1) Clinical data management is important for multi-center clinical trials to efficiently coordinate data capture, storage, and reporting across sites in real-time.
2) A clinical data management system is needed to handle the enormous volumes of data generated in clinical trials, ensuring the data is clean, consistent, accurate and can be easily analyzed.
3) Key functions of a clinical data management system include electronic data capture, data extraction, cleaning, validation, locking, and reporting to support clinical trial conduct and regulatory compliance.
1. Clinical data management systems are needed for multi-center clinical trials to manage large volumes of data from multiple sites in real-time.
2. India has potential to grow as a clinical data management hub due to its large, skilled workforce and lower costs compared to other countries.
3. Stakeholders in clinical data management include sponsors, CROs, sites, and regulators who require standardized, clean data to be efficiently captured and reported.
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.
Clinical Data Management Training @ Gratisol LabsGratisol Labs
Clinical data management involves processing clinical trial data using computer applications and database systems. It supports the collection, cleaning, and management of subject data. Key aspects of clinical data management include CRF design, database setup, data entry, discrepancy management, medical coding, quality control, and database lock. The goal is to ensure the integrity and quality of clinical trial data.
This document outlines the process of clinical data management. It discusses the key steps and technologies involved in digitization, electronic data capture, data analytics, document management, data standardization, and infrastructure/security. The main stages described are feasibility analysis, system selection, design and implementation, data collection, quality control, and regulatory submission preparation. Technologies mentioned include EDC tools like Oracle Clinical, data standards like CDISC SDTM, and document management solutions from vendors such as Documentum.
Appalla Venkataprabhakar and I presented this at the Oracle\'s Annual Clinical Development and Safety Conference 2010 at Hyderabad, India on 6th October 2010.
This document summarizes a webinar on streamlining data management for clinical trials. The webinar covered the need for streamlined approaches given rising drug development costs. It discussed areas for improving efficiencies, including using standards, a parallel approach, identifying key reviewers, and tailoring processes based on trial type (e.g. a "Premier Express" approach for small phase 1 trials). An example case study showed how streamlining tasks and performing work in parallel reduced timelines for developing case report forms, annotated case report forms, databases, and edit checks for a small phase 1 trial from 9 weeks to 5 weeks.
The document provides information on several clinical data management systems and software, including Oracle Clinical, SAS Clinical Software, TCS Clin-E2E Software, Cognos 8 Business Intelligence Software, Symetric Software, Akaza's OpenClinica Software, SigmaSoft's DMSys Software, and Progeny Clinical Software. It discusses their key features for managing clinical trials data such as electronic data capture, reporting, security, compliance with industry standards, and integration with other systems.
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.
1. Clinical data management systems are needed for multi-center clinical trials to manage large volumes of data from multiple sites in real-time.
2. India has potential to grow as a clinical data management hub due to its large, skilled workforce and lower costs compared to other countries.
3. Stakeholders in clinical data management include sponsors, CROs, sites, and regulators who require standardized, clean data to be efficiently captured and reported.
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.
Clinical Data Management Training @ Gratisol LabsGratisol Labs
Clinical data management involves processing clinical trial data using computer applications and database systems. It supports the collection, cleaning, and management of subject data. Key aspects of clinical data management include CRF design, database setup, data entry, discrepancy management, medical coding, quality control, and database lock. The goal is to ensure the integrity and quality of clinical trial data.
This document outlines the process of clinical data management. It discusses the key steps and technologies involved in digitization, electronic data capture, data analytics, document management, data standardization, and infrastructure/security. The main stages described are feasibility analysis, system selection, design and implementation, data collection, quality control, and regulatory submission preparation. Technologies mentioned include EDC tools like Oracle Clinical, data standards like CDISC SDTM, and document management solutions from vendors such as Documentum.
Appalla Venkataprabhakar and I presented this at the Oracle\'s Annual Clinical Development and Safety Conference 2010 at Hyderabad, India on 6th October 2010.
This document summarizes a webinar on streamlining data management for clinical trials. The webinar covered the need for streamlined approaches given rising drug development costs. It discussed areas for improving efficiencies, including using standards, a parallel approach, identifying key reviewers, and tailoring processes based on trial type (e.g. a "Premier Express" approach for small phase 1 trials). An example case study showed how streamlining tasks and performing work in parallel reduced timelines for developing case report forms, annotated case report forms, databases, and edit checks for a small phase 1 trial from 9 weeks to 5 weeks.
The document provides information on several clinical data management systems and software, including Oracle Clinical, SAS Clinical Software, TCS Clin-E2E Software, Cognos 8 Business Intelligence Software, Symetric Software, Akaza's OpenClinica Software, SigmaSoft's DMSys Software, and Progeny Clinical Software. It discusses their key features for managing clinical trials data such as electronic data capture, reporting, security, compliance with industry standards, and integration with other systems.
