1) The document discusses best practices for ensuring study success through the effective use of electronic data capture (EDC) technology. It outlines key factors like study setup, risk-based monitoring, data entry and reporting, safety reporting, data integrity and quality, and database lock.
2) Ensuring quick data entry and minimal errors is essential for success. The document advocates partnering with an EDC provider that can offer guidance, support innovative tools for data quality, and work flexibly with other systems.
3) OmniComm's EDC system TrialMaster is highlighted as providing features that streamline processes, catch errors early, and facilitate risk-based monitoring and post-study data access to meet regulatory standards
As per EU MDR, Post Marketing Clinical Follow-up (PMCF) is a continuous process where device manufacturers need to proactively collect and evaluate clinical data of the device when it is used as per the intended purpose. EU MDR gives more emphasize on PMCF data to confirm the safety and performance of the device throughout its expected lifetime, ensure continued acceptability of identified risks and detect emerging risks based on factual evidence.
In this report, ISR leverages the insights and real-world experiences investigative sites have with electronic medical records (EMRs) and clinical trials. The report examines how sites currently use EMRs for various clinical trial activities and provides recommendations to improve trial efficiency.
CDISC & Risk Based Monitoring to Compress Clinical Trial DurationClinical Data Inc .
Technology adoption in the clinical trial space has lagged other industries resulting in high cost / risk of new drug development and ongoing safety concerns of clinical trials.
The focus of this presentation is to educate bio-pharma companies, data managers and CROs on technological advances in the clinical trial space.
How to Improve Quality and Efficiency Using Test Data AnalyticsTequra Analytics
Discover 8 ways in our guide for advanced manufacturers.
Do you perform advanced manufacturing in an industry such as aerospace, automotive, medical devices or telecoms? Is product testing part of your manufacturing process? If you can answer yes to these questions, keep reading to learn how test data analytics can enable many improvements.
As per EU MDR, Post Marketing Clinical Follow-up (PMCF) is a continuous process where device manufacturers need to proactively collect and evaluate clinical data of the device when it is used as per the intended purpose. EU MDR gives more emphasize on PMCF data to confirm the safety and performance of the device throughout its expected lifetime, ensure continued acceptability of identified risks and detect emerging risks based on factual evidence.
In this report, ISR leverages the insights and real-world experiences investigative sites have with electronic medical records (EMRs) and clinical trials. The report examines how sites currently use EMRs for various clinical trial activities and provides recommendations to improve trial efficiency.
CDISC & Risk Based Monitoring to Compress Clinical Trial DurationClinical Data Inc .
Technology adoption in the clinical trial space has lagged other industries resulting in high cost / risk of new drug development and ongoing safety concerns of clinical trials.
The focus of this presentation is to educate bio-pharma companies, data managers and CROs on technological advances in the clinical trial space.
How to Improve Quality and Efficiency Using Test Data AnalyticsTequra Analytics
Discover 8 ways in our guide for advanced manufacturers.
Do you perform advanced manufacturing in an industry such as aerospace, automotive, medical devices or telecoms? Is product testing part of your manufacturing process? If you can answer yes to these questions, keep reading to learn how test data analytics can enable many improvements.
clinical data management in clinical research, helpful for pharmacy, nursing, medical, health care providers, clinical research organization, PharmD, CROs, Clinical trial industry, human biomedical research.
A unified platform providing functionality based on role is a logical progression in eClinical technology development, with the majority of sponsors/CROs preferring and supporting this evolution.
Enhancing Code Blue Performance with xAPIWatershed
Providing care to more than 500,000 patients each year, MedStar Health is the largest healthcare provider in the Washington, D.C./Maryland region. As an organization, they’re committed to providing the best care during emergency situations in which patients are in cardiopulmonary arrest (referred to as “Code Blues”).
During a Code Blue, the stakes are literally life and death—which is why it’s vital that MedStar resuscitation team members are well trained. Speed is vital in the seconds and minutes that follow a Code Blue, including the amounts of time for performing chest compressions and defibrillation and administering medications to a patient.
As a result, MedStar’s Code Blue training and learning resources have focused on improving clinician performance to reduce these times. However, MedStar didn’t have extensive information on the effectiveness of various training programs and learning resources.
Using Watershed and xAPI to aggregate and visualize data from multiple data sources, MedStar is now able to answer a range of questions about the usage and effectiveness of their training systems. They also have a better understanding of where they need to target their attention to improve performance. In particular, they can test the “chain of cause and effect” from training to simulations to final results.
This paper describes a study of the adoption of Picture Archiving and Communication Systems(PACS). The objective of this study is threefold. First, the adoption rate of PACS by European hospitals is described in relation to the use of other medical information systems. From this, a Medical Information Systems Maturity Scale (MISIS) for hospitals is statistically constructed. The second objective is to identify the key determinants of a hospitals’ score on MISIS, i.e. analyzing the situationality of the scale. The final objective of the paper is to explain and the variation in Medical Information System Maturity among hospitals in Europe. Using the results of this empirical analysis we set out general guidelines for the evolution of PACS maturity [1] within hospitals, based on principals of strategic alignment and situational growth.
