The use of electronic data capture tools in clinical trials: Web-survey of 259 Canadian trials

In Pursuit of Optimal Outcomes: A Framework for Quality Standards in Immune Effector Cell Therapy

Immune effector cell (IEC) therapy represents a transformative advancement in oncology, leveraging the immune system to combat various malignancies. This article outlines a comprehensive framework for establishing and maintaining quality standards in IEC therapy amidst rapid scientific and clinical advancements. We emphasize the integration of structured process measures, robust quality assurance, and meticulous outcome evaluation to ensure treatment efficacy and safety. Key components include multidisciplinary expertise, stringent accreditation protocols, and advanced data management systems, which facilitate standardized reporting and continual innovation. The collaborative effort among stakeholders-ranging from patients and healthcare providers to regulatory bodies-is crucial in delivering high-quality IEC therapies. This framework aims to enhance patient outcomes and cement the role of IEC therapy as a cornerstone of modern oncology, promoting continuous improvement and adherence to high standards across the therapeutic spectrum.

"Click and mortar" opportunities for digitization and consumerism in trials

BACKGROUND

Digitization (using novel digital tools and strategies) and consumerism (taking a consumer-oriented approach) are increasingly commonplace in clinical trials, but the implications of these changes are not well described.

METHODS

We assembled a group of trial experts from academia, industry, non-profit, and government to discuss implications of this changing trial landscape and provide guidance.

RESULTS

Digitization and consumerism can increase the volume and diversity of trial participants and expedite recruitment. However, downstream bottlenecks, challenges with retention, and serious issues with equity, ethics, and security can result. A "click and mortar" approach, combining approaches from novel and traditional trials with the thoughtful use of technology, may optimally balance opportunities and challenges facing many trials.

CONCLUSION

We offer expert guidance and three "click and mortar" approaches to digital, consumer-oriented trials. More guidance and research are needed to navigate the associated opportunities and challenges.

Ten Years of Medical Informatics and Standards Support for Clinical Research in an Infectious Diseases Network

BACKGROUND

It is 30 years since evidence-based medicine became a great support for individual clinical expertise in daily practice and scientific research. Electronic systems can be used to achieve the goal of collecting data from heterogeneous datasets and to support multicenter clinical trials. The Ligurian Infectious Diseases Network (LIDN) is a web-based platform for data collection and reuse originating from a regional effort and involving many professionals from different fields.

OBJECTIVES

The objective of this work is to present an integrated system of ad hoc interfaces and tools that we use to perform pseudonymous clinical data collection, both manually and automatically, to support clinical trials.

METHODS

The project comprehends different scenarios of data collection systems, according to the degree of information technology of the involved centers. To be compliant with national regulations, the last developed connection is based on the standard Clinical Document Architecture Release 2 by Health Level 7 guidelines, interoperability is supported by the involvement of a terminology service.

RESULTS

Since 2011, the LIDN platform has involved more than 8,000 patients from eight different hospitals, treated or under treatment for at least one infectious disease among human immunodeficiency virus (HIV), hepatitis C virus, severe acute respiratory syndrome coronavirus 2, and tuberculosis. Since 2013, systems for the automatic transfer of laboratory data have been updating patients' information for three centers, daily. Direct communication was set up between the LIDN architecture and three of the main national cohorts of HIV-infected patients.

CONCLUSION

The LIDN was originally developed to support clinicians involved in the project in the management of data from HIV-infected patients through a web-based tool that could be easily used in primary-care units. Then, the developed system grew modularly to respond to the specific needs that arose over a time span of more than 10 years.

Customer-centric product presentations for monoclonal antibodies

Delivering customer-centric product presentations for biotherapeutics, such as monoclonal antibodies (mAbs), represents a long-standing and paramount area of engagement for pharmaceutical scientists. Activities include improving experience with the dosing procedure, reducing drug administration-related expenditures, and ultimately shifting parenteral treatments outside of a controlled healthcare institutional setting. In times of increasingly cost-constrained markets and reinforced with the coronavirus pandemic, this discipline of "Product Optimization" in healthcare has gained momentum and changed from a nice-to-have into a must. This review summarizes latest trends in the healthcare ecosystem that inform key strategies for developing customer-centric products, including the availability of a wider array of sustainable drug delivery options and treatment management plans that support dosing in a flexible care setting. Three disease area archetypes with varying degree of implementation of customer-centric concepts are introduced to highlight relevant market differences and similarities. Namely, rheumatoid arthritis and inflammatory bowel disease, multiple sclerosis, and oncology have been chosen due to differences in the availability of subcutaneously dosed and ready-to-use self-administration products for mAb medicines and their follow-on biologics. Different launch scenarios are described from a manufacturer's perspective highlighting the necessity of platform approaches. To unfold the full potential of customer-centric care, value-based healthcare provider reimbursement schemes that incentivize the efficiency of care need to be broadly implemented.

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.

Utilization of the iOS Shortcuts App to Generate a Surgical Logbook Tool: Feasibility Study

Background

Surgical audit is an essential aspect of modern reflective surgical practice and is key to improving surgical outcomes. The surgical logbook is an important method of data collection for both personal and unit audits; however, current electronic data collection tools, especially mobile apps, lack the minimum recommended data fields.

Objective

This feasibility study details the creation of a free, effective surgical logbook tool with the iOS Shortcuts app and investigates the time investment required to maintain a surgical logbook with this tool. In addition, we investigate the potential utility of the Shortcuts app in creating medical data collection tools.

Methods

Using the iOS Shortcuts app, we created a shortcut “Operation Note,” which collects surgical logbook data by using the minimum and extended audit data sets recommended by the Royal Australasian College of Surgeons. We practically assessed the feasibility of the tool, assessing the time requirement for entry, accuracy, and completeness of the entered data.

Results

The shortcut collected accurate and useful data for a surgical audit. Data entry took on average 65 seconds per case for the minimum data set, and 135 seconds per case for the extended data set, with a mean difference of 68 seconds (P<.001; 95% CI 61.6-77.7).

Conclusions

This feasibility study demonstrates the utility of the iOS Shortcuts app in the creation of a surgical logbook and the time-consuming nature of data collection for surgical audit. Our iOS Operation Note shortcut is a free, rapid, and customizable alternative to currently available logbook apps and offers surgical trainees and consultants a method for recording surgical operations, complications, and demographic data.

Developing an FHIR-Based Computational Pipeline for Automatic Population of Case Report Forms for Colorectal Cancer Clinical Trials Using Electronic Health Records

PURPOSE

The Fast Healthcare Interoperability Resources (FHIR) is emerging as a next-generation standards framework developed by HL7 for exchanging electronic health care data. The modeling capability of FHIR in standardizing cancer data has been gaining increasing attention by the cancer research informatics community. However, few studies have been conducted to examine the capability of FHIR in electronic data capture (EDC) applications for effective cancer clinical trials. The objective of this study was to design, develop, and evaluate an FHIR-based method that enables the automation of the case report forms (CRFs) population for cancer clinical trials using real-world electronic health records (EHRs).