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 data management (CDM) ensures the collection, integration, and availability of high-quality data from clinical trials. It supports clinical research and analysis across different study types. CDM tools like CDMS help manage large amounts of multicenter trial data. Regulations like 21 CFR Part 11 require electronic records and validated systems to ensure accurate, reliable data. Guidelines from SCDM and CDISC provide standards for good CDM practices and data collection. CDM processes clinical research data from source documents through database entry, quality checking, analysis, and archiving to support regulatory approval and conclusions about clinical results.
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.
Scientific & systematic collection of data for clinical study is called as Clinical data management .
EDC
RDC
HISTORY
EVOLUTION OF CLINICAL DATA CAPTURE
CRITERIA FOR IDENTIFYING AN EDC
REGULATORY GUIDELINE ON EDC
EDC ISSUES
VALIDATING ELECTRONIC SOURCE DATA
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.
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 is the process of collecting, validating, and cleaning data from clinical trials. It aims to ensure data quality and integrity. Key aspects of clinical data management include electronic data capture, establishing data standards, using clinical data management systems, and performing activities like data collection, validation, and discrepancy management. It follows guidelines from organizations like SCDM and regulations like 21 CFR Part 11.
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.
This document provides guidance on clinical data management practices for analyzing research data. It discusses key aspects of clinical data management including planning, data collection, review, entry, coding, querying, output, and archiving. Ensuring accurate data capture and high quality databases is the objective. Adherence to good clinical data management practices and regulatory guidelines is emphasized. Effective planning, standardized processes, trained personnel, quality control measures, and system validation are seen as important for generating reliable data for analysis and reporting.
Clinical Data Management Plan_Katalyst HLSKatalyst 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.
Visit:www.acriindia.com
ACRI is a leading Clinical data management training Institute in Bangalore India.
ACRI creates a value add for every degree. Our PGDCRCDM course is approved by the Mysore University. Graduates and Post Graduates and even PhDs have trained with us and got enviable positions in the Clinical Research Industry. ACRI supplements University training with Industry based training, coupled with hands-on internships and projects based on real case studies. The ACRI brand gives the individual the confidence and expertise to join the ever-growing workforce both in the country and abroad.
The document outlines the process for setting up clinical data management and pharmacovigilance processes. It discusses developing the protocol and case report forms, designing the database, installing software like Oracle Inform and Argus, and preparing documents like the data management plan. It also describes the data entry, validation, query resolution, medical coding, biostatistics, and database locking and freezing aspects of the clinical data management and pharmacovigilance setup process.
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.
CRO
This document provides an overview of a CRO's full-service clinical data management capabilities. It has over 25 years of experience and a team of 70 staff across Europe and India. The CRO offers a full range of CDM services including project management, database design, data entry, validation, and IT infrastructure validated to industry standards. It has experience across therapeutic areas and geographies, using mature CDISC-compliant platforms. The CRO maintains data security, backup/recovery policies, and has qualified experienced staff to deliver high-quality CDM services.
YEARS
Track Record
Clinical data management (CDM) involves collecting, validating, and cleaning patient data from clinical trials to ensure it is complete, consistent, and compliant. A CDM team typically includes clinical data managers, programmers, and data entry associates. They are involved in all stages from study setup to completion. Key CDM activities include designing case report forms, programming data validation checks, overseeing data entry into clinical data management systems, manually and electronically cleaning the data, reconciling safety data with external sources, and locking the database once the trial is complete and the data is ready for analysis. The goal is to generate high-quality clinical trial data that can be analyzed to advance drug development timelines.
Clinical data management involves processing clinical trial data through activities like data entry, validation, query resolution and medical coding. It aims to ensure the integrity and quality of clinical trial data, which regulatory agencies rely on for drug approval. The document provides an overview of the clinical data management process and roles involved at each stage, from study set-up to closeout.
Introduction to Oracle Clinical Overview in Clinical Data Management in Clinical Trials of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Electronic Data Capture & Remote Data CaptureCRB Tech
CRB Tech is one of the best leading Software Development Company in Pune. We are offering Software Development Services as well as IT Training including Java, Dot Net, SEO and Clinical Research training in pune.
Study setup_Clinical Data Management_Katalyst HLSKatalyst HLS
Introduction to Study Setup in Clinical Data Management in Clinical Trials of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Is "healthcare intelligence" an oxymoron? What can we expect to accomplish with the data we have in healthcare? How do we transform data in electronic health records into superior clinical and financial outcomes? What are the information building blocks for a continuously learning health system? How important is technology in healthcare intelligence? What is the role of Big Data in healthcare and how do we prepare for it?
