Integrity Of Clinical Data Research Articles

Electronic health record impact on data quality: An integrated review

Few studies have assessed the impact of Electronic Health Record (EHR) on data quality and how it has impacted timeliness, accuracy, availability, integrity, consistency and uniqueness of health data. Assessing the current gaps between electronic health record utilisation in relation to enhancing data quality can help to identify and recommend key measures to improve healthcare delivery. This review examines the impact of electronic health record on data quality, and proposes recommendations desirable to improving healthcare delivery. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Integrative Review Frameworks (IRF) were adapted as a sign post for the study. These frameworks guided literature search, data analysis and presentation of findings. Boolean AND/OR operators were combined with search terms to increase relevant search results. Six (6) electronic databases that were used for literature search are Scopus, Web of Science, Science Direct, PubMed, ProQuest and Google Scholar. Searched articles were between 2012 to 2022, with the exception of two articles on the Technology Acceptance Model (TAM) published in 1986 and 1975. EHR implementation impact key health outcomes and contribute to timely and complete data submission. Implementation of Electronic Health Systems (EHS) embellish consistency, completeness, accuracy and integrity of clinical data significantly through embedded interoperability solutions. By reducing the scope of manual reviews, integrating a technology that automates data integrity and quality checks into EHR may potentially lessen the strain of maintaining data quality. Our findings depict that interoperability of clinical data sources will ensure consistent, timely and available health data when using EHR. The automated data integrity and consistency checks in the EHR improve data quality. The study contributes to advancing the theory of Technology Acceptance Model (TAM) in electronic health records and its impact on data quality in healthcare delivery.

How blockchain technology supports the business processes of clinical trials: a systematic review

PurposeThe authors propose a blockchain platform for managing clinical trial data to enhance data validity, integrity, trust and transparency in the pharmaceutical research process. The authors also provide an extensive review of how blockchain technology supports the business processes of clinical trials.Design/methodology/approachA systematic literature review was conducted to identify the existing applications of blockchain in pharmaceutical process management. A conceptual design for a blockchain infrastructure to address clinical trial challenges is developed by outlining the entire clinical trial value chain and identifying the coordination and communication among its stakeholders. A stakeholder analysis is conducted to ensure that the clinical trial processes satisfy the requirements and preferences of each stakeholder.FindingsThe proposed blockchain platform offers a promising solution for enhancing integrity, trust and transparency in the clinical trial process. Additionally, blockchain can help streamline communication and collaboration between stakeholders by enabling multiple parties to access and share data in real time, lowering the possibility of delays or errors in data analysis and reporting.Practical implicationsThe proposed blockchain platform can benefit patients by empowering them to have better-controlled access to their data and by allowing researchers to maintain adherence to reporting requirements. Additionally, the platform can benefit granting agencies, researchers and decision-makers by ensuring the integrity of clinical trial data and streamlining communication and collaboration between stakeholders.Originality/valueThis study builds on existing blockchain applications in pharmaceutical process management by developing a blockchain framework that can address clinical trial concerns from an integrated perspective.

A pipeline to further enhance quality, integrity and reusability of the NCCID clinical data

The National COVID-19 Chest Imaging Database (NCCID) is a centralized UK database of thoracic imaging and corresponding clinical data. It is made available by the National Health Service Artificial Intelligence (NHS AI) Lab to support the development of machine learning tools focused on Coronavirus Disease 2019 (COVID-19). A bespoke cleaning pipeline for NCCID, developed by the NHSx, was introduced in 2021. We present an extension to the original cleaning pipeline for the clinical data of the database. It has been adjusted to correct additional systematic inconsistencies in the raw data such as patient sex, oxygen levels and date values. The most important changes will be discussed in this paper, whilst the code and further explanations are made publicly available on GitLab. The suggested cleaning will allow global users to work with more consistent data for the development of machine learning tools without being an expert. In addition, it highlights some of the challenges when working with clinical multi-center data and includes recommendations for similar future initiatives.

