GLI TB Diagnostics Connectivity Guide 2016SystemOne
This document provides an overview of diagnostics connectivity solutions for tuberculosis (TB) programs. It discusses how connectivity solutions can enable remote monitoring of diagnostic devices, automatically send test results to clinicians and health information systems, facilitate inventory management, and enhance disease surveillance and program monitoring. The document also covers the necessary software, hardware, internet connectivity, data security, personnel needs, and budgeting considerations for implementing diagnostics connectivity solutions. Overall, the document presents connectivity solutions as a way for TB programs to improve patient care and management while strengthening laboratory systems.
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.
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.
Importance of data standards and system validation of software for clinical r...Wolfgang Kuchinke
We present our evaluation of existing data standards for clinical trials. For this purpose a survey about the importance of data standards for clinical trials centers and EDC software companies were conducted. Electronic data capture in clinical trials uses a computerized system designed for the collection of clinical data in electronic form in Case Report Forms (CRF). It also covers medical data captured during clinical trials, safety data related to clinical trials, and patient reported outcome. The degree of implementation of standards, like CDISC ODM in available EDC software products was evaluated. Failure to establish data standards will make it difficult or impossible to connect data between different systems for efficient clinical study execution. The next step after purchasing a software solution is the computer system validation. Validation is about bringing computerized systems into regulatory compliance and making them compliant with GCP, GLP and GMP and other regulations (e.g. data protection). The basis standard for validation is provided by the GAMP Good Practice Guide, which provides a framework of best practices to ensure that computer systems are suitable for use and compliant with the legislation. The newest version uses a risk-based approach to computer system validation A system is evaluated and assigned to a predefined category based on its intended use and complexity. For validation one should define how all elements of the computer system are supposed to work (functional requirements), develop corresponding scripts and test routines to validate it is functioning as it should.
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.
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 trial data collection and management involves processes to collect reliable clinical, control, and administrative data from trial sites and send it to a central location for management. Data collection is the process of gathering data according to agreed methods, while data management accumulates the collected data into a master database, ensuring security, validity, and completeness through quality reports. Data can be collected using pure paper-based, pure electronic-based, or hybrid systems, each with their own advantages and requirements. Communication tools help staff share information and exchange data. Processes before, during, and after data collection must be planned and executed successfully.
GLI TB Diagnostics Connectivity Guide 2016SystemOne
This document provides an overview of diagnostics connectivity solutions for tuberculosis (TB) programs. It discusses how connectivity solutions can enable remote monitoring of diagnostic devices, automatically send test results to clinicians and health information systems, facilitate inventory management, and enhance disease surveillance and program monitoring. The document also covers the necessary software, hardware, internet connectivity, data security, personnel needs, and budgeting considerations for implementing diagnostics connectivity solutions. Overall, the document presents connectivity solutions as a way for TB programs to improve patient care and management while strengthening laboratory systems.
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.
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.
Importance of data standards and system validation of software for clinical r...Wolfgang Kuchinke
We present our evaluation of existing data standards for clinical trials. For this purpose a survey about the importance of data standards for clinical trials centers and EDC software companies were conducted. Electronic data capture in clinical trials uses a computerized system designed for the collection of clinical data in electronic form in Case Report Forms (CRF). It also covers medical data captured during clinical trials, safety data related to clinical trials, and patient reported outcome. The degree of implementation of standards, like CDISC ODM in available EDC software products was evaluated. Failure to establish data standards will make it difficult or impossible to connect data between different systems for efficient clinical study execution. The next step after purchasing a software solution is the computer system validation. Validation is about bringing computerized systems into regulatory compliance and making them compliant with GCP, GLP and GMP and other regulations (e.g. data protection). The basis standard for validation is provided by the GAMP Good Practice Guide, which provides a framework of best practices to ensure that computer systems are suitable for use and compliant with the legislation. The newest version uses a risk-based approach to computer system validation A system is evaluated and assigned to a predefined category based on its intended use and complexity. For validation one should define how all elements of the computer system are supposed to work (functional requirements), develop corresponding scripts and test routines to validate it is functioning as it should.
