Decentralized Clinical Trials
•
1 like•617 views
Decentralized clinical trials (DCTs) allow patients to participate remotely with data captured using advanced equipment instead of visiting sites often. India can contribute significantly to DCTs by developing analytical tools to update clinical teams, training monitors remotely, managing documentation and helplines from India, and customizing roles like data access and quality control reviewers. DCTs have the potential to transform healthcare by improving access to trials, providing more comprehensive patient data to researchers, and opening up new areas for disease prevention and treatment evaluation using large datasets.
Report
Share
Report
Share
Download to read offline
Recommended
Decentralized Clinical Trials, presentaiton by Craig Lipset for mHealth Israel
Decentralized Clinical Trials, presentaiton by Craig Lipset for mHealth Israel, April 20, 2021. Origin Story: Centralization Enables Decentralization. Analogous potential for centralization
leading to decentralization in clinical trials. Decentralization: Purpose and potential benefits, including resilience and
business continuity. Pre-Pandemic DCT Timeline: 17-year History Prior to COVID-19. Seasons of Decentralization in 2020. Spring of Continuity, Summer of Restarts, Fall of Commitment, Winter of Pathways to Scale. 79% of sponsors / CROs increasing DCT. 90% of participants experiencing change. 75% focus on going hybrid. 73% of Sites Will continue to use telemedicine beyond the pandemic. 76% have accelerated their DCT Strategies.Leading Implementation Strategy: Pairing DCT Toolkit to Study Needs. Identify the decentralized research methods and tools needed by the medicine portfolio. Ensure aligned SOPs & training, identify new partners, modify protocols/templates. Pair the “right” method/tool to each study
based upon diverse criteria. Barriers to Scaled Adoption of Decentralized Trials: Regulatory ambiguity, Global variability, Technology interop & data flow, Investigator & patient readiness, Endpoint limitations, Organization culture. Forecasts and Futures. Choice & Flexibility for Participants on a Visit-by-Visit Basis. Research Sites Empowered to Use Their Existing Technology. New Opportunities to Engage Treating Physicians Enables Research as a Care Option. Observational
“All-Comer” Studies and Platform Trials with DCT Bring Research to People.
Decentralized Clinical Trials: Collaboration to Accelerate Adoption
The document discusses decentralized clinical trials (DCT) and the need for collaboration to accelerate their adoption. It defines DCT and outlines their 17-year history prior to the COVID-19 pandemic accelerating their use. In 2020, surveys found most sponsors and CROs increasing DCT and most participants experiencing changes due to the pandemic. The document advocates for pairing the right decentralized methods and tools to each study and identifies barriers to adoption like regulatory issues. It proposes a Decentralized Trials & Research Alliance to enable collaboration and proposes meta-collaboration across initiatives to streamline efforts and avoid redundancy.
Decentralized Clinical Trials_CSRPL.pptx
Decentralized clinical trials (DCT) are defined as studies “executed through telemedicine and mobile/local healthcare providers, using processes and technologies differing from the traditional clinical trial model.”
Remote decentralized clinical trials (RDCT) are defined as “an operational strategy for technology-enhanced clinical trials that are more accessible to [participants] by moving clinical trial activities to more local settings.”
Clinical Data Management Process Overview_Katalyst HLS
Introduction to Clinical Data Management Process Overview in Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Data Integrity in Decentralized Clinical Trials (DCTs)
Experts expand on the need for a comprehensive understanding of all sources of data in DCTs, and the need to evaluate those data centrally in real time to mitigate the risks associated with their capture (including data capture at the edge of the network (wearables)).
Every disruptive innovation must be complemented by adapted procedures, and this also applies to decentralized clinical trials (DCTs). Traditionally, sites entered clinical trial data in an Electronic Data Capture (EDC) system and these source data were verified at the site to confirm accuracy. Risk based monitoring focused on site level metrics such as screen failure rates, query rates, Serious Adverse Events (SAEs) reported, missed/late visits, etc. With DCTs, as source data are collected directly from participants this is no longer an option and a different approach is required to ensure the quality and integrity of the data. As a rule, a comprehensive understanding of all sources for data capture in a clinical trial and the process for centralization is essential. Also, it is important to evaluate the data collected in real time to allow early interventions that will ensure data integrity for regulatory submission.
In this webinar, Chitra Lele describes how centralized monitoring strategies can help aggregate and analyze data in real time and provide insights to a variety of functional teams across the trial continuum. Daniel Gutierrez describes how the Clinerion platform can boost data integrity in DCTs. The technology transforms global data sources to one query-able data model for structured medical data, while ensuring that the data keep its full resolution and integrity during aggregated queries.
Pierre Etienne talks about the expanding role of mobile Health Care Professionals (HCPs) and their crucial role in protecting data integrity. Clifton Chow finishes with a comparison of several artificial intelligence (AI) based binary classifiers for detecting the integrity of data obtained from Internet of Things (IoT) enabled wearable sensors.
Clinical data management
1. A clinical data management system (CDMS) is used to manage data from clinical trials by storing data entered in case report forms (CRFs) by investigators.
2. Data management involves planning, collection, entry, validation, manipulation, backup and documentation of data to create a high quality database. Commonly used CDMS tools include Oracle Clinical, ClinTrial, Macro and eClinical Suite.
3. Open source CDMS tools include OpenClinica, openCDMS, TrialDB and PhOSCo which are free. All CDMS tools ensure an audit trail and management of discrepancies according to roles and user access levels.
Workflow of CDM
Clinical Data Management (CDM) is a critical component of clinical research that involves the collection, cleaning, validation, and management of clinical trial data to ensure its accuracy, integrity, and compliance with regulatory requirements. The workflow of CDM typically consists of several key stages, each with specific activities and processes. Here is an overview of the typical workflow of CDM:
Study Startup:
Protocol Review: CDM teams begin by reviewing the clinical trial protocol to understand the study's objectives, endpoints, data collection requirements, and timelines.
Database Design: Based on the protocol, the team designs a data capture system or electronic data capture (EDC) system. This includes creating data entry forms, defining data validation checks, and setting up data dictionaries.
Data Collection:
Case Report Form (CRF) Design: CDM professionals design electronic or paper CRFs to collect data during the trial. CRFs capture specific data points required by the protocol.
Data Entry: Data is entered into the CRFs, either electronically by site personnel or through paper CRFs.
Data Validation: CDM teams implement validation checks to ensure data quality and consistency. Data validation checks may include range checks, consistency checks, and logic checks.
Query Management: Queries are generated when data discrepancies or inconsistencies are identified. CDM teams send queries to investigational sites for resolution.
Data Cleaning and Quality Control:
Data Cleaning: Data are cleaned to resolve discrepancies, discrepancies, and inconsistencies. This involves querying data discrepancies with clinical trial sites.
Data Review: CDM teams review data to ensure completeness and accuracy, and any outstanding queries are resolved.
Quality Control: Quality control processes are applied to verify the integrity and accuracy of data.
Database Lock:
Once the data are cleaned, reviewed, and validated, the database is locked, indicating that no further changes can be made to the data. Database lock is a critical step before data analysis begins.
