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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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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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Division of Neonatology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

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).

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Received : 11 December 2020

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Accepted : 06 January 2021

Published : 18 February 2021

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data management plan in clinical research

Clinical Trial Data Management and Professionals

By Andy Marker | January 16, 2020 (updated September 16, 2021)

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This guide provides professionals with everything they need to understand clinical data management, offering expert advice, templates, graphics, and a sample clinical data management plan. 

Included on this page, you'll find information on how to become a clinical trial data manager , a clinical data management plan template , a clinical data validation plan template , and much more.

What Is Clinical Trial Management?

Clinical trial management refers to the structured, organized regulatory approach that managers take in clinical trial projects to produce timely and efficient project outcomes. It includes developing and maintaining specified or general software systems, processes, procedures, training, and protocols.  

What Is a Clinical Trial Management System (CTMS)?

A clinical trial management system (CTMS) is a type of project management software specific to clinical research and clinical data management. It allows for centralized planning, reporting, and tracking of all aspects of clinical trials, with the end goal of ensuring that the trials are efficient, compliant, and successful, whether across one or several institutions. 

Companies use CTMS for their clinical data management to ensure they build trust with regulatory agencies. Trust is earned as the companies collect, integrate, and validate their clinical trial data with integrity over time. A comprehensive system helps them do so. 

In a 2017 paper, “ Artificial intelligence based clinical data management systems: A review ,” Gazali discusses CTMS and what makes it worthwhile for investigators — namely, that it helps to authenticate data. Accurate study results and a trail of data collection, as collected through a quality CTMS, lend credence to research study data. Clinical trial data management systems enable researchers to adhere to quality standards and provide assurance that they are appropriately collecting, cleaning, and managing the data.

A clinical data management system also offers remote data monitoring. The sponsor, or principal investigator, may want to monitor the trial from a distance, especially if the organization has many sites. Since the FDA mandates monitoring in clinical trials, and many studies generally consider it a large cost, remote monitoring offers a lower-priced option in which sponsors can identify issues and outliers and mitigate them quickly. 

Many data management systems are also incorporating artificial intelligence (AI). AI-based clinical data management systems support process automation, data insights analysis, and critical decision making. All of this can happen as your staff inputs the research data. According to a review of clinical data management systems , researchers note that automating all dimensions of clinical data management in trials can take them from mere electronic data capture to something that helps with findings in clinical trials.

The most helpful strategies for implementing clinical data management systems balance risk reduction and lead time. All trial managers want to have their software deployed rapidly. However, it is best to set up the databases thoroughly before the trial. When staff must make software changes during the trial, it can be costly and have implications on the trial data’s validity. 

Other strategies that help organizations implement a new system include making sure that, prior to deployment, the intended users give input. These users include entities such as the contract research organization (CRO), the sponsor, staff at the investigator site, and any onsite technical support. Staff should respond well to the graphical user interface (GUI). Additionally, depending on software support, the staff can gradually expand the modules to include more functionality, perform module-based programming, and duplicate the hardware. These actions give the staff the most functionality and the software the best chance at success.

How to Compare Clinical Data Management Systems

When deciding which clinical data management system to use, compare the program’s available features and those that your clinical sites need. Additionally, you can compare clinical data management systems by reviewing the installation platforms, pricing, technical support, and number of allowed users. 

For programs that collect data on paper and send it to data entry staff, the data entry portal should be simple and allow for double entry and regular oversight. 

In general, here are the main features to compare in a clinical data management system: 

  • 21 CFR Part 11 Compliance: Electronic systems must provide assurance of authentic records. 
  • Document Management: All documents should be in a centralized location, linked to their respective records. 
  • Electronic Data Capture (EDC): Direct clinical trial data collection, as opposed to paper forms. 
  • Enrollment Management: Research studies can use this data (from interactive web or voice response systems) to enroll, randomize, and manage patients. 
  • HIPAA compliance: Ensure compliance with the Health Insurance Portability and Accountability Act to protect patients’ information.
  • Installation: Identify whether you want a cloud-based or on-premises solution and if you need mobile deployment (iOS or Android).
  • Investigator and Site Profiling: Use this function to rapidly identify the feasibility of possible investigators and sites.
  • Monitoring: The system should offer a calendar, scheduling capabilities, adverse and severe adverse event tracking, trip reporting, site communication, and triggers. 
  • Number of Users: How many users can the software can handle? Is there a minimum number of required users? Does the software provide levels of accessibility and price based on the number of users?
  • Patient Database: Separate from recruitment and enrollment, the patient database is a record of previous contacts that you can potentially draw from for future trials. 
  • Payment Portal: Pay out stipends, contracts, and other finances related to the research project.
  • Pricing: Check whether the software company offers free trials, free or premium options, monthly subscriptions, annual subscriptions, or a one-time license. 
  • Recruiting Management: This function helps streamline recruitment by targeting potential trial patients with web recruitment and prescreening. 
  • Scheduling: Use this feature to keep track of visits and events.
  • Study Planning and Workflows: This function enables you to track all required study pieces from the beginning and optimize each piece with workflows. 
  • Support: Check if the software company offers 24-hour issue support and training on the software.

What Is Clinical Data Management?

Clinical data management (CDM) is the part of clinical trial management that deals specifically with information that comes out of the trials. In order to yield ethical, repeatable results, researchers must document their patients’ medical status — including everything relative to that status — and the trial’s interventions.

Clinical data management evolved from drug companies’ need for an honest path from their research to their findings; in short, their data had to be reproducible. CDM helps evolve a standards-based approach, and many regulators are continually imposing their requirements on it. For instance, paper is no longer favored as a collection method; most clinical trials prefer software systems that improve the timeliness and quality of data. 

In one model for data management, the cycle begins when the clinical trial is in the planning stages and goes through the final analysis and lockdown of the data. The stages for data management are as follows:

  • Plan: The data manager prepares the database, forms, and overall plan.
  • Collect: Staff gathers data in the course of the trial.
  • Assure: The data manager determines if the data plan and tools meet the requirements. 
  • Identify: Staff and the data manager identify any issues or risks.
  • Preserve: The data manager preserves the data already collected and mitigates risks.
  • Integrate: The data manager oversees different datasets and information mapped together for consistency.
  • Analyze: The statisticians analyze the mapped data trends and outcomes. 
  • Lock: The data manager locks the database for integrity.

Model for Data Management in Clinical Trials

CDM Model

When it comes to data, clinical research has several areas of responsibility. Sponsors can split these functions among several staff or, in smaller studies, assign them to the main data manager. These functions include the following:

Clinical systems: Any software or technology used. 

Data management: Data acquisition, coding, and standardization.

Data review and analytics: Quality management, auditing, and statistical analysis of the collected data. 

Data standards: Checking against regulatory requirements.

Innovation: Using tools and theory that coordinate with the developing field. For more innovative templates to use in clinical trials, see “ Clinical Trial Templates to Start Your Clinical Research .”

Clinical Research Data Areas of Responsibility

Study Data Areas of Responsibility

Clinical data management is one of the most critical functions in overall clinical trial management. Staff collects data from many different sources in a clinical trial — some will necessarily be from paper forms filled out by the patient, their representative, or a staff member on their behalf. However, instead of paper, some clinics may use devices such as tablets or iPads to fill out this direct-entry data electronically. 

Clinical data management also includes top-line data , such as the demographic data summary, the primary endpoint data, and the safety data. Together, this constitutes the executive summary for clinical trials. Companies often issue this data as a part of press releases. Additional clinical trial data management activities include the following:

  • Case report form (CRF) design, annotation, and tracking
  • Central lab data
  • Data abstraction and extraction
  • Data archiving
  • Data collection
  • Data entry and validation
  • Data extraction
  • Data queries and analysis
  • Data storage and privacy
  • Data transmission
  • Database design, build, and testing
  • Database locking
  • Discrepancy management
  • Medical data coding and processing
  • Patient recorded data
  • Severe adverse event (SAE) reconciliation
  • Study metrics and tracking
  • Quality control and assurance
  • User acceptance testing
  • Validation checklist

Since there are many different types of data coming from many different sources, some data managers have become experts in hybrid data management — the synchronization required to not only make disparate data relate to each other, but also to adequately manage each type of data. For example, one study could generate data on paper from both the trial site and from a contract research organization, electronic data from the site, and clinical data measurements from a laboratory.

The Roles and Responsibilities in Clinical Data Management

Clinical data management software assigns database access limitations based on the assigned roles and responsibilities of the users. This coding ensures there is an audit trail and the users can only access their respective required functionalities, without the ability to make other changes.

All staff members, whether a manager, programmer, administrator, medical coder, data coordinator, quality control staff, or data entry person, have differing levels of access to the software system, as delineated in the protocol. The principle investigator can use the CDMS to restrict these access levels.

What Is Clinical Trial Data Management (CDM)?

Clinical trial data management (CDM) is the process of a program or study collecting, cleaning, and managing subject and study data in a way that complies with internal protocols and regulatory requirements. It is simultaneously the initial phase in a clinical trial, a field of study, and an aspirational model. 