The United States Food and Drug Administration’s Innovative Alternative Tools to Evaluate Good Clinical Practice During the COVID-19 Public Health Emergency

The COVID-19 public health emergency limited the U.S. Food and Drug Administration’s ability to conduct on-site good clinical practice (GCP) inspections. Alternative tools have been used by the FDA during the pandemic to evaluate the reliability and integrity of clinical trial data for marketing applications. However, no systematic assessment of the pandemic's impact on in-person GCP inspections has been conducted. In addition, the alternative tools and their contribution to GCP oversight have not been reported.  This retrospective study reviewed databases internal to the FDA and identified and characterized alternative tools used in lieu of on-site GCP inspections in fiscal year (FY) 2020 and FY2021. The impact of the pandemic on on-site GCP inspections and the contribution of alternative tools to overall GCP activities were described. Between April 13, 2020, and September 30, 2021, FDA investigators conducted 77 GCP evaluations using alternative tools. Alternative tools were used most commonly for GCP evaluations of non-U.S. clinical investigators in support of mission critical, original New Drug Applications (NDAs). The FDA conducted 370 on-site GCP inspections in FY2020 and 451 in FY2021, which represented a 23% and 6% decrease, respectively, compared to the yearly average of 481 on-site GCP inspections in the five years preceding the pandemic. The use of alternative tools contributed 10% and 8% to total GCP activities in FY2020 and FY2021, respectively. GCP evaluations using alternative tools have played a significant role in GCP activities supporting the review of marketing applications during the COVID-19 public health emergency. 

Blockchain-based solutions for clinical trial data management: a systematic review

Blockchain technology can reduce the need for intermediaries in various types of transactions by providing a decentralized and secure ledger that can be accessed and updated by all parties involved in the transaction. Clinical trials are essential for bringing new drugs and therapies to market, but the current clinical research process is often marred by inefficiencies, data inaccuracies, and a lack of transparency. The implementation of blockchain technology in clinical trials has the potential to address these challenges by providing a secure and transparent platform for data management. By leveraging the power of blockchain, healthcare providers can improve the integrity and accuracy of clinical trial data, enhance trust in the clinical research process, and ultimately improve patient outcomes. In this article, we propose the use of blockchain technology in clinical trials and explore its potential benefits for the healthcare. The implementation of a blockchain-based data management system for clinical trials holds significant potential to address several challenges associated with the current clinical research process. By improving the integrity and security of medical data, enhancing trust, and easing regulatory burden, such a system can promote the efficient and effective conduct of clinical trials. The adoption of a blockchain-based solution for clinical trial data management has the potential to optimize costs, contributing to the sustainability of healthcare services. It also provides a model for future research and development of blockchain-based solutions in the field of clinical research.

Role of Regulatory Authorities on the Working of Contract Research Organization and Pharmaceutical Company’s Clinical Trials in India

The evolution of the drug development process and testing its efficacy is a primary responsibility of pharmaceutical companies. The time cost investment involved in identifying a compound suitable to its target disease and making it available to the masses eventually led to the rise of the Contract Research Organization (CRO) in the domain of clinical research. Pharmaceutical companies outsource the research and clinical trials to CRO’s. A CRO has a vital role from drug discovery to the launch and marketing of drugs. India is emerging as attractive location for global clinical trial. It has cost advantage compared to other countries and a well-developed associated services like data management, medical writing and pharmacovigilance. The Central Drug Standard Control Organization (CDSCO) is the National Regulatory Authority in India that aims to bring safe drugs and standardize clinical research. Pharmaceutical Companies benefit by strategically working with CRO to gain speed and efficiency in drug discovery, generation and retention of clinical data integrity. The risk associated with CRO relates to delays and inferior quality of work, thereby making CRO a critical decision for Pharmaceutical Company.