Data Export and Analysis:
Data is exported from the database and provided to biostatisticians and researchers for statistical analysis. This analysis is conducted to determine the study's outcomes, efficacy, and safety profile.
Data listings, summaries, and tables are generated for regulatory submissions, reports, and publications.
Final Study Reporting:
After data analysis, CDM teams contribute to the preparation of final study reports, which provide a comprehensive overview of the trial's results, data quality, and regulatory compliance.
Archiving and Documentation:
Clinical trial data, documentation, and databases are archived to ensure their long-term availability for regulatory audits and future reference.
Regulatory Submission: CDM teams provide support for regulatory submissions.
Clinical Research Coordinator
A Clinical Research Coordinator (CRC) is a healthcare professional who plays a crucial role in the execution and management of clinical research studies. The CRC acts as a liaison between the study sponsor, principal investigator (PI), study participants, and other members of the research team. Their primary responsibility is to ensure the smooth conduct of clinical trials and adherence to protocol requirements. Here are some key responsibilities of a Clinical Research Coordinator
Recommended
Decentralized Clinical Trials, presentaiton by Craig Lipset for mHealth Israel
Decentralized Clinical Trials, presentaiton by Craig Lipset for mHealth Israel, April 20, 2021. Origin Story: Centralization Enables Decentralization. Analogous potential for centralization
leading to decentralization in clinical trials. Decentralization: Purpose and potential benefits, including resilience and
business continuity. Pre-Pandemic DCT Timeline: 17-year History Prior to COVID-19. Seasons of Decentralization in 2020. Spring of Continuity, Summer of Restarts, Fall of Commitment, Winter of Pathways to Scale. 79% of sponsors / CROs increasing DCT. 90% of participants experiencing change. 75% focus on going hybrid. 73% of Sites Will continue to use telemedicine beyond the pandemic. 76% have accelerated their DCT Strategies.Leading Implementation Strategy: Pairing DCT Toolkit to Study Needs. Identify the decentralized research methods and tools needed by the medicine portfolio. Ensure aligned SOPs & training, identify new partners, modify protocols/templates. Pair the “right” method/tool to each study
based upon diverse criteria. Barriers to Scaled Adoption of Decentralized Trials: Regulatory ambiguity, Global variability, Technology interop & data flow, Investigator & patient readiness, Endpoint limitations, Organization culture. Forecasts and Futures. Choice & Flexibility for Participants on a Visit-by-Visit Basis. Research Sites Empowered to Use Their Existing Technology. New Opportunities to Engage Treating Physicians Enables Research as a Care Option. Observational
“All-Comer” Studies and Platform Trials with DCT Bring Research to People.
Decentralized Clinical Trials: Collaboration to Accelerate Adoption
The document discusses decentralized clinical trials (DCT) and the need for collaboration to accelerate their adoption. It defines DCT and outlines their 17-year history prior to the COVID-19 pandemic accelerating their use. In 2020, surveys found most sponsors and CROs increasing DCT and most participants experiencing changes due to the pandemic. The document advocates for pairing the right decentralized methods and tools to each study and identifies barriers to adoption like regulatory issues. It proposes a Decentralized Trials & Research Alliance to enable collaboration and proposes meta-collaboration across initiatives to streamline efforts and avoid redundancy.
Decentralized Clinical Trials_CSRPL.pptx
Decentralized clinical trials (DCT) are defined as studies “executed through telemedicine and mobile/local healthcare providers, using processes and technologies differing from the traditional clinical trial model.”
Remote decentralized clinical trials (RDCT) are defined as “an operational strategy for technology-enhanced clinical trials that are more accessible to [participants] by moving clinical trial activities to more local settings.”
Clinical Data Management Process Overview_Katalyst HLS
Introduction to Clinical Data Management Process Overview in Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Data Integrity in Decentralized Clinical Trials (DCTs)
Experts expand on the need for a comprehensive understanding of all sources of data in DCTs, and the need to evaluate those data centrally in real time to mitigate the risks associated with their capture (including data capture at the edge of the network (wearables)).
Every disruptive innovation must be complemented by adapted procedures, and this also applies to decentralized clinical trials (DCTs). Traditionally, sites entered clinical trial data in an Electronic Data Capture (EDC) system and these source data were verified at the site to confirm accuracy. Risk based monitoring focused on site level metrics such as screen failure rates, query rates, Serious Adverse Events (SAEs) reported, missed/late visits, etc. With DCTs, as source data are collected directly from participants this is no longer an option and a different approach is required to ensure the quality and integrity of the data. As a rule, a comprehensive understanding of all sources for data capture in a clinical trial and the process for centralization is essential. Also, it is important to evaluate the data collected in real time to allow early interventions that will ensure data integrity for regulatory submission.
In this webinar, Chitra Lele describes how centralized monitoring strategies can help aggregate and analyze data in real time and provide insights to a variety of functional teams across the trial continuum. Daniel Gutierrez describes how the Clinerion platform can boost data integrity in DCTs. The technology transforms global data sources to one query-able data model for structured medical data, while ensuring that the data keep its full resolution and integrity during aggregated queries.
Pierre Etienne talks about the expanding role of mobile Health Care Professionals (HCPs) and their crucial role in protecting data integrity. Clifton Chow finishes with a comparison of several artificial intelligence (AI) based binary classifiers for detecting the integrity of data obtained from Internet of Things (IoT) enabled wearable sensors.
Clinical data management
1. A clinical data management system (CDMS) is used to manage data from clinical trials by storing data entered in case report forms (CRFs) by investigators.
2. Data management involves planning, collection, entry, validation, manipulation, backup and documentation of data to create a high quality database. Commonly used CDMS tools include Oracle Clinical, ClinTrial, Macro and eClinical Suite.
3. Open source CDMS tools include OpenClinica, openCDMS, TrialDB and PhOSCo which are free. All CDMS tools ensure an audit trail and management of discrepancies according to roles and user access levels.
Workflow of CDM
Clinical Data Management (CDM) is a critical component of clinical research that involves the collection, cleaning, validation, and management of clinical trial data to ensure its accuracy, integrity, and compliance with regulatory requirements. The workflow of CDM typically consists of several key stages, each with specific activities and processes. Here is an overview of the typical workflow of CDM:
Study Startup:
Protocol Review: CDM teams begin by reviewing the clinical trial protocol to understand the study's objectives, endpoints, data collection requirements, and timelines.
Database Design: Based on the protocol, the team designs a data capture system or electronic data capture (EDC) system. This includes creating data entry forms, defining data validation checks, and setting up data dictionaries.
Data Collection:
Case Report Form (CRF) Design: CDM professionals design electronic or paper CRFs to collect data during the trial. CRFs capture specific data points required by the protocol.
Data Entry: Data is entered into the CRFs, either electronically by site personnel or through paper CRFs.
Data Validation: CDM teams implement validation checks to ensure data quality and consistency. Data validation checks may include range checks, consistency checks, and logic checks.