MATERIALS AND METHODS

We developed an FHIR-based computational pipeline of EDC with a case study for modeling colorectal cancer trials. We first leveraged an existing FHIR-based cancer profile to represent EHR data of patients with colorectal cancer, and then we used the FHIR Questionnaire and QuestionnaireResponse resources to represent the CRFs and their data population. To test the accuracy of and overall quality of the computational pipeline, we used synoptic reports of 287 Mayo Clinic patients with colorectal cancer from 2013 to 2019 with standard measures of precision, recall, and F1 score.

RESULTS

Using the computational pipeline, a total of 1,037 synoptic reports were successfully converted as the instances of the FHIR-based cancer profile. The average accuracy for converting all data elements (excluding tumor perforation) of the cancer profile was 0.99, using 200 randomly selected records. The average F1 score for populating nine questions of the CRFs in a real-world colorectal cancer trial was 0.95, using 100 randomly selected records.

CONCLUSION

We demonstrated that it is feasible to populate CRFs with EHR data in an automated manner with satisfactory performance. The outcome of the study provides helpful insight into future directions in implementing FHIR-based EDC applications for modern cancer clinical trials.

Modeling cancer clinical trials using HL7 FHIR to support downstream applications: A case study with colorectal cancer data

BACKGROUND AND OBJECTIVE

Identification and Standardization of data elements used in clinical trials may control and reduce the cost and errors during the operational process, and enable seamless data exchange between the electronic data capture (EDC) systems and Electronic Health Record (EHR) systems. This study presents a methodology to comprehensively capture the clinical trial data element needs.

MATERIALS AND METHODS

Case report forms (CRF) for clinical trial data collection were used to approximate the clinical information need, whereby these information needs were then mapped to a semantically equivalent field within an existing FHIR cancer profile. For items without a semantically equivalent field, we considered these items to be information needs that cannot be represented in current standards and proposed extensions to support these needs.

RESULTS

We successfully identified 62 discrete items from a preliminary survey of 43 base questions in four CRFs used in colorectal cancer clinical trials, in which 28 items are modeled with FHIR extensions and their associated responses for colorectal cancer. We achieved promising results in the data population of the CRFs with average Precision 98.5 %, Recall 96.2 %, and F-measure 96.8 % for all base questions. We also demonstrated the auto-filled answers in CRFs can be used to discover patient subgroups using a topic modeling approach.

CONCLUSION

CRFs can be considered as a proxy for representing information needs for their respective cancer types. Mining the information needs can serve as a valuable resource for expanding existing standards to ensure they can comprehensively represent relevant clinical data without loss of granularity.

Key components and IT assistance of participant management in clinical research: a scoping review

Objectives

Managing participants and their data are fundamental for the success of a clinical trial. Our review identifies and describes processes that deal with management of trial participants and highlights information technology (IT) assistance for clinical research in the context of participant management.

Methods

A scoping literature review design, based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses statement, was used to identify literature on trial participant-related proceedings, work procedures, or workflows, and assisting electronic systems.

Results

The literature search identified 1329 articles of which 111 were included for analysis. Participant-related procedures were categorized into 4 major trial processes: recruitment, obtaining informed consent, managing identities, and managing administrative data. Our results demonstrated that management of trial participants is considered in nearly every step of clinical trials, and that IT was successfully introduced to all participant-related areas of a clinical trial to facilitate processes.

Discussion

There is no precise definition of participant management, so a broad search strategy was necessary, resulting in a high number of articles that had to be excluded. Nevertheless, this review provides a comprehensive overview of participant management-related components, which was lacking so far. The review contributes to a better understanding of how computer-assisted management of participants in clinical trials is possible.

Implementing an Electronic Data Capture System to Improve Clinical Workflow in a Large Academic Radiation Oncology Practice

PURPOSE

To describe the feasibility and benefits of implementing a custom radiation oncology electronic data capture (EDC) system in a large academic radiation oncology practice.

PATIENTS AND METHODS

A Web-based point-and-click EDC known as Brocade was internally developed and implemented systemwide in 2016. Brocade captures key data elements, such as stage, histology, and patient and treatment characteristics; links this information to radiation dose data extracted from the record and verify system; and creates clinical notes that are automatically exported to the hospital electronic health record. We report the number of unique radiation episodes captured by Brocade in its first full year of implementation and describe the notes generated, toxicities captured, compliance with staging and quality assurance, and time of day in which documentation occurred with Brocade versus our prior human transcription system.

RESULTS

A median of 756 radiation episodes per month was captured for a total of 9,283 unique episodes captured in the first full year of implementation. The most common notes were for on-treatment visits (29,913) and simulations (13,220). Stage was captured for 92.2% of Brocade episodes (8,513 of 9,236) versus 29.7% of courses pre-Brocade (3,025 of 10,170; P < .001). Quality assurance was documented for 96.3% of completed courses (7,601 of 7,892). The most common grade ≥1 toxicities were pain (10,031), fatigue (7,490), and dermatitis (6,172). Brocade implementation was associated with a reduction in off-hours documentation and increase in the proportion of documentation created between 8:00 am and 12:00 pm.

CONCLUSION

Brocade is a reliable Web-based EDC tool that improves clinical documentation without detracting from clinical workflow. Moreover, Brocade has the advantage of capturing data in a structured manner that facilitates real-time analytics and outcome reporting.

A minimum evaluation protocol and stepped-wedge cluster randomized trial of ACCESS Open Minds, a large Canadian youth mental health services transformation project

BACKGROUND

Many Canadian adolescents and young adults with mental health problems face delayed detection, long waiting lists, poorly accessible services, care of inconsistent quality and abrupt or absent inter-service transitions. To address these issues, ACCESS Open Minds, a multi-stakeholder network, is implementing and systematically evaluating a transformation of mental health services for youth aged 11 to 25 at 14 sites across Canada. The transformation plan has five key foci: early identification, rapid access, appropriate care, the elimination of age-based transitions between services, and the engagement of youth and families.

METHODS

The ACCESS Open Minds Research Protocol has multiple components including a minimum evaluation protocol and a stepped-wedge cluster randomized trial, that are detailed in this paper. Additional components include qualitative methods and cost-effectiveness analyses. The services transformation is being evaluated at all sites via a minimum evaluation protocol. Six sites are participating in the stepped-wedge trial whereby the intervention (a service transformation along the key foci) was rolled out in three waves, each commencing six months apart. Two sites, one high-population and one low-population, were randomly assigned to each of the three waves, i.e., randomization was stratified by population size. Our primary hypotheses pertain to increased referral numbers, and reduced wait times to initial assessment and to the commencement of appropriate care. Secondary hypotheses pertain to simplified pathways to care; improved clinical, functional and subjective outcomes; and increased satisfaction among youth and families. Quantitative measures addressing these hypotheses are being used to determine the effectiveness of the intervention.

DISCUSSION

Data from our overall research strategy will help test the effectiveness of the ACCESS Open Minds transformation, refine it further, and inform its scale-up. The process by which our research strategy was developed has implications for the practice of research itself in that it highlights the need to actively engage all stakeholder groups and address unique considerations in designing evaluations of complex healthcare interventions in multiple, diverse contexts. Our approach will generate both concrete evidence and nuanced insights, including about the challenges of conducting research in real-world settings. More such innovative approaches are needed to advance youth mental health services research.