This document discusses the importance of data management in research. It notes that science is becoming more data-centric and data-intensive, and data is a valuable asset that should be reused. Examples are provided of different types of data from various domains like clinical trials, sensors, and simulations. Challenges of data management are outlined, like privacy, heterogeneity of data, and improving data management processes. The document recommends a mature, end-to-end approach to data management across the data lifecycle to better enable reuse of data through standards, metadata, cataloging, and dedicated expertise.
Clinical data management (CDM) ensures the collection, integration, and availability of high-quality data from clinical trials. It supports clinical research and analysis across different study types. CDM tools like CDMS help manage large amounts of multicenter trial data. Regulations like 21 CFR Part 11 require electronic records and validated systems to ensure accurate, reliable data. Guidelines from SCDM and CDISC provide standards for good CDM practices and data collection. CDM processes clinical research data from source documents through database entry, quality checking, analysis, and archiving to support regulatory approval and conclusions about clinical results.
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.
Scientific & systematic collection of data for clinical study is called as Clinical data management .
EDC
RDC
HISTORY
EVOLUTION OF CLINICAL DATA CAPTURE
CRITERIA FOR IDENTIFYING AN EDC
REGULATORY GUIDELINE ON EDC
EDC ISSUES
VALIDATING ELECTRONIC SOURCE DATA
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.
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 is the process of collecting, validating, and cleaning data from clinical trials. It aims to ensure data quality and integrity. Key aspects of clinical data management include electronic data capture, establishing data standards, using clinical data management systems, and performing activities like data collection, validation, and discrepancy management. It follows guidelines from organizations like SCDM and regulations like 21 CFR Part 11.
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.
This document provides guidance on clinical data management practices for analyzing research data. It discusses key aspects of clinical data management including planning, data collection, review, entry, coding, querying, output, and archiving. Ensuring accurate data capture and high quality databases is the objective. Adherence to good clinical data management practices and regulatory guidelines is emphasized. Effective planning, standardized processes, trained personnel, quality control measures, and system validation are seen as important for generating reliable data for analysis and reporting.
Clinical Data Management Plan_Katalyst HLSKatalyst 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.
Visit:www.acriindia.com
ACRI is a leading Clinical data management training Institute in Bangalore India.
ACRI creates a value add for every degree. Our PGDCRCDM course is approved by the Mysore University. Graduates and Post Graduates and even PhDs have trained with us and got enviable positions in the Clinical Research Industry. ACRI supplements University training with Industry based training, coupled with hands-on internships and projects based on real case studies. The ACRI brand gives the individual the confidence and expertise to join the ever-growing workforce both in the country and abroad.
The document outlines the process for setting up clinical data management and pharmacovigilance processes. It discusses developing the protocol and case report forms, designing the database, installing software like Oracle Inform and Argus, and preparing documents like the data management plan. It also describes the data entry, validation, query resolution, medical coding, biostatistics, and database locking and freezing aspects of the clinical data management and pharmacovigilance setup process.
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.
CRO
This document provides an overview of a CRO's full-service clinical data management capabilities. It has over 25 years of experience and a team of 70 staff across Europe and India. The CRO offers a full range of CDM services including project management, database design, data entry, validation, and IT infrastructure validated to industry standards. It has experience across therapeutic areas and geographies, using mature CDISC-compliant platforms. The CRO maintains data security, backup/recovery policies, and has qualified experienced staff to deliver high-quality CDM services.
YEARS
Track Record
Clinical data management (CDM) involves collecting, validating, and cleaning patient data from clinical trials to ensure it is complete, consistent, and compliant. A CDM team typically includes clinical data managers, programmers, and data entry associates. They are involved in all stages from study setup to completion. Key CDM activities include designing case report forms, programming data validation checks, overseeing data entry into clinical data management systems, manually and electronically cleaning the data, reconciling safety data with external sources, and locking the database once the trial is complete and the data is ready for analysis. The goal is to generate high-quality clinical trial data that can be analyzed to advance drug development timelines.
Clinical data management involves processing clinical trial data through activities like data entry, validation, query resolution and medical coding. It aims to ensure the integrity and quality of clinical trial data, which regulatory agencies rely on for drug approval. The document provides an overview of the clinical data management process and roles involved at each stage, from study set-up to closeout.
Introduction to Oracle Clinical Overview in Clinical Data Management in Clinical Trials of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Electronic Data Capture & Remote Data CaptureCRB Tech
CRB Tech is one of the best leading Software Development Company in Pune. We are offering Software Development Services as well as IT Training including Java, Dot Net, SEO and Clinical Research training in pune.
