data management plan in clinical research

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• Published: 18 February 2021

Essentials of data management: an overview

• Miren B. Dhudasia 1 , 2 ,
• Robert W. Grundmeier 2 , 3 , 4 &
• Sagori Mukhopadhyay 1 , 2 , 3  

Pediatric Research volume  93 ,  pages 2–3 ( 2023 ) Cite this article

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What is data management?

Data management is a multistep process that involves obtaining, cleaning, and storing data to allow accurate analysis and produce meaningful results. While data management has broad applications (and meaning) across many fields and industries, in clinical research the term data management is frequently used in the context of clinical trials. 1 This editorial is written to introduce early career researchers to practices of data management more generally, as applied to all types of clinical research studies.

Outlining a data management strategy prior to initiation of a research study plays an essential role in ensuring that both scientific integrity (i.e., data generated can accurately test the hypotheses proposed) and regulatory requirements are met. Data management can be divided into three steps—data collection, data cleaning and transformation, and data storage. These steps are not necessarily chronological and often occur simultaneously. Different aspects of the process may require the expertise of different people necessitating a team effort for the effective completion of all steps.

Data collection

Data source.

Data collection is a critical first step in the data management process and may be broadly classified as “primary data collection” (collection of data directly from the subjects specifically for the study) and “secondary use of data” (repurposing data that were collected for some other reason—either for clinical care in the subject’s medical record or for a different research study). While the terms retrospective and prospective data collection are occasionally used, 2 these terms are more applicable to how the data are utilized rather than how they are collected . Data used in a retrospective study are almost always secondary data; data collected as part of a prospective study typically involves primary data collection, but may also involve secondary use of data collected as part of ongoing routine clinical care for study subjects. Primary data collected for a specific study may be categorized as secondary data when used to investigate a new hypothesis, different from the question for which the data were originally collected. Primary data collection has the advantage of being specific to the study question, minimize missingness in key information, and provide an opportunity for data correction in real time. As a result, this type of data is considered more accurate but increases the time and cost of study procedures. Secondary use of data includes data abstracted from medical records, administrative data such as from the hospital’s data warehouse or insurance claims, and secondary use of primary data collected for a different research study. Secondary use of data offers access to large amounts of data that are already collected but often requires further cleaning and codification to align the data with the study question.

A case report form (CRF) is a powerful tool for effective data collection. A CRF is a paper or electronic questionnaire designed to record pertinent information from study subjects as outlined in the study protocol. 3 CRFs are always required in primary data collection but can also be useful in secondary use of data to preemptively identify, define, and, if necessary, derive critical variables for the study question. For instance, medical records provide a wide array of information that may not be required or be useful for the study question. A CRF with well-defined variables and parameters helps the chart reviewer focus only on the relevant data, and makes data collection more objective and unbiased, and, in addition, optimize patient confidentiality by minimizing the amount of patient information abstracted. Tools like REDCap (Research Electronic Data Capture) provide electronic CRFs and offer some advanced features like setting validation rules to minimize errors during data collection. 4 Designing an effective CRF upfront during the study planning phase helps to streamline the data collection process, and make it more efficient. 3

Data cleaning and transformation

Quality checks.

Data collected may have errors that arise from multiple sources—data manually entered in a CRF may have typographical errors, whereas data obtained from data warehouses or administrative databases may have missing data, implausible values, and nonrandom misclassification errors. Having a systematic approach to identify and rectify these errors, while maintaining a log of the steps performed in the process, can prevent many roadblocks during analysis.

First, it is important to check for missing data. Missing data are defined as values that are not available and that would be meaningful for analysis if they were observed. 5 Missing data can bias the results of the study depending on how much data is missing and what is the pattern of distribution of missing data in the study cohort. Many methods for handling missing data have been published. Kang 6 provide a practical review of methods for handling missing data. If missing data cannot be retrieved and is limited to only a small number of subjects, one approach is to exclude these subjects from the study. Missing data in different variables across many subjects often require more sophisticated approaches to account for the “missingness.” These may include creating a category of “missing” (for categorical variables), simple imputation (e.g., substituting missing values in a variable with an average of non-missing values in the variable), or multiple imputations (substituting missing values with the most probable value derived from other variables in the dataset). 7

Second, errors in the data can be identified by running a series of data validation checks. Some examples of data validation rules for identifying implausible values are shown in Table  1 . Automated algorithms for detection and correction of implausible values may be available for cleaning specific variables in large datasets (e.g., growth measurements). 8 After identification, data errors can either be corrected, if possible, or can be marked for deletion. Other approaches, similar to those for dealing with missing data, can also be used for managing data errors.

Data transformation

The data collected may not be in the form required for analysis. The process of data transformation includes recategorization and recodification of the data, which has been collected along with derivation of new variables, to align with the study analytic plan. Examples include categorizing body mass index collected as a continuous variable into under- and overweight categories, recoding free-text values such as “growth of an organism” or “no growth,” and into a binary “positive” or “negative,” or deriving new variables such as average weight per year from multiple weight values over time available in the dataset. Maintaining a code-book of definitions for all variables, predefined and derived, can help a data analyst better understand the data.

Data storage

Securely storing data is especially important in clinical research as the data may contain protected health information of the study subjects. 9 Most institutes that support clinical research have guidelines for safeguards to prevent accidental data breaches.

Data are collected in paper or electronic formats. Paper data should be stored in secure file cabinets inside a locked office at the site approved by the institutional review board. Electronic data should be stored on a secure approved institutional server, and should never be transported using unencrypted portable media devices (e.g., “thumb drives”). If all study team members do not require access to study data, then selective access should be granted to the study team members based on their roles.

Another important aspect of data storage is data de-identification. Data de-identification is a process by which identifying characteristics of the study participants are removed from the data, in order to mitigate privacy risks to individuals. 10 Identifying characteristics of a study subject includes name, medical record number, date of birth/death, and so on. To de-identify data, these characteristics should either be removed from the data or modified (e.g., changing the medical record number to study IDs, changing dates to age/duration, etc.). If feasible, study data should be de-identified when storing. If you anticipate that reidentification of the study participants may be required in future, then the data can be separated into two files, one containing only the de-identified data of the study participants, and one containing all the identifying information, with both files containing a common linking variable (e.g., study ID), which is unique for every subject or record in the two files. The linking variable can be used to merge the two files when reidentification is required to carry out additional analyses or to get further data. The link key should be maintained in a secure institutional server accessible only to authorized individuals who need access to the identifiers.