TRIAL REGISTRATION NUMBER

Clinicaltrials.gov, ISRCTN23349893 (Retrospectively registered: 16/02/2017).

Early warnings and repayment plans: novel trial management methods for monitoring and managing data return rates in a multi-centre phase III randomised controlled trial with paper Case Report Forms

Background

Monitoring and managing data returns in multi-centre randomised controlled trials is an important aspect of trial management. Maintaining consistently high data return rates has various benefits for trials, including enhancing oversight, improving reliability of central monitoring techniques and helping prepare for database lock and trial analyses. Despite this, there is little evidence to support best practice, and current standard methods may not be optimal.

Methods

We report novel methods from the Trial of Imaging and Schedule in Seminoma Testis (TRISST), a UK-based, multi-centre, phase III trial using paper Case Report Forms to collect data over a 6-year follow-up period for 669 patients. Using an automated database report which summarises the data return rate overall and per centre, we developed a Microsoft Excel-based tool to allow observation of per-centre trends in data return rate over time. The tool allowed us to distinguish between forms that can and cannot be completed retrospectively, to inform understanding of issues at individual centres. We reviewed these statistics at regular trials unit team meetings. We notified centres whose data return rate appeared to be falling, even if they had not yet crossed the pre-defined acceptability threshold of an 80% data return rate. We developed a set method for agreeing targets for gradual improvement with centres having persistent data return problems. We formalised a detailed escalation policy to manage centres who failed to meet agreed targets. We conducted a post-hoc, descriptive analysis of the effectiveness of the new processes.

Results

The new processes were used from April 2015 to September 2016. By May 2016, data return rates were higher than they had been at any time previously, and there were no centres with return rates below 80%, which had never been the case before. In total, 10 centres out of 35 were contacted regarding falling data return rates. Six out of these 10 showed improved rates within 6–8 weeks, and the remainder within 4 months.

Conclusions

Our results constitute preliminary effectiveness evidence for novel methods in monitoring and managing data return rates in randomised controlled trials. We encourage other researchers to work on generating better evidence-based methods in this area, whether through more robust evaluation of our methods or of others.

A Review of Clinical Data Management Systems Used in Clinical Trials

BACKGROUND

A clinical data management system is a software supporting the data management process in clinical trials. In this system, the effective support of clinical data management dimensions leads to the increased accuracy of results and prevention of diversion in clinical trials. The aim of this review article was to investigate the dimensions of data management in clinical data management systems.

METHODS

This study was conducted in 2017. The used databases included Web of Science, Scopus, Science Direct, ProQuest, Ovid Medline and PubMed. The search was conducted over a period of 10 years from 2007 to 2017. The initial number of studies was 101 reaching 19 in the final stage. The final studies were described and compared in terms of the year, country and dimensions of the clinical data management process in clinical trials.

RESULTS

The research findings indicated that none of the systems completely supported the data management dimensions in clinical trials. Although these systems were developed for supporting the clinical data management process, they were similar to electronic data capture systems in many cases. The most significant dimensions of data management in such systems were data collection or entry, report, validation, and security maintenance.

CONCLUSION

Seemingly, not sufficient attention has been paid to automate all dimensions of the clinical data management process in clinical trials. However, these systems could take positive steps towards changing the manual processes of clinical data management to electronic processes.

Acceptability of a German multicentre healthcare research study: a survey of research personnels' attitudes, experiences and work load

OBJECTIVES

The DACAPO study as a multicentre nationwide observational healthcare research study investigates the influence of quality of care on the quality of life in patients with acute respiratory distress syndrome. The aim of this study was to investigate the acceptability to the participating research personnels by assessing attitudes, experiences and workload associated with the conduct of the DACAPO study.

DESIGN, SETTING AND PARTICIPANTS

A prospective anonymous online survey was sent via email account to 169 participants in 65 study centres. The questionnaire included six different domains: (1) training for performing the study; (2) obtaining informed consent; (3) data collection; (4) data entry using the online documentation system; (5) opinion towards the study and (6) personal data. Descriptive data analysis was carried out.

RESULTS

A total of 78 participants took part (46%) in the survey, 75 questionnaires (44%) could be evaluated. 51% were senior medical specialists. 95% considered the time frame of the training as appropriate and the presentation was rated by 93% as good or very good. Time effort for obtaining consent, data collection and entry was considered by 41% as a burden. Support from the coordinating study centre was rated as good or very good by more than 90% of respondents. While the DACAPO study was seen as scientifically relevant by 81%, only 45% considered the study results valuable for improving patient care significantly.

CONCLUSION

Collecting feedback on the acceptability of a large multicentre healthcare research study provided important insights. Recruitment and data acquisition was mainly performed by physicians and often regarded as additional time burden in clinical practice. Reducing the amount of data collection and simplifying data entry could facilitate the conduct of healthcare research studies and could improve motivation of researchers in intensive care medicine.

TRIAL REGISTRATION NUMBER

NCT02637011; Pre-results.

Connecting healthcare and clinical research: Workflow optimizations through seamless integration of EHR, pseudonymization services and EDC systems

OBJECTIVE

In the last years, several projects promote the secondary use of routine healthcare data based on electronic health record (EHR) data. In multicenter studies, dedicated pseudonymization services are applied for unified pseudonym handling. Healthcare, clinical research and pseudonymization systems are generally disconnected. Hence, the aim of this research work is to integrate these applications and to evaluate the workflow of clinical research.

METHODS

We analyzed and identified technical solutions for legislation compliant automatic pseudonym generation and for the integration into EHR as well as electronic data capture (EDC) systems. The Mainzelliste was used as pseudonymization service, which is available as open source solution and compliant with the data privacy concept in Germany. Subject of the integration was the local EHR and an in-house developed EDC system. A time and motion study was conducted to evaluate the effects on the workflow.

RESULTS

Integration of EHR, pseudonymization service and EDC systems is technically feasible and leads to a less fragmented usage of all applications. Generated pseudonyms are obtained from the service hosted at a trusted third party and can now be used in the EDC as well as in the EHR system for direct access and re-identification. The evaluation of 90 registration iterations shows that the time for documentation has been significantly reduced in average by 39.6 s (56.3%) from 71 ± 8 s to 31 ± 5 s per registered study patient.

CONCLUSIONS

By incorporating EHR, EDC and pseudonymization systems, it is now feasible to support multicenter studies and registers out of an integrated system landscape within a hospital. Optimizing the workflow of patient registration for clinical research allows reduction of double data entry and transcription errors as well as a seamless transition from clinical routine to research data collection.

Comparison and transformation between CDISC ODM and EN13606 EHR standards in connecting EHR data with clinical trial research data

Objectives

Integrating Electronic Health Record (EHR) systems into the field of clinical trials still contains several challenges and obstacles. Heterogeneous standards and specifications are used to represent healthcare and clinical trial information. Therefore, this work investigates the mapping and data interoperability between healthcare and research standards: EN13606 used for the EHRs and the Clinical Data Interchange Standards Consortium Operational Data Model (CDISC ODM) used for clinical research.