DeepBlockShield: Blockchain Agent-Based Secured Clinical Data Management Model from the Deep Web Environment

With the growth of artificial intelligence in healthcare and biomedical research, many researchers are interested in large amounts of data in hospitals and medical research centers. Then the need for remote medicine services and clinical data utilization are expanding. However, since the misuse and abuse of clinical data causes serious problems, the scope of its use is bound to have a limited range physically and logically. Then a security-enhanced data distribution system for medical deep web environments. Therefore, in this paper, we propose a blockchain-based clinical data management model named DeepBlockshield to prevent information leakage between the deep web and the surface web. Blockchain supports data integrity and user validation to support data sharing in closed networks. Meanwhile, the agent performs integrity verification between the blockchain and the deep web and strengthens the security between the surface web and the deep web. DeepBlockShield verifies the user’s validity through the records of the deep web and blockchain. Furthermore, we wrap the results analyzed by the valid request into a web interface and provide information to the requester asynchronously. In the experiment, the block generation cycle and size on the delay time was analyzed for verifying the stability of the blockchain network. As a result, it showed that the proposed approach guarantees the integrity and availability of clinical data in the deep web environment.

The 2020 race towards SARS-CoV-2 specific vaccines.

The global outbreak of a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) highlighted a requirement for two pronged clinical interventions such as development of effective vaccines and acute therapeutic options for medium-to-severe stages of “coronavirus disease 2019" (COVID-19). Effective vaccines, if successfully developed, have been emphasized to become the most effective strategy in the global fight against the COVID-19 pandemic. Basic research advances in biotechnology and genetic engineering have already provided excellent progress and groundbreaking new discoveries in the field of the coronavirus biology and its epidemiology. In particular, for the vaccine development the advances in characterization of a capsid structure and identification of its antigens that can become targets for new vaccines. The development of the experimental vaccines requires a plethora of molecular techniques as well as strict compliance with safety procedures. The research and clinical data integrity, cross-validation of the results, and appropriated studies from the perspective of efficacy and potently side effects have recently become a hotly discussed topic.In this review, we present an update on latest advances and progress in an ongoing race to develop 52 different vaccines against SARS-CoV-2. Our analysis is focused on registered clinical trials (current as of November 04, 2020) that fulfill the international safety and efficacy criteria in the vaccine development. The requirements as well as benefits and risks of diverse types of SARS-CoV-2 vaccines are discussed including those containing whole-virus and live-attenuated vaccines, subunit vaccines, mRNA vaccines, DNA vaccines, live vector vaccines, and also plant-based vaccine formulation containing coronavirus-like particle (VLP). The challenges associated with the vaccine development as well as its distribution, safety and long-term effectiveness have also been highlighted and discussed.

How to Carry out Clinical Trials in Investigational Sites during COVID-19 Outbreak: A Single-Center Qualitative Study and System Review

Objectives: This article offers general advice on how to conduct clinical trials during a disease pandemic by summarizing the difficulties and risks of clinical trials confronting investigational sites during the outbreak of COVID-19 and the responses taken throughout the country during the pandemic so as to give references to performing clinical trials in a safe and effective manner during COVID-19 outbreak or the potential disease outbreak in the future. Methods: The systematic literature search was performed in Cochrane Library, MEDLINE, Baidu Search Engine, Google, and WeChat official Accounts platform. Studies published up to March 9, 2020 were searched. A pre-test for the title and abstract screening was performed by three reviewers. Results: A total of 468 records were identified through online searching. As there were few studies about carrying out clinical trials in investigational sites as of March 9, 2020, after duplicate removal only 299 records were retrieved. 263 records were assessed for eligibility after title screening of which 15 records provided a clear description of clinical trials in investigational sites during COVID-19 outbreak published up to March 9, 2020. Conclusion: Considering the summary of difficulties and risks arising from investigational sites and health institutions conducting clinical trials and the induction of relevant strategies and measures under the outbreak of COVID-19, following suggestions on pandemic are made accordingly based on informed analysis. First, hierarchical classification assessment of clinical trials is the top priority for conducting clinical trials under the pandemic. Second, conducting clinical trials in the way of non-contact is a necessary measure to ensure the safety of during the pandemic. Third, enhanced qualitative risk analysis of clinical trials and strict quality control are important means to ensure the integrity and authenticity of clinical trial data during pandemic.