With properly collected data in clinical trials, the study can progress and result in reliable, high-quality, statistically appropriate conclusions. Proper data collection also decreases the time from drug development to marketing. Further, proper data collection involves a multidisciplinary team, such as the research nurses, clinical data managers, investigators, support personnel, biostatisticians, and database programmers. Finally, CDM enables high-quality, understandable research, which can be capitalized on in its field and across many disciplines, according to the National Institutes of Health (NIH).

In clinical trials, data managers perform setup during the trial development phase. Data comes from the primary sources, such as site medical records, laboratory results, and patient diaries. If the project uses paper-based CRFs, staff members must transcribe them, then enter this source data into a clinical trial database. They enter paper-based forms twice, known as double data entry, and compare them, per best practice. This process significantly decreases the error rate from data entry mistakes. Electronic CRFs (eCRFs) enable staff to enter source data directly into the database. 

As with any project, the financial and human resources in clinical trials are finite. Coming up with and sticking to a solid data management plan is crucial — it should include structure for the research personnel, resources, and storage. A clinical trial is a huge investment of time, people, and money. It warrants expert-level management from its inception.

Clinical Data Management Plans

Clinical data management plans (DMPs) outline all the data management work needed in a clinical research project. This includes the timeline, any milestones, and all deliverables, as well as strategies for how the data manager will deal with disparate data sets. 

Regulators do not require a DMP, but they expect and audit them in clinical research. Thus, the DMPs should be comprehensive and all stakeholders should agree on them. They should also be living documents that staff regularly updates as the study evolves and the various study pieces develop. 

For example, during one study, the study manager might change the company used for laboratory work. This affects the DMP in two ways: First, staff needs to develop the data sharing agreement with the new company, and second, they need to integrate the data from both laboratories into one dataset at the end of the trial. The DMP should describe both.

When creating DMPs, you should also bear in mind any industry data standards, so the research can also be valuable outside of the discrete study. The Clinical Data Acquisitions Standards Harmonization (CDASH) recommends 16 standards for data collection fields for consistency in data across different studies.

The final piece of standardization in DMPs is the use of a template, which provides staff with a solid place to start developing a DMP specific to their study. Sponsors may have a standard template they use across their projects to help reduce the complexity inherent in clinical trials.

Data Management Plan Template for Clinical Trials

Sample Data Management Plan Template

This data management plan template provides the required contents of a standard clinical trial data management plan, with space and instructions to input elements such as the data validation process, the verification of database setup and implementation processes, and the data archival process. 

Download Data Management Plan Template - Word

Sample Data Management Plan for Clinical Trials

This sample data management plan shows a fictitious prospective, multicenter, single-arm study and its data management process needs. In two years of study, the data manager should regularly update this plan to demonstrate the study’s evolving needs, and document each change and update. Examples of sections include the databases used, how data will be entered and cleaned, and how staff will integrate different data sets collected in the study.

Download Sample Data Management Plan - Word

Clinical Trial Data Validation Plan

Data validation involves resolving database queries and inconsistencies by checking the data for accuracy, quality, and completeness. A data validation plan in clinical trials has all the variable calculations and checks that data managers use to identify any discrepancies in the dataset.

When the data is final, the database administrator locks it to ensure no further changes are made, as they could interrupt the integrity of the data. During reporting and analysis, experts may copy the data and reformat it into tables, lists, and graphs. Once the analysts complete their work, they report the results. When they have significant findings, they may create additional tables, lists, and graphs to present as part of the results. They then integrate these results into higher-level findings documentation. Examples of this type of documentation include investigator’s brochures or clinical case study reports (CSRs). Finally, the data manager archives the database. 

The above steps are important because they preserve the integrity of the data in the database. However, managers do not need to perform them in a strict order. Some studies may need more frequent data validation, due to the high volume of data they produce, while other studies may produce intermediate analysis and reporting as part of their predetermined requirements. Finally, due to the complexity of some studies, the data manager or analyst may need to query , which means running a data request in a database and determining cursory results so that they may adjust the protocol. 

Use this template to develop your own data validation plan. This Word template includes space and instructions for you to develop a data validation plan that you can include in your data management plan or use as a stand-alone document. Examples of sections include selecting and classifying the computer systems, validation protocol, and validation reporting.

Clinical trial Data Validation Plan Template

Download Data Validation Plan - Word

Data Management Workflow

A data management workflow is the process clinical research uses to deal with their data, from the data collection design to the electronic archival and findings presentation. This includes getting through the entry process, any batch validation, discrepancy management, coding, reconciliations, and quality control plans.

This workflow starts when researchers generate a CRF, whether manually or electronically, and continues through the final lock on the database. The data manager should perform quality checks and data cleaning throughout the workflow. The workflow steps for a data manager are as follows:

  • CRF Design: This initial design step forms the basis of initial data collection.
  • Database Design: The database should include space for all data collected in the study.
  • Data Mapping: This step integrates data from different forms or formats so researchers can consistently report it. 
  • SAE Reconciliation: Data managers should regularly review and correct severe adverse events and potential events. 
  • Database Locking: Once a study is complete, the database manager should lock the database so that no one can change the data.

Data Management Workflow

Clinical Trial Data Audits

A clinical trial data audit is a review of the information collected in order to ensure the quality, accuracy, and appropriateness for the stated research requirements, per the study protocol. Regulatory authorities, sponsors, and internal study staff can conduct two varieties of audit: overall and database-specific. 

Regulators use database audits to ensure that no one has tampered with the data. In general, there must be an audit trail to know which user made changes to what and when in the database. For example, the auditors will look at record creation, modification, and deletion, noting the usernames, dates, and times. FDA 21 CFR Part 11 includes this as a part of fraud detection, and requires that there is a complete history of the recordkeeping system and clinical trial data transparency.

The data manager develops templates for auditing the study during the study development phase and performs their own internal audits as a part of its quality management. 

This free clinical trial data management audit checklist template will help you develop your own checklist. This Excel template lets you show the status of your audit in an easy color-coded display, the category and tasks to review, and what criteria you require. It brings all your audit requirements and results together. 

CDM Clinical Data Management Audit Checklist

Download Clinical Data Management Audit Checklist - Excel

Quality Management in Clinical Trials

Data quality management (DQM) refers to the practices that ensure clinical information is of high value. In a clinical trial, DQM starts when staff first acquires the information and continues until the findings are distributed. DQM is critical in providing accurate outcomes. 

The factors that influence the quality of clinical data include how well the study investigators develop and implement each of the following data pieces: 

  • Case Report Forms (CRF): Design the CRF in parallel with the protocol so that the data collected by staff is complete, accurate, and consistent. 
  • Data Conventions: Data conventions include dates, times, and acronyms. Data managers should set these conventions during study development, especially if there are multiple study locations and investigators. 
  • Guidelines for Monitoring: The overall data quality is contingent on the quality of the monitoring guidelines established. 
  • Missing Data: Missing data are those values not available that could change the analysis findings. During study development, investigators and analysts should determine how they will handle missing data.
  • Verification of Source Data: Staff must verify that the source data is complete and accurate during data validation.

Regulations, Guidelines, and Standards in Clinical Data Management

Different regulations, guidelines, and standards govern clinical data management industry. The Clinical Data Interchange Standards Consortium (CDISC) is a global organization that holds clinical studies accountable to clinical trial data standards, international regulations, institutional and sponsor standard operating procedures (SOPs), and state laws.

There are standard operating procedures and best practices in clinical trial data management that are widespread. CDISC has two standards, the Study Data Tabulation Model Implementation Guide for Human Clinical Trials (SDTMIG), mandated by the U.S. Food and Drug Administration (FDA), and the Clinical Data Acquisition Standards Harmonization (CDASH). Also, in the industry, the Society for Clinical Data Management (SCDM) releases the Good Clinical Data Management Practices (GCDMP) guidelines and administers the International Association for Continuing Education and Training (IACET) credential for certified clinical data managers. The National Accreditations Board of Hospitals Health (NABH) provides additional guidance, such as pharmaceutical study auditing checklists. Finally, Good Clinical Practices (GCP) guidelines discuss ethical and quality standards in clinical research. 

A trial conducted under the appropriate standards ensures that staff has followed the protocol and treated the patients according to that protocol. Ultimately, this shows the integrity and reproducibility of the study and acceptance in the industry.

Case Report Forms in Data Management

In data management, CRFs are the main tool researchers use to collect information from their participants. Researchers design CRFs based on the study protocol; in them, they document all patient information per the protocol for the duration of the study’s requirements. 

CRFs should comply with all regulatory requirements and enable efficient analysis to decrease the need for data mapping during any data exchange. When longer than one page, the CRF is known as a CRF book, and each visit adds to the book. The main parts of a CRF are the header, the efficacy-related modules, and the safety-related modules:

  • CRF Header: This portion includes the patient identification information and study information, such as the study number, site number, and subject identification number.
  • Efficacy-Related Modules: This portion includes the baseline measurements, any diagnostics, the main efficacy endpoints of the trial, and any additional efficacy testing. 
  • Safety-Related Modules: The portion contains the patient’s demographic information, any adverse events, medical history, physical history, medications, confirmation of eligibility, and any reasons for release from the study.