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.
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 trial data collection and management involves processes to collect reliable clinical, control, and administrative data from trial sites and send it to a central location for management. Data collection is the process of gathering data according to agreed methods, while data management accumulates the collected data into a master database, ensuring security, validity, and completeness through quality reports. Data can be collected using pure paper-based, pure electronic-based, or hybrid systems, each with their own advantages and requirements. Communication tools help staff share information and exchange data. Processes before, during, and after data collection must be planned and executed successfully.
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.
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.
This in silico study examined potential methyltransferase inhibitor compounds to inhibit dengue virus production. Dengue virus is transmitted by mosquitoes and causes life-threatening dengue fever and hemorrhagic fever. The study hypothesized that inhibiting the methyltransferase enzyme, which transfers methyl groups needed for viral DNA replication, could stop viral production. Several compounds were docked in silico and ranked based on binding affinity to the methyltransferase. The top compound, DENV-M2_1, showed the strongest binding affinity and was predicted to best occupy the methyltransferase binding site, potentially ceasing viral replication.
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.
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.
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.
Introduction to Oracle Clinical Overview in Clinical Data Management in Clinical Trials of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Evaluation of the importance of standards for data and metadata exchange for ...Wolfgang Kuchinke
1) The document discusses the importance of standards for clinical research data and metadata exchange. It notes that electronic data capture (EDC) systems can increase efficiency and quality but require computer system validation.
2) A survey of German research networks found that over 80% saw exchanging metadata and clinical data as very or important. The top study partners for sharing data were universities, networks, and study groups.
3) Proper computer system validation is important for conducting GCP-compliant clinical studies. It ensures systems meet requirements through qualification and testing of components, computerized systems, and processes.
Overview of Validation in Pharma_Katalyst HLSKatalyst HLS
Introduction to Validation Concepts in Pharma, Bio-Pharma, Medical Device, Cosmetics, Food, Beverages industry.
Contact:
Katalyst Healthcare’s & Life Sciences
South Plainfield, NJ, USA 07080.
E-Mail: info@KatalystHLS.com
EpiDash 1.0 is a web-based dashboard that analyzes social media and other data to provide epidemiological context for gastrointestinal (GI) illnesses within a community. It aims to enhance surveillance, detect outbreaks earlier, and identify risk factors. The dashboard visualizes data through maps, word clouds, and time series graphs. It also provides case definitions, analytics to account for trends, and allows searching of keywords. An evaluation found it helped situational awareness for epidemiologists and integrated well into existing surveillance systems. Further work includes customizing it for different health districts and expanding data sources.
Appalla Venkataprabhakar and I presented this at the Oracle\'s Annual Clinical Development and Safety Conference 2010 at Hyderabad, India on 6th October 2010.
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.
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.
A data management plan (DMP) ensures consistent and effective clinical data management practices throughout a clinical trial. The DMP describes all data management activities, responsibilities, and deliverables to promote agreement among parties. It not only describes the data management process, but also provides documentation of data handling for regulatory compliance. The DMP components include data flow, capture, setup, entry, transfer, processing, coding, safety data, external data, database locking and unlocking, archiving, and quality reports. It serves to plan, communicate, and reference all data management tasks during a study.
IRIDA: A Federated Bioinformatics Platform Enabling Richer Genomic Epidemiolo...William Hsiao
Introducing BCCDC and Public Health Microbiology (PHM)
Current State of PHM
Sequence Technology Advancement -> revolution of PHM
Genomic Epidemiology
Amount of Sequence Data Produced
Need to Process the data – Introduction to IRIDA
Need of Metadata and Ontology
Software to improve data sharing
How research microbiology and PHM can joint effort
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.
Qualcomm Life Connect 2013: 2net System Overview, Security and PrivacyQualcomm Life
The document provides an overview of the 2net system, including its product overview describing how biometric data flows from devices to the cloud platform, as well as its security and privacy features leveraging Qualcomm's expertise in network operations. Key aspects covered include the 2net hub, cloud platform, and end-to-end data flows, as well as Qualcomm's focus on proactive data protection, cybersecurity initiatives, and use of a premier enterprise wireless data platform.
