The configuration that is generally used to set up a study in a clinical trial implies creating customizing folders, events, CRFs and setting up flex fields. Flex fields are used to capture the additional details required in a Flexfields are often created to record specific pieces of study information at a particular level. Oracle Clinical provides an intuitive, high-speed mechanism for study data entry into single or multiple clinical databases. This includes a data entry web interface, easy to use randomization and data entry and study specific edit checks. This guide will provide a comprehensive information and training about the full study setup in a clinical data trial including CRF design, define study conduct key, entering the data and making the most out of the different features available in oracle clinical.
The 1st chapter in the current guide is about an introduction to CDMS and the importance of clinical data management system. The 2nd chapter is about an introduction to Oracle Clinical, the features, advantages and database configuration used to set up a study. Oracle Clinical (OC) application is a part of Oracle Clinical suite. OC is integrated applications for designing, setting up, managing clinical trials. It is used to manage study setup, entry, analysis and this application is used by data management. Oracle clinical is used to maintain the speed, control, efficiency in data management process and to ensure higher quality and faster completion of clinical.
Importance of Clinical Data Management Software
The pharmaceutical industry today is perhaps the most organized in terms of structured documentation. Whether it is the development of new drugs, supporting clinical research, or conforming to industry regulations, the amount of data generated is monumental. The changing demographic trends, patent expiry, and intense competition from generic manufacturers are presenting a host of challenges forcing the industry to be much more cost-efficient. The key to remaining competitive in the pharmaceutical industry today is the ability to analyze clinical data and turn that analysis into actionable information. It is only with high-quality organized data that efficient analysis can take place. High-quality structured data can be aggregated and analyzed to generate safety surveillance reports or pooled with data from other studies to perform meta-analysis or can be used to create and monitor patient registries. All of these activities facilitate regulatory submissions and enable evidence-based decisions that affect the future of a company’s product or service. With pressure to reduce time to market and the cost of developing new drugs, there is a greater emphasis on biostatistical analysis and efficient data management in the clinical data process. This is the niche area of focus for most Clinical Data Management professionals. With the increased outsourcing of clinical trials globally and the offshoring of clinical data jobs, there are tremendous job opportunities in this IT-savvy bio-pharmaceutical industry. Clinical Data Management professionals in the pharmaceutical industry today should have a thorough understanding of industry-standard clinical data concepts and CDISC standards, good statistical and analytical skills, expertise in using mainstream clinical data management tools, with a willingness to constantly update knowledge with recent developments in IT and clinical research. Clinical Data Management has a diverse role and involves a series of tasks starting from CRF to database designing to data collection, data cleaning, applying various quality checks, and finally to generating different types of reports. The tasks are divided into various steps and generally in larger trials, there are separate teams for designing the database structures, data entry, and data cleaning. The entry-level clinical data management job is that of a clinical data coordinator. He is the person responsible for the data entry, and most companies prefer RN (registered nurse) to fill this position as he has a good understanding of medical terms and disease states. Gradually, with experience and/or additional educational qualifications in clinical data, he can move to positions like clinical data manager, clinical data analyst, team lead, and possibly to the position of a biostatistician. But the main entry to the clinical data management field still remains through a life science graduate opting for clinical data management courses. A life science graduate would find it easier to understand various disease states and medical terms and hence would have an added advantage provided he has a good understanding of IT techniques and programming logic.
Benefits of Training in Clinical Data Management Software
When an investment is made into a clinical data management system, training of the system is essentially required in order to obtain better quality production and effectiveness. This is in contrast with the traditional belief that training should be avoided because it takes staff away from the actual work. When users are inadequately trained, they use inefficient work-arounds which generally lead to lower quality results and can compromise the integrity of the clinical trial. Users who received software training generally spend less time on data management activities as well. This is often difficult to measure because training affects many different aspects of work, but in a study of training within clinical research organizations, Cline and Wyzgowski noted that subjects discussed how data entry seemed straightforward while sources reiterated difficulties with data entry, suggesting that trained data entry personnel anticipated and/or skirted potential problems. Finally, participants in a well-designed training program often express greater job satisfaction and a decrease in work-related stress. Considering these aspects, it is clear that the proper training of clinical data management software is beneficial to the sponsor, clinical research associate, data management personnel, and the whole clinical trial in the long run. The training of clinical data management software is beneficial to the quality of the trial in many ways. Higher quality data is obtained by improving the skills and knowledge of the data management personnel. This means ensuring data is more consistently captured, cleaned, and validated throughout the trial. Often, database setup occurs before the data management personnel have received much exposure to the study protocol and the clinical data. In a rush to build the Case Report Form and database, important aspects of the protocol are missed. This has resulted in the need for database modifications and re-cleaning of data. A knowledgeable data management team can provide a higher quality database that will decrease heavy reliance on edit check programming to clean and verify data.