Query Management: Queries are generated when data discrepancies or inconsistencies are identified. CDM teams send queries to investigational sites for resolution.
Data Cleaning and Quality Control:
Data Cleaning: Data are cleaned to resolve discrepancies, discrepancies, and inconsistencies. This involves querying data discrepancies with clinical trial sites.
Data Review: CDM teams review data to ensure completeness and accuracy, and any outstanding queries are resolved.
Quality Control: Quality control processes are applied to verify the integrity and accuracy of data.
Database Lock:
Once the data are cleaned, reviewed, and validated, the database is locked, indicating that no further changes can be made to the data. Database lock is a critical step before data analysis begins.
Data Export and Analysis:
Data is exported from the database and provided to biostatisticians and researchers for statistical analysis. This analysis is conducted to determine the study's outcomes, efficacy, and safety profile.
Data listings, summaries, and tables are generated for regulatory submissions, reports, and publications.
Final Study Reporting:
After data analysis, CDM teams contribute to the preparation of final study reports, which provide a comprehensive overview of the trial's results, data quality, and regulatory compliance.
Archiving and Documentation:
Clinical trial data, documentation, and databases are archived to ensure their long-term availability for regulatory audits and future reference.
Regulatory Submission: CDM teams provide support for regulatory submissions.
Clinical Research Coordinator
A Clinical Research Coordinator (CRC) is a healthcare professional who plays a crucial role in the execution and management of clinical research studies. The CRC acts as a liaison between the study sponsor, principal investigator (PI), study participants, and other members of the research team. Their primary responsibility is to ensure the smooth conduct of clinical trials and adherence to protocol requirements. Here are some key responsibilities of a Clinical Research Coordinator
Cra responsebility
The CRA oversees all stages of clinical trials from site selection to completion. They identify investigators, set up trial sites, train staff, monitor compliance, and verify informed consent and data collection. The CRA ensures protocols are followed, documents are collected, and supplies are accounted for throughout the trial. Effective communication, relationship building, attention to detail, and strong organizational skills are important for this role.
Project management in clinical research sanjay akhani 8 may
This document discusses project management in clinical research. It begins with a disclaimer from the presenter. It then provides an overview of key aspects of project management in clinical research, including project management tools and techniques used, components of a project management plan, managing contract research organizations and site management organizations, optimizing patient recruitment, and working with remote and multicultural teams. Challenges of working with remote and multicultural teams include differences in communication styles, work ethics, decision making, and views of time and change due to cultural differences between high and low context cultures.
Clinical data management
This document discusses clinical data management (CDM) systems and processes. It defines key terms like source data, source documents, and raw data. It then describes the essential steps in CDM including initial planning, data collection, review and verification, coding, query resolution, data entry and validation, output and archiving. Finally, it outlines requirements for a good CDM system including system validation, security, change control, and archiving. The goal of CDM is to generate an accurate, high-quality clinical trial database while ensuring compliance with regulations.
CDM
CLINICAL DATA MANAGEMENT
FOR MORE RELATED QUERIES CONTACT US ON- 9028839789
FOR ENROLLMENT IN NEXT BATCH CONTACT ON ABOVE MENTIONED NUMBER
Introduction to Clinical Research
An introduction for those who may be interested in a career in clinical research, but need to understand the industry and their potential for a role in it.
Provides an overview of the later stages of drug development, explaining the phases of drug studies and explores in brief the key roles for those participating.
Clinical Data Management: Best Practices and Key Considerations
Clinical data management (CDM) is a critical component of clinical research, involving the collection, processing, and analysis of data generated during clinical trials. Implementing best practices and considering key considerations is essential for ensuring data quality, integrity, and regulatory compliance. Here are some important considerations and best practices in clinical data management:
Data Standardization: Standardizing data collection and documentation across study sites is crucial for ensuring consistency and facilitating data analysis. Develop standardized data collection forms, case report forms (CRFs), and electronic data capture (EDC) systems that capture relevant data elements in a consistent manner.
Data Validation and Quality Control: Implement robust data validation procedures to ensure the accuracy and completeness of collected data. Conduct thorough quality control checks, including data validation checks, range checks, and consistency checks, to identify and resolve data discrepancies or errors.
Data Security and Privacy: Ensure data security and protect participant privacy by implementing appropriate measures such as data encryption, secure data transfer protocols, access controls, and adherence to applicable data protection regulations like GDPR or HIPAA.
Data Monitoring and Cleaning: Regularly monitor data collection processes to identify and address data discrepancies, missing data, or outliers. Implement data cleaning procedures to identify and resolve data errors, inconsistencies, and outliers that may impact the integrity and reliability of the study data.
Data Traceability and Audit Trail: Maintain a comprehensive audit trail that captures all changes and activities related to data entry, data modifications, and data review. This ensures data traceability and facilitates data validation and regulatory inspections.
Standard Operating Procedures (SOPs): Develop and adhere to well-defined SOPs for data management activities. SOPs should cover all aspects of data collection, processing, validation, cleaning, and archiving, ensuring consistency and adherence to regulatory requirements.
Audit and Inspection in Clinical Trial
This document discusses audits and inspections in clinical trials. It defines an audit as a systematic examination of trial activities and documents to determine compliance, while an inspection involves a regulatory review of documents, facilities, and resources related to a trial. The key differences between audits and inspections are that audits are conducted internally by sponsors or CROs, while inspections are done by regulatory authorities. Routine audits ensure compliance, while for-cause audits investigate non-compliance issues. Audits and inspections evaluate areas like personnel, trial conduct, documentation, drug accountability, and computer systems. Proper preparation and responding to document requests within time limits are important for audits and inspections.
Explaining the importance of a database lock in clinical research
One of the most crucial aspects of research is clinical data management or CDM. Proper CDM will generate results with excellent quality, integrity, and reliability. Quality data is essential in order to support the final conclusions of a certain study.
The person responsible for this area of research is called a clinical data manager. This job position can be filled by a PI, a study coordinator, or a CRA. No matter who fills this position at your site, data management has to be done promptly and correctly in order to generate the best results. Aside from all the other reasons why data management is so important, it’s also what determines the future IP (investigational product) development.
Clinical data management
Clinical Data Management (CDM) is a critical phase in clinical research that leads to generating high-quality, reliable data from clinical trials. CDM involves collecting, integrating, and ensuring the availability of appropriate quality and cost data. It encompasses entering, verifying, validating, and quality controlling the data gathered during clinical trials. The goal of CDM is to ensure the data supports conclusions drawn from the research.
Roles and responsibility of a CRC
A clinical research coordinator (CRC) is responsible for conducting clinical trials according to regulatory requirements and under the supervision of the principal investigator. The CRC acts as a vital link between all parties involved in the clinical trial. Their responsibilities include completing feasibility assessments, obtaining ethics committee approval, recruiting and retaining subjects, maintaining documentation, ensuring safety of patients, and closing out the trial in accordance with regulations.