The need for interoperability in blockchain-based initiatives to facilitate c...Massimiliano Masi
Slides for the IEEE Blockchain Symposium in Glasgow, https://blockchain.ieee.org/standards/clinicaltrialseurope18, https://blockchain.ieee.org/standards/clinicaltrialseurope18/speakers
IHE provides a framework for implementing multiple healthcare IT standards to address specific clinical needs. It has grown from a project launched in 1998 involving healthcare providers, vendors, and standards groups. IHE defines integration profiles that specify how standards should be implemented together to enable interoperability. Examples include profiles for image and report sharing within and between healthcare enterprises using standards like DICOM and HL7.
Use of personalized medicine tools for clinical research networksWolfgang Kuchinke
Patient-centric clinical trials can gain enormously from the employment of personalised medicine tools. Here we address software tools created by the p-medicine network, which developed thr ObTiMA data management system, Patient Empowerment Tool, data mining, data warehousing, biobank access, decision support, image annotation (DrEye) and simulation (Oncosimulator). We evaluated of some of these tools for their suitablity to perform clinical trials. Is their usage conform with regulations and standards (GCP, GDPR, GAMP, computer system validation)? Can these tools be integrated into the existing systems (IT infrastructure / organisational framework) of an international clinical trials network (ECRIN)?
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.
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.
This in silico study examined potential methyltransferase inhibitor compounds to inhibit dengue virus production. Dengue virus is transmitted by mosquitoes and causes life-threatening dengue fever and hemorrhagic fever. The study hypothesized that inhibiting the methyltransferase enzyme, which transfers methyl groups needed for viral DNA replication, could stop viral production. Several compounds were docked in silico and ranked based on binding affinity to the methyltransferase. The top compound, DENV-M2_1, showed the strongest binding affinity and was predicted to best occupy the methyltransferase binding site, potentially ceasing viral replication.
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.
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.
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.
Introduction to Oracle Clinical Overview in Clinical Data Management in Clinical Trials of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Evaluation of the importance of standards for data and metadata exchange for ...Wolfgang Kuchinke
1) The document discusses the importance of standards for clinical research data and metadata exchange. It notes that electronic data capture (EDC) systems can increase efficiency and quality but require computer system validation.
2) A survey of German research networks found that over 80% saw exchanging metadata and clinical data as very or important. The top study partners for sharing data were universities, networks, and study groups.
3) Proper computer system validation is important for conducting GCP-compliant clinical studies. It ensures systems meet requirements through qualification and testing of components, computerized systems, and processes.
Overview of Validation in Pharma_Katalyst HLSKatalyst HLS
Introduction to Validation Concepts in Pharma, Bio-Pharma, Medical Device, Cosmetics, Food, Beverages industry.
Contact:
Katalyst Healthcare’s & Life Sciences
South Plainfield, NJ, USA 07080.
E-Mail: info@KatalystHLS.com
EpiDash 1.0 is a web-based dashboard that analyzes social media and other data to provide epidemiological context for gastrointestinal (GI) illnesses within a community. It aims to enhance surveillance, detect outbreaks earlier, and identify risk factors. The dashboard visualizes data through maps, word clouds, and time series graphs. It also provides case definitions, analytics to account for trends, and allows searching of keywords. An evaluation found it helped situational awareness for epidemiologists and integrated well into existing surveillance systems. Further work includes customizing it for different health districts and expanding data sources.
Appalla Venkataprabhakar and I presented this at the Oracle\'s Annual Clinical Development and Safety Conference 2010 at Hyderabad, India on 6th October 2010.
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.
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.
A data management plan (DMP) ensures consistent and effective clinical data management practices throughout a clinical trial. The DMP describes all data management activities, responsibilities, and deliverables to promote agreement among parties. It not only describes the data management process, but also provides documentation of data handling for regulatory compliance. The DMP components include data flow, capture, setup, entry, transfer, processing, coding, safety data, external data, database locking and unlocking, archiving, and quality reports. It serves to plan, communicate, and reference all data management tasks during a study.
