Clinical Data Management System Research Articles

The genotype list string code syntax for exchanging nomenclature-level genotyping results in clinical and research data management and analysis systems.

The nomenclatures used to describe HLA and killer-cell immunoglobulin-like receptor (KIR) alleles distinguish unique nucleotide and peptide sequences, and patterns of expression, but are insufficient for describing genotyping results, as description of ambiguities and relations across loci require terminology beyond allele names. The genotype list (GL) String grammar describes genotyping results for genetic systems with defined nomenclatures, like HLA and KIR, documenting what is known and unknown about a given genotyping result. However, the accuracy of a GL String is dependent on the reference database version under which it was generated. Here, we describe the GL string code (GLSC) system, which associates each GL String with meta-data describing the specific reference context in which the GL String was created, and in which it should be interpreted. GLSC is a defined syntax for exchanging GL Strings in the context of a specific gene-family namespace, allele-name code-system, and pertinent reference database version. GLSC allows HLA and KIR genotyping data to be transmitted, parsed and interpreted in the appropriate context, in an unambiguous manner, on modern data-systems, including Health Level 7 Fast Healthcare Interoperability Resource systems. Technical specification for GLSC can be found at https://glstring.org.

A hybrid automated event adjudication system for clinical trials.

In clinical trials, event adjudication is a process to review and confirm the accuracy of outcomes reported by site investigators. Despite efforts to automate the communication between a clinical-data-and-coordination center and an event adjudication committee, the review and confirmation of outcomes, as the core function of the process, still fully rely on human labor. To address this issue, we present an automated event adjudication system and its application in two randomized controlled trials. Centrally executed by a clinical-data-and-coordination center, the automated event adjudication system automatedly assessed and classified outcomes in a clinical data management system. By checking clinically predefined criteria, the automated event adjudication system either confirmed or unconfirmed an outcome and automatedly updated its status in the database. It also served as a management tool to assist staff to oversee the process of event adjudication. The system has been applied in: (1) the Cardiovascular Outcomes for People Using Anticoagulation Strategies (COMPASS) trial and (2) the New Approach riVaroxaban Inhibition of Factor Xa in a Global trial versus Aspirin to prevenT Embolism in Embolic Stroke of Undetermined Source (NAVIGATE ESUS) trial. The automated event adjudication system first screened outcomes reported on a case report form and confirmed those with data matched to preset definitions. For selected primary efficacy, secondary, and safety outcomes, the unconfirmed cases were referred to a human event adjudication committee for a final decision. In the New Approach riVaroxaban Inhibition of Factor Xa in a Global trial versus Aspirin to prevenT Embolism in Embolic Stroke of Undetermined Source (NAVIGATE ESUS) trial, human adjudicators were given priority to review cases, while the automated event adjudication system took the lead in the Cardiovascular Outcomes for People Using Anticoagulation Strategies (COMPASS) trial. Outcomes that were adjudicated in a hybrid model are discussed here. The COMPASS automated event adjudication system adjudicated 3283 primary efficacy outcomes and confirmed 1652 (50.3%): 132 (21.1%) strokes, 522 (53%) myocardial infarctions, and 998 (59.7%) causes of deaths. The NAVIGATE ESUS one adjudicated 737 cases of selected outcomes and confirmed 383 (52%): 219 (51.5%) strokes, 34 (42.5%) myocardial infarctions, 73 (54.9%) causes of deaths, and 57 (57.6%) major bleedings. After one deducts the time needed for migrating the system to a new study, the automated event adjudication system helped to reduce the time required for human review from approximately 1303 to 716.5 h for the Cardiovascular Outcomes for People Using Anticoagulation Strategies trial and from 387 to 196 h for the New Approach riVaroxaban Inhibition of Factor Xa in a Global trial versus Aspirin to prevenT Embolism in Embolic Stroke of Undetermined Source trial. The automated event adjudication system in combination with human adjudicators provides a streamlined and efficient approach to event adjudication in clinical trials. To immediately apply automated event adjudication, one can first consider the automated event adjudication system and involve human assistance for cases unconfirmed by the former.