What Is the Role of a Clinical Data Manager?

In a clinical trial, the data manager is the person who ensures that the research staff collects, manages, and prepares the resulting information accurately, comprehensively, and securely. This is a key role in clinical research, as the person is involved in the study setup, conduct, closeout, and some analysis and reporting.

Melissa Peda

Melissa Peda , Clinical Data Manager at Fred Hutch Cancer Research Center , says, “Being a clinical data manager, you have to be very detail-oriented. We write up very specific instructions for staff. For example, the specifications to a program’s database include one document that could easily have 1,000 rows in Excel, and it needs to be perfect for queries to fire in real time. Code mistakes can put your project behind, so they must do their review with a close eye. You must also be logical and think through the project setup. A good clinical data manager must be detailed, so the programmers and other staff can do their thing.” 

Krishnankutty, et al. , developed an overview of best practices for data management in clinical research. In their article, published in the Indian Journal of Pharmacology, they say that the need for strong clinical data management has sprung up from the pharmaceuticals industry wanting to fast-track drug development by having high-quality data, regardless of the type of data. Clinical data managers can expect to work with many different types of clinical data; the most common types include the following:

  • Administrative data
  • Case report forms (CRFs)
  • Electronic health records
  • Laboratory data
  • Patient diaries
  • Patient or disease registries
  • Safety data

The clinical data managers often must oversee the analysis of the data as well. Data analysis conducted in clinical trial data management is very delicate: It requires a solid dataset and an analyst who can explain the findings. Regulatory agencies, along with other companies and professionals, check the findings and analysis, so they need to be accurate and understandable.

Education and Credentials of a Clinical Data Manager

Professionals in clinical data management receive data management in clinical trials training, and often have the Certified Clinical Data Manager (CCDM) credential. Their studies can have optimized outcomes since they are executed by a competent CDM team with validated skill sets and continued professional development. 

To become certified, the applicant must have the appropriate education and experience, including the following:

  • A bachelor’s degree and two or more years of full-time data management experience.
  • An associate’s degree and three or more years of full-time data management experience.
  • Four years of full-time data management experience.
  • Part-time data management experience that adds up to the requirements above. 

Raleigh Edelstein

Raleigh Edelstein , a clinical data manager and EDC programmer, discusses the credentialing in this field. “Anyone can excel in this profession,” she says. “A CRA — a clinical research associate, also called a clinical monitor or a trial monitor — may need this credential more, as their profession is more competitive, and their experience is more necessary in trials. But if the credential makes you more confident, then I say go for it. Your experience and confidence matter.”

There are several degrees with an emphasis on clinical research that can also teach the necessary technical skills. In addition to many online options, these include the following, or a combination of the following:

  • Associate of Science in biology, mathematics, or pharmacy.
  • Bachelor of Science in one of the sciences.
  • Post-Master's certificate in clinical data management, or a certificate related to medical device and drug development.
  • Master of Science in clinical research, biotechnology, bioinformatics.
  • Doctor of Nursing Practice.
  • Doctor of Philosophy in any clinical research area.

These degree programs include concepts that help data managers understand what clinical studies need. They especially focus on survey design and data collection, but also include the following:

  • Biostatistics
  • Clinical research management and safety surveillance
  • Compliance, ethics, and law
  • New product business and strategic planning
  • New product research and development

These degree programs offer coursework that improves the relevant clinical research skills. Many of the courses are introductory to clinical research, trials, and pharmacology, and others include the following:

  • Business processes
  • Clinical outsourcing
  • Clinical research biostatistics
  • Clinical trial design
  • Compliance and monitoring
  • Data collection strategies
  • Data management
  • Electronic data capture
  • Epidemiology
  • Ethics in research
  • Federal regulatory issues 
  • Health policy and economics
  • Human research protection
  • Medical devices and product regulation
  • Patient recruitment and informed consent
  • Pharmaceutical law
  • Review boards
  • Worldwide regulations for submission

Clinical data managers can get involved with several professional organizations worldwide, including the following:

  • The Association for Clinical Data Management (ACDM): This global professional organization supports the industry by providing additional resources and promoting best practices. 
  • The Association Française de Data Management Biomédicale (DMB): This French data management organization shares information and practices among professionals. 
  • International Network of Clinical Data Management Associations (INCDMA):  Based in the United Kingdom, this professional network exchanges information and discusses relevant professional issues. 
  • The Society for Clinical Data Management (SCDM): This global organization awards CCDM credential to professionals, provides additional education, and facilitates conferences in clinical data management.

FAQs about Clinical Trial Managers

The field of clinical management is quickly expanding in many forms to support the need for new research. Below are some frequently asked questions.

How do I become a clinical trial manager?  

To become a clinical trial manager, you must obtain the appropriate education, experience, and credentialing, as detailed above. 

What is better: a Master’s in Health Administration or a Master’s in Health Sciences?  

To work as a clinical data manager, either degree program is appropriate. Your choice depends on your interest.

What can you do with a degree in biotechnology or bioenterprise?

Biotechnology is involved in the technology that aids in biological research, and bioenterprise takes the products of biotechnology and markets and sells them. 

What is a clinical application analyst? 

A clinical application analyst is a professional who helps clinics evaluate software systems and vendors. 

What is a clinical data analyst?  

A clinical data analyst is a professional who analyzes data from clinical trials, and develops and maintains databases.

Contract Research Organizations for Data Management Services

Contract research organizations (CROs) are companies that provide outsourced research services to industries such as pharmaceutical, biotechnology, and research development. Designed to keep costs low, studies can hire them to perform everything from overall project management and data management to technical jobs. 

Studies can hire CROs that specialize as clinical trial data management companies so they don’t have to worry about having all the necessary skills in-house. According to Melissa Peda, “A consultant may have the expertise that someone already working in the organization may not have, so they make sense to bring in.” Further, a contractor outside of the business can bring a lack of bias to the project. 

According to Raleigh Edelstein, “A third-party person in charge of data management may be necessary because you don’t have to worry about the lack of company loyalty that the data may need.” 

CROs can offer skills such as the following:

  • Annotation and review
  • Coding and validation
  • Database export, transfer, and locking
  • Database integration
  • Database setup and validation
  • Double data entry and third-party review of discrepancies
  • Form design
  • Planning, such as project management and data management plans 
  • Quality assessments and auditing
  • Software implementation and training
  • SAE reconciliation

Related Topics in Clinical Data Management

The following are related topics to clinical data management:

  • Application Analyst: This position deals with the software side of clinical trials. Examples of their work include choosing software, designing databases, and writing queries. 
  • Clinical Data Analyst: A professional who examines and verifies that clinical study data is appropriate and means what it is supposed to mean. 
  • Clinical Research Academic Programs: Entry-level professional positions in clinical trials often require a minimum of a bachelor’s degree. 
  • Clinical Research Associate: This clinical trial staff member designs and performs clinical studies. 
  • Laboratory Informatics: The field of data and computational systems specialized for laboratory work. 
  • Laboratory Information Management System (LIMS): LIMS enables collection and analysis of data from laboratory work. LIMS is specialized to work in different environments, such as manufacturing and pharmaceuticals. 
  • Scientific Management: This management theory studies workflows, applying science to process engineering and management.

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Best Practices for Research Data Management

  • First Online: 15 June 2023

Cite this chapter

data management plan in clinical research

  • Anita Walden 4 ,
  • Maryam Garza 5 &
  • Luke Rasmussen 6  

Part of the book series: Health Informatics ((HI))

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Data is one of the most valuable assets to answer vital questions, so careful planning is needed to ensure quality while maximizing resources and controlling costs. Mature research and clinical trial organizations create and use data management plans to guide their processes from data generation to archiving. Today, clinical research activities are more complex due to a myriad of data sources, the use of new technologies, linkages between health care, patient reported, and research data environments, and the availability of digital tools. It is important to invest in training and hiring skilled data managers and informaticists to manage this rapidly changing landscape and to integrate variable and large-scale data. For everyone in the research enterprise, being aware of and implementing end-to-end data management best practices early in the research design phase can have a positive impact on data analysis.

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Walden, A., Garza, M., Rasmussen, L. (2023). Best Practices for Research Data Management. In: Richesson, R.L., Andrews, J.E., Fultz Hollis, K. (eds) Clinical Research Informatics. Health Informatics. Springer, Cham.

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National Institute of Arthritis and Musculoskeletal and Skin Diseases logo

Investigators should consider using this template when developing the Data and Safety Monitoring Plan (DSMP) for clinical studies funded by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS).