IRIDA: A Federated Bioinformatics Platform Enabling Richer Genomic Epidemiolo...William Hsiao
Introducing BCCDC and Public Health Microbiology (PHM)
Current State of PHM
Sequence Technology Advancement -> revolution of PHM
Genomic Epidemiology
Amount of Sequence Data Produced
Need to Process the data – Introduction to IRIDA
Need of Metadata and Ontology
Software to improve data sharing
How research microbiology and PHM can joint effort
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.
Qualcomm Life Connect 2013: 2net System Overview, Security and PrivacyQualcomm Life
The document provides an overview of the 2net system, including its product overview describing how biometric data flows from devices to the cloud platform, as well as its security and privacy features leveraging Qualcomm's expertise in network operations. Key aspects covered include the 2net hub, cloud platform, and end-to-end data flows, as well as Qualcomm's focus on proactive data protection, cybersecurity initiatives, and use of a premier enterprise wireless data platform.
The need for interoperability in blockchain-based initiatives to facilitate c...Massimiliano Masi
Slides for the IEEE Blockchain Symposium in Glasgow, https://blockchain.ieee.org/standards/clinicaltrialseurope18, https://blockchain.ieee.org/standards/clinicaltrialseurope18/speakers
IHE provides a framework for implementing multiple healthcare IT standards to address specific clinical needs. It has grown from a project launched in 1998 involving healthcare providers, vendors, and standards groups. IHE defines integration profiles that specify how standards should be implemented together to enable interoperability. Examples include profiles for image and report sharing within and between healthcare enterprises using standards like DICOM and HL7.
Use of personalized medicine tools for clinical research networksWolfgang Kuchinke
Patient-centric clinical trials can gain enormously from the employment of personalised medicine tools. Here we address software tools created by the p-medicine network, which developed thr ObTiMA data management system, Patient Empowerment Tool, data mining, data warehousing, biobank access, decision support, image annotation (DrEye) and simulation (Oncosimulator). We evaluated of some of these tools for their suitablity to perform clinical trials. Is their usage conform with regulations and standards (GCP, GDPR, GAMP, computer system validation)? Can these tools be integrated into the existing systems (IT infrastructure / organisational framework) of an international clinical trials network (ECRIN)?
Kuchinke Personalized Medicine tools for clinical research networksWolfgang Kuchinke
Personalized medicine for clinical trials networks.
The p-medicine project is presented. It deals with the creation of an integrative infrastructure for Personalised Medicine, which aims to accelerate personalized medicine and personal clinical research. For this purpose p-medicine developed a comprehensive set of software tools, including ObTiMA data management system, Patient Empowerment Tool, data
mining, data warehousing, biobank access, decision support, image annotation (DrEye) and simulation (Oncosimulator). Here we show the evaluation of some of the p-medicine tools for their suitablity to perform clinical trials. Is their usage conform with regulations and standards (GCP, GDPR, GAMP, computer system validation)? Can these tools be integrated into the existing systems (IT infrastructure / organisational framework) of an international clinical trials network (ECRIN)? To perform clinical trials, a legal and ethical framework based on international requirements and approved concepts for data security must be adopted. GCP (Good Clinical Practice) is such an international ethical and scientific quality standard for designing, recording and reporting trials that involve the participation of human subjects.
Evaluation of usability of p-medicine software tools for clinical trials was done with two surveys: (1) survey of p-medicine tools in the ECRIN network and (2) p-medicine developer survey. The tool integration topics contained questions about the employment of the right Clinical Data Management System (CDMS) at the many ECRIN centres. There is competition between different solutions, like VISTA (EORTC) MACRO, secuTrial, RAVE, OpenClinica. CDMS should be usable for all types of trials and the usability in clinical trials must be demonstrated by integration of biobank access / safety functions. Only ObTiMA is able to specifically address the challenges of personal medicine clinical trials. The results of the evaluation was that there exists some compliance gaps for quality management during software development, no complete GCP compliance yet and the missing of a robust business model for software sustainability. To address the latter, a Reciprocal Integration approach was developed to integrate p-medicine tools into clinical research networks.