Microservices-Based Architecture to Support the Adaptive RECORDS-Trial.

Information systems used by platform trials should handle changes that are not predefined. Unfortunately, the technical architecture of most existing clinical data management systems (CDMS) do not support changes to be incorporated into an ongoing trial. Adaptive clinical trials need advanced architectural solutions setup to enable biomarker stratification and enrichment strategy necessary for the adaptive clinical trial operation. This short paper presents the microservices-based architecture solution that is used to run and support the adaptive RECORDS-Trial.

Scrybe: A Secure Audit Trail for Clinical Trial Data Fusion.

Clinical trials are a multi-billion dollar industry. One of the biggest challenges facing the clinical trial research community is satisfying Part 11 of Title 21 of the Code of Federal Regulations [7] and ISO 27789 [40]. These controls provide audit requirements that guarantee the reliability of the data contained in the electronic records. Context-aware smart devices and wearable IoT devices have become increasingly common in clinical trials. Electronic Data Capture (EDC) and Clinical Data Management Systems (CDMS) do not currently address the new challenges introduced using these devices. The healthcare digital threat landscape is continually evolving, and the prevalence of sensor fusion and wearable devices compounds the growing attack surface. We propose Scrybe, a permissioned blockchain, to store proof of clinical trial data provenance. We illustrate how Scrybe addresses each control and the limitations of the Ethereum-based blockchains. Finally, we provide a proof-of-concept integration with REDCap to show tamper resistance.

Responsive neurostimulation of the thalamus improves seizure control in idiopathic generalised epilepsy: initial case series

ObjectivesUp to 40% of patients with idiopathic generalised epilepsy (IGE) are drug resistant and potentially could benefit from intracranial neuromodulation of the seizure circuit. We present outcomes following 2 years...

A Clinical Data Management System for Diabetes Clinical Trials.

Background The use of novel medications and methods to prevent, diagnose, treat, and manage diabetes requires confirmation of safety and efficacy in a well-designed study prior to widespread adoption. Diabetes clinical trials are the studies that examine these issues. The aim of the present study was to develop a web-based system for data management in diabetes clinical trials. Methods The present research was a mixed-methods study conducted in 2019. To identify the required data elements and functions to develop the system, 60 researchers completed a questionnaire. The designed system was evaluated using two methods. The usability of the system was initially evaluated by a group of researchers (n = 6) using the think-aloud method, and after system improvement, the system functions were evaluated by other researchers (n = 30) using a questionnaire. Results The main data elements which were required to develop a case report form included “study data,” “participant's personal data,” and “clinical data.” The functional requirements of the system were “managing the study,” “creating case report forms,” “data management,” “data quality control,” and “data security and confidentiality.” After using the system, researchers rated the system functions at a “good” level (6.3 ± 0.73) on a seven-point Likert scale. Conclusion Given the complexity of the data management processes in diabetes clinical trials and the widespread use of information technologies in research, the use of clinical data management systems in diabetes clinical trials seems inevitable. The system developed in the current study can facilitate and improve the process of creating and managing case report forms as well as collecting data in diabetes clinical trials.

ATRI EDC: a novel cloud-native remote data capture system for large multicenter Alzheimer's disease and Alzheimer's disease-related dementias clinical trails.