To conclude, effective data management is important to the successful completion of research studies and to ensure the validity of the results. Outlining the steps of the data management process upfront will help streamline the process and reduce the time and effort subsequently required. Assigning team members responsible for specific steps and maintaining a log, with date/time stamp to document each action as it happens, whether you are collecting, cleaning, or storing data, can ensure all required steps are done correctly and identify any errors easily. Effective documentation is a regulatory requirement for many clinical trials and is helpful for ensuring all team members are on the same page. When interpreting results, it will serve as an important tool to assess if the interpretations are valid and unbiased. Last, it will ensure the reproducibility of the study findings.

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Acknowledgements

This work was partially supported in part by the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health grant (K23HD088753).

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Miren B. Dhudasia & Sagori Mukhopadhyay

Center for Pediatric Clinical Effectiveness, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

Miren B. Dhudasia, Robert W. Grundmeier & Sagori Mukhopadhyay

Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

Robert W. Grundmeier & Sagori Mukhopadhyay

Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

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Dhudasia, M.B., Grundmeier, R.W. & Mukhopadhyay, S. Essentials of data management: an overview. Pediatr Res 93 , 2–3 (2023). https://doi.org/10.1038/s41390-021-01389-7

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Received : 11 December 2020

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Accepted : 06 January 2021

Published : 18 February 2021

Issue Date : January 2023

DOI : https://doi.org/10.1038/s41390-021-01389-7

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Data management in clinical research: An overview

Affiliation.

• 1 Global Medical Affairs, Dr. Reddy's Laboratories Ltd., Ameerpet, Hyderabad, India.
• PMID: 22529469
• PMCID: PMC3326906
• DOI: 10.4103/0253-7613.93842

Clinical Data Management (CDM) is a critical phase in clinical research, which leads to generation of high-quality, reliable, and statistically sound data from clinical trials. This helps to produce a drastic reduction in time from drug development to marketing. Team members of CDM are actively involved in all stages of clinical trial right from inception to completion. They should have adequate process knowledge that helps maintain the quality standards of CDM processes. Various procedures in CDM including Case Report Form (CRF) designing, CRF annotation, database designing, data-entry, data validation, discrepancy management, medical coding, data extraction, and database locking are assessed for quality at regular intervals during a trial. In the present scenario, there is an increased demand to improve the CDM standards to meet the regulatory requirements and stay ahead of the competition by means of faster commercialization of product. With the implementation of regulatory compliant data management tools, CDM team can meet these demands. Additionally, it is becoming mandatory for companies to submit the data electronically. CDM professionals should meet appropriate expectations and set standards for data quality and also have a drive to adapt to the rapidly changing technology. This article highlights the processes involved and provides the reader an overview of the tools and standards adopted as well as the roles and responsibilities in CDM.

Keywords: Clinical data interchange standards consortium; clinical data management systems; data management; e-CRF; good clinical data management practices; validation.

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Conflict of interest statement

Conflict of Interest: None declared.

Annotated sample of a Case…

Annotated sample of a Case Report Form (CRF). Annotations are entered in coloured…

Discrepancy management (DCF = Data…

Discrepancy management (DCF = Data clarification form, CRA = Clinical Research Associate, SDV…

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Data Collection and Management in Clinical Research

• First Online: 01 January 2012

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• Mario Guralnik PhD 3  

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Well-designed trials and data management methods are essential to the integrity of the findings from clinical trials, and the completeness, accuracy, and timeliness of data collection are key indicators of the quality of conduct of the study. The research data provide the information to be analyzed in addressing the study objectives, and addressing the primary objectives is the critical driver of the study. Since the data management plan closely follows the structure and sequence of the protocol, the data management group and protocol development team must work closely together. Accurate, thorough, detailed, and complete collection of data is critical, especially at baseline as this is the last time observations can be recorded before the effects of the trial interventions come into play. The shift from paper-based to electronic systems promotes efficient and uniform collection of data and can build quality control into the data collection process.

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Guralnik, M. (2012). Data Collection and Management in Clinical Research. In: Supino, P., Borer, J. (eds) Principles of Research Methodology. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3360-6_7

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Data Management in Clinical Research: Best Practices

What is clinical data management.

Clinical data management (CDM) is a field in healthcare that focuses on the accurate handling of data collected during clinical trials. It involves the collection, integration, and validation of clinical trial data to ensure it meets the highest standards of quality and reliability. CDM plays a pivotal role in the development of new drugs and medical devices, ensuring that the data used in clinical research is both accurate and suitable for analysis.

CDM forms the basis for understanding the efficacy and safety of new medical drugs, devices, or techniques based on patient data collected during trials.

Why is clinical data management important?

Clinical data management is crucial for ensuring patient safety, regulatory compliance, and cost efficiency in healthcare. By maintaining rigorous standards in data accuracy and integrity, CDM supports the safe development of new treatments, helping to prevent adverse outcomes. 

Compliance with regulatory guidelines is essential for the approval of drugs and therapies, and effective data management streamlines this process. Additionally, efficient handling of data reduces unnecessary costs by avoiding data-related errors and delays in clinical trials.

What are the main objectives of clinical data management?

Ensuring data accuracy.

One of the foremost objectives of clinical data management is to ensure that data collected during clinical trials is accurate. Accurate data is crucial for reliable results and conclusions in clinical research. Errors in data can lead to incorrect conclusions, potentially affecting patient safety and treatment efficacy.

Facilitating faster and safer drug development

Clinical data management aims to streamline the drug development process, making it both faster and safer. By efficiently managing data, CDM helps in speeding up the analysis and reporting phases, which can significantly shorten the time to market for new therapies while ensuring that safety protocols are rigorously followed.

