Paperless clinical trials: Myth or reality?

Electronic versus paper-based data collection for conducting health-care research: A cost-comparison analysis

Background

Containing expenditure and efficient resource use is essential to limit the increasing costs of health research. Electronic data collection (EDC) is thought to reduce the costs compared to paper-based data collection (PDC).

Objectives

As economic evidence in this area is scanty, especially in low- and middle-income countries, the objectives of the study are to perform an economic evaluation and compare the cost between EDC and PDC.

Methods

A cost-comparison study was conducted to compare between EDC and PDC from the institutional perspective for the year 2018, based on a community-based survey. Step-down cost accounting was adopted with a bottom-up approach for cost estimation. Total and unit costs were estimated with the base case comparison between EDC and PDC while using SPSS software (e-SPSS and p-SPSS, respectively). We conducted scenario analyses based on the usage of different software, R and STATA for both EDC and PDC (e-R, p-R, e-STATA, and p-STATA, respectively). One-way and probabilistic sensitivity analysis (PSA) was performed to examine the robustness of the observed results.

Results

In the base-case analysis, total costs of EDC and PDC were ₹72,617 ($1060.9) and 87,717 ($1281.5), respectively, with estimated cost reduction of ₹15,100 ($220.6). In other scenarios, the estimated cost reduction for e-R, e-STATA, p-R, p-STATA was ₹-274 ($4.0), 98 ($1.4), 14826 ($216.6), and 15,002 ($219.2), respectively, when compared to EDC-SPSS. On one-way and PSA, the results of the cost-comparison analysis were robust.

Conclusion

EDC minimizes institutional cost for conducting health research. This finding will help researchers in efficiently planning for the budget for their research.

An automated data verification approach for improving data quality in a clinical registry

BACKGROUND AND OBJECTIVE

The quality of data is crucial for clinical registry studies as it impacts credibility. In the regular practice of most such studies, a vulnerability arises from researchers recording data on paper-based case report forms (CRFs) and further transcribing them onto registry databases. To ensure the quality of data, verifying data in the registry is necessary. However, traditional manual data verification methods are time-consuming, labor-intensive and of limited-effect. As paper-based CRFs and electronic medical records (EMRs) are two sources for verification, we propose an automated data verification approach based on the techniques of optical character recognition (OCR) and information retrieval to identify data errors in a registry more efficiently.

METHODS

Three steps are involved to develop the automated verification approach. First, we analyze the scanned images of paper-based CRFs with machine learning enhanced OCR to recognize the checkbox marks and hand-writing. Then, we retrieve the related patient information from the EMRs using natural language processing (NLP) techniques. Finally, we compare the retrieved information in the previous two steps with the data in the registry, and synthesize the results accordingly. The proposed automated method has been applied in a Chinese registry study and the difference between automated and manual approach has been evaluated.

RESULTS

The automated approach has been implemented in The Chinese Coronary Artery Disease Registry. For CRF data recognition, the accuracy of recognition for checkboxes marks and hand-writing are 0.93 and 0.74, respectively. For EMR data extraction, the accuracy of information retrieval from textual electronic medical records is 0.97. The accuracy, recall and time consumption of the automated approach are 0.93, 0.96 and 0.5 h, better than the corresponding values of the manual approach, which are 0.92, 0.71 and 7.5 h.

CONCLUSIONS

Compared to the manual data verification approach, the automated approach enhances the recall of identify data errors and has a higher accuracy. The time consumed is far less. The results show that the automated approach is more effective and efficient for identifying incomplete data and incorrect data in a registry. The proposed approach has potential to improve the quality of registry data.

Defining and Developing a Generic Framework for Monitoring Data Quality in Clinical Research

Evidence for the need for high data quality in clinical research is well established. The rigor of clinical research conclusions rely heavily on good quality data, which relies on good documentation practices. Little attention has been given to clear guidelines and definitions to monitor data quality. To address this, a "fit-for-use" data quality monitoring framework (DQMF) for clinical research was developed based on a holistic design-oriented approach. An integrated literature review and feasibility study underpinned the framework development. Ontology of key terms, concepts, methods, and standards were recorded using a consensus approach and mind mapping technique. The DQMF is presented as a nested concentric network illustrating concept relationships and hierarchy. Face validation was conducted, and common terminology and definitions are listed. The consolidated DQMF can be adapted according to study context and data availability aiding in the development of a long-term strategy with increased efficacy for clinical data quality monitoring.

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.

Optimizing the Use of Electronic Data Sources in Clinical Trials: The Landscape, Part 1

BACKGROUND

TransCelerate BioPharma has created the eSource Initiative with the intent to facilitate the industry's movement toward optimal usage of electronic data sources. Although guidance and standards have been in place for some time, data collection methods and technology have not been utilized to their fullest capability, and transcription between electronic systems continues to be the norm.

METHODS

The TransCelerate approach for the eSource Initiative is to understand the current landscape and highlight factors that are influencing the adoption of new technologies. As a preliminary step in this process, TransCelerate surveyed member companies regarding eSource usage and barriers.

RESULTS

Literature review, stakeholder engagement, and the member survey have provided insight into the current landscape, which will help TransCelerate to develop proposals for best practices for industry utilization of electronic data collection tools and methods to benefit all stakeholders.

CONCLUSIONS

Based on survey results, companies generally have taken steps to leverage current eSource technologies and prepare for optimal utilization of electronic data sources. The TransCelerate eSource Initiative will continue to evaluate the technology, regulatory, standards, and health care landscape to support the goal of improving global clinical science and global clinical trial execution. Forthcoming publications will focus on technology landscape, future vision, and demonstration projects.