ObjectiveThe Alzheimer’s Therapeutic Research Institute (ATRI) developed a novel clinical data management system, the ATRI electronic data capture system (ATRI EDC), to address the complex regulatory, operational, and data requirements that arise in the conduct of multicenter Alzheimer’s disease and Alzheimer’s disease-related dementias (AD/ADRDs) clinical trials. We describe the system, its utility, and the broader implications for the field of clinical trials and clinical research informatics.Materials and MethodsThe ATRI EDC system was developed, tested, and validated using community-based agile software development methods and cloud-native single-page application design principles. It offers an increasing number of application modules, supports a high degree of study-specific configuration, and empowers study teams to effectively communicate and collaborate on the accurate and timely completion of study activities.ResultsTo date, the ATRI EDC system supports 10 clinical studies, collecting study data for 4596 participants. Three case descriptions further illustrate how the system’s capabilities support diverse study-specific requirements.DiscussionThe ATRI EDC system has several advantages: its modular capabilities can accommodate rapidly evolving research designs and technologies; its community-based agile development approach and community-friendly licensing model encourage collaboration per the principles of open science; finally, with continued development and community building efforts, the system has the potential to facilitate the effective conduct of clinical studies beyond the field of AD/ADRD.ConclusionBy effectively addressing the requirements of multicenter AD/ADRD studies, the ATRI EDC system supports ATRI’s scientific mission of rigorously testing new AD/ADRD therapies and facilitating the effective conduct of multicenter clinical studies.

Detecting of a Patient's Condition From Clinical Narratives Using Natural Language Representation.

The rapid progress in clinical data management systems and artificial intelligence approaches enable the era of personalized medicine. Intensive care units (ICUs) are ideal clinical research environments for such development because they collect many clinical data and are highly computerized. Goal: We designed a retrospective clinical study on a prospective ICU database using clinical natural language to help in the early diagnosis of heart failure in critically ill children. Methods: The methodology consisted of empirical experiments of a learning algorithm to learn the hidden interpretation and presentation of the French clinical note data. This study included 1386 patients' clinical notes with 5444 single lines of notes. There were 1941 positive cases (36% of total) and 3503 negative cases classified by two independent physicians using a standardized approach. Results: The multilayer perceptron neural network outperforms other discriminative and generative classifiers. Consequently, the proposed framework yields an overall classification performance with 89% accuracy, 88% recall, and 89% precision. Conclusions: This study successfully applied learning representation and machine learning algorithms to detect heart failure in a single French institution from clinical natural language. Further work is needed to use the same methodology in other languages and institutions.

Design and Application of Clinical Big Data Management System for Oncology

Design and Application of Clinical Big Data Management System for Oncology

Construction of Clinical Pathway Information Management System under the Guidance of Evidence-Based Medicine.

This study focuses on clinical pathways guided by evidence-based medicine (EBM). With the clinical pathway as the center, the subjective and objective medical knowledge of medical staff are collected, and a clinical pathway management system guided by EBM is established through a unified process; user demand analysis; main considerations; implementation, evaluation, and monitoring of the clinical path; and dictionary maintenance, to help hospitals fully regulate medical behaviors. Next, the study displays the path access prompt box, area 1 management page, table management page, exit prompt box, mutation record page, doctor order interface, revocation of execution, and monitoring interface, and the system designed is compared with the Beijing Shankang Technology (ASK) clinical data management system in terms of user experience. The results showed that the reporting rate of medical adverse events in the system in this study was 0.21%, and the work efficiency was increased by 14%. In terms of users' satisfaction, the hospital managers' satisfaction was 84 ± 5.36%, and it was 95 ± 4.72% for medical staff and 88 ± 4.91% for system administrators, superior to the ASK system; the differences were statistically significant (P < 0.05). In conclusion, the clinical pathway information management system is in line with the working environment of medical staff, and the synchronous monitoring and management of medical quality are achieved through digital means, which can reduce the occurrence of medical adverse events and improve the work efficiency of medical staff.

A Performant Web-Based Visualization, Assessment, and Collaboration Tool for Multidimensional Biosignals.