Maintaining data completeness

Clinical data management also aims to maintain the completeness of the data. This involves ensuring that all required data is captured throughout the clinical trial process. Complete data sets are vital for thorough analysis and to support the robustness of clinical study findings.

Securing data

Securing sensitive patient information is another key objective of clinical data management. This includes implementing strict data protection measures to comply with regulatory requirements and to maintain patient confidentiality. Effective data security practices prevent unauthorized access and ensure that the data is used solely for its intended research purposes.

Ensuring traceability

Traceability in clinical data management involves maintaining a clear and auditable trail for all data collected. This allows researchers to verify the data's origin, processing, and storage methods, which is crucial for addressing any discrepancies and upholding the integrity of the data throughout the trial.

What are the different professional organizations for clinical data management?

Professional organizations are essential in clinical data management, offering support, setting standards, and facilitating ongoing education and networking for professionals. The Society for Clinical Data Management (SCDM) stands out globally for its dedication to advancing clinical data management excellence through certification, educational resources, and conferences. 

In the UK, the Association for Clinical Data Management (ACDM) focuses on enhancing the skills and knowledge of data management professionals, providing specialized training and informative events. The Drug Information Association (DIA) offers a broader perspective, covering clinical data management within its extensive range of topics related to drug development and life sciences globally. 

Lastly, the Clinical Data Interchange Standards Consortium (CDISC) is instrumental in developing and promoting standardized data formats to improve data quality and consistency across clinical trials. 

While CDISC is predominant, FHIR and OMOP are also gaining traction in specific areas of clinical research, particularly where interoperability and real-world data are crucial. These organizations collectively help uphold the integrity and efficacy of clinical data management practices worldwide.

What are the roles and responsibilities of clinical data management?

The roles and responsibilities of clinical data management are essential to ensuring the success and integrity of clinical trials.

Data design and setup

The clinical data management team is responsible for designing the data collection tools, such as case report forms (CRFs). They ensure these tools are optimized to capture the necessary data efficiently and accurately. This process also involves setting up databases that are robust and secure.

Data collection and validation

Once data collection begins, the CDM team oversees the gathering of data, ensuring it adheres to the protocol and is logged correctly. They also perform rigorous checks to validate the data, ensuring that it is both accurate and complete. This involves identifying and resolving discrepancies and missing data.

Data cleaning and quality assurance

Data cleaning is a critical responsibility that involves correcting or removing any inaccurate, incomplete, or unreasonable data entries. The CDM team conducts regular audits to ensure data quality throughout the clinical trial process.

Data analysis and reporting

After data collection and cleaning, the clinical data management team works closely with biostatisticians to analyze the data. They ensure that the analysis is performed correctly and that the results are reported clearly and comprehensively.

Ensuring compliance and data security

The CDM team must ensure that all data management processes comply with regulatory requirements and ethical standards. They are also responsible for protecting the data against unauthorized access or breaches, maintaining patient confidentiality and data integrity.

What are the stages of clinical data management?

Clinical data management follows a structured series of stages to ensure the integrity and usability of data collected during clinical trials. Each stage is crucial for meeting regulatory requirements and achieving accurate trial outcomes.

• Protocol development This initial stage focuses on creating a comprehensive clinical trial protocol that outlines the study's objectives, design, methodology, and statistical considerations. The protocol is essential for guiding all future data management tasks.
• Setup and design The data management team is responsible for designing effective data collection tools, such as case report forms, and setting up the data management system. This stage ensures that the framework for data collection is robust and capable of handling the data needs of the trial.
• Data collection and entry As the trial progresses, data is systematically collected according to the protocol. This stage involves rigorous data entry processes to ensure that all data is accurately captured in the management system.
• Data validation and cleaning Data validation occurs after collection. During this stage, the data is scrutinized for errors or inconsistencies, and corrective measures are taken to clean and rectify any issues, ensuring the data's accuracy and completeness.
• Database lock and analysis Once data cleaning is complete, the database is locked to prevent further changes, ensuring the data's stability for accurate analysis. This locked database is then used for detailed statistical analysis to assess the trial's outcomes.
• Reporting and archiving The final stage involves compiling reports from the analyzed data and archiving all trial data and documentation. This ensures that all information is preserved in compliance with regulatory standards and is available for future reference.

Datavant Trials supports organizations throughout all stages of clinical trials by leveraging advanced tokenization and precision matching technology to manage and connect data efficiently. Datavant Trials helps optimize study design and cohort selection by linking trial patients to real-world data (RWD), providing insights into comorbidities and social determinants of health. 

Post-trial, Datavant helps track participant journeys by connecting real-world data such as claims data and clinical data, supporting long-term safety and efficacy assessments.

What tools are used for clinical data management?

Clinical data management utilizes a variety of tools to ensure the efficient handling and analysis of clinical trial data. These tools are designed to enhance accuracy, efficiency, and compliance with regulatory standards.

Clinical data management systems (CDMS)

CDMS are specialized software applications that facilitate the collection, storage, and management of clinical trial data. These systems are crucial for ensuring data integrity and support complex data validation and cleaning processes.

Electronic data capture (EDC) systems

EDC systems are commonly used for direct entry of clinical trial data at the site of the study. They provide real-time data capture capabilities, reducing the time and potential for errors associated with paper-based data collection.

Statistical software

Statistical software tools are essential for the analysis of clinical trial data. They allow biostatisticians to perform complex statistical tests and provide insights into the efficacy and safety of the investigational product.

Data warehousing and business intelligence tools

These tools are used for storing and analyzing large datasets, enabling more sophisticated data analysis and reporting capabilities. They help aggregate data from multiple trials or sources, providing a comprehensive view of the data landscape.

Risk-based monitoring software

This type of software uses algorithms to identify risks in data collection and management processes. It allows teams to focus resources on high-risk areas, improving overall data quality and trial efficiency.

Comparing clinical data management systems

When selecting a clinical data management system (CDMS), it's important to consider several key factors to ensure the system meets the specific needs of a clinical trial. Here's how to effectively compare different CDMS options:

Functionality and features

Evaluate the specific functionalities and features of each system, such as data capture, validation, querying capabilities, and reporting tools. Ensure that the system can handle the complexity of your clinical data and supports efficient data management processes.