TRI was founded as a subsidiary of Triumph Consultancy Services in 2013, following 12 years of consulting to the clinical trial industry. TRI has been evaluating the specific challenges facing the industry when implementing a risk-based monitoring strategy and the various approaches and products being utilized by organizations as they move into the RBM arena. This paper aims to summarize our findings and provide guidance as to how the main challenges can be overcome.
Modeling results from Health Sciences dataJudson Chase
Access to Heath Sciences data by Pharma, Academia, and Government has greater transparency is more generally available than ever before . . . offering untold possibilities through statistics and modeling to predict effect and impact BEFORE decisions are made.
Adaptive Clinical Trials: Role of Modelling and Simulation SGS
To increase the efficiency of trials in drug development, optimal experimental design has been used to successfully optimize dose allocation and sampling schedules. Better incremental decisions in Phase I and II result in greater likelihood that the safety and efficacy of the right dose is being studied, for the right indication and in the right patient population. This approach involves a pre-planned adaptation of aspects of study design based on statistical and/or pharmacokinetic/pharmacodynamic (PK/PD) analysis. From a modelling and simulation (M&S) perspective, a prior understanding of concentration (dose)-efficacy and of concentration (dose)-toxicity relationship is needed.
An brief introduction to the clinical data management process is described in this slides. These slides provides you the information regarding the data evaluation in the clinical trials , edit checks and data review finally data locking,then the data is submitted to the concerned regulatory body.
Clinical research and clinical data management - Ikya Globalikya global
Data management functions in clinical trials—extensive data cleaning, full query management, protocol deviation management, batch processing, as examples—have traditionally been served by stand-alone clinical data management systems (CDMS), whose input is from paper forms or from separate electronic data capture systems. Distinct electronic data capture and data management systems require data integration, with resulting timing and reconciliation issues.
Integrated Safety and Risk Management Solutions - Addressing the Needs of Sma...Covance
Premarketing clinical safety and PV activities, and the technology infrastructure that supports it, are typically outsourced to multiple contract research organisations (CROs) as part of their clinical trial programmes. **Disclaimer: This article was previously published. Sciformix is now a Covance company.
Technology Considerations to Enable the Risk-Based Monitoring Methodologywww.datatrak.com
TransCelerate BioPharma Inc developed a methodology based on the notion that shifting monitoring processes from an excessive concentration on source data verification to comprehensive risk-driven monitoring will increase efficiencies and enhance patient
safety and data integrity while maintaining adherence to good clinical practice regulations. This philosophical shift in monitoring processes employs the addition of centralized and off-site mechanisms to monitor important trial parameters holistically, and it uses adaptive on-site monitoring to further support site processes, subject safety, and data quality. The main tenet is to use available data to monitor, assess, and mitigate the overall risk associated with clinical trials. Having the right technology is critical to collect and aggregate data, provide analytical capabilities, and track issues to demonstrate that a thorough quality management framework is in place. This paper lays out the high-level considerations when designing and building an integrated technology solution that will aid in scaling the methodology across an organization’s portfolio.
In the past decade, there has been a significant increase in the use of Data Monitoring
Committees (DMC) and Adaptive Designs (AD) in clinical trials. While the monitoring of safety
data by a formal committee is not required for all clinical trials, it has become the norm to have
a formal DMC conduct periodic safety reviews for any controlled trial that evaluates treatments
intended to prolong life or reduce risk of major adverse health outcomes, or for trials that
compare rates of mortality or major morbidity. Confirmatory, pivotal, and adaptive design trials
have more complex operational issues requiring an external and independent DMC. The DMC
may have access to unblinded interim data, be required to make expert recommendations
about how the trial should continue, and then ensure that planned adaptations are
implemented as outlined in the protocol without involving the sponsor or exposing it to
unblinded data or results.
This added complexity creates a challenge and a question: how can the DMC, statisticians, and
sponsor effectively communicate, share blinded and unblinded data, perform analyses, and
implement adaptations without introducing operational bias or compromising the integrity of
the trial? One solution is to utilize a sophisticated computer system that can provide the
security and necessary firewalls to ensure that interim data is only accessible to those it is
intended for, that the rules and processes outlined in the protocol and DMC charter are
enforced, and that communication between the DMC and sponsor is effectively facilitated while
protecting the integrity of the trial and preventing the introduction of operational bias.
The system must also provide an audit trail that tracks “who saw what and when” providing
evidence to regulatory authorities that the protocol was strictly followed with a minimal
possibility of bias. This white paper describes the computer system, ACES, which Cytel has built,
that makes all of this possible. ACES (Access Control Execution System) has been purpose-built
to address the operational complexities inherent in adaptive design and pivotal clinical trials.
Journal for Clinical Studies: Close Cooperation Between Data Management and B...KCR
Every clinical trial is a source of multidimensional data, analyzed to answer questions on safety, efficacy and others. Invalid or incomplete data may lead to invalid conclusions and wrong decision. KCR’s Biostatistician, Adrian Olszewski, highlights the importance of cooperation between data management and biostatistics to improve data quality by introducing both statistical knowledge and the ability to create specialized, programmatic tools and advanced queries giving a good foundation for deeper and faster data investigations. Read more in the article published in the October Issue of Journal for Clinical Studies (p. 42-46).