Biosignal-based research is often multidisciplinary and benefits greatly from multi-site collaboration. This requires appropriate tooling that supports collaboration, is easy to use, and is accessible. However, current software tools do not provide the necessary functionality, usability, and ubiquitous availability. The latter is particularly crucial in environments, such as hospitals, which often restrict users' permissions to install software. This paper introduces a new web-based application for interactive biosignal visualization and assessment. A focus has been placed on performance to allow for handling files of any size. The proposed solution can load local and remote files. It parses data locally on the client, and harmonizes channel labels. The data can then be scored, annotated, pseudonymized and uploaded to a clinical data management system for further analysis. The data and all actions can be interactively shared with a second party. This lowers the barrier to quickly visually examine data, collaborate and make informed decisions.

Clinical Trial Data Management Software: A Review of the Technical Features.

Data management is an important, complex and multidimensional process in clinical trials. The execution of this process is very difficult and expensive without the use of information technology. A clinical data management system is software that is vastly used for managing the data generated in clinical trials. The objective of this study was to review the technical features of clinical trial data management systems. Related articles were identified by searching databases, such as Web of Science, Scopus, Science Direct, ProQuest, Ovid and PubMed. All of the research papers related to clinical data management systems which were published between 2007 and 2017 (n=19) were included in the study. Most of the clinical data management systems were web-based systems developed based on the needs of a specific clinical trial in the shortest possible time. The SQL Server and MySQL databases were used in the development of the systems. These systems did not fully support the process of clinical data management. In addition, most of the systems lacked flexibility and extensibility for system development. It seems that most of the systems used in the research centers were weak in terms of supporting the process of data management and managing clinical trial's workflow. Therefore, more attention should be paid to design a more complete, usable, and high quality data management system for clinical trials. More studies are suggested to identify the features of the successful systems used in clinical trials.

A Review of Clinical Data Management Systems Used in Clinical Trials.

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. 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. 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. 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.

Development of a new seamless data stream from EMR to EDC system using SS-MIX2 standards applied for observational research in diabetes mellitus.

Over the last decade, redundant entry of data in electronic medical records (EMR) for health care and electronic data capture (EDC) systems for research has been the typical medical research methodology. The corresponding data transcription this methodology requires not only increases the burden for clinician investigators and clinical research coordinators (CRCs), but it also decreases the quality of data. We designed and developed a new standards‐based and platform‐independent system to use data in the EMR to directly populate clinical data management systems in the EDC to eliminate the need for data transcription, streamline the clinical research process, and reduce clinician burden. Standardized structured medical information eXchange2 (SS‐MIX2) was implemented along with the Integrating the Healthcare Enterprise (IHE) Retrieve Form for Data Capture (RFD) Integration Profile. Standards from Clinical Data Interchange Standards Consortium (CDISC) were used to define metadata for research data collection forms and as a means to standardize data exchange semantics. These standards and the associated methodology were applied to observational research in patients with diabetes mellitus. The system we developed complies with global requirements for regulated research. It provides a standard‐based and platform‐independent method that can serve to accelerate the cycle of a learning health system.

A novel data management platform to improve image-guided precision preclinical biological research.

Preclinical biological research is mandatory for developing new drugs to investigate the toxicity and efficacy of the drug. In this paper, the focus is on radiobiological research as an example of advanced preclinical biological research. In radiobiology, recent technological advances have produced novel research platforms which can precisely irradiate targets in animals and use advanced onboard image-guidance, mimicking the clinical radiotherapy environment. These platforms greatly facilitate complex research combining several agents simultaneously (in our example, radiation and non-radiation agents). Since these modern platform can produce a large amount of wide-ranging data, one of the main impediments in preclinical research platforms is a proper data management system for preclinical studies. A preclinical data management system, inspired by current radiotherapy clinical data management systems was designed. The system was designed with InterSystems technology, i.e. a programmable Enterprise Service Bus solution. New DICOM animal imaging standards are used such as DICOM suppl. 187 for storing small animal acquisition context and the DICOM second generation course model. A small animal big data warehouse environment for research is designed to work with modern image-guided precision research platforms. Its modular design includes (1) a study workflow manager, (2) a data manager, and (3) a storage manager. The system provides interfaces to, e.g. preclinical treatment planning systems and data analysis plug-ins, and guides the user efficiently through the many steps involved in preclinical research. The system manages various data source locations, and arranges access to the data centrally. A novel preclinical data management system can be designed to improve preclinical workflow, facilitate data exchange between researchers, and support translation to clinical trials. A preclinical data management system such as the one proposed here would greatly benefit preparation, execution and analysis of biological experiments, and will eventually facilitate translation to clinical trials.