Compliance with regulatory standards

Check that the system is compliant with relevant regulatory requirements such as FDA 21 CFR Part 11, GDPR, or HIPAA. Compliance is crucial for ensuring that the data management processes adhere to legal and ethical standards.

User interface and usability

Assess the user interface and usability of the system. A user-friendly interface can significantly reduce training time and improve data entry accuracy. It’s important that the system is intuitive and easy for all users to navigate.

Integration capabilities

Consider the system's ability to integrate with other software and tools used in clinical trials, such as electronic health records (EHRs) or laboratory information systems (LIS). Seamless integration ensures smooth data flow and reduces the risk of data silos.

Vendor support and training

Examine the level of support and training provided by the vendor. Good vendor support can greatly enhance the system’s implementation and ongoing maintenance, while comprehensive training ensures that your team can use the system effectively.

Cost-effectiveness

Finally, consider the cost of the system in relation to its features and benefits. It’s important to find a balance between cost and the value it provides, ensuring it fits within the budget while meeting all necessary clinical trial requirements.

What are the best clinical data management practices?

Effective clinical data management practices are key to ensuring the reliability, accuracy, and integrity of data in clinical trials. Here are some fundamental practices:

• Implementing robust data management plans A comprehensive data management plan sets the foundation for all data management activities. It outlines clear protocols for data collection, entry, validation, and maintenance, ensuring consistency and compliance throughout the lifecycle of a clinical trial.
• Using standardized data collection methods Standardization is crucial for maintaining data quality. Using standardized data collection tools like case report forms (CRFs) and adhering to data standards like those from CDISC help in minimizing errors and improving data comparability across different studies.
• Ensuring continuous training and education Ongoing training for clinical data management teams is vital. It keeps the staff updated on the latest data management technologies, practices, and regulatory changes, ensuring the highest level of data integrity and compliance.
• Conducting regular data audits Regular audits are essential to identify and rectify any discrepancies, incomplete, or inaccurate data. These audits help in maintaining data quality and reliability throughout the trial.
• Leveraging technology for data security and integrity Using advanced security measures and data integrity tools ensures that the data is protected against unauthorized access and manipulation. Technologies such as encryption and secure data environments are crucial for safeguarding sensitive information.

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Improving hypertension management in primary care

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Learning implementation of a guideline based decision support system to improve hypertension treatment in primary care in China

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• Peer review
• Amy Pui Pui Ng , clinical assistant professor 1 ,
• Qingqi Chen , clinical practitioner 1 ,
• Diana Dan Wu , lecturer 1 ,
• Suk Chiu Leung , patient 2
• 1 Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
• 2 Patient author
• Correspondence to: A P P Ng amyppng{at}hku.hk

Clinical decision support increases guideline concordant care

Hypertension is a major risk factor for heart disease, stroke, and chronic kidney disease, and it is responsible for substantial disease burden and increased risk of mortality. 1 The global prevalence of hypertension ranges between 13% and 41%. 2 However, the effectiveness and use of hypertension treatment vary widely worldwide. 3 Studies show that doctors’ understanding of clinical practice guidelines influences choice of hypertensive drug, 4 and using computer generated prompts is one of the most effective methods to improve adherence to guidelines. 5 The latter has led to increasing development and use of clinical decision support systems (CDSSs) to assist healthcare professionals in decision making by providing important clinical knowledge, information about patients, and health related data. 6 Results from randomised controlled trials using CDSS for hypertension have been conflicting, however, with some showing benefit for blood pressure outcomes, and others not. 7 8 9

In the linked paper (doi:10.1136/bmj-2023-079143), Song and colleagues report the effectiveness of CDSS in improving primary care doctors’ adherence to hypertension …

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Adapting data management education to support clinical research projects in an academic medical center

Associated data.

The workshop evaluation form, resulting data, and slide deck from the “Clinical Research Data Management” workshop are available in Figshare at DOI: http://dx.doi.org/10.6084/m9.figshare.7105817.v1 .

Librarians and researchers alike have long identified research data management (RDM) training as a need in biomedical research. Despite the wealth of libraries offering RDM education to their communities, clinical research is an area that has not been targeted. Clinical RDM (CRDM) is seen by its community as an essential part of the research process where established guidelines exist, yet educational initiatives in this area are unknown.

Case Presentation

Leveraging my academic library’s experience supporting CRDM through informationist grants and REDCap training in our medical center, I developed a 1.5 hour CRDM workshop. This workshop was designed to use established CRDM guidelines in clinical research and address common questions asked by our community through the library’s existing data support program. The workshop was offered to the entire medical center 4 times between November 2017 and July 2018. This case study describes the development, implementation, and evaluation of this workshop.

Conclusions

The 4 workshops were well attended and well received by the medical center community, with 99% stating that they would recommend the class to others and 98% stating that they would use what they learned in their work. Attendees also articulated how they would implement the main competencies they learned from the workshop into their work. For the library, the effort to support CRDM has led to the coordination of a larger institutional collaborative training series to educate researchers on best practices with data, as well as the formation of institution-wide policy groups to address researcher challenges with CRDM, data transfer, and data sharing.

For over ten years, data management training has been identified as a need by the biomedical research community and librarians alike. From the perspective of biomedical researchers, the lack of good quality information management for research data [ 1 , 2 ] and an absence of training for researchers to improve their data management skills are recurring issues cited in the literature and a cause for concern for research overall [ 1 , 3 , 4 ]. Similarly, librarians practicing data management have identified that researchers generally receive no formal training in data management [ 5 ] yet have a desire to learn [ 6 ] because they lack confidence in their skills.

To address this need, librarians from academic institutions have been working to provide data management education and support to their communities. By developing specific approaches to creating data management education, libraries have found successful avenues in implementing stand-alone courses and one-shot workshops [ 7 ], integrating research data management into an existing curriculum [ 8 ], and offering domain-specific training [ 9 ]. Libraries have offered these training programs by providing general data management training to undergraduate and graduate students [ 10 – 12 ], doctoral scholars [ 13 ], and the general research community [ 14 – 20 ], whereas domain-specific data management can be seen most prominently in the life sciences [ 21 ], earth and environmental sciences [ 22 , 23 ], social sciences [ 24 ], and the digital humanities [ 25 ].