Safety & Regulatory Solutions for Small and Medium-sized Life Science Organiz...Covance
A key issue for small and medium-sized enterprises is the optimal utilization of their limited resources for moving their product pipeline through clinical development, and launching and marketing their approved product(s). This is further heightened as both clinical trials and post-marketing activities continue to grow in complexity and scope due to stringent regulatory pressures, patient involvement and globalization. Yet companies face overwhelming pressure to get their product to market as quickly as possible.
The Use of EDC in Canadian Clinical TrialsKhaled El Emam
Presentation at CHEO Research Rounds on a study to estimate the proportion of Canadian clinical trials that are using an Electronic Data Capture system during the period 2006-2007.
Scientific & systematic collection of data for clinical study is called as Clinical Data Management-
Clinical Data Management-Web Based Data Capture EDC & RDC , Oracle
SAS
Office software
UW Catalyst data collection (University of Washington)
REDCAP (Research electronic data capture)
OPENCLINICA
STUDY TRAX
clinical data management in clinical research, helpful for pharmacy, nursing, medical, health care providers, clinical research organization, PharmD, CROs, Clinical trial industry, human biomedical research.
A unified platform providing functionality based on role is a logical progression in eClinical technology development, with the majority of sponsors/CROs preferring and supporting this evolution.
Enhancing Code Blue Performance with xAPIWatershed
Providing care to more than 500,000 patients each year, MedStar Health is the largest healthcare provider in the Washington, D.C./Maryland region. As an organization, they’re committed to providing the best care during emergency situations in which patients are in cardiopulmonary arrest (referred to as “Code Blues”).
During a Code Blue, the stakes are literally life and death—which is why it’s vital that MedStar resuscitation team members are well trained. Speed is vital in the seconds and minutes that follow a Code Blue, including the amounts of time for performing chest compressions and defibrillation and administering medications to a patient.
As a result, MedStar’s Code Blue training and learning resources have focused on improving clinician performance to reduce these times. However, MedStar didn’t have extensive information on the effectiveness of various training programs and learning resources.
Using Watershed and xAPI to aggregate and visualize data from multiple data sources, MedStar is now able to answer a range of questions about the usage and effectiveness of their training systems. They also have a better understanding of where they need to target their attention to improve performance. In particular, they can test the “chain of cause and effect” from training to simulations to final results.
This paper describes a study of the adoption of Picture Archiving and Communication Systems(PACS). The objective of this study is threefold. First, the adoption rate of PACS by European hospitals is described in relation to the use of other medical information systems. From this, a Medical Information Systems Maturity Scale (MISIS) for hospitals is statistically constructed. The second objective is to identify the key determinants of a hospitals’ score on MISIS, i.e. analyzing the situationality of the scale. The final objective of the paper is to explain and the variation in Medical Information System Maturity among hospitals in Europe. Using the results of this empirical analysis we set out general guidelines for the evolution of PACS maturity [1] within hospitals, based on principals of strategic alignment and situational growth.
TRI was founded as a subsidiary of Triumph Consultancy Services in 2013, following 12 years of consulting to the clinical trial industry. TRI has been evaluating the specific challenges facing the industry when implementing a risk-based monitoring strategy and the various approaches and products being utilized by organizations as they move into the RBM arena. This paper aims to summarize our findings and provide guidance as to how the main challenges can be overcome.
Modeling results from Health Sciences dataJudson Chase
Access to Heath Sciences data by Pharma, Academia, and Government has greater transparency is more generally available than ever before . . . offering untold possibilities through statistics and modeling to predict effect and impact BEFORE decisions are made.
Adaptive Clinical Trials: Role of Modelling and Simulation SGS
To increase the efficiency of trials in drug development, optimal experimental design has been used to successfully optimize dose allocation and sampling schedules. Better incremental decisions in Phase I and II result in greater likelihood that the safety and efficacy of the right dose is being studied, for the right indication and in the right patient population. This approach involves a pre-planned adaptation of aspects of study design based on statistical and/or pharmacokinetic/pharmacodynamic (PK/PD) analysis. From a modelling and simulation (M&S) perspective, a prior understanding of concentration (dose)-efficacy and of concentration (dose)-toxicity relationship is needed.
An brief introduction to the clinical data management process is described in this slides. These slides provides you the information regarding the data evaluation in the clinical trials , edit checks and data review finally data locking,then the data is submitted to the concerned regulatory body.
Clinical research and clinical data management - Ikya Globalikya global
Data management functions in clinical trials—extensive data cleaning, full query management, protocol deviation management, batch processing, as examples—have traditionally been served by stand-alone clinical data management systems (CDMS), whose input is from paper forms or from separate electronic data capture systems. Distinct electronic data capture and data management systems require data integration, with resulting timing and reconciliation issues.
Integrated Safety and Risk Management Solutions - Addressing the Needs of Sma...Covance
Premarketing clinical safety and PV activities, and the technology infrastructure that supports it, are typically outsourced to multiple contract research organisations (CROs) as part of their clinical trial programmes. **Disclaimer: This article was previously published. Sciformix is now a Covance company.