CDISC-CDASH
CDISC is a non-profit organization that establishes clinical research data standards to support data acquisition, exchange, and submission. It has developed several standards including CDASH, which aims to standardize data collection fields across clinical trials to streamline data analysis and reduce errors. CDASH defines a set of common safety domains and variables that can be collected consistently across studies in a standardized way. This helps analyze data more efficiently, reduces training time for sites, and decreases potential errors from inconsistent data collection.
CLINICAL DATA MANGEMENT (CDM)
Have full fleged clinical trial data management systems which bring them a good amount of business and revenue.
CDM is a fundamental process which controls data accuracy of each trial besides helping the timelessness to be achieved.
It helps in linking clinical research co-ordinator = who monitor all the sites & collects the data.
it Links with biostatisticians = who analyze, interpret and report data in clinically meaningful way.
Clinical Data Management Plan_Katalyst HLS
A data management plan (DMP) ensures consistent and effective clinical data management practices throughout a clinical trial. The DMP describes all data management activities, roles, and responsibilities to promote standardized data handling. It provides an agreement between parties on data management deliverables. The DMP covers components like data flow, capture, setup, entry, transfer, processing, coding, safety handling, external data, and database locking. It serves to plan, communicate, and reference data management tasks. Developing a thorough DMP helps ensure quality and regulatory compliance in data collection and analysis.
eTMF in the fast lane
Learn about the Trial Master File, eTMF and how efficiencies can be during the clinical trial process.
Understanding Contracts in the Clinical Research Process
Presentation on key aspects of clinical trial agreements, with an emphasis on clauses impacting indemnification, confidentiality, material transfers, record retention, ownership of data,
Clinical Data Management
Clinical data is the most valuable asset to pharmaceutical companies as it serves as the basis for approval and marketing of new drugs. Clinical data is collected from various sources like clinical trial sites, laboratories, and subjects. It is important to manage clinical data carefully to minimize errors and ensure data quality. Clinical data management systems are used to store clinical trial data gathered at sites and help researchers analyze the data while maintaining accuracy and security. These systems employ features like double data entry, coding standards, and metadata repositories to organize data for regulatory submissions and clinical research.
Emerging Trends in Clinical Data Management
Appalla Venkataprabhakar and I presented this at the Oracle\'s Annual Clinical Development and Safety Conference 2010 at Hyderabad, India on 6th October 2010.
Investigator Site File (ISF) / Trial Master file in trial (TMF)
This document discusses the investigator site file (ISF) for a Phase 3 clinical trial of COVAXINE, a COVID-19 vaccine. It notes that the principal investigator is responsible for creating and maintaining the ISF. The contents of the ISF include essential documents like the protocol, informed consent forms, and case report forms. The document outlines procedures for maintaining the ISF, including updating it ongoing, filing documents without delay, and replacing outdated versions of documents. It also discusses who is responsible for single site studies and maintaining organization and version control of important documents in the ISF.
Clinical Data Management
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.
ICH-GCP Guidelines
The document provides an overview of ICH-GCP (Good Clinical Practice) guidelines, which are international ethical and scientific quality standards for designing, conducting, recording, and reporting trials that involve the participation of human subjects. The summary discusses the key sections and principles of ICH-GCP, which aim to protect trial subjects and ensure valid clinical trial data. It outlines the historical background and development of GCP standards from the Nuremberg Code to the ICH-GCP guidelines of 1996. The document reviews responsibilities of ethics committees, sponsors, investigators, clinical trial protocols, and informed consent processes.
Bridging Health Care and Clinical Trial Data through Technology
Karim Damji, SVP of Product and Marketing, presented at the Bridging Clinical Research and Clinical Health Care conference held at the Gaylord in National Harbor on April 4-5, 2018.
Machine Learning in Healthcare: A Case Study
Check out what machine learning can do when implemented by Hospital administrators for their operational services. We used historical data to test out and got results that could turn around ROIs for many hospitals suffering loses today
More Related Content
What's hot
Cra responsebility
The CRA oversees all stages of clinical trials from site selection to completion. They identify investigators, set up trial sites, train staff, monitor compliance, and verify informed consent and data collection. The CRA ensures protocols are followed, documents are collected, and supplies are accounted for throughout the trial. Effective communication, relationship building, attention to detail, and strong organizational skills are important for this role.
Project management in clinical research sanjay akhani 8 may
This document discusses project management in clinical research. It begins with a disclaimer from the presenter. It then provides an overview of key aspects of project management in clinical research, including project management tools and techniques used, components of a project management plan, managing contract research organizations and site management organizations, optimizing patient recruitment, and working with remote and multicultural teams. Challenges of working with remote and multicultural teams include differences in communication styles, work ethics, decision making, and views of time and change due to cultural differences between high and low context cultures.
Clinical data management
This document discusses clinical data management (CDM) systems and processes. It defines key terms like source data, source documents, and raw data. It then describes the essential steps in CDM including initial planning, data collection, review and verification, coding, query resolution, data entry and validation, output and archiving. Finally, it outlines requirements for a good CDM system including system validation, security, change control, and archiving. The goal of CDM is to generate an accurate, high-quality clinical trial database while ensuring compliance with regulations.
CDM
CLINICAL DATA MANAGEMENT
FOR MORE RELATED QUERIES CONTACT US ON- 9028839789
FOR ENROLLMENT IN NEXT BATCH CONTACT ON ABOVE MENTIONED NUMBER
Introduction to Clinical Research
An introduction for those who may be interested in a career in clinical research, but need to understand the industry and their potential for a role in it.
Provides an overview of the later stages of drug development, explaining the phases of drug studies and explores in brief the key roles for those participating.
Clinical Data Management: Best Practices and Key Considerations
Clinical data management (CDM) is a critical component of clinical research, involving the collection, processing, and analysis of data generated during clinical trials. Implementing best practices and considering key considerations is essential for ensuring data quality, integrity, and regulatory compliance. Here are some important considerations and best practices in clinical data management:
Data Standardization: Standardizing data collection and documentation across study sites is crucial for ensuring consistency and facilitating data analysis. Develop standardized data collection forms, case report forms (CRFs), and electronic data capture (EDC) systems that capture relevant data elements in a consistent manner.
Data Validation and Quality Control: Implement robust data validation procedures to ensure the accuracy and completeness of collected data. Conduct thorough quality control checks, including data validation checks, range checks, and consistency checks, to identify and resolve data discrepancies or errors.
Data Security and Privacy: Ensure data security and protect participant privacy by implementing appropriate measures such as data encryption, secure data transfer protocols, access controls, and adherence to applicable data protection regulations like GDPR or HIPAA.
Data Monitoring and Cleaning: Regularly monitor data collection processes to identify and address data discrepancies, missing data, or outliers. Implement data cleaning procedures to identify and resolve data errors, inconsistencies, and outliers that may impact the integrity and reliability of the study data.
Data Traceability and Audit Trail: Maintain a comprehensive audit trail that captures all changes and activities related to data entry, data modifications, and data review. This ensures data traceability and facilitates data validation and regulatory inspections.
Standard Operating Procedures (SOPs): Develop and adhere to well-defined SOPs for data management activities. SOPs should cover all aspects of data collection, processing, validation, cleaning, and archiving, ensuring consistency and adherence to regulatory requirements.