Xue-Fu-Zhu-Yu capsule in the treatment of qi stagnation and blood stasis syndrome: a study protocol for a randomised controlled pilot and feasibility trial

BackgroundQi stagnation and blood stasis syndrome (QS&BSS) is one of the common Zhengs in traditional Chinese medicine (TCM), which manifests as various symptoms and signs, such as distending pain or a tingling sensation in a fixed position. In recent years, a number of clinical trials have focused on the effectiveness and safety of XFZYC in patients with a QS&BSS subtype disease, such as coronary heart disease, hyperlipidaemia, ischaemic cerebrovascular disease, gastritis, dysmenorrhoea, or arthritis, in terms of the outcomes of relevant diseases. However, there is lack of evidence of the effects of XFZYC in patients with QS&BSS with different diseases, focusing on the outcomes of Zhengs.Methods/DesignA randomised, controlled, pilot and feasibility trial will be employed in this study, using a 7-week study period. Participants will be recruited from Guang’anmen Hospital, Huguosi TCM Hospital, Wangjing Hospital in China. One hundred and twenty participants will be randomised to a treatment group (Xue-Fu-Zhu-Yu Capsule (XFZYC)) and placebo group in a 1:1 ratio. Participants included in the study must be diagnosed with Qi stagnation and blood stasis syndrome criteria. The outcome measurements will include the traditional Chinese medicine patient-reported outcome (PRO) scale for QS&BSS, the single symptom and sign scale of QS&BSS, and the pain scale of QS&BSS. The clinical data management system (http://www.tcmcec.net/) will be used to collect and manage the data. Quality control will be used, according to Good Clinical Practice (GCP).DiscussionPrevious studies were expected to evaluate whether the addition of XFZYC to standard routine treatment would enhance the treatment effectiveness and improve the biomedical parameters pertaining to relevant disease. However, this trial is focused on the outcome of Zhengs, and we chose a range of outcome measurements to assess the improvement of relevant symptoms and signs. This trial is the first study designed to define and optimise the outcome measurements of Zhengs of XFZYC in the treatment of patients with QS&BSS.Trial registrationClinicalTrials.gov, NCT03091634. Registered on 12 August 2018. Release date 6 May 2017.

A standard-driven approach for electronic submission to pharmaceutical regulatory authorities

ObjectiveUsing standards is not only useful for data interchange during the process of a clinical trial, but also useful for analyzing data in a review process. Any step, which speeds up approval of new drugs, may benefit patients. As a result, adopting standards for regulatory submission becomes mandatory in some countries. However, preparing standard-compliant documents, such as annotated case report form (aCRF), needs a great deal of knowledge and experience. The process is complex and labor-intensive. Therefore, there is a need to use information technology to facilitate this process. Materials and methodsInstead of standardizing data after the completion of a clinical trial, this study proposed a standard-driven approach. This approach was achieved by implementing a computer-assisted “standard-driven pipeline (SDP)” in an existing clinical data management system. SDP used CDISC standards to drive all processes of a clinical trial, such as the design, data acquisition, tabulation, etc. ResultsA completed phase I/II trial was used to prove the concept and to evaluate the effects of this approach. By using the CDISC-compliant question library, aCRFs were generated automatically when the eCRFs were completed. For comparison purpose, the data collection process was simulated and the collected data was transformed by the SDP. This new approach reduced the missing data fields from sixty-two to eight and the controlled term mismatch field reduced from eight to zero during data tabulation. ConclusionThis standard-driven approach accelerated CRF annotation and assured data tabulation integrity. The benefits of this approach include an improvement in the use of standards during the clinical trial and a reduction in missing and unexpected data during tabulation. The standard-driven approach is an advanced design idea that can be used for future clinical information system development.