Study setup_Clinical Data Management_Katalyst HLSKatalyst HLS
Introduction to Study Setup in Clinical Data Management in Clinical Trials of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Is "healthcare intelligence" an oxymoron? What can we expect to accomplish with the data we have in healthcare? How do we transform data in electronic health records into superior clinical and financial outcomes? What are the information building blocks for a continuously learning health system? How important is technology in healthcare intelligence? What is the role of Big Data in healthcare and how do we prepare for it?
This document discusses the importance of data management in research. It notes that science is becoming more data-centric and data-intensive, and data is a valuable asset that should be reused. Examples are provided of different types of data from various domains like clinical trials, sensors, and simulations. Challenges of data management are outlined, like privacy, heterogeneity of data, and improving data management processes. The document recommends a mature, end-to-end approach to data management across the data lifecycle to better enable reuse of data through standards, metadata, cataloging, and dedicated expertise.
1) The data management process ensures collection of high quality clinical research data according to the study protocol and GCP standards. This increases confidence in the research results.
2) Key aspects of the data management process include designing CRFs based on the protocol, implementing electronic CRFs in a data capture system, developing a data dictionary and validation plan, and conducting quality assurance audits to identify errors.
3) Maintaining a "chain of evidence" through documentation allows for accurate reporting, interpretation, and verification of clinical trial data as required by GCP.
Pragmatics Driven Issues in Data and Process Integrity in EnterprisesAmit Sheth
Keynote/Invited Talk
IFIP TC-11 First Working Conference on
Keynote/Invited Talk at the IFIP TC-11 First Working Conference on
Integrity and Internal Control in Information Systems
Zurich, Switzerland, December 4-5, 1997
Selecting Core Clinical It Solutions For Life Sciences Organizations – Key S...Vinoth Kumar T
This document summarizes a presentation on selecting core clinical IT solutions for life sciences organizations. It discusses the challenges life sciences companies face with drug development timelines and costs. Effective clinical data management can help address these challenges by streamlining processes, improving data quality and security, and enabling faster data review and regulatory submissions. When selecting clinical IT solutions, companies should evaluate vendors, ensure regulatory compliance, and consider costs, integration, usability, and ongoing support. A successful implementation requires understanding existing workflows, conducting pilots, addressing security risks, and having open communication channels to resolve problems.
Healthcare institutions are aggressively moving towards meeting compliance with MU1 and MU2 with the implementation of full-featured Electronic Health Records. Concomitantly, there will be a massive increase in the amount of clinical data captured electronically. Business intelligence (BI) which traditionally has focused on financial data can be leveraged to use clinical data to support providers in delivering high quality, efficient care. In addition, BI coupled with population health analytics can help meet many Accountable Care Organization needs. This presentation will discuss the Denver Health journey in using BI in a variety of was to facilitate the attainment of high quality care.
Bridging Health Care and Clinical Trial Data through TechnologySaama
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.
The document discusses developing an electronic patient medical records system. It outlines the objectives of automating medical records management, which include achieving the highest efficiency given limited resources by providing faster, complete, and error-free access to records on demand. The document also describes how a computer network can integrate and link different types of patient records, allowing generation of complex reports from multiple data sources and high availability of records through the world wide web.
Lightning Talk, Coates: Clinical Data Management strategies: How can they imp...ASIS&T
This document discusses strategies for managing unregulated clinical data. It outlines how clinical data management practices have been developed based on principles from regulations like HIPAA and ICH GCP to improve efficiency, efficacy, safety, accuracy and confidentiality. While regulations can be burdensome, clinical data management standards provide best practices for areas like data collection, validation, database design and implementation. The document recommends starting with the end in mind by designing data collection tools like case report forms to efficiently collect data that can be easily entered, processed, validated and analyzed.
Information Quality and Metadata in Healthcare ManagementSeth Johnson
This presentation is targeted to department heads and frontline staff who produce provider, member and medical treatment information in a Medicaid Managed Care enterprise. It covers the quality approach to information, while fostering a work culture of information stewardship by clarifying information producer roles and how they can foster improving enterprise information and their own daily processes through updating and sharing of metadata in an annual process of completing the National Committee for Quality Assurance's Baseline Assessment Tool (now the HEDIS Roadmap).
National Patient Safety Foundation 2012 Dashboard DemoEdgewater
Edgwater attended the NPSF 2012 Patient Safety Congress in order to showcase our proven expertise in developing Patient Safety & Quality systems and processes. This presentation highlights some Edgewater client success stories as well as a demonstration of dashboards developed as part of our projects.