Next generation proteomics in precision oncology: 1000s of proteome and phosphoproteome profiles of tumors and matching healthy tissues as meaningful layer in multi-omics database.

e15672 Background: The rise of precision oncology therapeutics requires deep understanding of all molecular mechanisms involved in cancer biology. IndivuType offers the world’s first multi-omics database for individualized cancer therapy, analyzing the highest quality cancer biospecimens to generate the most comprehensive dataset, including genomics (WGS), transcriptomics, proteomics, and clinical outcome information. Indivumed is committed to the quality of the IndivuType ecosystem starting with stringent SOP-driven sample collection combined with thorough validation of clinical information and data integrity. The availability of multi-omics data from the same tumor can provide a comprehensive molecular picture of cancer for a given patient. Protein expression and activation are directly related to cellular function and hence provide actionable information about druggable targets. Until recently, the proteomics technology could not match the scale of next-gen sequencing and consequently precision medicine has almost exclusively been based on gene level data. Here we present the first large-scale data set for protein expression and phosphorylation. Enabled by the data independent acquisition (DIA) workflow, a mass spectrometric method provided by Biognosys that obtains peptide fragmentation data in a highly parallelized way with high sensitivity, more than 7,000 proteins in the whole proteome (WP) and 20,000 phospho-peptides in the phospho-proteome (PP) workflow were profiled. Methods: Sample processing from 5 mg of tissue per sample was performed using liquid handling robot. Phospho-peptide enrichment was carried out with a Kingfisher Flex device and MagReSyn Ti-IMAC magnetic beads. DIA LC-MS/MS was performed on multiple platforms consisting of a Thermo Scientific Q Exactive HF-X mass spectrometer coupled to a Waters M-Class LC. Chromatography was operating at 5 µL/min, and separation was achieved using 45 min (WP) and 60 min (PP) gradients. Results: Several thousands of high-quality patient samples of various cancer types have been analyzed to date. The resulting proteome and phospho-proteome data has been integrated into the IndivuType database, thereby providing a solid foundation to advance our understanding of cancer. Conclusions: With the ongoing addition of more samples and associated deep and rich data, the platform could unravel key molecular events and is expected to transform knowledge into actionable treatments and personalized therapies.

Application of blockchain technology to enhance data security of the medical records

Introduction: Blockchain is a rapidly evolving data security practice which employs a growing list of records linked using cryptographic hash of the block proceeding, a timestamp and transaction data. Electronic medical records (EMR) are gaining popularity in health sector. Most of these EMRs are based on relational databases. Data tampering is a known issue found in relational data base approach and it is fairly simple to alter a relational data base record with the administrative privileges. This research explores the potentials of blockchain technology with its resistant to modification of the data, as a tamper protection mechanism for medical records. Methods: A blockchain-based program was developed using Proof-of-Work, to maintain the integrity of the digital record independent of access permissions. This was preceded by a series of interviews with clinicians and EMR administers to understand the possible data tampering in association with paper-based and electronic medical records in the State health sector of Sri Lanka. The blockchain algorithm was applied to the Hospital Health Information Management System (HHIMS) clinical database. A security audit was used to assess the tamper-protection capabilities of the relational database. Results: It was possible to incorporate the blockchain algorithm to the relational database of Hospital HHIMS successfully. An independent blockchain-based data repository was created in association with the native data base of HHIMS. With this approach, a ‘block’ is being created for each new record added to HHIMS database. Hence, the blockchain generated was instrumental in verifying the authenticity of EMR transactions. Conclusion: With the increasing penetration of EMR in health sector, the integrity of the digital stored clinical data is becoming a vital necessity. In centralized database administration, possibility of tampering is high. Clinical data can be counted as ‘high risk’ for such occurrences. The blockchain technology found to be capable of making relational data base tamper-protected by verifying the authenticity of the transactions and data being stored in HHIMS.