While it is clear that libraries have made inroads into domain-specific areas to provide training in data management, the clinical research community—clinical faculty, project and research coordinators, postdoctoral scholars, medical residents and fellows, data analysts, and medical or doctoral degree (MD/PhD) students—is one that has not received much attention. Clinical research data management (CRDM), an integral part of the clinical research process, differs from the broader concept of research data management because it involves rigorous procedures for the standardized collection and careful management of patient data to protect patient privacy and ensure quality and accuracy in medical care. The clinical research community understands the importance of data standardization [ 26 – 29 ], data quality [ 30 – 33 ], and data collection [ 28 , 34 – 36 ] and has established good clinical data management practices (GCDMP) [ 37 ] to ensure that CRDM is conducted at the highest level of excellence.

Despite this community-driven goal toward CRDM excellence, there is a dearth of literature about data management training for clinical research, with the only evidence coming from nursing training programs [ 35 , 38 ], whose research practices are further afield in that they focus on quality improvement rather than clinical investigations. This lack of evidence is surprising considering that the need for CRDM training has been communicated [ 1 , 3 , 4 , 6 ].

My library, located in an academic medical center, has supported CRDM through National Library of Medicine informationist projects by collaborating with clinical research teams to improve data management practices [ 39 ] and, more recently, by serving as the front line of support for REDCap (an electronic data capture system for storing research data) by offering consultations and comprehensive training [ 40 ]. Through REDCap training, I identified a need to expand my knowledge of CRDM to better support the needs of our research community. While REDCap is a tool to help researchers collect data for their studies, the majority of issues that our clinical research community encountered were related to data management. These issues included developing data collection plans, assigning and managing roles and responsibilities throughout the research process, ensuring that the quality of data remains intact throughout the course of the study, and creating data collection instruments. As this recurring thread of issues expanded the learning needs of our community beyond those provided via our REDCap training, I decided to expand my knowledge to address the questions that our researchers asked, to develop a curriculum to support CRDM, and to offer and evaluate CRDM training for our community.

STUDY PURPOSE

This case study will discuss (a) the development and implementation of a 1.5-hour CRDM workshop for the medical center research community, (b) the results and outcomes from teaching the CRDM workshop, and (c) the next steps for the library in this area.

CASE PRESENTATION

Workshop development, gaining skills.

Beyond the experience I gained from working closely with researchers on their clinical research projects and through REDCap support, I took two particularly valuable training opportunities that improved my skills in CRDM: the “Data Management for Clinical Research” Coursera course [ 41 ] and “Developing Data Management Plans” course [ 42 ] offered through the online educational program sponsored by the Society for Clinical Data Management. These two courses provided me with the knowledge that I needed to teach a CRDM workshop but more importantly gave me the confidence to teach it because they provided a depth of knowledge I did not have before. These courses also served to reinforce that the issues and challenges encountered at my own institution were common data management concerns across the broader clinical research community.

Identifying core competencies and building workshop content

The primary focus for developing a 1.5-hour CRDM workshop was to use the GCDMP core guidelines [ 37 ] as the baseline structure for the workshop. The core guidelines are separated into chapters in the GCDMP, which were used as the foundation for the core competencies of the workshop. Once this baseline structure was established, my goal was to weave in answers to the common questions that our clinical research community has asked through our existing REDCap training. These questions related to how to create codebooks and data dictionaries for research projects, how to structure roles in a research team, how to use best practices for building data collection instruments, how to protect their data according to Health Insurance Portability and Accountability Act (HIPAA) regulations that they should be aware of, how to improve the quality of their data throughout a study, and how to best document procedures throughout a study.

The goal of the workshop was to tie as many examples back to REDCap as possible, because the use of REDCap was written into institutional policy as the recommended tool for research data collection, which made it essential to highlight its data management capabilities. The core competencies combined with the questions mentioned above served as the foundation for developing the learning objectives and interactive learning activities for the workshop ( Table 1 ).

Clinical research data management workshop core competencies

The core competencies and learning objectives were designed to make the workshop as practical as possible. While the theoretical components of CRDM are important and are emphasized in the workshop, the main focus was to consistently incorporate interactive learning throughout so that attendees could both apply and contextualize what they learned to their own research. Another goal of this workshop was to encourage communication between attendees to highlight common CRDM errors and provide avenues for attendees to learn about successful and unsuccessful approaches from their peers. To this end, after each core competency was taught, the workshop was designed to have attendees discuss their own experiences.

In addition to the core competencies listed in Table 1 , the overarching theme and intention applied across the workshop was the importance of maintaining good documentation throughout a clinical research project (e.g., data collection plan, roles and responsibilities documents, statistical analysis plan). By stressing the importance of documentation for each competency, I hoped that attendees would understand the value of and be able to develop their own detailed documentation at each stage of the research process. The time dedicated to developing this workshop—which included reviewing the GCDMP core competencies, outlining commonly asked questions from the research community, establishing learning objectives, building the slide deck, and creating the workshop activities—took between 80 and 100 hours to complete.

Workshop implementation

The CRDM workshop was offered broadly throughout the medical center three separate times in November 2017, January 2018, and February 2018. These workshops were promoted using our library’s email discussion list of attendees from previous data classes and the Office of Science and Research and Clinical and Translational Science Institute’s announcements emails. Direct outreach was also extended to residency directors and research coordinators, both of whom regularly attend the library’s REDCap training. A fourth workshop was offered in July 2018 as part of the library’s established Data Day to Day series [ 43 ], which the library has substantially marketed through posters, write-ups in institutional newsletters, and broadcast emails.