Methods

Based on the specifications of CDISC ODM 1.3.2 and EN13606, a mapping between the structure and components of both standards has been performed. Archetype Definition Language (ADL) forms built with the EN13606 editor were transformed to ODM XML and reviewed. As a proof of concept, clinical sample data has been transformed into ODM and imported into an electronic data capture system. Reverse transformation from ODM to ADL has also been performed and finally reviewed concerning map-ability.

Results

The mapping between EN13606 and CDISC ODM shows the similarities and differences between the components and overall record structure of the two standards. An EN13606 archetype corresponds with a group of items within CDISC ODM. Transformations of element names, descriptions, different languages, datatypes, cardinality, optionality, units, value range and terminology codes are possible from EN13606 to CDISC ODM and vice versa.

Conclusion

It is feasible to map data elements between EN13606 and CDISC ODM and transformation of forms between ADL and ODM XML format is possible with only minor limitations. EN13606 can accommodate clinical information in a more structured manner with more constraints, whereas CDISC ODM is more suitable and specific for clinical trials and studies. It is feasible to transform EHR data in the EN13606 form to ODM to transfer it into research database. The attempt to use EN13606 to build a study protocol (that was already built with CDISC ODM) also suggests the possibility of using EN13606 standard in place of CDISC ODM if needed to avoid transformations.

ODM Data Analysis-A tool for the automatic validation, monitoring and generation of generic descriptive statistics of patient data

INTRODUCTION

A required step for presenting results of clinical studies is the declaration of participants demographic and baseline characteristics as claimed by the FDAAA 801. The common workflow to accomplish this task is to export the clinical data from the used electronic data capture system and import it into statistical software like SAS software or IBM SPSS. This software requires trained users, who have to implement the analysis individually for each item. These expenditures may become an obstacle for small studies. Objective of this work is to design, implement and evaluate an open source application, called ODM Data Analysis, for the semi-automatic analysis of clinical study data.

METHODS

The system requires clinical data in the CDISC Operational Data Model format. After uploading the file, its syntax and data type conformity of the collected data is validated. The completeness of the study data is determined and basic statistics, including illustrative charts for each item, are generated. Datasets from four clinical studies have been used to evaluate the application's performance and functionality.

RESULTS

The system is implemented as an open source web application (available at https://odmanalysis.uni-muenster.de) and also provided as Docker image which enables an easy distribution and installation on local systems. Study data is only stored in the application as long as the calculations are performed which is compliant with data protection endeavors. Analysis times are below half an hour, even for larger studies with over 6000 subjects.

DISCUSSION

Medical experts have ensured the usefulness of this application to grant an overview of their collected study data for monitoring purposes and to generate descriptive statistics without further user interaction. The semi-automatic analysis has its limitations and cannot replace the complex analysis of statisticians, but it can be used as a starting point for their examination and reporting.

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.

Integrating technology into complex intervention trial processes: a case study

BACKGROUND

Trials of complex interventions are associated with high costs and burdens in terms of paperwork, management, data collection, validation, and intervention fidelity assessment occurring across multiple sites. Traditional data collection methods rely on paper-based forms, where processing can be time-consuming and error rates high. Electronic source data collection can potentially address many of these inefficiencies, but has not routinely been used in complex intervention trials. Here we present the use of an on-line system for managing all aspects of data handling and for the monitoring of trial processes in a multicentre trial of a complex intervention. We custom built a web-accessible software application for the delivery of ENGAGE-HD, a multicentre trial of a complex physical therapy intervention. The software incorporated functionality for participant randomisation, data collection and assessment of intervention fidelity. It was accessible to multiple users with differing levels of access depending on required usage or to maintain blinding. Each site was supplied with a 4G-enabled iPad for accessing the system. The impact of this system was quantified through review of data quality and collation of feedback from site coordinators and assessors through structured process interviews.

RESULTS

The custom-built system was an efficient tool for collecting data and managing trial processes. Although the set-up time required was significant, using the system resulted in an overall data completion rate of 98.5% with a data query rate of 0.1%, the majority of which were resolved in under a week. Feedback from research staff indicated that the system was highly acceptable for use in a research environment. This was a reflection of the portability and accessibility of the system when using the iPad and its usefulness in aiding accurate data collection, intervention fidelity and general administration.

CONCLUSIONS

A combination of commercially available hardware and a bespoke online database designed to support data collection, intervention fidelity and trial progress provides a viable option for streamlining trial processes in a multicentre complex intervention trial. There is scope to further extend the system to cater for larger trials and add further functionality such as automatic reporting facilities and participant management support.

TRIAL REGISTRATION

ISRCTN65378754 , registered on 13 March 2014.

Concept and implementation of a study dashboard module for a continuous monitoring of trial recruitment and documentation

BACKGROUND

The difficulty of managing patient recruitment and documentation for clinical trials prompts a demand for instruments for closely monitoring these critical but unpredictable processes. Increasingly adopted Electronic Data Capture (EDC) applications provide novel opportunities to reutilize stored information for an efficient management of traceable trial workflows. In related clinical and administrative settings, so-called digital dashboards that continuously visualize time-dependent parameters have recently met a growing acceptance. To investigate the technical feasibility of a study dashboard for monitoring the progress of patient recruitment and trial documentation, we set out to develop a propositional prototype in the form of a separate software module.

METHODS

After narrowing down functional requirements in semi-structured interviews with study coordinators, we analyzed available interfaces of a locally deployed EDC application, and designed the prototypical study dashboard based on previous findings. The module thereby leveraged a standardized export format in order to extract and import relevant trial data into a clinical data warehouse. Web-based reporting tools then facilitated the definition of diverse views, including diagrams of the progress of patient accrual and form completion at different granularity levels. To estimate the utility of the dashboard and its compatibility with current workflows, we interviewed study coordinators after a demonstration of sample outputs from ongoing trials.

RESULTS

The employed tools promoted a rapid development. Displays of the implemented dashboard are organized around an entry page that integrates key metrics for available studies, and which links to more detailed information such as study-specific enrollment per center. The interviewed experts commented that the included graphical summaries appeared suitable for detecting that something was generally amiss, although practical remedies would mostly depend on additional information such as access to the original patient-specific data. The dependency on a separate application was seen as a downside. Interestingly, the prospective users warned that in some situations knowledge of specific accrual statistics might undermine blinding in a subtle yet intricate fashion, so ignorance of certain patient features was seen as sometimes preferable for reproducibility.

DISCUSSION

Our proposed study dashboard graphically recaps key progress indicators of patient accrual and trial documentation. The modular implementation illustrates the technical feasibility of the approach. The use of a study dashboard might introduce certain technical requirements as well as subtle interpretative complexities, which may have to be weighed against potential efficiency gains.

REDLetr: Workflow and tools to support the migration of legacy clinical data capture systems to REDCap

OBJECTIVE

A memory clinic at an academic medical center has relied on several ad hoc data capture systems including Microsoft Access and Excel for cognitive assessments over the last several years. However these solutions are challenging to maintain and limit the potential of hypothesis-driven or longitudinal research. REDCap, a secure web application based on PHP and MySQL, is a practical solution for improving data capture and organization. Here, we present a workflow and toolset to facilitate legacy data migration and real-time clinical research data collection into REDCap as well as challenges encountered.

