematologic outcomes and low risk of adverse events for children with SCD. A REDCap-based registry may facilitate data entry and analysis of clinical outcomes to allow for comparison between different types of splenectomy.

The clinical assessment and remote administration tablet

Electronic data capture of case report forms, demographic, neuropsychiatric, or clinical assessments, can vary from scanning hand-written forms into databases to fully electronic systems. Web-based forms can be extremely useful for self-assessment; however, in the case of neuropsychiatric assessments, self-assessment is often not an option. The clinician often must be the person either summarizing or making their best judgment about the subject’s response in order to complete an assessment, and having the clinician turn away to type into a web browser may be disruptive to the flow of the interview. The Mind Research Network has developed a prototype for a software tool for the real-time acquisition and validation of clinical assessments in remote environments. We have developed the clinical assessment and remote administration tablet on a Microsoft Windows PC tablet system, which has been adapted to interact with various data models already in use in several large-scale databases of neuroimaging studies in clinical populations. The tablet has been used successfully to collect and administer clinical assessments in several large-scale studies, so that the correct clinical measures are integrated with the correct imaging and other data. It has proven to be incredibly valuable in confirming that data collection across multiple research groups is performed similarly, quickly, and with accountability for incomplete datasets. We present the overall architecture and an evaluation of its use.