A systematic quality assurance framework for the upgrade of radiation oncology information systems

In spite of its importance, no systematic and comprehensive quality assurance (QA) program for radiation oncology information systems (ROIS) to verify clinical and treatment data integrity and mitigate against data errors/corruption and/or data loss risks is available. Based on data organization, format and purpose, data in ROISs falls into five different categories: (1) the ROIS relational database and associated files; (2) the ROIS DICOM data stream; (3) treatment machine beam data and machine configuration data; (4) electronic medical record (EMR) documents; and (5) user-generated clinical and treatment reports from the ROIS. For each data category, this framework proposes a corresponding data QA strategy to very data integrity. This approach verified every bit of data in the ROIS, including billions of data records in the ROIS SQL database, tens of millions of ROIS database-associated files, tens of thousands of DICOM data files for a group of selected patients, almost half a million EMR documents, and tens of thousands of machine configuration files and beam data files. The framework has been validated through intentional modifications with test patient data. Despite the ‘big data’ nature of ROIS, the multiprocess and multithread nature of our QA tools enabled the whole ROIS data QA process to be completed within hours without clinical interruptions. The QA framework suggested in this study proved to be robust, efficient and comprehensive without labor-intensive manual checks and has been implemented for our routine ROIS QA and ROIS upgrades.

Strategic Shift of Statistical Review on Data Quality Assessment for New Drug Applications in China.

Data quality is critical for clinical trials to obtain robust conclusions about drug safety and efficacy evaluation. Effective data quality evaluation has been one of the major obstacles to new drug approvals in China, which hinders innovation in drug discovery and development ultimately. To improve the data quality submitted for regulatory drug approval, the China Food and Drug Administration (CFDA) has issued serial official announcements and industry guidelines regarding improvement of the clinical trial data integrity and quality since 2015. These announcements and follow-up measures are shaping up the entire pharmaceutical industry in China. While data quality is being strongly emphasized more than ever at the trial conduction phase, it is still an open question about how to assess data quality effectively at the review stage. Thus, this article describes the authors' standpoints to assess the quality and integrity of submitted clinical data via statistical review methods including advanced risk-based approaches, which may bring significant impact to new drug applications and motivate sustainable development of innovative medicines in China.

Experiences and Lessons from a Multicountry NIDIAG Study on Persistent Digestive Disorders in the Tropics.

Experiences and Lessons from a Multicountry NIDIAG Study on Persistent Digestive Disorders in the Tropics.

The spectre of ghostwriting: eroding public trust in physicians, clinical trial integrity and biomedical authorship.

The impact of medical ghostwriting, a violation of authorship ethics, remains unclear within the biomedical literature and among the public, potentially raising concerns about the integrity of the biomedical evidence base. Core texts in authorship and ghostwriting from the clinical literature and the 2010 Senate Minority Report on ghostwriting were reviewed as were uses of the term 'ghostwriting' in contemporary (2009-2011) and more recent (2015-2016) journalistic news coverage originally printed in English. Journalistic coverage oversimplified key concerns about ghostwriting identified by the medical community and the US government. More recent journalistic uses of the term 'ghostwriting' suggest confusion with topics such as financial disclosures or patient monitoring. Pharmaism in the medical literature, an expression of bias against pharmaceutical companies that casts doubt on the credibility of physicians and scientists, may be a source for confusion. The tendency for medical journal editors to discuss ghostwriting in the context of clinical trial transparency or data integrity is another possible source for misinterpretation via oversimplification. Journalistic descriptions of ghostwriting consistently downplay the critical reasoning abilities and competence of practising physicians and deflect attention away from patient concerns and back to pharmaceutical companies. Some uses of the term ghostwriting in news coverage may implicitly undercut belief in the competence of physicians, a troubling trend. Further work is needed to characterise the impact of ghostwriting in the medical literature and to reassure the public that their trust in medical practitioners is well placed.

Navigating Through a Complex and Inconsistent Regulatory Framework: Section 503B of the Federal Food Drug and Cosmetic Act Outsourcing Facilities Engaged in Clinical Investigation.

The integrity of unbiased clinical data is essential to the future of the health care system by facilitating the discovery of lifesaving medicines and ensuring investigational drugs are safe and effective. Since 2002, the US pharmaceutical industry has invested over $500 billion, which is the largest research and development investment of any sector of the US economy. As a consequence of this significant investment, pharmacy compounders and other stakeholders must be acutely aware of the consequences of noncompliance. Pharmacy compounders are required to navigate through a complex and ever-changing regulatory landscape governed by US federal and state authorities competing for oversight and enforcement authority. In particular, pharmacy compounders participating in clinical investigation are faced with inconsistent federal and state drug labeling regulations, which can lead to enforcement for violating acceptable standards for clinical investigation and informed consent. As FDA registrants, Outsourcing Facilities are able to produce large volumes of clinical supplies without the need for prescriptions for individually named patients. Some states, however, may have prescription drug labeling laws that could thwart the ability to provide these clinical supplies. Accordingly, researchers should be aware of the implications of federal and state laws, including any inconsistencies, prior to engaging in clinical investigation.