Audit and Inspection in Clinical Trial
This document discusses audits and inspections in clinical trials. It defines an audit as a systematic examination of trial activities and documents to determine compliance, while an inspection involves a regulatory review of documents, facilities, and resources related to a trial. The key differences between audits and inspections are that audits are conducted internally by sponsors or CROs, while inspections are done by regulatory authorities. Routine audits ensure compliance, while for-cause audits investigate non-compliance issues. Audits and inspections evaluate areas like personnel, trial conduct, documentation, drug accountability, and computer systems. Proper preparation and responding to document requests within time limits are important for audits and inspections.
Explaining the importance of a database lock in clinical research
One of the most crucial aspects of research is clinical data management or CDM. Proper CDM will generate results with excellent quality, integrity, and reliability. Quality data is essential in order to support the final conclusions of a certain study.
The person responsible for this area of research is called a clinical data manager. This job position can be filled by a PI, a study coordinator, or a CRA. No matter who fills this position at your site, data management has to be done promptly and correctly in order to generate the best results. Aside from all the other reasons why data management is so important, it’s also what determines the future IP (investigational product) development.
Clinical data management
Clinical Data Management (CDM) is a critical phase in clinical research that leads to generating high-quality, reliable data from clinical trials. CDM involves collecting, integrating, and ensuring the availability of appropriate quality and cost data. It encompasses entering, verifying, validating, and quality controlling the data gathered during clinical trials. The goal of CDM is to ensure the data supports conclusions drawn from the research.
Roles and responsibility of a CRC
A clinical research coordinator (CRC) is responsible for conducting clinical trials according to regulatory requirements and under the supervision of the principal investigator. The CRC acts as a vital link between all parties involved in the clinical trial. Their responsibilities include completing feasibility assessments, obtaining ethics committee approval, recruiting and retaining subjects, maintaining documentation, ensuring safety of patients, and closing out the trial in accordance with regulations.
CDISC-CDASH
CDISC is a non-profit organization that establishes clinical research data standards to support data acquisition, exchange, and submission. It has developed several standards including CDASH, which aims to standardize data collection fields across clinical trials to streamline data analysis and reduce errors. CDASH defines a set of common safety domains and variables that can be collected consistently across studies in a standardized way. This helps analyze data more efficiently, reduces training time for sites, and decreases potential errors from inconsistent data collection.
CLINICAL DATA MANGEMENT (CDM)
Have full fleged clinical trial data management systems which bring them a good amount of business and revenue.
CDM is a fundamental process which controls data accuracy of each trial besides helping the timelessness to be achieved.
It helps in linking clinical research co-ordinator = who monitor all the sites & collects the data.
it Links with biostatisticians = who analyze, interpret and report data in clinically meaningful way.
Clinical Data Management Plan_Katalyst HLS
A data management plan (DMP) ensures consistent and effective clinical data management practices throughout a clinical trial. The DMP describes all data management activities, roles, and responsibilities to promote standardized data handling. It provides an agreement between parties on data management deliverables. The DMP covers components like data flow, capture, setup, entry, transfer, processing, coding, safety handling, external data, and database locking. It serves to plan, communicate, and reference data management tasks. Developing a thorough DMP helps ensure quality and regulatory compliance in data collection and analysis.
eTMF in the fast lane
Learn about the Trial Master File, eTMF and how efficiencies can be during the clinical trial process.
Understanding Contracts in the Clinical Research Process
Presentation on key aspects of clinical trial agreements, with an emphasis on clauses impacting indemnification, confidentiality, material transfers, record retention, ownership of data,
Clinical Data Management
Clinical data is the most valuable asset to pharmaceutical companies as it serves as the basis for approval and marketing of new drugs. Clinical data is collected from various sources like clinical trial sites, laboratories, and subjects. It is important to manage clinical data carefully to minimize errors and ensure data quality. Clinical data management systems are used to store clinical trial data gathered at sites and help researchers analyze the data while maintaining accuracy and security. These systems employ features like double data entry, coding standards, and metadata repositories to organize data for regulatory submissions and clinical research.
Emerging Trends in Clinical Data Management
Appalla Venkataprabhakar and I presented this at the Oracle\'s Annual Clinical Development and Safety Conference 2010 at Hyderabad, India on 6th October 2010.
Investigator Site File (ISF) / Trial Master file in trial (TMF)
This document discusses the investigator site file (ISF) for a Phase 3 clinical trial of COVAXINE, a COVID-19 vaccine. It notes that the principal investigator is responsible for creating and maintaining the ISF. The contents of the ISF include essential documents like the protocol, informed consent forms, and case report forms. The document outlines procedures for maintaining the ISF, including updating it ongoing, filing documents without delay, and replacing outdated versions of documents. It also discusses who is responsible for single site studies and maintaining organization and version control of important documents in the ISF.
Clinical Data Management
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.
ICH-GCP Guidelines
The document provides an overview of ICH-GCP (Good Clinical Practice) guidelines, which are international ethical and scientific quality standards for designing, conducting, recording, and reporting trials that involve the participation of human subjects. The summary discusses the key sections and principles of ICH-GCP, which aim to protect trial subjects and ensure valid clinical trial data. It outlines the historical background and development of GCP standards from the Nuremberg Code to the ICH-GCP guidelines of 1996. The document reviews responsibilities of ethics committees, sponsors, investigators, clinical trial protocols, and informed consent processes.
What's hot (20)
Project management in clinical research sanjay akhani 8 may
Project management in clinical research sanjay akhani 8 may
Clinical Data Management: Best Practices and Key Considerations
Clinical Data Management: Best Practices and Key Considerations
Explaining the importance of a database lock in clinical research
Explaining the importance of a database lock in clinical research
Understanding Contracts in the Clinical Research Process
Understanding Contracts in the Clinical Research Process
Investigator Site File (ISF) / Trial Master file in trial (TMF)
Investigator Site File (ISF) / Trial Master file in trial (TMF)
Similar to Decentralized Clinical Trials
Bridging Health Care and Clinical Trial Data through Technology
Karim Damji, SVP of Product and Marketing, presented at the Bridging Clinical Research and Clinical Health Care conference held at the Gaylord in National Harbor on April 4-5, 2018.
Machine Learning in Healthcare: A Case Study
Check out what machine learning can do when implemented by Hospital administrators for their operational services. We used historical data to test out and got results that could turn around ROIs for many hospitals suffering loses today
How to Architect Smarter Systems for Healthcare
The document discusses using intelligent data and connectivity in healthcare. It outlines several use cases for real-time data integration like clinical decision support, remote patient monitoring, and medical device interoperability. The needs of intelligent healthcare systems include reliable and secure communication between devices, location abstraction, and scalability. The RTI Connext DataBus is presented as a solution to distribute data across these complex healthcare systems in real-time. A deep dive on DocBox, a clinical decision support system, shows how it can improve patient safety by integrating data from various sources and devices like PCA pumps. The Internet of Things is transforming healthcare by enabling smart devices and analytics to share data to deliver better care.