The goal of the DSMP is to provide a general description of a plan that you intend to implement for data and safety monitoring. The DSMP should specify the following:

  • Primary and secondary outcome measures/endpoints
  • Sample size and target population
  • Inclusion and exclusion criteria
  • A list of proposed participating sites and centers for multi-site trials
  • Potential risks and benefits for participating in the study
  • Procedures for data review and reportable events
  • Project Director (PD)/Principal Investigator (PI) (required)
  • Institutional Review Board (IRB) (required)
  • Designated medical monitor
  • Internal Committee or Board
  • Independent, NIAMS-appointed Monitoring Body (MB) which can include a Data and Safety Monitoring Board (DSMB), an Observational Study Monitoring Board (OSMB), a Safety Officer (SO) or Dual SOs
  • Content and format of the safety report
  • Data Management, Quality Control and Quality Assurance

Note that all sample text should be replaced with the study specific text. There is no need to include sections that are not relevant to the particular study. Please do not use the sample text verbatim .


1.0  Study Overview

  • 1.1  Study Description
  • 1.2  Study Management

2.0  Participant Safety

  • 2.1.1  Potential Risks
  • 2.1.2  Potential Benefits
  • 2.2.1  Informed Consent Process

3.0  Reportable Events

  • 3.1.1  Adverse Events (AEs)
  • 3.1.2  Serious Adverse Events (SAEs)
  • 3.1.3  Unanticipated Problems (UPs)
  • 3.1.4  Protocol Deviations
  • 3.2  Collection and Assessment of AEs, SAEs, UPs, and Protocol Deviations
  • 3.3.1  AE Reporting Procedures
  • 3.3.2  SAE Reporting Procedures
  • 3.3.3  UP Reporting Procedures
  • 3.3.4  Protocol Deviation Reporting Procedures
  • 3.3.5  Serious or Continuing Noncompliance
  • 3.3.6  Suspension or Termination of IRB Approval

4.0  Interim Analysis & Stopping Rules

5.0  Data and Safety Monitoring

  • 5.1  Frequency of Data and Safety Monitoring
  • 5.2  Content of Data and Safety Monitoring Report
  • 5.3  Monitoring Body Membership and Affiliation
  • 5.4  Conflict of Interest for Monitoring Bodies
  • 5.5  Protection of Confidentiality
  • 5.6  Monitoring Body Responsibilities

6.0  Data Management, Quality Control, and Quality Assurance

1.0 Study Overview

1.1 study description.

This section outlines the overall goal of this project. It also describes the study design, primary and secondary outcome measures/endpoints, sample size/power calculation and target population, inclusion and exclusion criteria.

1.2 Study Management

This section includes the proposed participating sites and their responsibilities. In addition, this section should include the planned enrollment timetable (i.e. projected enrollment).

2.0 Participant Safety

2.1 potential risks and benefits for participants.

This section outlines the potential risks and benefits of the research for the study participants and for society. It should include a description of all expected adverse events (AEs), the known side effects of the intervention, and all known risks or complications of the outcomes being assessed.

2.1.1 Potential Risks

Outline potential risks for study participants including a breach of confidentiality.

{Begin sample text}

{End sample text}

2.1.2 Potential Benefits

Outline potential benefits for study participants or if there are no direct benefits to the participants.

2.2 Protection Against Study Risks

This section provides information on how risks to participants will be managed. It should specify any events that would preclude a participant from continuing in the study. In general, the format and content of this section are similar to the Human Participants section of the grant application.

In addition, this section describes measures to protect participants against study specific risks including the data security to protect the confidentiality of the data.

2.2.1 Informed Consent Process

This section explains the informed consent process. It should include, but not be limited to, who will be consenting the participant, how and under what conditions will a participant be consented, and that participation is voluntary. The informed consent process should meet the revised Common Rule requirements for consenting. For further details on this requirement, please visit: .

3.0 Data and Safety Monitoring

3.1 definitions.

This section should describe how to identify AEs, SAEs and UPs. In the case where the intervention is a Food and Drug Administration (FDA) regulated drug, device or biologic, it should include the FDA definition, grading scale and “study relatedness” criteria of AEs.

3.1.1 Adverse Events (AEs)

The definition of adverse event here is drawn from the OHRP guidance ( ); for some studies, the ICH E6 definition may be more appropriate. Expected and unexpected AEs should be listed in this section.

3.1.2 Serious Adverse Events (SAEs)

SAEs are a subset of all AEs.

3.1.3 Unanticipated Problems (UPs)

The OHRP definition of UPs can be accessed using the link provided in Section 3.1.1 above.

 {End sample text}

3.1.4 Protocol Deviations

This section should include the study definition of protocol deviations and define the events placing the participant at increased risk of harm or compromising the integrity of the safety data.

3.2 Collection and Assessment of AEs, SAEs, UPs, and Protocol Deviations

The section should include who is responsible for collecting these events, how the information will be captured, where the information will be collected from (e.g., medical records, self-reported), and what study form(s) will be used to collect the information (e.g., case report forms, direct data entry). This section should also include what type of information will be collected (e.g., event description, time of onset, assessment of seriousness, relationship to the study intervention, severity, etc.). Note that it is the NIAMS requirement to collect all AEs regardless of the expectedness or relatedness.

This section should also describe who is responsible for assessing these events. The individual(s) responsible should have the relevant clinical expertise to make such an assessment (e.g., physician, nurse practitioner, physician assistant, nurse). When assessing AEs and SAEs, the following information should be included:

  • Possibly/Probably (may be related to the intervention)
  • Definitely (clearly related to the intervention)
  • Not Related (clearly not related to the intervention)

3.3 Reporting of AEs, SAEs, UPs, Protocol Deviations, Serious or Continuing Noncompliance, and Suspension or Termination of IRB Approval

This section should describe who is responsible for reporting these events and the roles and responsibilities of each person on the clinical study team who is involved in the safety reporting to the IRB, FDA (if applicable), Monitoring Body, and NIAMS (through the NIAMS Executive Secretary). It should also include the Office for Human Research Protections (OHRP) and FDA reporting requirements. See NIAMS Reportable Events Requirements and Guidelines for more details.

3.3.1 AE Reporting Procedures

All non-serious AEs (regardless of expectedness or relatedness) are reported to the Monitoring Body and NIAMS (through the NIAMS Executive Secretary) semi-annually or as determined by the NIAMS.

3.3.2 SAE Reporting Procedures

All SAEs (regardless of expectedness or relatedness ) must be reported in an expedited manner to the NIAMS and the Monitoring Body. There may be different timeline for reporting SAE to the IRBs, FDA (if applicable), Monitoring Body and the NIAMS. The timeline for reporting SAEs to the Monitoring Body and NIAMS (through the NIAMS Executive Secretary) is within 48 hours of the investigator becoming aware of the event so that a real time assessment can be conducted, and the outcome shared in a timely manner.

3.3.3 UP Reporting Procedures

All events that meet the criteria of a UP must be reported in an expedited manner to the NIAMS and the Monitoring Body. There may be different timeline for reporting UPs to the IRBs, OHRP/FDA (if applicable), Monitoring Body, and the NIAMS. The timeline for reporting UPs to the Monitoring Body and NIAMS (through the NIAMS Executive Secretary) is within 48 hours of the investigator becoming aware of the event so that a real time assessment can be conducted, and the outcome shared in a timely manner.

3.3.4 Protocol Deviation Reporting Procedures

Protocol deviations impacting participant safety are subject to expedited reporting to the Monitoring Body and NIAMS (through the NIAMS Executive Secretary) within 48 hours of the investigator becoming aware of the even t so that a real time assessment can be conducted, and the outcome shared in a timely manner. All other events should be reported at the time of the routine DSMB meeting or submission of the safety report.

3.3.5 Serious or Continuing Noncompliance

This section should include the process in place at your institution to capture and report serious or continuing noncompliance. It should include who is responsible for reporting. Serious or continuing noncompliance must be reported to the NIAMS Program Officer and Grants Management Specialist within 3 business days of IRB determination. A copy of the IRB submission and determination must be submitted along with the report to the NIAMS. The guidance on reporting incidents to OHRP should also be followed to provide the timeline of reporting to this regulatory body.

3.3.6 Suspension or Termination of IRB Approval

This section should include the process for reporting study suspension or termination by the IRB. It should also include who is responsible for reporting to the NIAMS, OHRP, and the timeline for reporting of these events. Suspension or termination of IRB approval must include a statement of the reason(s) for the action and must be reported promptly to the NIAMS Program Officer and Grants Management Specialist within 3 business days of receipt by the PI.

4.0 Interim Analysis & Stopping Rules

This section provides information on planned interim analysis. Interim analysis may be conducted either due to pre-specified stopping rules as outlined in the protocol and at predetermined intervals, or as determined necessary by the Monitoring Body to assess safety concerns or study futility based upon accumulating data. An interim analysis may be performed for safety, efficacy and/or futility, and the reports are prepared by the unmasked study statistician or data coordinating center responsible for generating such reports. Rules for stopping the study, based on interim analysis, should be described.

If no interim analysis is planned, this should be noted within this section.

5.0 Data and Safety Monitoring

This section identifies the name of the individual or entity responsible for data and safety monitoring, what information will be reviewed, and frequency of such reviews. A brief general introduction regarding data and safety monitoring oversight should be provided in section 5.0, and further details should be provided in the subsequent sections.