Technology Considerations to Enable the Risk-Based Monitoring Methodologywww.datatrak.com
TransCelerate BioPharma Inc developed a methodology based on the notion that shifting monitoring processes from an excessive concentration on source data verification to comprehensive risk-driven monitoring will increase efficiencies and enhance patient
safety and data integrity while maintaining adherence to good clinical practice regulations. This philosophical shift in monitoring processes employs the addition of centralized and off-site mechanisms to monitor important trial parameters holistically, and it uses adaptive on-site monitoring to further support site processes, subject safety, and data quality. The main tenet is to use available data to monitor, assess, and mitigate the overall risk associated with clinical trials. Having the right technology is critical to collect and aggregate data, provide analytical capabilities, and track issues to demonstrate that a thorough quality management framework is in place. This paper lays out the high-level considerations when designing and building an integrated technology solution that will aid in scaling the methodology across an organization’s portfolio.
In the past decade, there has been a significant increase in the use of Data Monitoring
Committees (DMC) and Adaptive Designs (AD) in clinical trials. While the monitoring of safety
data by a formal committee is not required for all clinical trials, it has become the norm to have
a formal DMC conduct periodic safety reviews for any controlled trial that evaluates treatments
intended to prolong life or reduce risk of major adverse health outcomes, or for trials that
compare rates of mortality or major morbidity. Confirmatory, pivotal, and adaptive design trials
have more complex operational issues requiring an external and independent DMC. The DMC
may have access to unblinded interim data, be required to make expert recommendations
about how the trial should continue, and then ensure that planned adaptations are
implemented as outlined in the protocol without involving the sponsor or exposing it to
unblinded data or results.
This added complexity creates a challenge and a question: how can the DMC, statisticians, and
sponsor effectively communicate, share blinded and unblinded data, perform analyses, and
implement adaptations without introducing operational bias or compromising the integrity of
the trial? One solution is to utilize a sophisticated computer system that can provide the
security and necessary firewalls to ensure that interim data is only accessible to those it is
intended for, that the rules and processes outlined in the protocol and DMC charter are
enforced, and that communication between the DMC and sponsor is effectively facilitated while
protecting the integrity of the trial and preventing the introduction of operational bias.
The system must also provide an audit trail that tracks “who saw what and when” providing
evidence to regulatory authorities that the protocol was strictly followed with a minimal
possibility of bias. This white paper describes the computer system, ACES, which Cytel has built,
that makes all of this possible. ACES (Access Control Execution System) has been purpose-built
to address the operational complexities inherent in adaptive design and pivotal clinical trials.
Journal for Clinical Studies: Close Cooperation Between Data Management and B...KCR
Every clinical trial is a source of multidimensional data, analyzed to answer questions on safety, efficacy and others. Invalid or incomplete data may lead to invalid conclusions and wrong decision. KCR’s Biostatistician, Adrian Olszewski, highlights the importance of cooperation between data management and biostatistics to improve data quality by introducing both statistical knowledge and the ability to create specialized, programmatic tools and advanced queries giving a good foundation for deeper and faster data investigations. Read more in the article published in the October Issue of Journal for Clinical Studies (p. 42-46).
Safety & Regulatory Solutions for Small and Medium-sized Life Science Organiz...Covance
A key issue for small and medium-sized enterprises is the optimal utilization of their limited resources for moving their product pipeline through clinical development, and launching and marketing their approved product(s). This is further heightened as both clinical trials and post-marketing activities continue to grow in complexity and scope due to stringent regulatory pressures, patient involvement and globalization. Yet companies face overwhelming pressure to get their product to market as quickly as possible.
The Use of EDC in Canadian Clinical TrialsKhaled El Emam
Presentation at CHEO Research Rounds on a study to estimate the proportion of Canadian clinical trials that are using an Electronic Data Capture system during the period 2006-2007.
Scientific & systematic collection of data for clinical study is called as Clinical Data Management-
Clinical Data Management-Web Based Data Capture EDC & RDC , Oracle
SAS
Office software
UW Catalyst data collection (University of Washington)
REDCAP (Research electronic data capture)
OPENCLINICA
STUDY TRAX
Integrating Clinical Operations and Clinical Data Management Through EDCwww.datatrak.com
When electronic data capture was first introduced there was a great deal of discussion surrounding how the technology would alter the roles of those in clinical operations and clinical data management. Through the review of a case study, we will explore how EDC is used as a tool to more tightly integrate clinical operational staffs with those in clinical data management resulting in a more streamlined process from study initiation to database lock.
Appalla Venkataprabhakar and I presented this at the Oracle\'s Annual Clinical Development and Safety Conference 2010 at Hyderabad, India on 6th October 2010.
Risk-based Monitoring Strategies for Improved Clinical Trial PerformanceCognizant
To address draft regulatory guidance for risk-based clinical trial monitoring, sponsors should consider strategies that utilize social, mobile, analytics and cloud technologies to create responsive methodologies that satisfy both the spirit and the letter of these new guidelines.
Clinical Trial Management System Implementation GuidePerficient, Inc.
Clinical trials account for the majority of the cost in new drug development – a cost that is
constantly increasing. Not only are clinical trials expensive, but they are lengthy, complex
and highly scrutinized. Technology solutions play a significant role in helping life sciences
organizations oversee these critical tasks.
