An Examination of eClinical Technology Usage and CDISC Standards Adoption

OVERVIEW Fewer than 1 in 20 adult cancer patients enroll in cancer clinical trials. But although barriers to trial participation have been the subject of frequent study, the rate of trial participation has not changed substantially over time. Barriers to trial participation are structural, clinical, and attitudinal, and differ according to demographic and socioeconomic factors. In this paper, we characterize the nature of cancer clinical trial barriers, and we consider global and local strategies for reducing barriers. We also consider the specific case of adolescents with cancer, and show that the low rate of trial enrollment in this age group strongly correlates with limited improvements in cancer population outcomes compared to other age groups. Our analysis suggests that a clinical trial system that enrolls patients at higher rates produces treatment advances at a faster rate and corresponding improvements in cancer population outcomes. Viewed in this light, the issue of clinical trial enrollment is foundational, lying at the heart of the cancer clinical trial endeavor. Fewer barriers to trial participation would allow trials to be completed more quickly and would improve the generalizability of trial results. Moreover, increased accrual to trials is important to patients, since trials provide patients the opportunity to receive the newest treatments. In an era of increasing emphasis on a treatment decision-making process that incorporates the patient perspective, the opportunity for patients to choose trial participation for their care is vital.

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“…104 The Quorum Review IRB offered considerations for a plan to use social media in research. 105,106 These considerations are to:…”

Section: Global and Local Strategies To Improve Clinical Trial Particmentioning

confidence: 99%

The Role of Clinical Trial Participation in Cancer Research: Barriers, Evidence, and Strategies

Unger

Cook

Tai

et al. 2016

American Society of Clinical Oncology Educational Book

453

284

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“…Earlier research has been limited to analyzing qualitative data only, or investigating the adoption of various clinical trial technologies without addressing the preference of end users. 2,3 The survey questionnaire was developed by the authors of this article. The authors have several years of experience in working with IRT systems and clinical end users of IRT in clinical trials.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Use of telephone and web interfaces of interactive response technology at clinical investigator sites in clinical trials

2019

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Background: Interactive response technologies are used in clinical trials to provide services such as automated randomization and medication logistics management. The objective of this article is to investigate the usage of telephone (Interactive Voice Response) and web (Interactive Web Response) interfaces of interactive response technologies at clinical investigator sites in clinical trials, to obtain information about the preferences of interactive response technology end users between the telephone and web interfaces, and to explore the relevance of the telephone interface in this setting. Methods: The data consist of an online survey conducted in spring 2016 with clinical investigators, study nurses, and pharmacists in 13 countries. Results: Ninety-eight percent of survey respondents preferred the web interface over the telephone interface, the most important reason being superior usability. However, the respondents indicated the usability of interactive response technology interfaces is not optimal, and lack of integration and consistency across systems is common. A vast majority of interactive response technology end users at clinical sites prefer to use the web interface over the telephone interface, but most also feel there would need to be a backup system. Conclusions: Based on the results, it would be beneficial to improve the usability of the interactive response technology interfaces, and to increase consistency across systems from the current level. Support to and training of the users, as well as clarifying the responsibilities between sites and the sponsor should also be a focal point. Study sponsors should explore with interactive response technology service providers how removing the telephone interface would impact future studies, and whether there could be a more efficient means to achieve a reliable backup to the web interface instead of a dedicated telephone interface.

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“…According to a business case published by Gartner in 2006, when clinical trials were conducted in the early stages using the CDISC standards, the start-up phase (until the first patient is enrolled) could save 70 to 90% of the time and resources, the implementation phase could save 40%, and the analysis and reporting phase could save 50%. [5][6][7] Thus, the utilization of the CDISC standards enables data exchange between systems and between organizations, ensures data quality, and increases the efficiency of clinical trial operations. However, converting legacy clinical trial data into the CDISC SDTM format requires significant resources because the process involves many steps such as creation of data conversion specifications, development of data conversion programs, and verification to ensure the quality of the data.…”

Section: Introductionmentioning

confidence: 99%

Semi-automated Conversion of Clinical Trial Legacy Data into CDISC SDTM Standards Format Using Supervised Machine Learning

2021

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Objective This study aimed to develop a semi-automated process to convert legacy data into clinical data interchange standards consortium (CDISC) study data tabulation model (SDTM) format by combining human verification and three methods: data normalization; feature extraction by distributed representation of dataset names, variable names, and variable labels; and supervised machine learning. Materials and Methods Variable labels, dataset names, variable names, and values of legacy data were used as machine learning features. Because most of these data are string data, they had been converted to a distributed representation to make them usable as machine learning features. For this purpose, we utilized the following methods for distributed representation: Gestalt pattern matching, cosine similarity after vectorization by Doc2vec, and vectorization by Doc2vec. In this study, we examined five algorithms—namely decision tree, random forest, gradient boosting, neural network, and an ensemble that combines the four algorithms—to identify the one that could generate the best prediction model. Results The accuracy rate was highest for the neural network, and the distribution of prediction probabilities also showed a split between the correct and incorrect distributions. By combining human verification and the three methods, we were able to semi-automatically convert legacy data into the CDISC SDTM format. Conclusion By combining human verification and the three methods, we have successfully developed a semi-automated process to convert legacy data into the CDISC SDTM format; this process is more efficient than the conventional fully manual process.

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An Examination of eClinical Technology Usage and CDISC Standards Adoption

Cited by 10 publications

References 2 publications

The Role of Clinical Trial Participation in Cancer Research: Barriers, Evidence, and Strategies

The Role of Clinical Trial Participation in Cancer Research: Barriers, Evidence, and Strategies

Use of telephone and web interfaces of interactive response technology at clinical investigator sites in clinical trials

Semi-automated Conversion of Clinical Trial Legacy Data into CDISC SDTM Standards Format Using Supervised Machine Learning

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