EP-1648: A robust automated approach to clinical data integrity management

EP-1648: A robust automated approach to clinical data integrity management

Capturing Patient-Reported Outcome (PRO) Data Electronically: The Past, Present, and Promise of ePRO Measurement in Clinical Trials.

Patient-reported outcomes (PROs) are an important means of evaluating the treatment benefit of new medical products. It is recognized that PRO measures should be used when assessing concepts best known by the patient or best measured from the patient’s perspective. As a result, there is growing emphasis on well defined and reliable PRO measures. In addition, advances in technology have significantly increased electronic PRO (ePRO) data collection capabilities and options in clinical trials. The movement from paper-based to ePRO data capture has enhanced the integrity and accuracy of clinical trial data and is encouraged by regulators. A primary distinction in the types of ePRO platforms is between telephone-based interactive voice response systems and screen-based systems. Handheld touchscreen-based devices have become the mainstay for remote (i.e., off-site, unsupervised) PRO data collection in clinical trials. The conventional approach is to provide study subjects with a handheld device with a device-based proprietary software program. However, an emerging alternative for clinical trials is called bring your own device (BYOD). Leveraging study subjects’ own Internet-enabled mobile devices for remote PRO data collection (via a downloadable app or a Web-based data collection portal) has become possible due to the widespread use of personal smartphones and tablets. However, there are a number of scientific and operational issues that must be addressed before BYOD can be routinely considered as a practical alternative to conventional ePRO data collection methods. Nevertheless, the future for ePRO data collection is bright and the promise of BYOD opens a new chapter in its evolution.

Good clinical practice (GCP): A universal call for ethics in biomedical research

Today, the principles of good clinical practice (GCP) form such an integral part of the development of new medicines that they could easily be taken for granted. Yet, the road to a universal code of ethics in human experimentation is paved with tragedies which have only gradually led to tightened rules on human experimentation. Awareness of the historical roots of GCP helps explain that GCP, rather than representing a seemingly endless series of regulations, finally provides an international ethical and scientific quality standard designed to protect the rights and safety of individuals consenting to participate in clinical trials and to ensure the integrity and credibility of clinical research data. For medical writers, familiarity with the principles of GCP, which in the European Union are now a legal obligation, is an essential prerequisite for providing documentation in compliance with the ethical and scientific principles of GCP: not only are medical writers expected to frame clinical research into a language that enables independent assessors to evaluate the methodological validity of a study and the safety and efficacy of a given drug, they also compose documents that may be instrumental in assuring the rights and safety of clinical trial participants.

Assessing the Integrity of Clinical Data: When is Statistical Evidence Too Good to be True?

Evidence, as viewed through the lens of statistical significance, is not always as it appears! In the investigation of clinical research findings arising from statistical analyses, a fundamental initial step for the emerging fraud detective is to retrieve the source data for cross-examination with the study data. Recognizing that source data are not always forthcoming and that, realistically speaking, the investigator may be uninitiated in fraud detection and investigation, this paper will highlight some key methodological procedures for providing a sounder evidence base for withdrawing from a study on grounds of integrity. The promotion of patient safety is paramount. However, there is a broader rationale for disseminating these ideas. This includes empowering researchers to optimize their personal integrity, make informed choices regarding membership of future research collaborations and successfully voice their concerns to journal editors, particularly where a conflict of interests can render such dialogues particularly difficult. Recommendations will be supported by topical case studies and practical steps involving data exploration, testing of baseline data and application of Benford’s Law. While this paper has a clinical focus, the advice provided is transferrable to a wide range of multidisciplinary research settings outside of Medicine.