In this slideshare, we discuss:
1. Signs that a CTMS is needed
2. Benefits of a CTMS
3. Preparing to explore CTMS options
4. Developing a CTMS selection checklist
5. Choosing an implementation partner
6. Considering post-implementation support
Overview of Risk Based Monitoring in Clinical Trial ProcessesEditorIJTSRD1
Risk based monitoring RBM has emerged as a transformative approach in clinical trial processes. This paper provides an overview of RBM and its impact on the field of clinical research. By moving away from traditional on site monitoring and adopting a targeted and efficient approach, RBM has demonstrated numerous benefits in terms of cost effectiveness, data quality, and patient safety. This abstract summarizes the key findings discussed in the conclusion. The proactive identification and management of risks throughout the trial lifecycle have led to improved decision making, increased study participant compliance, and enhanced overall trial success rates. With advancing technology, RBM approaches are expected to evolve further, allowing for greater optimization and streamlining of clinical trial processes. The abstract concludes by emphasizing the potential of risk based monitoring to shape the future of clinical research and contribute to the development of safe and effective therapies for patients worldwide. Kelam Himasri | Sankara Narayanan. K "Overview of Risk-Based Monitoring in Clinical Trial Processes" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-3 , June 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd58586.pdf Paper URL: https://www.ijtsrd.com.com/pharmacy/pharmacy-practice/58586/overview-of-riskbased-monitoring-in-clinical-trial-processes/kelam-himasri
Study start up activities in clinical data managementsoumyapottola
Study start-up (SSU) is so much more than a one-time document management exercise. It’s a global, strategic operation that can get new drugs approved faster – and it’s ripe for innovation – from Site Selection to Site Activation and Site Training.
Many SSU tech solutions deployed by sponsors don’t deliver the results promised because they add burden without benefits to clinical research sites. The result? Site staff simply avoid using them.
When that happens, document exchange and tracking falls back to paper, email and Excel formats – with CRAs holding the processes together. The tools that were supposed to solve a problem become part of the problem – and consume preThe implementation and conduct of a study can be a complex process that involves a
team from various disciplines and multiple steps that are dependent on one another. This
document offers guidance for navigating the study start-up processcious clinical trial budget.
A successful clinical study start-up is a crucial first step and an important factor for the overall success of the trial. For this reason, SCRO has experienced study start-up teams, offering customized services depending on your needs, whether it be fuWhile the definition varies across companies, study startup typically includes the process of identifying and qualifying sites, collecting essential documents at the study and site level, and submitting these documents for ethics approval. Successful study startup requires coordination between sites, sponsors, and contract research organizations (CROs) to achieve critical milestones in a compliant manner.ll-service or single activities.
How to achieve better time management in EDC start up
Clinical data management requires strict time management processes, especially in study start up within an electronic data capture (EDC) system. Three steps that clinical data management teams can take to outline the planning and executing of each task that needs to be considered are as follows:
Make a List: Create a daily or weekly task list and schedule when each task will be completed. This strategy will assist you in maintaining focus and staying organized.
Set realist goals: Be realistic about what you can finish in the amount of time you have. When setting unrealistic goals, failure is almost certain to follow.
Explore time-saving techniques: Examples of techniques that could help save time include grouping similar tasks together or using a timer to stay focused.
To help get started, here is a list of EDC considerations for Study Start-Up deadlines:
Protocol finalization and study enrollment
Split go-live considerations
eCRF Specification meetings (this will ensure proper collaboration and minimize any back-and-forth communication)
EDC add-on modules (which will be required and need validation?)
ePRO/eCOA used with licensed questionnaires.
IRB requirements for add-on modules (eConsent/ePRO)
Who needs fast data? - Journal for Clinical Studies KCR
How “no news” during the life of a trial is bad news, and what data management (among other things) can do to help when ensuring access to fast data? Get to know this and more about smart e-solutions in the newest article of Kaia Koppel, Associate Director, Biometrics & Clinical Trial Data Execution Systems at KCR, in the recent issue of Journal for Clinical Studies (p.40-21).
ML operations comprise a set of practices and methods specifically crafted for streamlined management of the complete lifecycle of machine learning models in production environments. It encompasses the iterative process of model development, deployment, monitoring, maintenance and integrating the model into operational systems, ensuring reliability, scalability, and performance.ML operations comprise a set of practices and methods specifically crafted for streamlined management of the complete lifecycle of machine learning models in production environments. It encompasses the iterative process of model development, deployment, monitoring, maintenance and integrating the model into operational systems, ensuring reliability, scalability, and performance.
ML operations comprise a set of practices and methods specifically crafted for streamlined management of the complete lifecycle of machine learning models in production environments. It encompasses the iterative process of model development, deployment, monitoring, maintenance and integrating the model into operational systems, ensuring reliability, scalability, and performance.
ML operations comprise a set of practices and methods specifically crafted for streamlined management of the complete lifecycle of machine learning models in production environments. It encompasses the iterative process of model development, deployment, monitoring, maintenance and integrating the model into operational systems, ensuring reliability, scalability, and performance. In certain cases, ML operations are solely employed for deploying machine learning models.
When testing new software functionality, it is important to have access to high-quality test data. This can be challenging due to large data volumes or different sources of data with varying permissions.