MATERIALS AND METHODS

Legacy data consisted of neuropsychological tests stored in over 4000 Excel workbooks. Functions for data extraction, norm scoring, converting to REDCap-compatible formats, accessing the REDCap API, and clinical report generation were developed and executed in Python.

RESULTS

Over 400 unique data points for each workbook were migrated and integrated into our REDCap database. Moving forward, our REDCap-based system replaces the Excel-based data collection method as well as eases the integration into the standard clinical research workflow and Electronic Health Record.

CONCLUSION

In the age of growing data, efficient organization and storage of clinical and research data is critical for advancing research and providing efficient patient care. We believe that the workflow and tools described in this work to promote legacy data integration as well as real time data collection into REDCap ultimately facilitate these goals.

Open-source, Rapid Reporting of Dementia Evaluations

The National Institutes of Health Alzheimer's Disease Center consortium requires member institutions to build and maintain a longitudinally characterized cohort with a uniform standard data set. Increasingly, centers are employing electronic data capture to acquire data at annual evaluations. In this paper, the University of Kansas Alzheimer's Disease Center reports on an open-source system of electronic data collection and reporting to improve efficiency. This Center capitalizes on the speed, flexibility and accessibility of the system to enhance the evaluation process while rapidly transferring data to the National Alzheimer's Coordinating Center. This framework holds promise for other consortia that regularly use and manage large, standardized datasets.

Online electronic data capture and research data repository system for clinical and translational research

Data is at the core of any clinical and translational research (CTR). In many studies, the electronic data capture (EDC) method has been found to be more efficient than standard paper-based data collection methods in many aspects, including accuracy, integrity, timeliness, and cost-effectiveness. The objective of this article is to present a secure, web-based EDC system for CTR that has been implemented by the Institute for Clinical and Translational Science (iCATS) at the University of Missouri School of Medicine.

Meeting the security requirements of electronic medical records in the ERA of high-speed computing

This study has two objectives. First, it aims to develop a system with a highly secured approach to transmitting electronic medical records (EMRs), and second, it aims to identify entities that transmit private patient information without permission. The NTRU and the Advanced Encryption Standard (AES) cryptosystems are secured encryption methods. The AES is a tested technology that has already been utilized in several systems to secure sensitive data. The United States government has been using AES since June 2003 to protect sensitive and essential information. Meanwhile, NTRU protects sensitive data against attacks through the use of quantum computers, which can break the RSA cryptosystem and elliptic curve cryptography algorithms. A hybrid of AES and NTRU is developed in this work to improve EMR security. The proposed hybrid cryptography technique is implemented to secure the data transmission process of EMRs. The proposed security solution can provide protection for over 40 years and is resistant to quantum computers. Moreover, the technique provides the necessary evidence required by law to identify disclosure or misuse of patient records. The proposed solution can effectively secure EMR transmission and protect patient rights. It also identifies the source responsible for disclosing confidential patient records. The proposed hybrid technique for securing data managed by institutional websites must be improved in the future.

Experience of using an open source clinical trials data management software system in Kenya

BACKGROUND

Clinical trials data management (CTDM) remains one of the many challenges in running state of the art trials in resource-poor settings since most trials do not allocate, or have available, sufficient resources for CTDM and because of poor internet connectivity. Open-source software like OpenClinica could be a solution in such scenarios.

FINDINGS

In 2007, the KEMRI-Wellcome Trust Research Programme (KWTRP) adopted OpenClinica (OC) community edition, an open-source software system and we share our experience and lessons learnt since its adoption. We have used OC in three different modes; direct remote data entry from sites through Global System for Mobile Communications (GSM) modems, a centralized data centre approach where all data from paper records were entered at a central location and an off-line approach where data entry was done from a copy of database hosted on a field-site server laptop, then data uploaded to a centralized server later. We have used OC in eleven trials/studies with a cumulative number of participants in excess of 6000. These include large and complex trials, with multiple sites recruiting in different regions of East Africa. In the process, we have developed substantial local capacity through hands-on training and mentorship, which we have now begun to share with other institutions in the region.

CONCLUSIONS

Our experience demonstrates that an open source data management system to manage trials' data can be utilized to international industry standards in resource-poor countries.

Clinical data miner: an electronic case report form system with integrated data preprocessing and machine-learning libraries supporting clinical diagnostic model research

BACKGROUND

Using machine-learning techniques, clinical diagnostic model research extracts diagnostic models from patient data. Traditionally, patient data are often collected using electronic Case Report Form (eCRF) systems, while mathematical software is used for analyzing these data using machine-learning techniques. Due to the lack of integration between eCRF systems and mathematical software, extracting diagnostic models is a complex, error-prone process. Moreover, due to the complexity of this process, it is usually only performed once, after a predetermined number of data points have been collected, without insight into the predictive performance of the resulting models.

OBJECTIVE

The objective of the study of Clinical Data Miner (CDM) software framework is to offer an eCRF system with integrated data preprocessing and machine-learning libraries, improving efficiency of the clinical diagnostic model research workflow, and to enable optimization of patient inclusion numbers through study performance monitoring.

METHODS

The CDM software framework was developed using a test-driven development (TDD) approach, to ensure high software quality. Architecturally, CDM's design is split over a number of modules, to ensure future extendability.

RESULTS

The TDD approach has enabled us to deliver high software quality. CDM's eCRF Web interface is in active use by the studies of the International Endometrial Tumor Analysis consortium, with over 4000 enrolled patients, and more studies planned. Additionally, a derived user interface has been used in six separate interrater agreement studies. CDM's integrated data preprocessing and machine-learning libraries simplify some otherwise manual and error-prone steps in the clinical diagnostic model research workflow. Furthermore, CDM's libraries provide study coordinators with a method to monitor a study's predictive performance as patient inclusions increase.

CONCLUSIONS

To our knowledge, CDM is the only eCRF system integrating data preprocessing and machine-learning libraries. This integration improves the efficiency of the clinical diagnostic model research workflow. Moreover, by simplifying the generation of learning curves, CDM enables study coordinators to assess more accurately when data collection can be terminated, resulting in better models or lower patient recruitment costs.

Five-year experience with setup and implementation of an integrated database system for clinical documentation and research

In radiation oncology, where treatment concepts are elaborated in interdisciplinary collaborations, handling distributed, large heterogeneous amounts of data efficiently is very important, yet challenging, for an optimal treatment of the patient as well as for research itself. This becomes a strong focus, as we step into the era of modern personalized medicine, relying on various quantitative data information, thus involving the active contribution of multiple medical specialties. Hence, combining patient data from all involved information systems is inevitable for analyses. Therefore, we introduced a documentation and data management system integrated in the clinical environment for electronic data capture. We discuss our concept and five-year experience of a precise electronic documentation system, with special focus on the challenges we encountered. We specify how such a system can be designed and implemented to plan, tailor and conduct (multicenter) clinical trials, ultimately reaching the best clinical performance, and enhancing interdisciplinary and clinical research.