Enterprise Analytics: Serving Big Data Projects for HealthcareDATA360US
Andrew Rosenberg's Presentation on "Enterprise Analytics: Serving Big Data Projects for Healthcare" at DATA 360 Healthcare Informatics Conference - March 5th, 2015
The presentation discusses how cognitive sciences and next generation clinical data management can transform clinical trials. It notes that currently, 72% of studies are one month behind schedule, 70% experience patient enrollment delays, and 20% do not recruit any subjects. It advocates centralizing and contextualizing data in a clinical data lake to enable evidence generation and reduce time and costs. The presentation outlines Saama Technologies' clinical data-as-a-service solution which uses metadata-driven transformation, analytics applications, and data pipelines to generate insights from varied data sources in real time. It argues that disruptive thinking is now required to achieve clean, longitudinal data and operational efficiencies through cognitive systems and a patient-centric, "Silicon Valley" mindset
Mark Anderson is the CEO of AC Group, a national healthcare IT consulting firm. He has over 36 years of experience in healthcare IT, including serving as CIO for multiple regional healthcare systems. He regularly speaks on electronic health records (EHRs) and healthcare IT. The document discusses EHR trends, challenges with adoption and use, and the need for a strategic, enterprise-wide approach to business intelligence to improve outcomes and efficiencies across the healthcare organization. It also addresses issues around data integration, clinician buy-in, and the importance of an accountable culture for dashboard and scorecard applications.
Neil Mason presents on Data Mining and Predictive Analytics at Emetrics San F...Foviance
This document discusses applications of data mining and predictive analytics techniques. It provides examples of how segmentation, propensity modeling, forecasting, and anomaly detection can be used. Specific applications discussed include visitor segmentation to understand website users, predictive modeling to improve email targeting and conversion rates, and analyzing multi-visit behavior to determine key drivers of online purchases. The document emphasizes that these analytical techniques can help reveal patterns in data that traditional querying may miss.
This document discusses policy-based data management using the Integrated Rule-Oriented Data System (iRODS). iRODS enables flexible, customizable data management through policy-based controls mapped to computer rules and workflows. It has been applied to various use cases including data grids, digital libraries, and repositories. The document provides examples of rules that can automate tasks like validating data integrity and initializing workflow parameters in iRODS.
HIMSS National Data Warehousing WebinarDale Sanders
BMJ and other sources
• Integrated into Cerner EMR
• Action sets, order sets, reference
• Chronic condition management
• Population health monitoring
• Local quality improvement
• Data analytics and reporting
• Continuous improvement
• Outcomes and process measures
• Cost and utilization measures
• Staff education and training
• Governance and oversight
• Continuous refinement
• Continuous expansion of content
• Continuous expansion of use
• Continuous expansion of benefits
• Continuous expansion of users
• Continuous expansion of evidence
• Continuous expansion of data
• Continuous expansion of analytics
• Continuous expansion of improvement
• Continuous
Centralizing Data to Address Imperatives in Clinical DevelopmentSaama
Karim Damji presents at SCDM 2017 Annual Conference in Orlando, Florida in the Unstructured and Structured Big Data Convergence for Bridging Clinical, Regulatory, and Commercialization session.
Abstract:
Are you fully leveraging the data you generate from trials, regulatory submissions and post-approval marketing to maximize business outcomes? With the deluge of structured, unstructured, and syndicated data, the use of varied data for targeted outcomes remains difficult, despite increased industry efforts to address the issue. New technologies are federating the ability to leverage analytic-ready data for innovations in clinical development and drug commercialization. With the application of clinical data-as-a-service and meta-data core, centralized clinical data lakes have the power to improve data quality, evidence generation, and time-to-insights.
eWave MD is an international healthcare software company that provides secure, web-based medical platforms and solutions like electronic medical records, telemedicine, patient portals, and disease management tools. Their products have been implemented in national health plans and hospitals in Israel, the US, and other countries to improve care quality, reduce costs, and support remote diagnostics. Case studies demonstrate how their platforms have benefited organizations by streamlining workflows, centralizing medical records, and enabling telehealth services.
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This document discusses study designs for evaluating the bioequivalence of new drug doses and dosage forms through pharmacokinetic or pharmacodynamic studies. It covers topics such as parallel vs. crossover study designs, sample size calculations, evaluating modified release dosage forms, and considering food effects and non-linear pharmacokinetics. The document also discusses using in vitro studies like dissolution testing and permeation studies to establish bioequivalence for some drug products like topical creams when in vivo studies are challenging.
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This presentation mainly deals with clinical development of biosimilar products. It also gives enough on non-clinical development so that the audience is well oriented.
This document discusses bioequivalence standards for highly variable drug products. It begins by defining highly variable drug substances and products as those with intra-subject variabilities greater than 30%. It notes the sources of high variability can include administration conditions, physiological factors, and technical aspects. The usual standards for passing bioequivalence require average AUC and Cmax values to fall within 80-125% intervals. However, these criteria may be impossible to meet for highly variable drugs even with large sample sizes. The document therefore proposes alternative approaches for demonstrating bioequivalence of highly variable drugs, including replicate study designs and reference scaled average bioequivalence criteria. It provides examples and discusses some issues that can arise with these alternative approaches.