5.1 Frequency of Data and Safety Monitoring

This section describes the frequency of data and safety monitoring reviews. As the reviews of reportable events (AEs, SAEs, UPs, and protocol deviations) are included in Section 3, this section should focus on the routine and ad hoc review of the full data and safety monitoring reports.   {Begin sample text}

5.2 Content of Data and Safety Monitoring Report

This section describes the content of the data and safety monitoring reports. The specifics of the study and the requests of the Monitoring Body will guide requirements for additional tables and listings. Tables for multi-site studies will present aggregated data as well as data by site. For studies with more than one intervention group, this section should indicate the plans for providing data stratified by masked intervention group (i.e., Group A vs. Group B) as part of the closed report to the Monitoring Body, while the open report should have data presented in aggregate without stratification by groups. The complete data and safety monitoring report template should be included as an appendix. Refer to the NIAMS Data and Safety Monitoring Board Report Templates ( ) for guidance.

5.3 Monitoring Body Membership and Affiliation

This section includes a roster of the Monitoring Body’s name(s) and affiliation(s). For studies with a NIAMS-appointed Monitoring Body, the NIAMS Executive Secretary will provide the name(s) and affiliation(s) of the individual(s) serving once the Monitoring Body has been assembled. However, if this is an Internally-appointed Monitoring Body (i.e., PI-appointed), the study team should enter the information in this section once the NIAMS has confirmed that no conflicts of interest with the Monitoring Body member(s) are identified.

5.4 Conflict of Interest for Monitoring Bodies

This section describes the conflict of interest procedure for Monitoring Body members. For studies with a NIAMS-appointed Monitoring Body, the NIAMS Executive Secretary will conduct a conflict of interest check on each member prior to beginning their service and on an annual basis thereafter. For studies with an Internally-appointed Monitoring Body (i.e., PI-appointed), the study team should provide the name, affiliation, and curriculum vitae (if available) of the proposed Monitoring Body member(s) to the NIAMS Executive Secretary for a conflict of interest check to be conducted. Once the conflict of interest check is complete, this section should be updated to indicate that the NIAMS did not identify any conflicts of interest for the Monitoring Body member(s).

5.5 Protection of Confidentiality

This section describes how confidentiality of data presented to the Monitoring Body will be protected.

5.6 Monitoring Body Responsibilities

A charter provides a detailed list of the Monitoring Body’s responsibilities. Listed in the sample text below are the responsibilities for a NIAMS-appointed Monitoring Body.  Please ensure that all of the items are applicable for this study. For studies with an Internally-appointed Monitoring Body, the study team should ensure that a detailed list of the Monitoring Body’s responsibilities are provided in this section.

6.0 Data Management, Quality Control, and Quality Assurance

This section describes how the site will collect, document, and review the data. Who will be responsible for data entry and ensure they are accurate and complete? Which database will be used? Does it have audit tracking capabilities? What is the data query process and frequencies? Are there any planned mitigation strategies in the event of non-compliance? What is the process for locking the final study datasets? Are there any procedures on data access and sharing as appropriate? Is there a description of security measures in place? (If you have a separate Clinical Monitoring and Data Management Plan, please reference it and utilize that information to help populate this section).

Each study should have standard operating procedures (SOPs) and/or a quality management plan that describe the following (if this is a multi-site study, each site should have SOPs and a plan): 

  • Staff training methods and how such training will be tracked
  • How data will be evaluated for compliance with the protocol and for accuracy in relation to source documents
  • The documents to be reviewed (e.g., case report forms, clinic notes, product accountability records, specimen tracking logs, questionnaires), who is responsible, and the frequency for reviews
  • Who will be responsible for addressing quality assurance (QA) issues (correcting procedures that are not in compliance with protocol) and quality control issues (correcting data entry errors). It is anticipated that QA review and data verification will be performed by someone other than the individual originally collecting the data, or by double-data entry.  The frequency of internal QA review and measures to be taken for corrective action (e.g., for trends in errors) should be included 
  • QA measures for participant recruitment, enrollment, enrollment targets, and for the validity and integrity of the data. E6 Good Clinical Practice (R1): 1.46 defines quality assurance as “All those planned and systematic actions that are established to ensure that the trial is performed and the data are generated, documented (recorded), and reported in compliance with Good Clinical Practice (GCP) and the applicable regulatory requirement(s)”

data management plan in clinical research

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How oct clinical completed patient recruitment…, completing recruitment in three countries…, resource library, why data management is essential for clinical trials.

CDM (Clinical Data Management) is essential to the success of clinical studies, including in multinational studies, where CDM is vital for the handling of vast amounts of data generated simultaneously from different sites across several countries. The process itself is considered the key to ensuring reliable, fast and flexible clinical trials. To accomplish this goal, all steps need to be fully  planned with an integrated scheme, as CDM activities start from the beginning.

The success of clinical trials depends on the accurate gathering, generation, and verification of data. This is because pharmaceutical companies, device manufacturers, researchers, and other life science organizations rely on data management to improve the outcomes and speed of clinical trials.  

Here is a brief explanation aimed at assessing why CDM is considered decisive for studies.

What is Clinical Data Management?

Clinical Data Management is intended to deliver quality data that support the purpose of a study and find answers to the research question and the conclusion to hypotheses.

CDM helps to obtain faster and better results with higher accuracy, as it allows the building of a valid, error-free and solid database.

Data management in clinical studies involves main two steps:

1.   Data collection

2.   Data cleaning 

The main objective of these steps is to collect the maximum amount of information for statistical analysis and to ensure the data are of high integrity and quality by eliminating any errors and likelihood of missing data.

Our team at OCT is dedicated to delivering accurate and high-quality CDM solutions by selecting the most advanced and innovative approach to improve the efficiency of your trial.

Also we have an experienced team of biostatisticians collaborates, scientific experts and medical writers who will optimize data accuracy and integrity and enable you to deliver faster and more accurate results. Read more about our Clinical Data Management services  here .

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Advancing Clinical Research Through Effective Data Delivery

Novel data collection and delivery strategies help usher the clinical research industry into its next era..

A photo of Rose Kidd, the president of Global Operations Delivery at ICON.

The clinical research landscape is rapidly transforming. Instead of viewing patients as subjects, sponsors now use the patients’ input to help reduce the burden they face during trials. This patient-centric approach is necessary to ensure that the clinical trial staff recruit and retain enough participants and it has led the industry to modify all stages of the clinical trial life cycle, from design to analysis. “What we are seeing is a lot more openness to innovations, digitization, remote visits for the patient, and telemedicine, for example,” said Rose Kidd, the president of Global Operations Delivery at ICON, who oversees a variety of areas including site and patient solutions, study start up, clinical data science, biostatistics, medical writing, and pharmacovigilance. “It is becoming a lot more decentralized in terms of how we collect clinical data, which is really constructive for the industry, and also hugely positive for patients.” 

The Increasing Complexity of Clinical Trials

Accurate data is central to the success of a clinical trial. “Research results are only as reliable as the data on which they are based,” Kidd remarked. “If your data is of high quality, the conclusions of that data are trustworthy.” Sponsors are now collecting more data than ever through their trials. 1 This allows them to observe trends and make well-informed decisions about a drug’s or device’s development. 

However, these changes in data volume complicate how clinicians design and run their clinical trials. They must capture enough data to fully assess the drug or device without severely disrupting a patient’s lifestyle. Additionally, the investigational sites must ensure that they have enough staff to collect the data in the clinic or through home visits and keep up with their country’s clinical trial regulations. They also must develop efficient data collection and delivery strategies to ensure a trial’s success. While poorly collected data can introduce noise, properly collected data allows clinical trial leads to quickly consolidate and analyze this information. 2 And they often require support with this process. 

Innovative Solutions to Improve Data Collection and Delivery 

Fortunately, sponsors can find that support with ICON, the healthcare intelligence and clinical research organization. “We essentially advance clinical research [by] providing outsourced services to the pharmaceutical industry, to the medical device industry, and also to government and public health organizations,” Kidd explained. With expertise in numerous therapeutic areas, such as oncology, cell and gene therapies, cardiovascular, biosimilars, vaccines, and rare diseases to mention just a few, ICON helps the pharmaceutical industry efficiently bring devices and drugs to the patients that need them, while ensuring patient safety and meeting local regulations. 

One of the areas that Kidd’s team is specifically focused on is providing solutions to advance the collection, delivery, and analysis of clinical data.

The platform that ICON provides to support sponsors in this regard not only stores data directly entered into the system by clinicians during their site or home visits, but also serves as an electronic diary for patients to remotely record their symptoms as they happen. This makes it easier for patients to participate in clinical trials while maintaining their jobs and familial responsibilities. Moreover, this solution provides clinical trial staff with insights into their data as they emerge, such as adverse event profiles and the geographical spread of these events. However, this requires that the data is input into the system in the same manner at every participating site. 

To address this problem, ICON’s solutions also include a site-facing web portal that helps to reduce the training burden by standardizing data capture and allowing site teams to learn key information about a drug or device. The portal also offers a visit-by-visit guide to ensure that clinicians are asking the necessary questions for a particular visit and helps them remember how to record the data correctly. “It is training at their fingertips when they need it most,” Kidd said. Solutions like these help sponsors obtain the high-quality clinical data that they need to progress from the trial to the market.