Comparison of two data collection processes in clinical studies: electronic and paper case report forms

Background

Electronic Case Report Forms (eCRFs) are increasingly chosen by investigators and sponsors of clinical research instead of the traditional pen-and-paper data collection (pCRFs). Previous studies suggested that eCRFs avoided mistakes, shortened the duration of clinical studies and reduced data collection costs.

Methods

Our objectives were to describe and contrast both objective and subjective efficiency of pCRF and eCRF use in clinical studies. A total of 27 studies (11 eCRF, 16 pCRF) sponsored by the Paris hospital consortium, conducted and completed between 2001 and 2011 were included. Questionnaires were emailed to investigators of those studies, as well as clinical research associates and data managers working in Paris hospitals, soliciting their level of satisfaction and preferences for eCRFs and pCRFs. Mean costs and timeframes were compared using bootstrap methods, linear and logistic regression.

Results

The total cost per patient was 374€ ±351 with eCRFs vs. 1,135€ ±1,234 with pCRFs. Time between the opening of the first center and the database lock was 31.7 months Q1 = 24.6; Q3 = 42.8 using eCRFs, vs. 39.8 months Q1 = 31.7; Q3 = 52.2 with pCRFs (p = 0.11). Electronic CRFs were globally preferred by all (31/72 vs. 15/72 for paper) for easier monitoring and improved data quality.

Conclusions

This study found that eCRFs and pCRFs are used in studies with different patient numbers, center numbers and risk. The first ones are more advantageous in large, low–risk studies and gain support from a majority of stakeholders.

Application of methods for central statistical monitoring in clinical trials

Background

On-site source data verification is a common and expensive activity, with little evidence that it is worthwhile. Central statistical monitoring (CSM) is a cheaper alternative, where data checks are performed by the coordinating centre, avoiding the need to visit all sites. Several publications have suggested methods for CSM; however, few have described their use in real trials.

Methods

R-programs were created to check data at either the subject level (7 tests within 3 programs) or site level (9 tests within 8 programs) using previously described methods or new ones we developed. These aimed to find possible data errors such as outliers, incorrect dates, or anomalous data patterns; digit preference, values too close or too far from the means, unusual correlation structures, extreme variances which may indicate fraud or procedural errors and under-reporting of adverse events. The methods were applied to three trials, one of which had closed and has been published, one in follow-up, and a third to which fabricated data were added. We examined how well the methods work, discussing their strengths and limitations.

Results

The R-programs produced simple tables or easy-to-read figures. Few data errors were found in the first two trials, and those added to the third were easily detected. The programs were able to identify patients with outliers based on single or multiple variables. They also detected (1) fabricated patients, generated to have values too close to the multivariate mean, or with too low variances in repeated measurements, and (2) sites which had unusual correlation structures or too few adverse events. Some methods were unreliable if applied to centres with few patients or if data were fabricated in a way which did not fit the assumptions used to create the programs. Outputs from the R-programs are interpreted using examples.

Limitations

Detecting data errors is relatively straightforward; however, there are several limitations in the detection of fraud: some programs cannot be applied to small trials or to centres with few patients (<10) and data falsified in a manner which does not fit the program’s assumptions may not be detected. In addition, many tests require a visual assessment of the output (showing flagged participants or sites), before data queries are made or on-site visits performed.

Conclusions

CSM is a worthwhile alternative to on-site data checking and may be used to limit the number of site visits by targeting only sites which are picked up by the programs. We summarise the methods, show how they are implemented and that they can be easy to interpret. The methods can identify incorrect or unusual data for a trial subject, or centres where the data considered together are too different to other centres and therefore should be reviewed, possibly through an on-site visit.

QAIT: a quality assurance issue tracking tool to facilitate the improvement of clinical data quality

In clinical and translational research as well as clinical trial projects, clinical data collection is prone to errors such as missing data, and misinterpretation or inconsistency of the data. A good quality assurance (QA) program can resolve many such errors though this requires efficient communications between the QA staff and data collectors. Managing such communications is critical to resolving QA problems but imposes a major challenge for a project involving multiple clinical and data processing sites. We have developed a QA issue tracking (QAIT) system to support clinical data QA in the Clinical Breast Care Project (CBCP). This web-based application provides centralized management of QA issues with role-based access privileges. It has greatly facilitated the QA process and enhanced the overall quality of the CBCP clinical data. As a stand-alone system, QAIT can supplement any other clinical data management systems and can be adapted to support other projects.

ClinData Express--a metadata driven clinical research data management system for secondary use of clinical data

Aim to ease the secondary use of clinical data in clinical research, we introduce a metadata driven web-based clinical data management system named ClinData Express. ClinData Express is made up of two parts: 1) m-designer, a standalone software for metadata definition; 2) a web based data warehouse system for data management. With ClinData Express, what the researchers need to do is to define the metadata and data model in the m-designer. The web interface for data collection and specific database for data storage will be automatically generated. The standards used in the system and the data export modular make sure of the data reuse. The system has been tested on seven disease-data collection in Chinese and one form from dbGap. The flexibility of system makes its great potential usage in clinical research. The system is available at http://code.google.com/p/clindataexpress.

Deficiencies in the transfer and availability of clinical trials evidence: a review of existing systems and standards

Background

Decisions concerning drug safety and efficacy are generally based on pivotal evidence provided by clinical trials. Unfortunately, finding the relevant clinical trials is difficult and their results are only available in text-based reports. Systematic reviews aim to provide a comprehensive overview of the evidence in a specific area, but may not provide the data required for decision making.

Methods

We review and analyze the existing information systems and standards for aggregate level clinical trials information from the perspective of systematic review and evidence-based decision making.

Results

The technology currently used has major shortcomings, which cause deficiencies in the transfer, traceability and availability of clinical trials information. Specifically, data available to decision makers is insufficiently structured, and consequently the decisions cannot be properly traced back to the underlying evidence. Regulatory submission, trial publication, trial registration, and systematic review produce unstructured datasets that are insufficient for supporting evidence-based decision making.

Conclusions

The current situation is a hindrance to policy decision makers as it prevents fully transparent decision making and the development of more advanced decision support systems. Addressing the identified deficiencies would enable more efficient, informed, and transparent evidence-based medical decision making.

The Epilepsy Phenome/Genome Project (EPGP) informatics platform

BACKGROUND

The Epilepsy Phenome/Genome Project (EPGP) is a large-scale, multi-institutional, collaborative network of 27 epilepsy centers throughout the U.S., Australia, and Argentina, with the objective of collecting detailed phenotypic and genetic data on a large number of epilepsy participants. The goals of EPGP are (1) to perform detailed phenotyping on 3750 participants with specific forms of non-acquired epilepsy and 1500 parents without epilepsy, (2) to obtain DNA samples on these individuals, and (3) to ultimately genotype the samples in order to discover novel genes that cause epilepsy. To carry out the project, a reliable and robust informatics platform was needed for standardized electronic data collection and storage, data quality review, and phenotypic analysis involving cases from multiple sites.