Database design in the context of Clinical Data Management (CDM) is a crucial aspect of organizing and managing clinical trial data effectively and efficiently. A well-designed database ensures that data collected during a clinical trial is accurate, consistent, and accessible, facilitating data analysis, reporting, and regulatory submissions. Clinical Data Management involves various steps, including data collection, validation, cleaning, and reporting
The challenges facing in pharmaceutical maintenanceMANUEL PACINI
Maintenance strategies for the pharmaceutical industry.
Maintenance and service-related items are often the second-largest budget element in a laboratory after salaries and benefits
The Role of Technology in Streamlining Clinical Trial ProcessesClinosolIndia
Technology plays a significant role in streamlining clinical trial processes, enhancing efficiency, data quality, and participant engagement. Here are some key areas where technology contributes to the optimization of clinical trials
Similar to The_Essential_EDC_Partnership_Strategy_for_Ensuring_Study_Success (20)
2. 2
Contents
Introduction.................................................................................................................... 3
Study Setup .................................................................................................................... 5
Risk-Based Monitoring ................................................................................................ 6
Reporting & Data Entry................................................................................................ 8
Safety..............................................................................................................................10
Data Integrity & Quality..............................................................................................12
Database Lock .............................................................................................................14
Conclusion.....................................................................................................................15
3. 3
Introduction
Results of a 2014 study examining FDA drug approval
rates and trends of new molecular entities showed that
26 percent of all submissions were never approved.
Data-related factors — specifically, missing data, data
integrity, and inconsistencies — accounted for 16
percent of new molecular entity submissions that
were never approved.1
Electronic data capture (EDC)
technology, and the way it is utilized, can be directly tied
to every one of these factors.
The metric "cycle time from patient visit to completed
eCRF data entry" is highlighted in an executive summary
published by The Metrics Champion Consortium
as being central to the Risk-Based Monitoring
environment.2
Arguably, the referenced cycle metric is
essential to any use of an EDC solution. The longer the
gap, the higher the probability for errors – missing data,
compromised data integrity, and inconsistencies.
Sponsors often employ teams of SAS programmers who
carry the weight of getting poorly organized data ready
for submission after study close-out. But there is no
reason that the in-house team should be left to wrestle
with data at the end of a study to prepare it for the
FDA. There is a better way to approach data that yields
far more favorable results. It really does start with your
choice of an electronic data capture (EDC) technology
and the company that provides it.
1 http://jama.jamanetwork.com/article.
aspx?articleid=1817795
2 http://www.appliedclinicaltrialsonline.com/mcc-metric-
month-blog-risk-based-monitoring-metric
There is a better way
to approach data
that yields far more
favorable results. It
really doesstart with
your choice of an
electronic data capture
(EDC) technology and
the company that
provides it.
26 %
of all submissions were
never approved
4. 4
Challenge
Data is a continuous, connected, singular arch throughout
a study. It is not separate from the study, and it is not a
different component at various stages along the way. The
study sponsor and partner EDC solution provider are
responsible for creating study protocols and building out
the system to better ensure site adherence and quality
data. It is therefore imperative to partner with a provider
that demonstrates a commitment to success through
continued innovation.
What features have they developed for their system
to minimize data errors? Are you confident that their
support team has the experience to guide your study to
successful close? Do you know what site users say about
using their system? How are they working to maximize
operational efficiencies for example, lowering monitoring
costs through advancements in Risk-Based Monitoring
(Dynamic Monitoring)?
Overview
This white paper discusses the data needs throughout a
clinical trial and explains how an EDC partner should work
in concert with the study sponsor, providing the tools,
guidance, and support necessary to help ensure study
success. OmniComm System’s approach of embracing
and actively facilitating open integration between its
TrialMaster®
EDC solution and other electronic clinical
systems changes the relationship between the EDC
solution provider and the sponsor and/or contract
research organization (CRO).
5. 5
Study Setup
Doing it correctly leads
to smoother study
management and a
successful database
lock at the end.
Doing it incorrectly
means spending
valuable time and
money on cleaning up
data throughout the
study and delays the
database lock.
To initiate a new study, a sponsor and/or CRO finalizes the study
protocol, selects investigative sites, and sets up the study management
systems, including the EDC solution. The way the EDC system is set
up affects the entire clinical trial. Doing it correctly leads to smoother
study management and a successful database lock at the end. Doing it
incorrectly means spending valuable time and money on cleaning up
data throughout the study and delays the database lock.
Ideally, an EDC partner works with a sponsor and/or CRO during study
setup to design a system that will meet the ultimate goal of minimizing
unnecessary data. With its TrialMaster®
EDC Suite, OmniComm provides
not just the necessary software, but expertise and support from its
team of experienced industry professionals.
OmniComm will work during study setup to:
• Reduce cost and time by streamlining study build time lines and enforcing
standards across studies.
• Re-use complex study designs and components across trials leveraging industry
standards
• Manage data standards for submission.
• Provide rapid study build and design support regardless of clinical phase,
therapeutic area or study style.
• Share study design using industry standards with related applications, ex. ePRO.
TrialMaster®
EDC comes equipped with a library of standard forms for
the study team’s use during system build and throughout the study.