Workshop evaluation

The CRDM workshop evaluation consisted of both quantitative and qualitative methods using a questionnaire administered at the conclusion of each workshop ( supplemental Appendix ). This study was deemed exempt by our institutional review board (IRB). Using Likert scales, questions asked attendees to evaluate the difficulty level of the material presented in the workshop, their willingness to recommend the workshop to others, and their intention to use what they had learned in their work. Free-text questions asked attendees to specify how they would use what they learned in their current roles in the institution and what other course topics they would be interested in learning about. For the question that asked attendees to describe how they would use what they learned in their current roles, I hand coded responses in a spreadsheet using the emergent coding technique [ 44 ] to identify the competencies that attendees stated as the most applicable to their work.

Workshop results

Of the 145 attendees at the 4 workshops, 113 provided fully or partially completed evaluation forms. Overall registration to and attendance at all 4 workshops was very high, with substantial waitlists accumulating for each class offered ( Figure 1 ). In fact, the workshop offered in February 2018 was a direct result of having 60 people on the waitlist from the January session. Waitlists were useful for identifying communities that I had not reached through training to date as well as for understanding the popularity of the topic for the research community. If the waitlist was high in number, it provided another opportunity to offer the workshop or reach out to attendees to see if there was an opportunity to teach a smaller class in their departments.

Total attendance, registration, and waitlist numbers for the four clinical research data management (CRDM) workshops

There was a wide range of attendees at these workshops ( Figure 2 ), as there were no restrictions on who could attend. Project/research coordinators (n=38), faculty (n=18), and managers (n=13) were prominent attendees at the workshop, and their comments in the evaluation form reflected its value and the importance of someone from the library teaching this material.

Roles of attendees of the four CRDM workshops

Research coordinators and project managers specifically indicated that the CRDM workshop was helpful in multiple ways for their roles, including how to set up the organization of their data collection procedures, how to establish and clarify roles in a research team, and how to develop documentation for both data collection and the roles and responsibilities of their staff. Research coordinators also indicated that no other stakeholders in the institution taught this kind of material and that this type of training was essential for their work.

Faculty indicated that the workshop was beneficial for developing project management skills, gaining an awareness of the benefits of using REDCap to both collect and manage data, and clarifying the roles and responsibilities of statisticians on their team. They also mentioned the benefits of their study team taking a workshop of this kind at the beginning of a study.

Attendees more generally described the value of the resources presented in the workshop, specifically stating that using REDCap, locating resources for identifying relevant data collection standards, gaining awareness of institutional data storage options, and using the workshop slide deck to guide their CRDM processes were particularly helpful.

Overall, the evaluation data indicated positive results, with the majority of those who responded (94%) indicating the level of material was just right and almost all who responded stating they would recommend the class to others (99%) and would use what they learned in their work (98%). Additionally, responses from attendees who indicated how they would use what they learned and apply it to their current role helped provide additional context for the benefits of the CRDM workshop ( Figure 3 ) with improving documentation (37%), planning work flows (34%), using REDCap (22%), and assigning roles and responsibilities (17%) being the most prominent applications of the core competencies learned.

How attendees would use what they learned in their current roles

Finally, attendees expressed interest in many additional topics that they would like to see taught in future classes. These topics included statistics, research compliance, the legal implication of data sharing, and IRB best practices for study design. It is important to mention that attendees indicated that they would like to see these additional topics taught in tandem with the CRDM workshop so that they could gain a better understanding of CRDM from the perspective of an established institutional work flow for clinical research projects.

Considering that this was the first time that I had offered CRDM training to our research community, the overall attendance, high waitlist numbers, and percentage of attendees who said the course content was at the appropriate level validated the educational approach that I used. One major concern during the workshop development phase was that the content would be too rudimentary for our research community; however, the evaluations suggested that this was not the case. Furthermore, since one of the central goals of the CRDM workshop was to emphasize the importance of documentation for each core competency, the fact that this was the most commonly cited application of what attendees learned was further validation of the CRDM workshop’s course content.

While my approach was to utilize REDCap as a resource to demonstrate good CRDM practices because it served a direct purpose for our research community, this workshop can be taught without reference to it. The core competencies of this workshop ( Table 1 ) are based on fundamental guidelines of good CRDM practice, and these competencies and skills are applicable to any stakeholder who participates in clinical research, no matter what tool or format they decide to use to collect their data.

The positive reviews of the four broadly offered courses led to seven additional CRDM training sessions that were requested by specific departments and research teams, indicating a strong need from our research community for this material. Evaluation forms were not distributed during these seven sessions due to the consult-like nature of these requests. During these sessions, several research coordinators indicated that the CRDM workshop should be required for all clinical research teams before their studies begin. This call for additional training presents an opportunity for our library to incorporate CRDM education into existing institutional initiatives. Specifically, I identified our institutional education and training management system, residency research blocks, and principal investigator training as logical next steps for integrating CRDM education into institutional research work flows.

The evaluation data initiated the development of partnerships with other institutional stakeholders to better support clinical research training efforts. Our library has begun conversations with stakeholders from research compliance, general counsel, the IRB, the Office of Science and Research, and information technology (IT) to identify ways to better address the needs of clinical researchers. The CRDM workshop highlighted a level of uncertainty on the part of clinical researchers about how best to conduct research in the medical center and whom to contact when faced with certain questions or issues.

Subsequent discussions with the aforementioned stakeholders have emphasized a need to provide more clarity to our community about the research process. To this end, our library is leading the coordination of these groups to offer a comprehensive clinical data education series with representatives from each major department providing their own training to complement the library’s existing REDCap and CRDM workshops. This training series will likely be offered through our library’s existing “Data Day to Day” series so that the research community can take all of the classes within a short time span.

The lack of institutional clarity that attendees and the aforementioned stakeholders identified has also led to policy discussions related to data transfer, sharing, and compliance, as our current institutional procedures are unclear and poorly utilized. Through the development of new standard operating procedures and increased educational initiatives, our library is driving awareness of institutional best practices with the hopes of improving clinical research efficiency. Members from our library now sit on institutional policy working groups that are working to improve institutional data transfer and data sharing work flows.