Clinicaldatamanagementindiaasahub 130313225150-phpapp01
Clinical data management systems (CDMS) are important for managing large volumes of data from multinational clinical trials efficiently and accurately. India is becoming a major hub for CDMS due to its large skilled workforce, lower costs, and concentration of clinical trial resources. CDMS provide electronic tools for remote data capture, monitoring compliance and workflows, processing data, and generating reports. Standards are crucial for harmonizing data across regions and facilitating regulatory review. India offers many advantages for hosting CDMS and their associated databases and pharmacovigilance activities.
Big data in healthcare
Big data solutions are enabling healthcare providers to transform into more patient-centered, collaborative care models driven by analytics. As basic needs are met and advanced applications emerge, new use cases will arise from sources like wearable devices and sensors. Predictive analytics using big data can help fill gaps by predicting things like missed appointments, noncompliance, and patient trajectories in order to proactively manage care. However, barriers to using big data include a lack of expertise and the fact that big data has a different structure and is more unstructured than traditional databases.
City of hope research informatics common data elements
This document discusses City of Hope's Research Informatics Common Data Elements (RI-CDE) and Research Informatics Enterprise Architecture Framework (RI-EAF). The RI-CDE is a repository that harmonizes common data elements and their relationships to enable decision support and interoperability. The RI-EAF is an architectural framework based on standards like TOGAF and HL7 that facilitates research information systems. It then analyzes diagnosis workflows and systems, identifies issues, and proposes improvements like leveraging the data warehouse to collect quality metrics.
Data science / Big Data
This document provides an introduction to big data and data science. It defines big data as large and complex datasets that cannot be processed by traditional software. Big data comes from various sources like mobile devices, IoT, social media, and more. Data science is the process of extracting knowledge from raw data through techniques like machine learning. The document outlines the data-to-knowledge process and popular tools used in data science like Hadoop, Spark, Python libraries Scikit-Learn and Keras. It also discusses how big data is important in applications like healthcare, predicting hospital admissions, and improving telehealth services.
Business ideas pitch deck - multi project - Canada
This is a presentation and pitch deck that introduces my ideas, its technical and business views for the investors and companies.Projects proposed: Job Cafe, AI Lawyer, AI wearable, & AI based food tester
4D Geospatial Analytics in Digital Healthcare PDF
This document discusses digital health technologies and 4D geospatial analytics in digital healthcare. It describes how digital health utilizes categories like digital imaging, robotic surgery, patient monitoring, and biomedical data. It also discusses how geospatial information systems (GIS) and spatial data analysis are used with digital healthcare to map and analyze medical data based on location. GIS allows integration of data from sources like medical imaging, GPS tracking, and biomedical streaming to analyze and distribute health information spatially. The document proposes a model called "The Cone" to cluster and analyze patient data based on attributes like acute/chronic conditions to derive actionable clinical insights.
The Data Operating System: Changing the Digital Trajectory of Healthcare
This is the next evolution in health information exchanges and data warehouses, specifically designed to support analytics, transaction processing, and third party application development, in one platform, the Data Operating System.
Decentralized Monitoring in Clinical Trials
Decentralized monitoring (DM) in clinical trials is a relatively new approach to monitoring that is becoming increasingly popular. DM involves the use of technology and remote monitoring methods to monitor the conduct of clinical trials, as opposed to the traditional on-site monitoring approach.
Under DM, monitoring activities are conducted remotely using electronic data capture (EDC) systems, risk-based monitoring tools, and other technologies. This allows the monitoring team to access and review trial data in real-time, without the need to be physically present at the trial site. DM can include a range of activities, such as remote source data verification, central review of imaging data, and remote monitoring of electronic health records.
There are several benefits to DM. First, it can reduce the burden and costs associated with on-site monitoring, such as travel expenses and time spent away from the office. DM can also allow for more frequent monitoring and earlier detection of issues, as monitoring activities can be conducted in real-time. Additionally, DM can increase patient safety and improve data quality, as remote monitoring tools can be used to identify potential safety concerns or data inconsistencies more quickly.
However, there are also some challenges associated with DM. For example, there may be concerns about the security and confidentiality of trial data when accessed remotely. Additionally, DM may not be suitable for all types of clinical trials, and there may be regulatory or legal requirements that need to be considered when implementing DM.
The Data Operating System: Changing the Digital Trajectory of Healthcare
In 1989, John Reed, the CEO of Citibank and the early pioneer for ATMs, said, “I can see a future in which the data and information that is exchanged in our transactions are worth more than the transactions themselves.” We are at an interesting digital nexus in healthcare. Few of us would argue against the notion that data and digital health will play a bigger and bigger role in the future. But, are we on the right track to deliver on that future? It required $30B in federal incentive money to subsidize the uptake of Electronic Health Records (EHRs). You could argue that the federal incentives stimulated the first major step towards the digitization of health, but few physicians would celebrate its value in comparison to its expense. As the healthcare market consolidates through mergers and acquisitions (M&A), patching disparate EHRs and other information systems together becomes even more important, and challenging. An organization is not integrated until its data is integrated, but costly forklift replacements of these transaction information systems and consolidating them with a single EHR solution is not a viable financial solution.
Business ideas pitch deck - multi projects
This is a pitch deck for showcasing ideas that helps creation of quality smart products for health, food quality , AI lawyer and many more ideas that help in smart living, smart health, & smart eating
Regulatory Intelligence
Regulatory intelligence involves acquiring knowledge through analyzing various internal and external information sources to enable timely, data-driven decision making in the complex and evolving global regulatory landscape. A regulatory intelligence platform can integrate internal quality and compliance data with external sources like FDA reports, clinical studies, and regulations. This provides a holistic view to identify risks, prioritize improvements, and help maintain market advantage. However, many organizations still struggle with data access, analysis, and using analytics to impact business outcomes. Assessing internal audit and complaint data alongside external benchmarks can help evaluate inspection readiness and prioritize compliance issues.
Transforming GE Healthcare with Data Platform Strategy
Data and Analytics is foundational to the success of GE Healthcare’s digital transformation and market competitiveness. This use case focuses on a heavy platform transformation that GE Healthcare drove in the last year to move from an On prem legacy data platforming strategy to a cloud native and completely services oriented strategy. This was a huge effort for an 18Bn company and executed in the middle of the pandemic. It enables GE Healthcare to leap frog in the enterprise data analytics strategy.
iHT2 Health IT Chicago Summit
Keynote Presentation: "Patient Matching Decoded: A Proposed National Patient Matching Framework" by Mariann Yeager
International Quality Clinical HIV/AIDS Registry tool
IQChart is a patient management database that collects clinical data from HIV/AIDS patients to generate accurate monthly and quarterly reports for monitoring and evaluation. It was developed by AIDS Relief and ICAP to computerize paper-based patient registers and improve data analysis and clinical decision making. The tool is freely available, open source software that is used in over 90 treatment facilities in Rwanda to track over 54,000 patients. Future plans include integrating geographic information system mapping capabilities to help identify underserved areas and monitor program outcomes.