Key Features of Clinical Data Management Software
This refers to the code systems that are so important in clinical study data and a data dictionary function that can be used for future reference or revision to code new data. Automatic edit checks have the advantage of relatively less need for data cleaning, as it prevents the entering of incorrect data. The ability to define and track data and its queries is particularly important in the quick-paced environment of clinical study data. Fast digital technology has led to an explosion of electronic data collection and data capture, and the necessity and importance of good data quality control is higher than ever. The speed and ease of data collection lead to an increase in both volume and complexity of data. This necessarily leads to a greater frequency and complexity of queries and increasing difficulty in keeping track of the data and the queries. These factors often lead to compromises in data quality just to keep up with the momentum of the study, but in the end result in more time and effort trying to clean and reconcile the data. Code data, data dictionary, edit checks, and the ability to define and track queries represent a proactive approach to maintaining data quality and can prevent many issues of data quality control before they occur.
Data entry is the process of entering data or information into a computer for the purpose of processing, analysis, and storage. There are two different methods of data entry available: paper-based and electronic. Paper-based data entry involves collecting data on paper and then later entering that information into a computer using selected software. Electronic data entry is the direct collection of data into a computer. In the present world, electronic data entry is used in clinical studies. It has a greater advantage than paper-based data entry, such as the avoidance of re-entry of data. It is a safer method as data is directly saved into the computer with no chances of loss, and it is easier to edit. Clinical data management software has a feature of coding systems that helps users code clinical data and a data dictionary that can be used for future reference or revision to code new data. Automatic edit checks have the advantage of relatively less need for data cleaning, as it prevents the entering of wrong data. Clinical data management software should have the ability to define and track data and its queries.
Data Entry and Validation
Data entry functionality allows the clinical data to be recorded directly into the electronic database. This is usually done by data entry screens which are programmed into the CDM system to match the case report form. Generally, a range of edit checks are programmed in to examine the data as it is being entered. These range from simple validity checks (e.g. a check that the data falls within a certain range, e.g. a male patient does not have a pregnancy test) to checks for consistency with other data already entered (e.g. a check to confirm a change in a patient’s therapy is reflected by a change in their laboratory results). In some systems, data entry can be done offline with the data being later uploaded into the database. Validation is an important step in assuring that data is of high quality. It generally involves comparing the data to that held on the original paper source and/or running a range of logic and error checks to detect errors and missing data. Automatic validation routines can save a great deal of time, but it is important to retain a data trail so that data errors can be corrected. It may be necessary to freeze the database for validation checks to be performed alongside revalidation of any data which is corrected or changed as a result of the checks.
Query Management
A query is a question or an inquiry into the correctness or completion of data. Queries should be issued as soon as discrepancies are found or whenever clarification is needed. Effective query management facilitates a quick turnaround time for query resolution, thereby minimizing any delays in the data cleaning process. When using paper CRFs, query management is a time-consuming process that involves physically locating the CRF, writing the query, and then making copies for all involved parties. This process can be expedited using EDC, but the real benefit of query management is in the tracking and resolution of queries. The software should provide a means to categorize, prioritize, and record the status of all queries. This is essential for managing large volumes of queries and for generating reports on query status. A good query management system should have an intuitive interface that enables the user to view, edit, and search queries. Automatic email notifications can be a useful feature for informing investigative sites of new queries. Finally, the system should have an audit trail to log all query-related activities, and the queries should be linkable to the data in question. As with discrepancies, this traceability is crucial for regulatory inspections.
Data Cleaning and Quality Control
While there are many areas of data entry and storage that require data cleaning, the programming and implementation of study edit checks can be viewed as a preventative measure in maintaining data quality. An edit check is a computerized check that is performed on data to identify errors and inconsistencies. For example, a range check is an edit check that identifies whether a value is within a specified range. Edit checks can be implemented at the time of data entry into a data form and/or on the data form at a later time and run centrally on the database. If an error or inconsistency is found, it will be identified and a corrective action can be performed. The implementation of edit checks at the time of data entry can prompt the data entry operator to take corrective action immediately.
A system within a CDM software that identifies data discrepancies, termed data cleaning, is essential in mitigating data quality issues. Data cleaning activities might be performed regularly on the data in the underlying database or at the time of producing output. The nature of such software often stores data in units called data forms (e.g. CRFs). Data cleaning activities are performed on each individual data form. Automatic cleaning is preferred, although some data cleaning activities may still need to be performed on the data in the underlying database after an automatic procedure has taken place. The objective of the data cleaning process should be to identify and correct all errors and inconsistencies.