Clinical research is evolving and data strategies that support sites and patients alike must similarly evolve. With the right expertise, experience, and technology solutions, ICON is supporting better decision-making by sponsors.

  • Crowley E, et al. Using systematic data categorisation to quantify the types of data collected in clinical trials: The DataCat project . Trials . 2020;21(1):535.
  • McGuckin T, et al. Understanding challenges of using routinely collected health data to address clinical care gaps: A case study in Alberta, Canada . BMJ Open Qual . 2022;11(1):e001491.

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  • v.107(1); 2019 Jan

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: .

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.


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.


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.


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.

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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.

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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.

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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


Supplemental file.

Stanford Medicine

Stanford Cancer Institute

Search stanford cancer institute, new nih requirements for proposals in january 2023.

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  • Training (T)
  • Fellowships (Fs)
  • Construction (C06)
  • Conference Grants (R13)
  • Resource (Gs)
  • Research-Related Infrastructure Programs (e.g., S06)
  • Start planning now for the selection of the appropriate repository for data sharing and secondary uses.
  • Note that non-compliance with this new requirement for a DMS plan could result in your proposal not moving forward for scientific review
  • Resources and support are available through the Lane Library.

The information below is for NIH-sponsored PI and their research teams, and staff responsible for supporting sponsored research.

What are the new NIH requirements? 

From January 25, 2023, the National Institutes of Health (NIH) will require all new proposals that involve the generation of scientific data to include a 2-page Data Management and Sharing Plan (DMSP or DMP). This information must be provided in  a new “other Plan(s)” attachment field in the PHS 398 Research Plan form. Applicants must attach the DMSP/DMP in this new field in  FORMS-H applications .

The plan must include six elements (as specified in  NOT-OD-21-014 ):

  • Related Tools, Software, and/or Code
  • Data Preservation, Access, and Associated Timelines
  • Data Access, Distribution, or Reuse Considerations
  • Oversight of Data Management and Sharing

Getting Assistance

The VPDoR, School of Medicine, and a community of Stanford-led teams are partnering to coordinate support for principal investigators and research teams across campus who are preparing new proposals for the coming funding cycle.

Resources are available to Principal Investigators and research teams that will be affected by these changes.

How should I prepare?

Researchers who foresee submitting new proposals for data-generating studies should start planning now, by prioritizing the following:

  • Selection of the appropriate  repository  for data sharing and secondary uses;
  • Planning for  curation ,  deposit, local management, preservation, and sharing  of data (if applicable). This includes building the costs of these activities into project budgets.  (Information regarding categories of allowable costs can be found  here .)
  • Preparing to draft a  DSMP/DMP  (Data Sharing and Management Plan/Data Management Plan) to be included in submissions on or after 1/25/23. DMPs must account for  human subjects protections  and privacy. If your project involves data derived from human participants, it is paramount that researchers uphold  six operational privacy principles  in developing the plan and throughout the project:         
  • Proactive assessment of protections
  • Clear communication of data sharing and use in consent forms
  •  Considerations of justifiable limitations on data sharing
  • Institutional review of the conditions for data sharing
  • Protections for all data used in research
  • Remaining vigilant regarding data misuse

Where do I find information to help me estimate repository costs?

Costs depend on the repository selected and the size of the data sets to be stored.  NIH provides a list of approved domain-specific and  generalist repositories. In addition, Stanford provides  DMP consultation services  to assist in creating a budget for your data management needs and free archival of data in the  Stanford Digital Repository , which meets or exceeds NIH repository requirements.

The National Academies of Science Engineering and Medicine have posted resources to help researchers forecast  biomedical data sharing costs .

Is there guidance on designating data for ‘controlled access’ v ‘openly without access restrictions?

Yes.  The Policy provides  points to consider  factoring into controlled access decisions.

Are there tips and guidance for generating high-quality metadata, preparing files for sharing, and documentation?

Yes. Through the Generalist Repository Ecosystem Initiative (GREI), the NIH Office of Data Science Strategy has created a publicly available  webinar series  on repository sharing best practices.

What about data curation resources?

In addition to DMP data management services provided by the Stanford University Library and  Lane Library , the following external links describe curation and archival services using various cost and delivery models including:

  • Dryad  (Stanford subscribes)
  • Center for Open Science
  • Elsevier Digital Commons
  • Vivli (clinical trial data)   (Stanford subscribes)
  • Harvard Dataverse  (includes curation and price table)

See this  chart comparing generalist repositories .

What resources are available to help me?

  • NIH maintains  a website dedicated to scientific data sharing  designed as a centralized landing page for resources.
  • Visit NIH’s  12 tips for compliance  with the new policy. 
  • If you have questions about the NIH requirements, data management, repository selection, or assistance with your Data management and Sharing Plan, please contact the new  Stanford DMP Service .
  • If you have questions about the policy’s provisions concerning privacy, security, or ELSI (ethical, legal, or social implications), please e-mail Scott Edmiston  [email protected]
  • The Lane Library  in the School of Medicine has created a  resource guide  accessible to anyone interested in gaining better knowledge on the process.
  • The  Stanford Data Farm  is a research tool built on the Redivis platform enabling a host of features aligned with NIH requirements, such as persistent identifiers, logging/auditing, governance, privacy, security, and secure analytics for a variety of structured and unstructured data types.

Additional sources of information and assistance:

  • Read the final NIH Policy Data Management and Sharing and review NIH’s new  site to understand the policy changes further. 
  • Watch the recent  School of Medicine Faculty Townhall  presentation on the upcoming changes.
  • Plan to attend  The Stanford Program on Research Rigor and Reproducibility  ( SPORR ),  SoM colloquium and Help-a-Thon  on January 23, 2023, with sign-up for onsite data consultations. Hosted in collaboration with the Lane Library and Quantitative Sciences Unit. Registration details are forthcoming. 

We continue to monitor the changes and encourage you to stay connected to the Research Administrators in your schools for additional updates.

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Suicide Prevention in an Emergency Department Population: ED-SAFE

  • Contact Innovator

Suicide is the 12 th leading cause of death in the United States, and the 3 rd leading cause of death for people ages 15-24. 1 More than 4% of all emergency department visits are attributed to psychiatric conditions 2 and 3–8% of all patients have suicidal ideation when screened in the ED. 3 In addition, there are approximately 420,000 ED visits every year for intentional self-harm. 4 The emergency department (ED) is an ideal place to implement interventions designed to reduce suicidal behavior. However, there have been few trials conducted in clinical settings to reduce suicidal behavior.

Brown University and Butler Hospital created the Emergency Department Safety Assessment and Follow-Up Evaluation (ED-SAFE) innovation to reduce suicidal behavior among patients who present to the ED with suicidal ideation. They published the results from the initial clinical trial, ED-SAFE 1, a multicenter study of eight EDs that assessed the ED-SAFE innovation. The ED-SAFE 1 innovation provided participants with a standard universal suicide risk screening (any standard universal screening tool can be applied) plus a secondary suicide risk screening by an ED physician. It also included discharge resources (including a self-administered safety plan) and post-ED telephone calls based on the Coping with Long Term Active Suicide Program (CLASP) 5 focused on reducing suicide risk. 6 In ED-SAFE 1, there was a 5% absolute reduction in suicide attempts between the treatment as usual and intervention phases. 6 During the intervention phase, participants had 30% fewer total suicide attempts than participants in the treatment as usual phase. 6 The study found that universal screening alone did not reduce suicide attempts, and therefore, the reduction is most likely tied to the innovation itself. 6

The ED-SAFE 2 trial implemented two key elements that built on ED-SAFE 1: a Lean continuous quality improvement (CQI) approach and collaborative safety planning between patients and caregivers. Data were collected from 2014 to 2018 and analyzed from April 2022 to December 2022. 3 The trial included three phases: baseline (retrospective), implementation, and maintenance. 3 During implementation, each of the eight EDs formed a Lean team consisting of staff, management, information technology (IT), patient safety, and quality assurance members. The teams attended a one-day training and monthly followup meetings on Lean principles with an industrial engineering Lean expert with doctoral training. 3 The teams evaluated their workflows, identified gaps in care, designed solutions to close these gaps, and oversaw the implementation of ED-SAFE 2. 3 Additionally, the innovators implemented collaborative safety planning. Collaborative safety planning involved six-step safety plans created by clinicians and patients to help patients manage their individual suicidal crises. 3 In addition to these changes, teams were expected to increase the number of suicide risk screenings for patients. 3 The primary outcome measured was a suicide composite measure. The measure included 1) an ED visit or hospitalization due to suicidal ideation/behavior or 2) death by suicide in the six months after the index visit. 3 The composite measure improved over the three phases (baseline by 21%; implementation by 22%; and maintenance by 15.3%; p=.001). 3

Innovation Patient Safety Focus

Although the National Action Alliance for Suicide Prevention and The Joint Commission both identify EDs as an essential setting for suicide prevention, suicide prevention interventions in EDs remain underdeveloped and understudied. 3 The Joint Commission identifies suicide within 72 hours of discharge from a healthcare setting that provides around-the-clock care, including the ED, as a sentinel event (a patient safety event that results in death, permanent harm, or severe temporary harm). 3

Resources Used and Skills Needed

When implementing this innovations, organizations should consider the following:

  • Buy-in from hospital leadership and all staff involved in the continuum of care.
  • Staff to conduct post-visit phone calls.
  • Physicians willing to conduct secondary suicide prevention screenings.
  • Physicians to serve as treatment advisers for the post-visit phone calls.
  • Time and resources to train staff on the intervention.
  • Staff to conduct data analysis.
  • Leaders to train multidisciplinary teams on the Lean CQI strategy.
  • Staff to participate in the Lean teams and create collaborative safety plans with patients.
  • Clinicians with the bandwidth to create collaborative safety plans with patients. These clinicians must attend at least one training related to collaborative safety planning and they must demonstrate competency in collaborative safety planning through observation or other work samples.
  • Staff to champion the maintenance stage of the innovation.
  • Staff to measure and report results: at least one person with data related skills who can use the EHR for reporting per site; 40-80 hours are needed as an initial investment for setting up reports followed by one to two hours per month after the initial investment per site.