The document discusses healthcare challenges and the evolution of healthcare information technology (HCIT). It notes that chronic diseases account for over 75% of US medical costs. It also describes different levels of HCIT integration, from legacy systems to continuum-focused systems using standards-based structured data. The document advocates for an approach using a Global Clinical Data Archive to provide seamless interoperability and transparency of clinical data across the healthcare system.
Tomasz Sablinski from Transparency Life Sciences showed at the DayOne Expert event - Next Generation Clinical Trials ways to virtualize clinical trials or parts of them.
From Edge Case to Main Case, Michelle Longmire of Medable_mHealth IsraelLevi Shapiro
Presentation by Michelle Longmire, CEO of Medable, April 20, 2021, for mHealth Israel. During CoVID, as physical access to clinics was limited, Medable enabled patients to continue participating in critical research efforts. Medable Supporting over 100 Studies Across a Diverse Array of Therapeutic Areas. Medable provides a platform for seamless evidence
generation, across the entire patient journey. Connecting patients globally for community, care, and research. Improve patient experience and retention. Reduce site burden. Data Cloud & Platform should be flexible and modular to enable protocol-fit digital. Medable Digitome, for data driven decentralized trials and a new era of understanding patients, therapies, and conditions. Clinical research is a small component of the broader healthcare journey. Enable health data and evidence generation from clinical to commercial, from day one. Continuous health data & evidence from clinical to commercial and beyond. The Digitome can provide a
primary observational protocol that collects large scale baseline data in a framework that enables streamlined recruitment, enrollment, and participation into interventional clinical substudies.
UCSF Informatics Day 2014 - Sorena Nadaf, "Translational Informatics OnCore C...CTSI at UCSF
Translational Informatics at UCSF aims to:
1) Bridge the gap between research labs and clinical care by accelerating development of targeted agents and biomarkers through integration of genomics, molecular diagnostics, and therapeutics.
2) Leverage informatics standards and platforms to enable high-throughput translational research through infrastructure for collection, management, and analysis of clinical, biomedical and biospecimen data.
3) Deliver a suite of services including clinical research informatics, decision support, biospecimen informatics, and high performance computing to support translational research and clinical care improvement through centralized data management and coordination.
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.
This document discusses disease registries and the benefits of centralized data. It explains that disease registries collect uniform clinical and research data from multiple sources to study outcomes for populations with specific diseases or exposures. Centralizing registry data provides several advantages, including easier data entry and analysis across locations, more robust research on risk factors and disease patterns, and quicker decision making for health managers and researchers. The document advocates for web-based registry software to facilitate anytime access to real-time centralized data without geographical boundaries, allowing greater data sharing and collaborative research efforts.
Canada's Integrated Rapid Infectious Disease Analysis Platform for Genomic Epidemiology (IRIDA) is an open source platform designed to support real-time disease outbreak investigations using genomic sequencing data. IRIDA provides tools for rapid processing of genomic data, sample and metadata management, built-in analysis workflows, and data sharing between public health agencies. The platform is being developed and tested collaboratively with Canadian and international public health partners.
The document summarizes the XNAT imaging informatics platform. XNAT is an open-source platform for managing and sharing imaging and related data across clinical/translational research, institutional repositories, and multi-center studies. It provides features such as DICOM integration, automated analytics, extensibility through plugins, and user access control. XNAT uses a modular architecture and containers to enable scalable execution of analytic routines on imaging data. It has been adopted by many imaging centers and studies to support clinical workflows and research initiatives in areas like connectomics and cancer.
The document discusses clinical data mining and data warehousing. It begins by introducing clinical data mining as a process to analyze and interpret available clinical data for decision making and knowledge building. It then describes approaches to clinical data mining including data collection, pre-processing, parsing, and applying knowledge to create new databases and queries. The document also discusses online clinical data mining tools, advantages of data warehousing, challenges of clinical data warehousing, and applications of data mining such as creating electronic patient files and improving healthcare quality.