Reporting and Analysis
With the increasing amount of data and complexity of clinical trials, statistical analysis plans are becoming more and more complex. It is important to be able to manage the analysis process in the same way a CDMS manages the data management process. This includes management of data and metadata, a full audit trail, and a complete archive of the analysis and program environment so that it is reproducible on the raw data at any time in the future. Finally, with the trend in modern collaborative clinical trials towards outsourcing both data management and statistical analysis, it is important to bring the various aspects of a trial together in an integrated way and share the same data, data collection, queries, and final data conclusions.
The ultimate goal of research in clinical trials is to produce useful information, which means to draw conclusions so that they can make informed decisions. Regardless of what type of analysis is planned, the common thing to all data analysis is that data analysis needs to prepare the data for analysis. Data preparation is the process of cleaning and transforming raw data prior to processing and analysis. Data cleaning needs to be done with a very methodical approach, including the use of listing and checking routines, so that the process is reproducible and well documented.
Training Program for Clinical Data Management Software
Step 1.0 Training Modules and Curriculum The training modules are structured for the Clinical Data Management professional and build on each other. They are presented in a logical fashion to enhance comprehension and retention of the material and are taught in a variety of ways including PowerPoint presentation, lecture, hands-on and practical application, and case studies. Topics include: – Basics of Clinical Data Management – Study Protocol and Case Report Form Review – Data Management Plan – CRF Design – Database Design and Build – Data Entry; SAE Reconciliation – Data Validation – Database Testing and Implementation – Quality Control and Query Management
In order to meet the growing need for properly trained clinical data management personnel, we developed a modular training program aimed at individuals involved in clinical trials who have a desire to learn clinical data management and the associated data management issues. The training program provides an overview of the role of the Clinical Data Manager and the process of clinical data management. Various aspects of clinical data management are discussed in the training modules, with the end result being a thorough understanding of the data management process and a strong foundation in the principles of clinical data management. The training modules include comprehensive coverage of the clinical data management process, managing clinical trials data, database systems, data validation, and quality control. Our training program has trainees apply the concepts learned in the training modules during practical training sessions utilizing Oracle Clinical. This provides for a better understanding of the material as trainees can see the data management process in action. Finally, trainees will put it all together during a series of case studies meant to simulate “real life” examples of clinical trial data management and issues that can arise. This will ensure that trainees have a strong understanding of clinical data management and are well prepared to take on a CDM role.
Overview of the Training Program
Understanding that the student’s time is valuable and that it is very difficult to take a break from the workforce, the CDMS training program offers flexible scheduling including evening, weekend, or intensive courses for accelerated learning. This program can also be customized to better suit the needs of a given organization. In addition, training modules can be taken separately to coincide with the Module Self-Directed Learning Package, which allows for participants to study CDMS at their own pace with online support available when needed. This is a key feature for those who want to get licensed and start working as a Clinical Data Manager with minimal CDMS education. With some scheduling, modules may even provide a preceptorship opportunity with an experienced CD manager and potential to gain experience through completion of a module.
In order to ensure a strong professional grasp of the use of CDMS, a comprehensive training program has been developed. This training program generates a solid educational foundation which will benefit those who are fairly new to clinical data management, as well as provide those with some experience a broader understanding of CDMS. The practical knowledge and experience gained from this training program will be a strong complement to some of the more theory-based knowledge offered from traditional classroom education. This training program is theme-based and offers a variety of learning techniques to accommodate the many different styles of adult learning. This training program is also punctuated with exercises and tests which are designed to reinforce participant learning. This CDMS training program allocation is listed in the table below.
Training Modules and Curriculum
My favourite module has a proven history from previous training courses of much debate and of being a learning area that participants, once successfully conquering the module (usually after the second or third attempt at a real study), feel significant improvement in their proficiency. This is Module 4 which covers the coding of clinical data and application of coding lists with a methodology to effectively store and retrieve coded data. High quality coding can substantially reduce downstream statistical programming and data management time because coded terms, if used efficiently within the database, can readily replace complex search algorithms and data queries. It is not unusual for coded data to be recoded at some stage due to changes in coding dictionaries, so it is important to build a coding structure that can adapt with minimal impact on the underlying collected data.
Modules will begin with a focus on relevant study background, ensuring all participants have a foundation understanding of the therapeutic area and data management best practices for Clinical Data Management Software application. This will particularly focus on those data management tasks which are specific to the therapeutic area. Module 2 will provide an overview of the CDMS itself, functional design and application of the system. This will be examined in the context of the study protocol and the most efficient way to achieve the specified data management outcomes. Following on from this, Module 3 will cover various Study build activities, from form and edit check design, through to defining and implementing a particular data entry workflow. This module will refer heavily to the specific study for practical examples and exercises.