Use By Other Organizations

The innovator has received regular inquiries from other EDs about their ED-SAFE innovation. Much of the ED-SAFE innovation aligns with the Zero Suicide model, an emerging model for suicide prevention in healthcare. 7

Date First Implemented

Problem addressed.

The innovators created the Emergency Department Safety Assessment and Follow-Up Evaluation (ED-SAFE) 2 to build on the successes from ED-SAFE 1 (the reduction of suicidal behavior). The innovators wanted to sustain the suicide risk management that was achieved in ED-SAFE 1. 3 They deduced that a continuous quality improvement (CQI) strategy was the best way to ensure consistency, effectiveness, and the standard use of the new procedures and interventions from ED-SAFE 1. 3 This led to the development of the Lean CQI strategy, one of the key elements of ED-SAFE 2, and a focus on collaborative safety planning as a standard part of treating patients at risk for suicide who are discharged from the ED.

Description of the Innovative Activity

The ED-SAFE study was designed to assess the effect of universal screening for suicide risk and an intervention for people at risk for suicide in the ED setting. 6 ED-SAFE 1 included elements like standard universal screening, secondary risk screening, collaborative safety plans, and post-ED phone calls. Universal screening and secondary screening were carried over into the ED-SAFE 2 study. 3 The primary and secondary screener are both referred to as the Patient Safety Screener (PSS-3). 8 The first part of the PSS-3 is used to identify suicide risk for all patients who come to the acute care setting. 8 The second part of the PSS-3 takes those who have been identified with risk of suicide and guides risk stratification. 8 The second part of PSS-3 guides physicians through care pathways and mitigation procedures based on the patient’s risk level. The screeners also include other risk factors, like psychiatric hospitalization. This makes PSS-3 different than many other predominate screeners that focus entirely on suicidal ideation and behavior. When all elements of ED-SAFE 1 are implemented, the intervention costs around $1,000 per patient, per month. 9

For ED-SAFE 2, two key improvements were made: the implementation of the Lean CQI strategy and the introduction of collaborative safety planning with clinicians and patients. 3 Each of the eight EDs created a Lean CQI multidisciplinary team. 3 The team included members from all areas of patient care and support services, including frontline providers, patient safety professionals, information technology (IT) staff, and quality assurance staff. 3 Each team participated in a day-long training on Lean principles. 3 Following the training, the team attended monthly coaching calls to ensure that they had consistent knowledge of the Lean principles. 3 The Lean teams evaluated their workflows to find gaps that could be targeted with interventions. 3 The interventions to close these gaps focused on addressing the root causes associated with negative patient care outcomes. 3 In addition, the Lean teams oversaw the implementation of the interventions they established. 3 The eight ED Lean teams reported their metrics to a coordinating center each month to ensure continuity across the whole system. 3

Collaborative safety planning involved a six-step safety plan created by a clinician and a patient; the goal of the safety plan was to help manage individual suicidal crises for patients who screened positive for suicidal ideation but who were discharged from the ED. 3 When patients were in crisis states, they used the collaborative safety plans. The plans helped patients to cope with the crisis by providing collaborative interventions to curb suicidal thoughts. The plans included interventions that patients could do on their own and ideas for interventions they could do with others. The plans also directed patients on how and when to reach out for additional professional help. Although created collaboratively, the plans were written in the patient’s voice. The clinicians attended a training with a safety planning intervention trainer. The trainers followed up with the clinicians each month to provide additional training as needed, to review safety plans, and to provide feedback to the clinicians. 3

Context of the Innovation

Emergency departments treat many patients who are at risk for suicidal behavior. High-risk individuals are susceptible to suicide attempts after their ED visit. 10 In addition, a significant number of those who die by suicide received care in an ED in the period prior to death. 11 In a study using Medicaid data from 2008 to 2018, researchers looked at data from national cohorts of patients with mental health ED visits due to suicide attempts. Researchers wanted to determine the rate of suicide for these patients up to one year after discharge. 12 Among these patients, the suicide rate was 325.4 per 100,000 person years. 12 This finding contrasts with the rate of suicide in the general population as reported by the Centers for Disease Control and Prevention (CDC), which was 14.1 per 100,000 person years in 2021. 13 Because of these key patient safety factors, the innovators chose to conduct this intervention to improve patient safety for those at risk of suicide or suicidal behavior who are presenting to the ED.

The positive results from ED-SAFE 1 prompted the development of ED-SAFE 2. In ED-SAFE 1, compared with the treatment as usual phase, patients in the intervention phase showed a 5% absolute reduction in the risk of a suicide attempt (23% vs 18%; p=0.05). 6 Participants in the intervention phase in ED-SAFE 1 had 30% fewer total suicide attempts than participants in the treatment as usual phase. 3 For ED-SAFE 2, across all three phases (baseline, implementation, and maintenance), 2,761 patient encounters were included in the study analysis. 3 The intervention was based on a suicide composite measure. The measure included an ED visit or hospitalization due to suicidal ideation/behavior or death by suicide in the six months after the index visit. 3 The percentage of patient encounters with a suicide composite outcome decreased from 21% as baseline (216 of 1030) and 22% at implementation (213 of 967) to 15.3% during the maintenance phase (117 of 764; p=0.001). 3 The adjusted odds ratio of risk demonstrated a 43% reduction in the maintenance phase compared to the implementation phase and a 39% reduction in the maintenance phase compared to the baseline phase. 3

Planning and Development Process

When planning and developing this innovation, it is important to ensure that the innovating site is committed to change, there is support from senior leadership, a multidisciplinary team is convened, a standard definition of suicide risk is established, and a gap analysis is completed.

The innovators recommend convening a multidisciplinary team when planning and developing this innovation. The team must consist of members of the healthcare organization who are involved in the reduction of suicide and care pathways within the innovating organization, if possible. This may include senior leaders, frontline staff, patient safety officers, and nurse managers. The team will be instrumental in developing the innovation. Because there is no one-size-fits-all approach for EDs, the team should adapt ED-SAFE to fit their organization. Senior leadership should give the multidisciplinary team the authority to make changes in the organization that are necessary for the innovation’s success. For example, there must be a suicide risk screener administered with the innovation. However, an innovating organization should have the flexibility to pick which screener they would like to use, when they would like to screen patients, who they would like to administer the screener, and whether the screener is universal for all patients who present to the ED.

Before this innovation is implemented, it is critical that the innovation organization agree on a standard definition of suicide risk. This definition will be instrumental in determining who is eligible for the innovation.

In addition, the innovating organization should conduct a gap analysis. The gap analysis will evaluate the current state of managing risk of suicide among patients presenting to the ED versus what the innovating organization would like to achieve. The organization can determine the current state by convening meetings with management and frontline staff to reconcile the differences between the policies in place to reduce suicide risk and what is actually occurring at the organization. The organization will remedy the gaps identified in the analysis with targeted interventions. In addition, the organization will use data discovered in the gap analysis to monitor the success of the innovation.

If this innovation was used in a smaller emergency department, the smaller emergency department would likely need to adjust the scope and pace of the innovation to make it successful. However, if the ED has a strong champion, the innovation could be successful. For example, the smaller organization may need to establish who their quality improvement team will be, as they may not have a dedicated quality improvement department. A nurse manager could serve as the quality improvement lead. In addition, the smaller ED could conduct a modest amount of chart reviews rather than using EHR data reports to evaluate performance.

Funding Sources

The National Institute of Mental Health funded this innovation.

Getting Started with This Innovation

The innovators found that a deployment plan was necessary when getting started with this innovation. A deployment plan lays out the step-by-step process for implementing the innovation. It includes details like what to do first and includes the ways that every person involved in the innovation will be trained. The deployment plan also describes how the organization will gather and measure metrics and tracks implementation successes and challenges when implementing the innovation. For example, the deployment plan may include metrics that track whether the innovation is being adopted within the organization. If the innovator discovers that the organization is not adopting certain elements, they can make changes to rectify the situation. For example, the innovator discovered via their deployment plan that the screener was not being used as intended. Because of this, the innovator created targeted trainings to remedy the situation and improve the success of the innovation.