High variability in PK can be a characteristic of certain drug products which require different from ordinary strategies and study designs for establishing bioequivalence.
Protein binding of drugs and screening of drugs by physicochemical propertiesBhaswat Chakraborty
This document discusses protein binding of drugs. It notes that drug-protein binding is usually a reversible interaction between drugs and proteins in plasma, blood cell membranes, and other tissues. The main binding proteins are albumin, α1-acid glycoprotein, and lipoproteins. Protein binding can be measured in vitro using ultrafiltration or equilibrium dialysis. Higher binding (lower Kd) means more drug is bound and less is unbound, while lower K2 means a greater drug effect. Protein binding is important for pharmacokinetics, pharmacodynamics, and toxicity. It can impact volume of distribution, toxicity risks, and the relationship between plasma concentration and effect. Different binding sites on proteins allow some drugs to displace others
5-hydroxytryptamine or 5-HT or Serotonin is a neurotransmitter that serves a range of roles in the human body. It is sometimes referred to as the happy chemical since it promotes overall well-being and happiness.
It is mostly found in the brain, intestines, and blood platelets.
5-HT is utilised to transport messages between nerve cells, is known to be involved in smooth muscle contraction, and adds to overall well-being and pleasure, among other benefits. 5-HT regulates the body's sleep-wake cycles and internal clock by acting as a precursor to melatonin.
It is hypothesised to regulate hunger, emotions, motor, cognitive, and autonomic processes.
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Cell Therapy Expansion and Challenges in Autoimmune DiseaseHealth Advances
There is increasing confidence that cell therapies will soon play a role in the treatment of autoimmune disorders, but the extent of this impact remains to be seen. Early readouts on autologous CAR-Ts in lupus are encouraging, but manufacturing and cost limitations are likely to restrict access to highly refractory patients. Allogeneic CAR-Ts have the potential to broaden access to earlier lines of treatment due to their inherent cost benefits, however they will need to demonstrate comparable or improved efficacy to established modalities.
In addition to infrastructure and capacity constraints, CAR-Ts face a very different risk-benefit dynamic in autoimmune compared to oncology, highlighting the need for tolerable therapies with low adverse event risk. CAR-NK and Treg-based therapies are also being developed in certain autoimmune disorders and may demonstrate favorable safety profiles. Several novel non-cell therapies such as bispecific antibodies, nanobodies, and RNAi drugs, may also offer future alternative competitive solutions with variable value propositions.
Widespread adoption of cell therapies will not only require strong efficacy and safety data, but also adapted pricing and access strategies. At oncology-based price points, CAR-Ts are unlikely to achieve broad market access in autoimmune disorders, with eligible patient populations that are potentially orders of magnitude greater than the number of currently addressable cancer patients. Developers have made strides towards reducing cell therapy COGS while improving manufacturing efficiency, but payors will inevitably restrict access until more sustainable pricing is achieved.
Despite these headwinds, industry leaders and investors remain confident that cell therapies are poised to address significant unmet need in patients suffering from autoimmune disorders. However, the extent of this impact on the treatment landscape remains to be seen, as the industry rapidly approaches an inflection point.
Promoting Wellbeing - Applied Social Psychology - Psychology SuperNotesPsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
The skin is the largest organ and its health plays a vital role among the other sense organs. The skin concerns like acne breakout, psoriasis, or anything similar along the lines, finding a qualified and experienced dermatologist becomes paramount.
Mercurius is named after the roman god mercurius, the god of trade and science. The planet mercurius is named after the same god. Mercurius is sometimes called hydrargyrum, means ‘watery silver’. Its shine and colour are very similar to silver, but mercury is a fluid at room temperatures. The name quick silver is a translation of hydrargyrum, where the word quick describes its tendency to scatter away in all directions.
The droplets have a tendency to conglomerate to one big mass, but on being shaken they fall apart into countless little droplets again. It is used to ignite explosives, like mercury fulminate, the explosive character is one of its general themes.
Travel Clinic Cardiff: Health Advice for International TravelersNX Healthcare
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Osteoporosis - Definition , Evaluation and Management .pdfJim Jacob Roy
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In this document , a brief outline of osteoporosis is given , including the risk factors of osteoporosis fractures , the indications for testing bone mineral density and the management of osteoporosis
8 Surprising Reasons To Meditate 40 Minutes A Day That Can Change Your Life.pptxHolistified Wellness
We’re talking about Vedic Meditation, a form of meditation that has been around for at least 5,000 years. Back then, the people who lived in the Indus Valley, now known as India and Pakistan, practised meditation as a fundamental part of daily life. This knowledge that has given us yoga and Ayurveda, was known as Veda, hence the name Vedic. And though there are some written records, the practice has been passed down verbally from generation to generation.