Hands-on Practice and Case Studies
When scientists or healthcare professionals have a good understanding of the software and its capabilities, however, the current scenario imposes them to record data and processes trial information in another location outside the ongoing clinical trials or even to send the data overseas for data entry by offshore data entry technicians. It is a very inefficient and costly way of executing a clinical trial. Nowadays, some of the multinational medical companies have established a network that enables data entry into a web-based system directly through a part-time data entry technician and sometimes the investigator’s site personnel themselves. With some capabilities, Clinical Data Management can help both the data entry technicians and investigator’s site personnel to review the status of data entry and source document verification. Part-time data entry technicians who are located at a different location from the investigator’s site personnel often make mistakes by entering wrong data or entering personal conclusions concerning the data clarification queries. Concerning the queries submitted by data entry technicians, they can be made directly to the investigator site personnel. By this online data entry, we can decrease the time spent for DBCS activities.
Choosing the Right Software for Clinical Data Management
High-quality software selection is the cornerstone of any successful clinical data management system. All of the considerations detailed in previous sections must carefully be weighed against all available software options. Almost all clinical trial data management is accomplished through use of Electronic Data Capture (EDC) systems and the vendors who provide them often provide CDMS options as well. Many other software systems have been specifically designed for certain types of studies, some of these can be very good options but tend to be limited in functionality to a specific study type or medical specialty. In general, commercially available systems are often more flexible and easier to use, whereas home-grown databases are frequently more cost effective. In the current climate CDMS vendors with software as a service (SaaS) pricing and functionality hosted on their own servers may be the most attractive option of all. Global outsourcing to offshore locations has also become a widespread practice and should be mentioned when discussing software options.
Factors to Consider
If you are working within the pharmaceutical industry, another factor to consider is the need to comply with industry standards and regulations. In recent years, there has been a shift from paper-based to electronic data management systems for clinical and preclinical studies, and many companies are outsourcing to clinical data management services. In light of this, it is important to ensure that the software package chosen will facilitate the production of data that meets industry standards and regulations. Failure to do so may result in having to invest additional time and resources to convert or reproduce data at a later stage in order to comply with industry standards.
For those involved in large scale research projects in the clinical or pharmaceutical industry, a more comprehensive software system will be required. The more sophisticated packages are often quite expensive, so it is important to weigh up the cost of the package in relation to the funding available for the project. When considering cost, check what each package has to offer in terms of specific features and only purchase what is necessary to fulfill the needs of your project. A common mistake when selecting software is to pay for features that will never be used, so it is important to accurately define the requirements of your project before investing in a package.
When deciding on what software package to select, you must consider various factors. Firstly, take into account the scope and size of your projects. If you are a postgraduate student or an academic involved in a relatively small scale research project, you may get away with using the most basic package. Such packages are often free and can be easily downloaded for use. For example, Dmacc (www.dmacc.nl) provides a simple database system that’s suitable for small projects. However, it should be noted that free software often has its limitations, and you may find that after investing time and effort into using a free package, it may not be sufficient to accommodate the volume of data that you wish to store.
Comparison of Popular Software Options
Non-functional requirements. These define important attributes of the system that the software must satisfy and cover issues such as system performance, the development environment, and supplier characteristics such as reputation and stability.
Functional requirements. These define what the software must do and cover issues such as the depth and breadth of functionality, desired features, and user interface.
One of the best ways to determine which software to invest in is to compare the existing options. However, this is no simple task! There are a vast number of software packages available, and it would take considerable time and resources to examine even a fraction of these in detail. One approach to simplifying the decision-making process is to prepare a set of requirements or selection criteria and use these to compare software packages. Generally, the requirements will fall into two categories:
Implementation and Integration Considerations
When evaluating potential software choices, it is important to research the ease and cost of implementation, as well as the ability to integrate applications. Most software companies will tell you that their systems are easy to implement and use, but these statements are subjective. To get a true understanding, it is wise to speak with current users of the system to see what their experiences have been. Ask if the implementation went smoothly and was on schedule. Delays in implementation can be costly as it can hold up study start-up or patient enrollment. Find out if any problems were encountered and how responsive the software company was in addressing those issues. Often there are unforeseen circumstances that arise during the implementation process, a vendor with good customer service and a flexible programming team will be the most successful in seeing the project through to completion. The most telling information may come from references regarding what the software can’t do. High praise for what it can but long wish lists of what it can’t may reveal that the software is not fully functional in its current state. A similar line of questioning can be used to determine how the software integrates with other applications. With the increasing amount of technology involved in data collection, it is rare that a single software application can meet all data collection needs. It is common for different EDC systems to be utilized on studies and there may be data that is collected on paper case report forms which later needs to be entered into the system. EDC and ePRO systems often utilize randomization and trial supply management systems which require data transfer between systems. In each of these scenarios, the ability to seamlessly integrate data from one source to another is crucial in avoiding manual data entry and costly reconciliation efforts. Ask for examples of how the software has been used in conjunction with other applications and the success in doing so. This is another area where the squeaky clean demo may not reflect the reality of system capabilities.