METHODS

EPGP developed its own suite of web-based informatics applications for participant tracking, electronic data collection (using electronic case report forms/surveys), data management, phenotypic data review and validation, specimen tracking, electroencephalograph and neuroimaging storage, and issue tracking. We implemented procedures to train and support end-users at each clinical site.

RESULTS

Thus far, 3780 study participants have been enrolled and 20,957 web-based study activities have been completed using this informatics platform. Over 95% of respondents to an end-user satisfaction survey felt that the informatics platform was successful almost always or most of the time.

CONCLUSIONS

The EPGP informatics platform has successfully and effectively allowed study management and efficient and reliable collection of phenotypic data. Our novel informatics platform met the requirements of a large, multicenter research project. The platform has had a high level of end-user acceptance by principal investigators and study coordinators, and can serve as a model for new tools to support future large scale, collaborative research projects collecting extensive phenotypic data.

CONSORT-EHEALTH: improving and standardizing evaluation reports of Web-based and mobile health interventions

BACKGROUND

Web-based and mobile health interventions (also called "Internet interventions" or "ehealth/mhealth interventions") are tools or treatments, typically behaviorally based, that are operationalized and transformed for delivery via the Internet or mobile platforms. These include electronic tools for patients, informal caregivers, healthy consumers, and health care providers. The "Consolidated Standards of Reporting Trials" (CONSORT) was developed to improve the suboptimal reporting of randomized controlled trials (RCTs). While broadly the CONSORT statement can be applied to provide guidance on how ehealth and mhealth trials should be reported, RCTs of web-based interventions pose very specific issues and challenges, in particular related to reporting sufficient details of the intervention to allow replication and theory-building.

OBJECTIVE

To develop a checklist, dubbed CONSORT-EHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile HEalth Applications and onLine TeleHealth), as an extension of the CONSORT statement that provides guidance for authors of ehealth and mhealth interventions.

METHODS

A literature review was conducted, followed by a survey among ehealth experts and a workshop.

RESULTS

An instrument and checklist was constructed as an extension of the CONSORT statement. The instrument has been adopted by the Journal of Medical Internet Research (JMIR) and authors of ehealth RCTs are required to submit an electronic checklist explaining how they addressed each subitem.

CONCLUSIONS

CONSORT-EHEALTH has the potential to improve reporting and provides a basis for evaluating the validity and applicability of ehealth trials. Subitems describing how the intervention should be reported can also be used for non-RCT evaluation reports. As part of the development process, an evaluation component is essential, therefore feedback from authors will be solicited, and a before-after study will evaluate whether reporting has been improved.

CONSORT-EHEALTH: improving and standardizing evaluation reports of Web-based and mobile health interventions

BACKGROUND

Web-based and mobile health interventions (also called "Internet interventions" or "ehealth/mhealth interventions") are tools or treatments, typically behaviorally based, that are operationalized and transformed for delivery via the Internet or mobile platforms. These include electronic tools for patients, informal caregivers, healthy consumers, and health care providers. The "Consolidated Standards of Reporting Trials" (CONSORT) was developed to improve the suboptimal reporting of randomized controlled trials (RCTs). While broadly the CONSORT statement can be applied to provide guidance on how ehealth and mhealth trials should be reported, RCTs of web-based interventions pose very specific issues and challenges, in particular related to reporting sufficient details of the intervention to allow replication and theory-building.

OBJECTIVE

To develop a checklist, dubbed CONSORT-EHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile HEalth Applications and onLine TeleHealth), as an extension of the CONSORT statement that provides guidance for authors of ehealth and mhealth interventions.

METHODS

A literature review was conducted, followed by a survey among ehealth experts and a workshop.

RESULTS

An instrument and checklist was constructed as an extension of the CONSORT statement. The instrument has been adopted by the Journal of Medical Internet Research (JMIR) and authors of ehealth RCTs are required to submit an electronic checklist explaining how they addressed each subitem.

CONCLUSIONS

CONSORT-EHEALTH has the potential to improve reporting and provides a basis for evaluating the validity and applicability of ehealth trials. Subitems describing how the intervention should be reported can also be used for non-RCT evaluation reports. As part of the development process, an evaluation component is essential, therefore feedback from authors will be solicited, and a before-after study will evaluate whether reporting has been improved.

Concept and implementation of a computer-based reminder system to increase completeness in clinical documentation

PURPOSE

Medical documentation is often incomplete. Missing information may impede or bias analysis of study data and can cause delays. In a single source information system, clinical routine documentation and electronic data capture (EDC) systems are connected in the hospital information system (HIS). In this setting, both clinical routine and research would benefit from a higher rate of complete documentation.

METHODS

We designed a HIS-based reminder system which identifies not yet finalized forms and sends reminder e-mails to responsible physicians depending on escalation level. The generic concept to create reminder e-mail messages consists in database queries on not-finalized forms and generation of e-mail messages based on this output via the communication server. We compared completeness of electronic HIS forms before and after introduction of the reminder system three months each.

RESULTS

Completeness increased highly significantly (p<0.0001) for each form type (medical history form 93% (145 of 156 forms) vs 100% (206 forms), stress injection protocol 90% (142 of 157 forms) vs 100% (198 forms) and rest injection protocol 31% (45 of 147 forms) vs 100% (208 forms)). Forty-six reminder e-mails to the responsible study physician and 53 reminder e-mails to the principal investigator were sent to finish 2 medical history forms, 8 stress and 20 rest injection protocols. These 2 medical history forms were completed after 1 and 56 days. The median processing time of the stress injection protocols in the post-implementation phase was 18 days (range from 1 to 60 days). The median processing time of the rest injection protocols was 26 days (range from 5 to 37 days).

CONCLUSION

A computer-based reminder system to identify incomplete documentation forms with a notification and escalation mechanism can improve completeness of finalized forms significantly. It is technically feasible and effective in the clinical setting.

How strong are passwords used to protect personal health information in clinical trials?

BACKGROUND

Findings and statements about how securely personal health information is managed in clinical research are mixed.

OBJECTIVE

The objective of our study was to evaluate the security of practices used to transfer and share sensitive files in clinical trials.

METHODS

Two studies were performed. First, 15 password-protected files that were transmitted by email during regulated Canadian clinical trials were obtained. Commercial password recovery tools were used on these files to try to crack their passwords. Second, interviews with 20 study coordinators were conducted to understand file-sharing practices in clinical trials for files containing personal health information.

RESULTS

We were able to crack the passwords for 93% of the files (14/15). Among these, 13 files contained thousands of records with sensitive health information on trial participants. The passwords tended to be relatively weak, using common names of locations, animals, car brands, and obvious numeric sequences. Patient information is commonly shared by email in the context of query resolution. Files containing personal health information are shared by email and, by posting them on shared drives with common passwords, to facilitate collaboration.

CONCLUSION

If files containing sensitive patient information must be transferred by email, mechanisms to encrypt them and to ensure that password strength is high are necessary. More sophisticated collaboration tools are required to allow file sharing without password sharing. We provide recommendations to implement these practices.