Just as librarians at the University of Washington carved out a role for themselves in supporting clinical research efforts [ 45 ], we seized the opportunity to do the same by offering CRDM education. As the first line of defense for teaching researchers, identifying their data management issues, and hearing their concerns, our library is serving as the conduit for ensuring clinical research is conducted according to GCDM practices at our institution. Establishing partnerships with research compliance, general counsel, the Office of Science and Research, and IT provides us with additional knowledge of their institutional roles and subsequently enables us to send researchers in the right direction to receive the necessary expertise and support. As this service model develops, our library plans to monitor and assess referrals to these other departments to demonstrate the value of increasing compliance in the institution and to integrate CRDM education services into any newly developed policy (which we were successful in doing for the new institutional data storage policy and REDCap). With our library serving as the driving force behind the improvement of CRDM support, the ultimate goal is that these new partnerships will result in our research community being better trained, more compliant, and increasingly aware of established institutional work flows for clinical research

DATA AVAILABILITY STATEMENT

Supplemental file.

Working together, we can reimagine medicine to improve and extend people’s lives.

Senior Clinical Research Associate

About the role.

Your Key Responsibilities:

Trial Monitoring strategy:

• Serves as the primary site manager for assigned clinical investigative sites (first point of contact between investigative site staff and Novartis)

Allocation, initiation and conduct of trials: • Is the frontline liaison between Novartis and sites to ensure successful collaboration, meeting Novartis expectation on milestone and deliveries • Manages assigned study sites/networks, conducting phase I-IV protocols according to the monitoring plan and Novartis procedures • Facilitates the preparation and collection of site and country level documents • Performs Site Initiation Visit, ensures site personnel are fully trained on all trial related aspects and performs continuous training for amendments and new site personnel as required.

• Conducts continuous monitoring activities (onsite and/or remote). Implements site management activities to ensure compliance with protocol, GCP, global and local regulations, global and local processes to secure data integrity and patient safety.

• Accountable for continuously updating all relevant electronic systems to perform job functions

• Takes on the responsibility as SME (Subject Matter Expert) as needed

Delivery of quality data and compliance to quality standards:

• Monitors studies as per current legislations, ICH/GCP and Novartis standards • Ensures timely delivery, of high quality, robust and reliable data of the monitored sites to support the goals of Trial Monitoring as defined by Trial Monitoring.

•  Identifies, resolves & escalates issues appropriately • Collaborates with internal stakeholders and site personnel to manage data query resolution process to ensure timely and accurate data entry

• Proactively collaborates with the Clinical Project Manager (CPM) and CRA Manager as well as Medical Scientific Liaison (MSL), Clinical Regional Medical Director (CRMD), medical advisor and Strategic Site Partner to achieve key accountabilities

•Partners with SSU CRA to ensure seamless transition of site responsibility

Role Requirements:

BS/BA degree. Scientific or healthcare discipline preferred • Minimum of 3 years’ experience in site monitoring strongly preferred • Excellent knowledge of the drug development process specifically clinical trial/research • Knowledge of international standards (GCP/ICH, FDA, EMEA) • Ability to manage multiple priorities and manage time efficiently.

• Excellent Site management capabilities with demonstrated negotiating and problem-solving skills

• Strong communicator and presentation skills (oral and written)

• Fluent in both written and spoken English

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The Novartis Group of Companies are Equal Opportunity Employers and take pride in maintaining a diverse environment. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, gender identity or expression, marital or veteran status, disability, or any other legally protected status. We are committed to building diverse teams, representative of the patients and communities we serve, and we strive to create an inclusive workplace that cultivates bold innovation through collaboration and empowers our people to unleash their full potential.

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ChatGPT: Disruptive or Constructive?

Thursday, Jul 18, 2024 • Jeremiah Valentine : [email protected]

What is Chat GPT?

ChatGPT is a popular emerging technology using Artificial Intelligence. GPT stands for Generative Pre-trained Transformer, which describes an AI program that looks for patterns in language and data learning to predict the next word in a sentence or the next paragraph in an essay. The website has a friendly interface that allows users to interact with AI in a n efficient conversational tone . ChatGPT provides another opportunity for students, instructors, researchers, workers, and others to find practical solutions to everyday and complicated problems.

At the root of this conversation is Artificial Intelligence. I plan to explore applicable uses of AI and ChatGPT in the classroom , entrepreneurial potential uses, and applications in industry .

Everyday Uses of Artificial Intelligence

The use of Artificial I ntelligence varies based on the user and their end goal. While many individuals will use certain programs or websites to meet specific objectives , many companies and apps have begun to utilize this emerging technology to better meet their customer's needs.

Duolingo is a popular foreign language learning application that I use to supplement my Spanish studies . The app uses Artificial Intelligence to assess users' knowledge and understanding as they interact with the program , thus streamlining users learning outcomes.

As another example, Khan Academy is a free online resource that helps teachers and students learn any level of math or other grade school topics for free. They have created Khanmigo , using AI. The model acts as a tutor that helps work through a problem while not directly providing the answer. It can assist in writing an essay or solving a complex math problem step by step.

These everyday applications continue a trend of companies implementing this new technolog y into students and teachers' lives . . This new AI technology also allows business professionals to enhance aspects of their processes.

Entrepreneurs, A.I. and the Advantages

While AI already provides companies and organizations with new ways to interact with and better support their customers, AI could also provide emerging industries and entrepreneurs with new paths to business success. 

According to Entrpreneur.com, most businesses currently use AI for customer service purposes , however , AI could also help entrepreneurs create effective spreadsheets cataloging useful data with accuracy that can be incredibly specific or broad. Specifically with customer service, AI can quickly find what a customer needs and solve their problems efficiently. It could also analyze how effective marketing campaigns are influencing customers’ purchases.

As I researched for more information about this topic, I found an article in The Journal of Business Venturing Insights published in March 2023, sharing different techniques business students can use ChatGPT as an asset to generate entrepreneurial business pitches. The article titled “ The Artificially Intelligent Entrepreneur” written by Cole Short, an Assistant Professor of Strategy at Pepperdine University, and Jeremy C. Short, a UTA alumni and Professor at the University of North Texas at Denton, showcased different elevator pitch scenarios.