3. Clinical Trials:
Why Do We Need a Data Management System?
• Multi-centre co-operative trials
– Multiple sites capturing data
– Multiple disparate databases
– Multiple levels of reporting
– Critical, very specific information
– Multitude decision making at multiple sites
– Co-ordination demands details
– Real time query and real time response
4. Knowledge Investigational
Sites
Contracts
Partners &
Affiliates CROs
Relationship
Building
Meetings Communication
IRB Data Capture
Regulatory
Data Management
Documents Product
Safety Management
Project eMails
Management
Resource
Management
Information
Drug & Clinical Trial Development
Extended Picture
Multidirectional Flow of Data and Decisions
5. Clinical Trials:
Why Do We Need a Data Management System?
• Enormous volumes of data
– Example, a Phase-III trial in 10 centres with 100 patients
each
– 60 pages of CRF for each recruited patient
• 20 fields each page
– 40 pages of screening form for each candidate patient
• 20 fields each page
– [1000 (60 x 20)] + [1500 (40 x 20)]
= 12, 00000 + 12, 00000
= 24,00000 specific data points
6. Clinical Trial Data
• Useful only if it is clean & up to date.
• Data processing must be
– real-time
• subject randomization
• management of clinical trials materials
• laboratory uploads
• patient diary data
– Integrated
– Consistent
– Accurate
• Data structures must be
– Standard
– Validated
• Data transfer method must be
– Standard
– Validated
7. Data Management Services:
What Exactly Do They Do?
• Case report forms (CRFs) design
• Database design
• Database programming
• 21 CFR part 11 compliant validation process
• Loading, reconciliation and integration of external data
• Medical coding
• Status reporting
• Forms management
• Data entry and cleaning
• Data locking
• Statistical analysis
• Report generation
8. Clinical Data Management System
(CDMS)
Data Capture Strategy Processes
Remote Data Capture Adverse Event Monitoring System
Portal Data Capture Compliance (GCP/GLP) Monitoring
Workflow Monitoring
Analytical Data Processing
Systems
Statistical Data Processing
Data Extraction
GLIB
TMS/Dictionaries
Reports
Validation
9. Data Capture (1)
CRF
Manual data
Electronic data No
Raw data
to be combined?
(Manual)
Yes
Electronic Get approval
data
Raw data
A
10. Clinical Data Management System
(CDMS)
Data Capture Strategy Processes
Remote Data Capture Adverse Event Monitoring System
Portal Data Capture Compliance (GCP/GLP) Monitoring
Workflow Monitoring
Analytical Data Processing
Systems
Statistical Data Processing
Data Extraction
GLIB
TMS/Dictionaries
Reports
Validation
11. Data Extraction, Cleaning & Locking (2)
A
Real time query
No
Are the
queries answered? Approval required
Yes
Repeat No
Data cleaning Observation/
Can this data
be locked?
1. Detecting & diagnosing errors Omission
2. Editing incorrect data
3. Integrated data passage Yes
4. Outlier determination
5. Robust estimation of analytical parameters
Clean data Locked data B
12. Clinical Data Management System
(CDMS)
Data Capture Strategy Processes
Remote Data Capture Adverse Event Monitoring System
Portal Data Capture Compliance (GCP/GLP) Monitoring
Workflow Monitoring
Analytical Data Processing
Systems
Statistical Data Processing
Data Extraction
GLIB
TMS/Dictionaries
Reports
Validation
13. Data Processing & Reporting (3)
B
Locked Clean Data
No Data Summary,
Statistical analysis Charts/Graphs
required?
Yes
SAS Data Sets
Statistical Data Analysis
Tests of Hypotheses
Cohort Analyses
Report
Results
14. CRF
Maker CRF
Data Entry Editor
(Form)
Layout
CRF
Database
Edited
Hard Copy
Electronic Case Report Forms
15. Electronic Data Capture (EDC)
Define
gn
Desi
Bu
ild
C
en
ry R tr
nt ) ep a
E
ite os l D a
Compliance
21CFR Part 11
ta
tD
a lS ito ta
na
Test
c o ry
u bje ati (H
S tig U
v es B
(In )
Data Review
Sponsor/Monitor Use
16. CDMS Market Size in India
• [Gobally ~$1.5 billion]
• Estimated Indian Market
• The total Clinical Trial market in India is ~$600 million
• CDMS is about 7-8% of CTs
• Thus the CDMS market is estimated to around 40 – 45 million
dollars
• For big MNCs, it is still a very small portion
• But it has a huge potential to grow
18. Drivers of EDC & CDMS
• Context – why India and why EDC & CDMS
• Technology & market forces
• Cost advantage
• Concentration of resources
• Expertise (and lathes of expertise)
• Regulators are insisting on comprehensive risk
management and PV
• Large trials have dozens of international sites and
corresponding chunks of data
19. Facilitators of EDC & CDMS
• Context – why India and why EDC & CDMS
• Consultants who can integrate different parts
• One stop shopping
– Patients, diversity
– investigators
– CT conduct experience
– Top CROs
• Research subsidiaries of pharma MNCs and int’l CROs
• CDMS & EDC offer efficiency and timeliness of data collection
and reporting
• Understanding of harmonized data & analysis requirements
20. Stakeholders in CDMS & EDC
Sahoo U. (2005). Clinical data capture shifts paradigm. Pharmabiz, July 14, 2005
21.