Sustaining This Innovation

The innovators found a continuous quality improvement (CQI) approach to be the primary investment in sustaining the innovation. The CQI approach identifies areas for improvement throughout the lifetime of the innovation. The CQI approach monitors the training of personnel, reviews performance, identifies gaps, creates ways to remediate gaps, implements, and evaluates remediation measures, and remeasures performance after remediation. The CQI approach should be iterative and provide flexibility for staff who take part in the innovation.

One component of the innovator’s CQI process were spot checks. The innovator conducted spot checks using the same measures that were created during the gap analysis. Spot checks help the innovators gauge whether the innovation is reaching the final target that was established during the gap analysis. If the spot checks find there are reoccurring gaps, an innovator may want to consider going back to a previous implementation phase to increase adoption of the innovation. The innovators found that conducting spot checks during suicide awareness month garnered strong support from leadership and frontline staff, as there was already a renewed focus on suicide prevention at that time.

The innovators found the minimum investment to maintain results included quarterly meetings with the innovation team (usually four to ten people for one hour), a core steering committee (usually three to five people for two hours a quarter, plus leading the quarterly meetings), and trainers focused on training new employees and current staff as needed (three to five hours a quarter). An annual review of current protocols to determine if the protocols should be updated due to new care expectations or evidence-based best practices is also beneficial when sustaining this innovation.

References/Related Articles Emergency Department Safety Assessment and Follow-Up Evaluation (ED-SAFE).

Miller I, Gaudiano B, Weinstock L. The Coping Long Term with Active Suicide Program (CLASP): description and pilot data. Suicide Life Threat Behav . 2016;46(6):752-761. doi:10.1111/sltb.12247

  • Owens P, Mutter R, Stocks C. Mental Health and Substance Abuse-Related Emergency Department Visits Among Adults, 2007 . Statistical Brief #92. July 2010. Agency for Healthcare Research and Quality, Rockville, MD.
  • Boudreaux ED, Larkin C, Vallejo Sefair A, et al. Effect of an emergency department process improvement package on suicide prevention: the ED-SAFE 2 cluster randomized clinical trial. JAMA Psychiatry 2023;80(7):665-674. doi:10.1001/jamapsychiatry.2023.1304
  • Ting SA, Sullivan AF, Boudreaux ED, Miller I, Camargo CA Jr. Trends in US emergency department visits for attempted suicide and self-inflicted injury, 1993-2008. Gen Hosp Psychiatry . 2012;34(5):557-565. doi:10.1016/j.genhosppsych.2012.03.020
  • Miller I, Gaudiano B, Weinstock L. The Coping Long Term with Active Suicide Program (CLASP: A Clinicians Guide to a Multi-Modal Intervention for Suicide Prevention. Oxford University Press; 2022.
  • Miller IW, Camargo Jr CA, Arias SA, et al. Suicide prevention in an emergency department population: the ED-SAFE Study. JAMA Psychiatry . 2017;74(6):563-570. doi:10.1001/jamapsychiatry.2017.0678
  • Education Development Center. Zero Suicide. Accessed February 12, 2024.
  • Boudreaux ED. The Patient Safety Screener: A Brief Tool to Detect Suicide Risk. Accessed March 18, 2024.
  • Dunlap L, Orme S, Zarkin G, Miller I. Screening and Intervention for Suicide Prevention: A Cost-Effectiveness Analysis of the ED-SAFE Interventions. Accessed March 19 2024.
  • Olfson M, Marcus SC, Bridge JA. Focusing suicide prevention on periods of high risk. JAMA . 2014;311(11):1107-1108. doi:10.1001/jama.2014.501
  • Ahmedani BK, Simon GE, Stewart C, et al. Health care contacts in the year before suicide death. J Gen Intern Med . 2014;29(6):870-877. doi:10.1007/s11606-014-2767-3
  • Olfson M, Gao YN, Xie M, Wiesel Cullen S, Marcus SC. Suicide risk among adults with mental health emergency department visits with and without suicidal symptoms. J Clin Psychiatry . 2021;82(6):20m13833. doi:10.4088/JCP.20m13833
  • Garnett MF, Curtin SC. Suicide mortality in the United States, 2001-2021 . NCHS Data Brief, no. 464. April 2023. National Center for Health Statistics, Hyattsville, MD.

The inclusion of an innovation in PSNet does not constitute or imply an endorsement by the U.S. Department of Health and Human Services, the Agency for Healthcare Research and Quality, or of the submitter or developer of the innovation.

Contact the Innovator

ED-SAFE 1: Dr. Ivan Miller, [email protected]

ED-SAFE 2: Dr. Edwin Boudreaux, [email protected]


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Evaluating independent double checks in the pediatric intensive care unit: a human factors engineering approach. February 21, 2024

Pediatric safety incidents from an intensive care reporting system. May 27, 2009

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A Crisis in Health Care: A Call to Action on Physician Burnout. January 30, 2019

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Diagnostic Error in Medicine. October 7, 2009

Simulation in Maternal Fetal Medicine. June 26, 2013

The 2013 John M. Eisenberg Patient Safety and Quality Awards. April 30, 2014

Iatrogenic Disease. March 5, 2008

Making Healthcare Safer III. March 18, 2020

Patient Safety and Quality. March 3, 2010

2011 John M. Eisenberg Patient Safety and Quality Awards. June 27, 2012

The Second Society for Simulation in Healthcare Research Summit: Beyond Our Boundaries. August 1, 2018

Risk, Safety and Reliability Special Issue. May 20, 2009

Quality and Safety Competencies in Nursing Education. January 13, 2010

Contributions from Ergonomics and Human Factors. November 17, 2010

Innovation in Perioperative Patient Safety. February 27, 2013

Making Health Care Safer: A Critical Review of Modern Evidence Supporting Strategies to Improve Patient Safety. March 6, 2013

The Science of Simulation in Healthcare: Defining and Developing Clinical Expertise. November 19, 2008

Special Issue: Progress at the Intersection of Patient Safety and Medical Liability. December 14, 2016

Patient Safety Papers 3. April 23, 2008

Quality and Safety in Medicine. December 9, 2009

Patient Safety. November 21, 2018

Special Issue on Health Information Technology. April 16, 2008

Patient Safety Papers 4. September 2, 2009

Patient Safety Innovations

Risk Mitigation Using the Anesthesia Risk Alert Program: Applying a Proactive Approach With Data Review & Collaborating With a Second Practitioner

Should dignity preservation be a precondition for safety and a design priority for healing in inpatient psychiatry spaces? April 10, 2024

Patient and Family Centered I-PASS (Family-Centered Communication Program to Reduce Medical Errors and Improve Family Experience and Communication Processes)

The e-autopsy/e-biopsy: a systematic chart review to increase safety and diagnostic accuracy innovation.

Clinicians' insights on emergency department boarding: an explanatory mixed methods study evaluating patient care and clinician well-being. August 23, 2023

Ambulatory Safety Nets to Reduce Missed and Delayed Diagnoses of Cancer

Remote response team and customized alert settings help improve management of sepsis, preventing falls through patient and family engagement to create customized prevention plans.

Effect of an emergency department process improvement package on suicide prevention: the ED-SAFE 2 cluster randomized clinical trial. May 31, 2023

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    Clinical Data Management is intended to deliver quality data that support the purpose of a study and find answers to the research question and the conclusion to hypotheses. CDM helps to obtain faster and better results with higher accuracy, as it allows the building of a valid, error-free and solid database. Data management in clinical studies ...

  19. Advancing Clinical Research Through Effective Data Delivery

    Through her 30-year career with ICON, Rose Kidd has watched the company and the clinical research industry evolve. Now her team provides the clinical data science, biostatistics, medical writing, pharmacovigilance, study start up, and site and patient solutions that sponsors need to improve their clinical trials.

  20. Adapting data management education to support clinical research

    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) to ensure that CRDM is conducted at the highest level of excellence.

  21. New NIH proposal requirements: Data Management and Sharing Plan

    From January 25, 2023, the National Institutes of Health (NIH) will require all new proposals that involve the generation of scientific data to include a 2-page Data Management and Sharing Plan (DMSP or DMP). This information must be provided in a new "other Plan(s)" attachment field in the PHS 398 Research Plan form.

  22. Clinical Research Associate

    Monitors patient data & study-related information related to clinical study sites and clinical trial participation.. Ensures the investigator adheres to research protocols, regulatory requirements and good clinical practices and provides input into data validation plan. Provides timely and accurate monitoring of patient data and study-related information from source documents, research records ...

  23. Suicide Prevention in an Emergency Department Population: ED-SAFE

    Summary. Suicide is the 12 th leading cause of death in the United States, and the 3 rd leading cause of death for people ages 15-24. 1 More than 4% of all emergency department visits are attributed to psychiatric conditions 2 and 3-8% of all patients have suicidal ideation when screened in the ED. 3 In addition, there are approximately 420,000 ED visits every year for intentional self-harm ...