The OmniComm team works with sponsors to select and customize the
standard case report forms (CRFs) and corresponding electronic case
report forms (eCRFs) at the beginning of a study, an integral component
to building a successful EDC framework. The team references the Clinical
Data Acquisition Standards Harmonization as the standard in the Clinical
Data Interchange Standards Consortium portfolio that integrates the
Submission Data Tabulation Model requirements into the CRFs.
6. 6
The decision of whether to use risk-based monitoring,
also called targeted monitoring, is a vital decision with
the potential to affect the entire study. Implementing
100 percent source data verification (SDV) in a study
significantly increases monitoring costs. Such costs
can account for 30 percent of the total study budget,
according to some estimates1
. In the past, the
pharmaceutical industry has been reluctant to adopt
new methods that could reduce monitoring costs due
to the ambiguousness of the FDA’s guidance on the
subject.
However, in 2013, when the FDA issued its final
guidance for risk-based monitoring in clinical
investigations2
, the industry made a drastic change. The
new guidance supports selective monitoring, including
less than 100 percent SDV, provided that the selective
approach is justified in a risk-based monitoring plan.3
Suddenly, sponsors and CROs needed support for
the new approach, so OmniComm added three
capabilities to its TrialMaster®
EDC Suite to meet this
demand throughout the clinical trial process:
3 Risk-BasedMonitoringwithTrialMaster®
4 http://www.fda.gov/downloads/Drugs/.../Guidances/UCM269919.pdf
5 Risk-Based Monitoring with TrialMaster®
Implementing 100
percent source data
verification (SDV) in
a study significantly
increases monitoring
costs.
Risk-Based Monitoring
7. 7
Dynamic monitoring
Allows sponsors/CROs to selectively mark
forms for SDV.
Central source review
Enables central review and/or SDV of a
scanned, auto-redacted copy of selected
patient source documents.
Centralized monitoring
Reports highlight questionable data and
high-risk sites using a robust reporting
solution and advanced key risk indicator
reports, optimized for proactive and
reliable issue detection.
In concert with the rest of its capabilities, TrialMaster®
EDC delivers high
levels of automation and control for companies looking to adopt a risk-
based approach to monitoring.
8. 8
Data Entry & Reporting
Traditionally, site monitors spend countless hours
reviewing source documents and CRFs, matching them
to the entered data. Visits that could be completed in
one or two days are extended to three as monitors sort
through data entry errors and create queries for each
one. Site staff must then go back over all the queries to
correct them. Even so, at the end of the study, site staff
and monitors are left resolving thousands of queries to
meet database lock timelines. When a study utilizes a
system that makes entry simple for the site staff, with
standardized eCRFs and ongoing training, the number
of errors and resulting queries are minimized, saving
the sponsor time and money. The queries that do
arise are submitted immediately for resolution. Timely
reporting also gives project managers the opportunity
to assess the quality of the CRFs and site staff training
— and to make changes, if necessary.
In a recent survey, three out of five investigative sites
reported that they preferred TrialMaster®
to other
market leaders.4
The built-in user-friendly features
simplify implementation and ongoing training. Available,
ongoing e-learning gives site staff and monitors the
opportunity to train themselves and refresh their
knowledge of the EDC system at their own pace rather
than having to learn everything there is to know about
the system in one half-day investigator meeting.
6 OmniComm Systems
TrialMaster®
makes
data entry and
reporting a far less
cumbersome process
for site staff and
monitors.
9. 9
Guided Data Entry
One of TrialMaster’s®
standout features,
Guided Data Entry, saves time and money
while guaranteeing more accurate data. In
contrast to other systems, which wait until
after submission to the database, to crawl the
reports for errors, TrialMaster®
saves site staff
and monitors countless hours of searching
through queries by flagging errors as soon as
they are entered.
Reduce costs
Monitoring typically accounts for 30 to 40
percent of the entire study budget, with
the majority of that cost funding on-site
visits. Combined with risk-based monitoring,
OmniComm’s innovative software for data
entry and reporting means far fewer hours
and resources are needed for monitoring
activities. This reduction in the number and
length of visits is one reason that direct users
prefer TrialMaster®
over other programs.
10. 10
Safety is vital to the running and outcome of every
clinical trial. While the goal is to avoid adverse events,
some are inevitable. According to 21 CFR 312.64,
investigators are required to “immediately report to
the sponsor any serious adverse event (SAE), whether
or not considered drug related, including those listed
in the protocol or investigator brochure and must
include an assessment of whether there is a reasonable
possibility that the drug caused the event.5
” Immediate
reporting directly to the safety system eliminates lag
time and gives the sponsor time to react accordingly.
Faster notification of adverse events can lead to earlier
and better decision-making, potentially saving hundreds
or thousands of patients from exposure to unsafe
medication.
In the past, doctors and investigators had to fill out
separate forms to be submitted to different entities for
every SAE, jeopardizing accuracy and consistency along
the way. With a robust EDC system, investigators enter
the data just once. The CDISC/CDASH Serious Adverse
Event Supplement published in November 2013 directly
addresses collecting SAE information through an EDC
rather than on paper.
7 https://www.gpo.gov/fdsys/pkg/CFR-2011-title21-vol5/pdf/CFR-2011-title21-vol5-sec312-64.pdf
In the past, doctors and
investigators had to fill
out separate forms to be
submitted to different
entities for every SAE,
jeopardizing accuracy
and consistency along
the way.