22. Data Standards & Harmonization
• It is estimated that ~ 200 million dollars are wasted yearly
because of a lack of globally accepted clinical data format
• Following organizations are working for data standardization:
• Clinical Data Interchange Standards Consortium (CDISC)
• Health Level 7 (Hl7)
• WHO
• US National Cancer Institute (NCI)
• National Library of Medicine (NLM)
• Academia
• ISO
• …
24. Standards for Data Management
• Very important for the regulatory agencies
• Without a standard, sponsors file data in different
arrangements
• Once data is in a standardized structure, regulatory
agencies can preprogram software to run a macro
script
• Thereby data coming from different sources will
automatically format to conform to the regulatory
agencies requirements
25. MNC PV Activities & Databases in
India
• Example: Novartis
• Activities/Databses
– Periodic Safety Update Report (PSUR)
– Risk Management Plan (RMP) updates and associated activities
– safety signal detection
– management of large datasets
– analysis of large databases
– responses to external authorities
– review of clinical protocols
– other regulatory activities
– clinical review and evaluation of cases including input for follow-up
and data cleaning
– ….many other relevant activities
26. Advantages to MNCs: Outsourcing to
India
• Better, safer drugs to market faster
• Improve efficiency
• Improve communication
• Improve data collection
• Reduce redundant data submissions
• Other benefits
• Improve communication
• Decrease redundant data submission
• Decrease “learning curve”
• Cross study analysis
• User friendly tools
• Decrease delays
27. Technical Advantages
• Cloud computing possible
• Real-time access to all clinical trial data
• Easy filling of e-CRFs with
– Radiobutton choices
– Checkboxes
– Drop-down selections
– Unlimited text boxes for comments
• Real-time data entry validation checks
• Secure database
• Back-end clinical data management and programmed data
validation checks
• Electronic and automatic Audit Trail
• Simple e-mail query resolution or by on-line query database
• Configurable access rights
• Electronic signatures fully compliant with FDA's 21 CFR Part 11
28. Concluding Remarks
• CDMS provide a range of IT tools that give the trials personnel
the required information throughout clinical management
• CDMS mainly manages data capture, systems and analytical
process electronically
• EDC definitely adds value – efficiency and accuracy, however,
high costs and some technology issues remain
• Technical and automational advantages are countless
• The CDMS market in India is estimated to around 40 – 45
million dollars and growing
• Provides for data standardization and interchange in
universally acceptable formats
29. Concluding Remarks: India as a Hub
• India offers many advantages as a CDMS hub
• Cost
• Concentration of resources
• Expertise
• Comprehensive risk management databases, analysis, mitigation and
PV centres
• Consolidation of various databases (especially large ones)
• India’s IT sector is growing at ~25% per year thus maintaining
complex CDMSs at competitive costs in India is an added
advantage
• Abundant skilled personnel in all areas of CDM available
• Hub of almost all clinical trial activities in coming years
GLIB: global library is an organization wide central repository for containing standardized data definitions. TMS: thesaurus management system; e.g., Oracle TMS provides terminology services for Oracle Clinical, Oracle Remote Data Capture, Oracle Adverse Event Reporting System, and Oracle Life Sciences Data Hub. Allows access to any number of dictionaries, including multiple versions of the same dictionary ; supports any number of hierarchy levels and supports custom or commonly used dictionaries, such as MedDRA, MedDRAJ, MedDRA SMQs, SNOMED, ICD9, WHO-ART, and WHO-Drug. MedDRA or Medical Dictionary for Regulatory Activities is a clinically validated international medical terminology used by regulatory authorities and the regulated biopharmaceutical industry during the regulatory process, from pre-marketing to post-marketing activities, and for data entry, retrieval, evaluation, and presentation. In addition, it is the adverse event classification dictionary endorsed by the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH). MedDRA is used in the United States, European Union, and Japan. Its use is currently mandated in Europe and Japan for safety reporting.