Big data in healthcare
Big data is generating hype in healthcare, but true value will come as technical expertise and security improve. While most healthcare organizations currently have limited big data use beyond basic analytics, needs will grow as data sources expand through devices and the "internet of things". Predictive analytics using socioeconomic and other data could help predict patient outcomes and appointments. Prescriptive analytics may eventually provide personalized treatment paths for patients. Drug discovery may also be enhanced through big data. However, barriers like a lack of skills and integrated security currently limit big data to research applications.
Healthcare Analytics Adoption Model
In this webinar, Dale Sanders will provide a pragmatic, step-by-step, and measurable roadmap for the adoption of analytics in healthcare-- a roadmap that organizations can use to plot their strategy and evaluate vendors; and that vendors can use to develop their products. Attendees will have a chance to learn about:
1) The details of his eight-level model, 2) A brief introduction to the HIMSS/IIA DELTA Model, 3) The importance of permanent organizational teams to sustain improvements from analytic investments, 4) The process of curating and maturing data governance, and 5) The coordination of a data acquisition strategy with payment and reimbursement strategies
Wellness presentation
This document discusses challenges in managing explosive growth in healthcare IT and provides a 5-stage approach and checklist for effective IT asset management. It recommends analyzing workflows, auditing assets, refreshing/replacing devices, ensuring quality assurance, and properly disposing of or repurposing old devices. The approach aims to support sustainability and regulatory compliance while improving efficiency and cost-effectiveness of IT logistics for healthcare organizations.
Similar to Decentralized Clinical Trials (20)
Bridging Health Care and Clinical Trial Data through Technology
Bridging Health Care and Clinical Trial Data through Technology
Clinicaldatamanagementindiaasahub 130313225150-phpapp01
Clinicaldatamanagementindiaasahub 130313225150-phpapp01
City of hope research informatics common data elements
City of hope research informatics common data elements
Business ideas pitch deck - multi project - Canada
Business ideas pitch deck - multi project - Canada
The Data Operating System: Changing the Digital Trajectory of Healthcare
The Data Operating System: Changing the Digital Trajectory of Healthcare
The Data Operating System: Changing the Digital Trajectory of Healthcare
The Data Operating System: Changing the Digital Trajectory of Healthcare
Transforming GE Healthcare with Data Platform Strategy
Transforming GE Healthcare with Data Platform Strategy
International Quality Clinical HIV/AIDS Registry tool
International Quality Clinical HIV/AIDS Registry tool
Recently uploaded
Best practices for project execution and delivery
A select set of project management best practices to keep your project on-track, on-cost and aligned to scope. Many firms have don't have the necessary skills, diligence, methods and oversight of their projects; this leads to slippage, higher costs and longer timeframes. Often firms have a history of projects that simply failed to move the needle. These best practices will help your firm avoid these pitfalls but they require fortitude to apply.
一比一原版新西兰奥塔哥大学毕业证（otago毕业证）如何办理
一模一样【微信：A575476】【新西兰奥塔哥大学毕业证（otago毕业证）成绩单Offer】【微信：A575476】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：A575476】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：A575476】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Event Report - SAP Sapphire 2024 Orlando - lots of innovation and old challenges
Holger Mueller of Constellation Research shares his key takeaways from SAP's Sapphire confernece, held in Orlando, June 3rd till 5th 2024, in the Orange Convention Center.
The Genesis of BriansClub.cm Famous Dark WEb Platform
BriansClub.cm, a famous platform on the dark web, has become one of the most infamous carding marketplaces, specializing in the sale of stolen credit card data.
Call 8867766396 Satta Matka Dpboss Matka Guessing Satta batta Matka 420 Satta...
CALL 8867766396 SATTA MATKA | DPBOSS | KALYAN MAIN BAZAR | FAST MATKA | DPBOSS GUESSING | TARA MATKA | KALYAN CHART | MATKA BOSS
Income Tax exemption for Start up : Section 80 IAC
A presentation on the concept of Exemption of Profits of Start ups from Income Tax
Satta Matka Dpboss Matka Guessing Kalyan Chart Indian Matka Kalyan panel Chart
Satta Matka Dpboss Matka Guessing Kalyan Chart Indian Matka Kalyan panel Chart➒➌➎➏➑➐➋➑➐➐Dpboss Matka Guessing Satta Matka Kalyan Chart Indian Matka
SATTA MATKA SATTA FAST RESULT KALYAN TOP MATKA RESULT KALYAN SATTA MATKA FAST RESULT MILAN RATAN RAJDHANI MAIN BAZAR MATKA FAST TIPS RESULT MATKA CHART JODI CHART PANEL CHART FREE FIX GAME SATTAMATKA ! MATKA MOBI SATTA 143 spboss.in TOP NO1 RESULT FULL RATE MATKA ONLINE GAME PLAY BY APP SPBOSSCall8328958814 satta matka Kalyan result satta guessing
Satta Matka Kalyan Main Mumbai Fastest Results
Satta Matka ❋ Sattamatka ❋ New Mumbai Ratan Satta Matka ❋ Fast Matka ❋ Milan Market ❋ Kalyan Matka Results ❋ Satta Game ❋ Matka Game ❋ Satta Matka ❋ Kalyan Satta Matka ❋ Mumbai Main ❋ Online Matka Results ❋ Satta Matka Tips ❋ Milan Chart ❋ Satta Matka Boss❋ New Star Day ❋ Satta King ❋ Live Satta Matka Results ❋ Satta Matka Company ❋ Indian Matka ❋ Satta Matka 143❋ Kalyan Night Matka..
DearbornMusic-KatherineJasperFullSailUni
My powerpoint presentation for my Music Retail and Distribution class at Full Sail University
How are Lilac French Bulldogs Beauty Charming the World and Capturing Hearts....
“After being the most listed dog breed in the United States for 31
years in a row, the Labrador Retriever has dropped to second place
in the American Kennel Club's annual survey of the country's most
popular canines. The French Bulldog is the new top dog in the
United States as of 2022. The stylish puppy has ascended the
rankings in rapid time despite having health concerns and limited
color choices.”
The 10 Most Influential Leaders Guiding Corporate Evolution, 2024.pdf
In the recent edition, The 10 Most Influential Leaders Guiding Corporate Evolution, 2024, The Silicon Leaders magazine gladly features Dejan Štancer, President of the Global Chamber of Business Leaders (GCBL), along with other leaders.
The latest Heat Pump Manual from Newentide
𝐔𝐧𝐯𝐞𝐢𝐥 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐄𝐧𝐞𝐫𝐠𝐲 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 𝐰𝐢𝐭𝐡 𝐍𝐄𝐖𝐍𝐓𝐈𝐃𝐄’𝐬 𝐋𝐚𝐭𝐞𝐬𝐭 𝐎𝐟𝐟𝐞𝐫𝐢𝐧𝐠𝐬
Explore the details in our newly released product manual, which showcases NEWNTIDE's advanced heat pump technologies. Delve into our energy-efficient and eco-friendly solutions tailored for diverse global markets.
Anny Serafina Love - Letter of Recommendation by Kellen Harkins, MS.