HIS-Based Support of Follow-Up Documentation - Concept and Implementation for Clinical Studies

OBJECTIVE

Follow-up data must be collected according to the protocol of each clinical study, i.e. at certain time points. Missing follow-up information is a critical problem and may impede or bias the analysis of study data and result in delays. Moreover, additional patient recruitment may be necessary due to incomplete follow-up data. Current electronic data capture (EDC) systems in clinical studies are usually separated from hospital information systems (HIS) and therefore can provide limited functionality to support clinical workflow. In two case studies, we assessed the feasibility of HIS-based support of follow-up documentation.

METHODS

We have developed a data model and a HIS-based workflow to provide follow-up forms according to clinical study protocols. If a follow-up form was due, a database procedure created a follow-up event which was translated by a communication server into an HL7 message and transferred to the import interface of the clinical information system (CIS). This procedure generated the required follow-up form and enqueued a link to it in a work list of the relating study nurses and study physicians, respectively.

RESULTS

A HIS-based follow-up system automatically generated follow-up forms as defined by a clinical study protocol. These forms were scheduled into work lists of study nurses and study physicians. This system was integrated into the clinical workflow of two clinical studies. In a study from nuclear medicine, each scenario from the test concept according to the protocol of the single photon emission computer tomography/computer tomography (SPECT/CT) study was simulated and each scenario passed the test. For a study in psychiatry, 128 follow-up forms were automatically generated within 27 weeks, on average five forms per week (maximum 12, minimum 1 form per week).

CONCLUSION

HIS-based support of follow-up documentation in clinical studies is technically feasible and can support compliance with study protocols.

[From the design of use study to the assessment of the benefit: with or without pharmaceutical industry?]

The financing of clinical studies by the pharmaceutical industry is a controversial topic both internationally and in here in Germany. The well-known unacceptable shortcomings require no further confirmation. It is, however, indisputable that the pharmaceutical industry and medical science are co-dependent. Neither the marketing of industrial products nor the research and education of clinical scientists could function without this cooperation. Therefore, all partners need suggestions concerning goal orientation and consensus. The aim of this discussion is to formulate just such suggestions. To structure this discussion, we have raised the following questions: Must we always be suspicious of the results of studies financed by the pharmaceutical industry? We have to keep in mind that in Germany all clinical trials leading to approval of a drug were supported by the industry. What, exactly, do we want to achieve with our explicit and often justified criticism of these studies? What should be done to achieve a higher validity of the published data if we avoid answering the decisive question of whether we accept the challenge of continuing to let research and teaching be financed by the pharmaceutical industry or reject this kind of cooperation and support altogether.

IT Infrastructure Components for Biobanking

OBJECTIVE

Within translational research projects in the recent years large biobanks have been established, mostly supported by homegrown, proprietary software solutions. No general requirements for biobanking IT infrastructures have been published yet. This paper presents an exemplary biobanking IT architecture, a requirements specification for a biorepository management tool and exemplary illustrations of three major types of requirements.

METHODS

We have pursued a comprehensive literature review for biobanking IT solutions and established an interdisciplinary expert panel for creating the requirements specification. The exemplary illustrations were derived from a requirements analysis within two university hospitals.

RESULTS

The requirements specification comprises a catalog with more than 130 detailed requirements grouped into 3 major categories and 20 subcategories. Special attention is given to multitenancy capabilities in order to support the project-specific definition of varying research and bio-banking contexts, the definition of workflows to track sample processing, sample transportation and sample storage and the automated integration of preanalytic handling and storage robots.

CONCLUSION

IT support for biobanking projects can be based on a federated architectural framework comprising primary data sources for clinical annotations, a pseudonymization service, a clinical data warehouse with a flexible and user-friendly query interface and a biorepository management system. Flexibility and scalability of all such components are vital since large medical facilities such as university hospitals will have to support biobanking for varying monocentric and multicentric research scenarios and multiple medical clients.

Heterogeneity prevails: the state of clinical trial data management in Europe - results of a survey of ECRIN centres

BACKGROUND

The use of Clinical Data Management Systems (CDMS) has become essential in clinical trials to handle the increasing amount of data that must be collected and analyzed. With a CDMS trial data are captured at investigator sites with "electronic Case Report Forms". Although more and more of these electronic data management systems are used in academic research centres an overview of CDMS products and of available data management and quality management resources for academic clinical trials in Europe is missing.

METHODS

The ECRIN (European Clinical Research Infrastructure Network) data management working group conducted a two-part standardized survey on data management, software tools, and quality management for clinical trials. The questionnaires were answered by nearly 80 centres/units (with an overall response rate of 47% and 43%) from 12 European countries and EORTC.

RESULTS

Our survey shows that about 90% of centres have a CDMS in routine use. Of these CDMS nearly 50% are commercial systems; Open Source solutions don't play a major role. In general, solutions used for clinical data management are very heterogeneous: 20 different commercial CDMS products (7 Open Source solutions) in addition to 17/18 proprietary systems are in use. The most widely employed CDMS products are MACRO and Capture System, followed by solutions that are used in at least 3 centres: eResearch Network, CleanWeb, GCP Base and SAS. Although quality management systems for data management are in place in most centres/units, there exist some deficits in the area of system validation.

CONCLUSIONS

Because the considerable heterogeneity of data management software solutions may be a hindrance to cooperation based on trial data exchange, standards like CDISC (Clinical Data Interchange Standard Consortium) should be implemented more widely. In a heterogeneous environment the use of data standards can simplify data exchange, increase the quality of data and prepare centres for new developments (e.g. the use of EHR for clinical research). Because data management and the use of electronic data capture systems in clinical trials are characterized by the impact of regulations and guidelines, ethical concerns are discussed. In this context quality management becomes an important part of compliant data management. To address these issues ECRIN will establish certified data centres to support electronic data management and associated compliance needs of clinical trial centres in Europe.

Technical challenges in designing post-marketing eCRFs to address clinical safety and pharmacovigilance needs

To identify key challenges and propose technical considerations in designing electronic case report form (eCRF) for post-marketing studies, the author undertakes a comprehensive literature review of peer reviewed and grey literature to assess the key aspects, processes, standards, recommendations, and best practices in designing eCRFs based on industry experience in designing and supporting electronic data capture (EDC) studies. Literature search using strings on MEDLINE and PUBMED returned few papers directly related to CRF design. Health informatics and general practice journals were searched and results reviewed. Many conference, government commission, health professional and special interests group websites provide relevant information from practical experience - summarization of this information is presented. Further, we presented a list of concrete technical considerations in dealing with EDC technology/system limitations based on literature assessment and industry implementation experience. It is recognized that cross-functional teams be involved in eCRF design process and decision making. To summarize the keys in designing eCRFs to address post-market study safety and pharmacovigilance needs, the first is to identify required data elements from the study protocol supporting data analyses and reporting requirements. Secondly, accepted best practices, CDASH & CDISC guidelines, and company internal or therapeutic unit standard should be considered and applied. Coding (MedDRA & WHODD) mapping should be managed and implemented as well when possible. Finally, we need to be on top of the EDC technologies, challenge the technologies, drive EDC improvement via working with vendors, and utilize the technologies to drive clinical effectiveness.