Safety
With a robust EDC
system, investigators
enter the data just
once.
11. 11
Appendix B of the supplement6
states:
Electronic data capture (EDC) is recognized as an efficient and time saving
method for capturing clinical data. EDC also offers a more efficient process
for SAE information capture than the traditional paper form; sponsors
can use information already available in the Clinical Data Management
System (CDMS) to populate the same data elements on an SAE report form.
Typically, such data are housed in a clinical study database. All SAE data that
are not extracted from the clinical study database are typically housed in a
separate safety database. The relationship between drug safety data and
clinical trial data that commonly manifests in two distinct data acquisition
processes can be enhanced by minimizing duplicative data collection and
easing the safety data reconciliation processes.
SAEs are typically reported to a pharmacovigilance or drug safety group that
is independent of the clinical research team and is dedicated to receiving,
clarifying, analyzing, and reporting SAE information in conjunction with
the investigative sites. TrialMaster’s®
SafetyLink module gives sponsors
and CROs the ability to set up safety reporting in line with study protocols,
regulatory guidelines, and the CDASH SAE Supplement. SAEs can then be
transmitted seamlessly to the safety system of choice.
8 http://www.cdisc.org/system/files/all/standard_category/application/pdf/cdash_sae_supplement_v1___3_.pdf
12. 12
The FDA expects data to be attributable, legible,
contemporaneous, original, and accurate (ALCOA).9
The Good Clinical Practice Inspectors Working Group
released its “Reflection paper on expectations for
electronic source data and data transcribed to
electronic data collection tools in clinical trials”10
in 2010, which added four additional attributes –
complete, consistent, enduring, and available when
needed (ALCOA+).
Data Integrity & Quality
Data integrity
To ensure data integrity, data must be main-
tained throughout its life cycle to make sure that
it is accurate and consistent11
. Collected data
must be genuine, with an audit trail to show
any changes. Adherence to ALCOA+ guarantees
that at every stage, a reviewer can return to the
original data and ensure that the correct changes
were made.
1 2 3
9 http://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/
ucm328691.pdf
10 http://www.ema.europa.eu/docs/en_GB/document_library/Regulatory_and_procedural_guide-
line/2010/08/WC500095754.pdf
11 IS Practitioners Views on Core Concepts of Information Integrity
13. 13
Data quality
Data quality, meanwhile, means making sure
that the meaning, context, and intent of the
data are clear.7
A key aspect of data quality is
providing user access to data along with the
ability to organize it for business purposes.
These calculations, derivations, and other
transformations permit evaluation of a batch
of product and ultimately determine the
outcome of the study.
Database Lock
12 ahima.org
Together, data integrity and quality are the most important aspects of your study
data after safety. Both are products of a well-designed system. TrialMaster®
EDC provides the functionality to maintain data integrity and quality throughout
the entire study while OmniComm’s team provides the necessary support,
giving sponsors what they need to end their studies with clean data that meets
ALCOA+ standards.
14. 14
1 2
13 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3326906/
14 TrialMaster® by OmniComm
Database lock, as dictated by industry regulations13
,
is the point after which data can no longer be edited.
However, there are several reasons that having data
available when needed was one of the attributes added
to the original ALCOA list. Sponsors require access to
their data during the study for ongoing management
and for export once the study has ended. Unfortunately,
database lock as experienced by many sponsors means
being locked out of their data — in other words, losing
access to the results of their own studies.
OmniComm works in conjunction with sponsor/CRO
systems while adhering to the FDA’s requirements for
data submission. Gone are the days when EDC required
just a data transactional system; now, it is expected
that data will be exported in a form that is ready to be
analyzed, so results can be promptly submitted to the
FDA. Sponsors can obtain CDISC SDTM datasets
within days of database lock with TrialMaster’s®
built-in data mapping and export utility tool.14
Through continued innovation, OmniComm has built a
system that meets a sponsor’s database lock and export
needs every time. A true EDC partner will not hold data
hostage the way an ordinary vendor might.
Through continued
innovation, OmniComm
has built a system
that meets sponsor
database lock and
export needs every
time.
Database Lock
15. 15
50,000+
Study sites
4,800+
Clinical studies
20,000+
Trained users
250+
Sponsors
55+
Countries
www.omnicomm.com
Conclusion
OmniComm is changing the way the
pharmaceutical industry approaches data,
because we know that data is at the core of
a clinical trial, not the result of a clinical trial.
Our innovative technology and philosophy
as an open integration system is unlike that
of any other EDC company. This is one major
aspect of our company that sets OmniComm
and our TrialMaster®
EDC Suite apart. We are
dedicated to being the industry’s leading EDC
provider.
Quality is the key
The quality of our software is exceeded only
by the support we provide. Our experienced
teams built the system we offer today, and
they are here to help our clients successfully
run their clinical trials. Our OmniComm
service records span 50,000+ study sites,
4,800+ clinical studies, 20,000+ trained
TrialMaster®
EDC users, and 250+ sponsors
across more than 55 countries. OmniComm
EDC technologies are used by four of the
top five CROs, seven of the 10 largest phase
I clinics, and all 10 of the top 10 cancer
research centers. We are an aid to the sites
and we are a true partner to every client.