This letter, written by Kellen Harkins, Course Director at Full Sail University, commends Anny Love's exemplary performance in the Video Sharing Platforms class. It highlights her dedication, willingness to challenge herself, and exceptional skills in production, editing, and marketing across various video platforms like YouTube, TikTok, and Instagram.
Dpboss Matka Guessing Satta Matta Matka Kalyan Chart Satta Matka
Dpboss Matka Guessing Satta Matta Matka Kalyan Chart Satta Matka➒➌➎➏➑➐➋➑➐➐Dpboss Matka Guessing Satta Matka Kalyan Chart Indian Matka
Dpboss Matka Guessing Satta Matta Matka Kalyan Chart Indian Matka Indian satta Matka Dpboss Matka Kalyan Chart Matka Boss otg matka Guessing Satta Digital Transformation Frameworks: Driving Digital Excellence
[To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This presentation is a curated compilation of PowerPoint diagrams and templates designed to illustrate 20 different digital transformation frameworks and models. These frameworks are based on recent industry trends and best practices, ensuring that the content remains relevant and up-to-date.
Key highlights include Microsoft's Digital Transformation Framework, which focuses on driving innovation and efficiency, and McKinsey's Ten Guiding Principles, which provide strategic insights for successful digital transformation. Additionally, Forrester's framework emphasizes enhancing customer experiences and modernizing IT infrastructure, while IDC's MaturityScape helps assess and develop organizational digital maturity. MIT's framework explores cutting-edge strategies for achieving digital success.
These materials are perfect for enhancing your business or classroom presentations, offering visual aids to supplement your insights. Please note that while comprehensive, these slides are intended as supplementary resources and may not be complete for standalone instructional purposes.
Frameworks/Models included:
Microsoft’s Digital Transformation Framework
McKinsey’s Ten Guiding Principles of Digital Transformation
Forrester’s Digital Transformation Framework
IDC’s Digital Transformation MaturityScape
MIT’s Digital Transformation Framework
Gartner’s Digital Transformation Framework
Accenture’s Digital Strategy & Enterprise Frameworks
Deloitte’s Digital Industrial Transformation Framework
Capgemini’s Digital Transformation Framework
PwC’s Digital Transformation Framework
Cisco’s Digital Transformation Framework
Cognizant’s Digital Transformation Framework
DXC Technology’s Digital Transformation Framework
The BCG Strategy Palette
McKinsey’s Digital Transformation Framework
Digital Transformation Compass
Four Levels of Digital Maturity
Design Thinking Framework
Business Model Canvas
Customer Journey Map
2022 Vintage Roman Numerals Men Rings
Discover timeless style with the 2022 Vintage Roman Numerals Men's Ring. Crafted from premium stainless steel, this 6mm wide ring embodies elegance and durability. Perfect as a gift, it seamlessly blends classic Roman numeral detailing with modern sophistication, making it an ideal accessory for any occasion.
https://rb.gy/usj1a2
Recently uploaded (20)
Event Report - SAP Sapphire 2024 Orlando - lots of innovation and old challenges
Event Report - SAP Sapphire 2024 Orlando - lots of innovation and old challenges
The Genesis of BriansClub.cm Famous Dark WEb Platform
The Genesis of BriansClub.cm Famous Dark WEb Platform
Call 8867766396 Satta Matka Dpboss Matka Guessing Satta batta Matka 420 Satta...
Call 8867766396 Satta Matka Dpboss Matka Guessing Satta batta Matka 420 Satta...
Income Tax exemption for Start up : Section 80 IAC
Income Tax exemption for Start up : Section 80 IAC
Satta Matka Dpboss Matka Guessing Kalyan Chart Indian Matka Kalyan panel Chart
Satta Matka Dpboss Matka Guessing Kalyan Chart Indian Matka Kalyan panel Chart
Call8328958814 satta matka Kalyan result satta guessing
Call8328958814 satta matka Kalyan result satta guessing
How are Lilac French Bulldogs Beauty Charming the World and Capturing Hearts....
How are Lilac French Bulldogs Beauty Charming the World and Capturing Hearts....
The 10 Most Influential Leaders Guiding Corporate Evolution, 2024.pdf
The 10 Most Influential Leaders Guiding Corporate Evolution, 2024.pdf
Anny Serafina Love - Letter of Recommendation by Kellen Harkins, MS.
Anny Serafina Love - Letter of Recommendation by Kellen Harkins, MS.
Dpboss Matka Guessing Satta Matta Matka Kalyan Chart Satta Matka
Dpboss Matka Guessing Satta Matta Matka Kalyan Chart Satta Matka
Digital Transformation Frameworks: Driving Digital Excellence
Digital Transformation Frameworks: Driving Digital Excellence
Decentralized Clinical Trials
- 1. Decentralized Clinical Trials by Preyas Parikh Clinical Research Professional
- 2. What is DCT? • DCTs are the trials where Patients/Subjects cater remotely and data captured using technological advanced equipments. • Protocol and other CSDs are designed in a way that all trial activities can be performed at Patient’s Home or at a Remote Location. • Subjects have to visit the site at rare occasions only • Hybrid Trials are good solutions for complex projects
- 3. India Centric Business Model
- 4. Technology which we use • Cloud Computing (To Store, communicate and integrate the data) • AI & ML (To Do Analytical Part, To generate data through Stats) • MS Office 365 (To record important information on real time) • Tablet and Wearable Trackers (To Capture the Vital and real time info.) • eCRFs (Small Office Team need to manage the important parts) • Tab to eCRF Integration (Need to do for Most of the Subject Level Records. It serves as a Source and directly integrated to eCRF) • SAE Monitoring through Virtual PI Calls (Helpline Number for any immediate Assistance and Data Capture) • IVRS/IWRS Reminders for Visits to be conducted, IP Management
- 5. How we can contribute from India in DCT • By Creating Analytical tools, we may be very helpful in updating the CL Team on real time basis • Central Monitors can learn few techniques and need to train the Phlebo, Nurse and Pharmacists about masking the data and any other legal implications (If Any) based on Country regulations. They can review and evaluate most of the trial data remotely. • We can also manage the SAE Documentation and Subject Helpline activities remotely from India • We can customize our role for DCT as per the requirements like Access providers, training providers, QC reviewer and many.
- 6. DCT as a Whole - Conclusion • The concept which will change the healthcare industry from scratch • Data what we produce will have a great weightage to manage the complicated health issues • The Data can be able to work on AI ML Bot trainings and to train the new Doctors • Patient History data will be available in just one touch to any Physician for better treatment and evaluations of underlying disease • Insurance sector will also open up where we can be able to pitch in and get a good remuneration by analyzing the Subject Data • Clinical trials will be within the reach to all communities and populations • Stats will become more judgmental and clearer • Many other Sectors will open up like Disease spread evaluations in regions, Weather impact on health to the region, How to overcome a Disease spread (Planning & Strategic thinking) and so on and so forth.
- 7. Thanks. You can reach me for more info. Preyas Parikh Clinical Research Professional +91-9725956241