Design and implementation of Guillain-Barré syndrome clinical date management and analysis system

Objective To design a scientific, standardized, fully functional, easy-to-use Guillain-Barre syndrome (GBS) clinical informatization management system, with the purpose to meet the needs of diagnosis and clinical research in patients with GBS. This system can manage and excavate clinical data and provide a platform for clinical research of GBS. Methods The clinical data items of GBS were identified. The Visual Basic 6.0 and Vista DB software development environment was used to develop the system and the related database based on embedded database. Results A GBS database including 560 clinical indicators were set up. Through the built-in data mining and analysis functions, the system can realize the functions of visualized data input, combination search, statistical analysis, data exchange and literature update. This system has been used in Xijing Hospital, and a total 274 clinical and prognostic records were recorded from the patients with GBS. The practical results proved that the system can achieve the design goals. Conclusions Based on the information technology and GBS-related clinical epidemiology and neurology expertise, a standard GBS clinical data management and analysis system and related database were established, which could provide a basis for GBS information storage, update, epidemiological investigation and prognosis. Key words: Guillain-Barre syndrome; Clinical date; Data mining; Date management; Database

Web based Clinical Data Management System

Web based Clinical Data Management System

Tips and Traps: Lessons From Codesigning a Clinician E-Monitoring Tool for Computerized Cognitive Behavioral Therapy.

BackgroundComputerized cognitive behavioral therapy (cCBT) is an acceptable and promising treatment modality for adolescents with mild-to-moderate depression. Many cCBT programs are standalone packages with no way for clinicians to monitor progress or outcomes. We sought to develop an electronic monitoring (e-monitoring) tool in consultation with clinicians and adolescents to allow clinicians to monitor mood, risk, and treatment adherence of adolescents completing a cCBT program called SPARX (Smart, Positive, Active, Realistic, X-factor thoughts).ObjectiveThe objectives of our study were as follows: (1) assess clinicians’ and adolescents’ views on using an e-monitoring tool and to use this information to help shape the development of the tool and (2) assess clinician experiences with a fully developed version of the tool that was implemented in their clinical service.MethodsA descriptive qualitative study using semistructured focus groups was conducted in New Zealand. In total, 7 focus groups included clinicians (n=50) who worked in primary care, and 3 separate groups included adolescents (n=29). Clinicians were general practitioners (GPs), school guidance counselors, clinical psychologists, youth workers, and nurses. Adolescents were recruited from health services and a high school. Focus groups were run to enable feedback at 3 phases that corresponded to the consultation, development, and postimplementation stages. Thematic analysis was applied to transcribed responses.ResultsFocus groups during the consultation and development phases revealed the need for a simple e-monitoring registration process with guides for end users. Common concerns were raised in relation to clinical burden, monitoring risk (and effects on the therapeutic relationship), alongside confidentiality or privacy and technical considerations. Adolescents did not want to use their social media login credentials for e-monitoring, as they valued their privacy. However, adolescents did want information on seeking help and personalized monitoring and communication arrangements. Postimplementation, clinicians who had used the tool in practice revealed no adverse impact on the therapeutic relationship, and adolescents were not concerned about being e-monitored. Clinicians did need additional time to monitor adolescents, and the e-monitoring tool was used in a different way than was originally anticipated. Also, it was suggested that the registration process could be further streamlined and integrated with existing clinical data management systems, and the use of clinician alerts could be expanded beyond the scope of simply flagging adolescents of concern.ConclusionsAn e-monitoring tool was developed in consultation with clinicians and adolescents. However, the study revealed the complexity of implementing the tool in clinical practice. Of salience were privacy, parallel monitoring systems, integration with existing electronic medical record systems, customization of the e-monitor, and preagreed monitoring arrangements between clinicians and adolescents.