Students and entrepreneurs study CEOs who have impacted an industry dynamically; the CEO's mentality is an asset . I had the opportunity to question Dr. Jeremy Short on how he arrived at the initial question of using AI as a CEO archetype business consultant. An archetype is a symbol, term, or pattern of behavior which others have replicated or emulated.

He responded, “ We used this existing framework and selected a CEO from each archetype and used ChatGPT to create elevator pitches, social media pitches, and crowdfunding pitches. The strength of ChatGPT is based largely on the creativity of the prompt, which is where we aim as authors.”

CEO Archetypes and Prompt Engineering

ChatGPT allows the user to understand the archetypes of successful CEOs and collaborate with entrepreneurial styles. These archetypes are accessible options to consult with AI. Let ’ s break down different CEO archetypes students used during this study:

Creator CEOs are typically serial entrepreneurs and serve during the growth stages of developing new businesses. These individuals are risk takers recognizing opportunities that others don ’ t see. Elon Musk, CEO of Tesla, SpaceX, and Twitter is the creator archetype.

Transformer CEOs are created by climbing the ladder of a successful business and adding new ideas . They have a firm understanding of the company's culture and work to dramatically change the company, separating it from missteps in the past. Indra Nooyi CEO of PepsiCo is the transformer archetype.

Savior CEOs rescue businesses on the verge of failure with disciplined actions, unique experience and insights they forge a successful path forward for declining businesses. Lisa Su, CEO of AMD is the savior archetype.

ChatGPT was prompted to write an elevator pitch in the style of the previously listed CEOs. 

The response for Elon Musk included language about “ building” a product with “ cutting-edge technology.” 

Indra Nooyi ’s response included phrases like “ the world is changing” and making “ a positive impact in the world.” 

Lisa Su's response produced a pitch speaking about being “ accountable, tough and disciplined” with an emphasis on “ a strong focus on efficiency and performance.”

However, I believe these positions can help entrepreneurs develop their own successful business practices; creating a product your former employer could use to gain an advantage over the competition is disruptive. B uying a company on the brink of bankruptcy that has been mismanaged is a scenario entrepreneurs have explored and practiced .

Prompt engineering is the description of a task AI can accomplish , with instructions embedded in the input. Using prompt engineering, users can fine-tune their input to achieve a desired output incorporating a task description to guide the AI model. 

Conversation around ChatGPT and Artificial Intelligence

I asked Dr. Short about how students could use this technology as an asset that guides their learning and, additionally, how instructors can use this as well. He spoke about an assignment he is currently using in his classes. “ Chat GPT might be valuable in helping create a recipe for material that students can then refine. For example, in my social entrepreneurship class students create crowdfunding campaigns for either DonorsChoose , a platform that caters to public school teachers or GoFundMe , a service which allows a variety of project types to a larger userbase . I plan on students using ChatGPT to create a ‘rough draft’ to show me so I can see how they refine their responses for their particular campaigns this upcoming fall.” Th is approach allows students to take advantage of popular technology in a constructive way.

The journal article provided some notable conclusions about ChatGPT , i ncluding “ quality control is essential when using automated tools; a hallmark of success for large language models is their vast associative memory, this strength can also be a weakness. Specifically, models such as OpenAI’s GPT-3.5 and GPT-4 are capable of confidently generating “ hallucinated” output that appears correct but, it is incorrect or completely fabricated. ChatGPT serves as an emerging tool that can efficiently and flexibly produce a range of narrative content for entrepreneurs and serve to inspire future research at the intersection of entrepreneurship and AI.” ChatGPT ’s limitations and potential applications are continually being explored.

Industry Application

After researching various applications of AI, I spoke with Dr. George Benson, Professor and Department Chair of the Department of Management at The University of Texas at Arlington, about AI and ChatGPT from an industry perspective. His research focuses on Artificial Intelligence with Human Resource Management .

Dr. Benson told me that Artificial Intelligence is being invested heavily by human resource departments who are looking to automate hiring practices. Specifically, he mentioned “ HR is using this as a market opportunity. AI is a useful tool to sift through potential applicants by scanning their resumes for qualifications and experiences. Allowing professionals to hire applicants faster.”

This application allows the technology to handle low-level tasks, but the results generated are being handed to a human to review and act on. He spoke about the potential of A.I. “ There are a lot of unknowns, but the technology is new and getting better.” Looking towards the future, technology is already being applied in different ways . These applications are being explored in the classrooms of UTA as well.

Exploration of AI at UTA

The College of Business conduct ed a survey to understand the faculty’s attitude towards A I in the classroom. It was a part of the “Teaching with Chat GPT” workshop on Friday February 9 th , which focus ed on how to integrate Chat GPT and other AI platforms into teaching . 

Dr. Kevin Carr, a Clinical Assistant Professor of Marketing at UTA, was a part of the workshop ; he currently teaches Advanced Business Communication . I talked to him about the purpose of the workshop and what he hopes to gain from the group's sessions. 

Dr. Carr explained "The point of the workshop is designed to give faculty ideas for instruction and to develop classroom activities to work with students . Our goal for th e workshop is to introduce Artificial Intelligence as a teaching tool for faculty, including showing what AI can do potentially in the classroom. We are going to be very open to faculty’s direction, in terms of ongoing discu ssions and meetings.”

Personal Take

Artificial Intelligence or Chat GPT , in my view, is another useful tool in the toolbox of technology. It will take the air out of certain industries, and it will change jobs, yet every major technological advancement has the potential to do so. The automobile was considered radical, the use of plastic, computers in the workplace, and alternative energy have been impactful on society. 

Alternative energy was headlined as the end of oil use. The automobile changed the way cities were formed and led to the creation of a national highway system. Society has always found a way to adapt and overcome major technological innovations, artificial intelligence is not any different.

AI is the technology of tomorrow. It reminds me of something Dr. George Benson said , “ It's cool software that is a sophisticated search engine.” Google, one of the most popular search engines, reshaped the internet, as you search for resources, it is a natural starting point. AI and ChatGPT are an evolution, for students it is a tremendous resource consulting a CEO archetype, creating business pitches, and most importantly shaping the future .

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