Hitachi provides connected health solutions across the patient care continuum from devices and data to analytics and population health management. Their portfolio includes infrastructure, clinical data exchange, mobility and analytics solutions. The goal is to improve patient outcomes by connecting stakeholders and providing actionable insights from data. Population health management is the ultimate aim of reducing healthcare costs through preventative and personalized care enabled by Hitachi's connected health offerings.
Infomed CS is a Greek software company established in 1993 that specializes in developing and supporting Laboratory Information Systems (LIS) and Radiology Information Systems (RIS). It has over 25 years of experience and 800 active installations. Infomed CS develops highly customizable solutions that adapt to customer workflows and demands, and integrates with other hospital systems through standards like HL7. Their flagship product, sLis Enterprise, is a fully integrated LIS/RIS platform that provides functionality across diagnostic departments and enhances laboratory management.
Computer Software Assurance (CSA): Understanding the FDA’s New Draft GuidanceGreenlight Guru
Understand the FDA's new draft guidance on Computer Software Assurance (CSA).
This presentation originally aired during the 2022 Future of QMS Requirements Virtual Summit.
Similar to CDX Platform Availability - InSTEDD & FIND (20)
Technology to Scale Health - for Neglected Tropical DiseasesEduardo Jezierski
At a Bill & Melinda Gates Foundation summit on NTD (Neglected Tropical Diseases) I gave this ignite talk to share some practices to "bend the curve" and scale the coverage of prevention & treatment of NTDs. Interventions that are "operational improvements" are useful, but won't get to cover the extra 1/2 billion kids who need access to these treatments,
PlanWise - InSTEDD and Concern Worldwide Tools for Health Resource PlanningEduardo Jezierski
PlanWise is an initiative by Concern Worldwide and InSTEDD to help planners everywhere use data to optimize where to invest in health resources. It connects to global world population densities (courtesy of WorldPop), Map networks (Google and OSM) and local facility databases and use advanced modeling and algorithms to optimize where to place facilities, ambulances and health services
The document provides a brief history of photography, noting that the first photo ever taken was called "View from the Window at Le Gras" by French inventor Joseph Nicéphore Niépce in 1826. It then shows graphs depicting the rapid increase in the number of photos taken each year from 1930 to 2012, rising from 1 billion photos in 1960 to over 380 billion photos in 2012. The rest of the document discusses feedback cycles in diagnostic technologies and opportunities for connected diagnostics.
A brief introduction to InSTEDD, our mission, the role of our iLabs in creating world-class technology for health, safety and development, and examples of tools available for free to use.
Switchpoint Presentation - Information for Crisis VictimsEduardo Jezierski
A 7.0 magnitude earthquake struck Léogâne, Haiti on January 12, 2010, killing over 200,000 people. In response, many groups collaborated to create an Emergency Information System (EIS) that provided critical and lifesaving information to Haitians via SMS, Twitter, and other platforms. A survey found that 92% of EIS recipients found the messages "very useful" and 74% took action based on the information received, with many sharing it with others. The majority of information requested and shared regarded health issues. Feedback showed how the information helped people find assistance and protect their health and safety.
With a little bit of help, program managers designed, configured and demonstrated tools for population information hotlines using Verboice, the most widely used voice platform for global humanitarian projects.
Mobile tools in Disease Surveillance in Low Income Countries - ASTMH 2012 Pre...Eduardo Jezierski
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2. EIS aggregates information from various sources and disseminates it via SMS, email, and RSS feeds to help survivors make decisions about safety, aid locations, medical facilities, and finding missing relatives.
3. Relief responders and local journalists can also use EIS to monitor situation updates, share their own information, and receive and disseminate bulletins to help coordination and response efforts.
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Presented in Yangon the week after Haiti, sharing some personal experience from having done remote support for systems running in Haiti and years of work in technology for Crisis. Be prepared!
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During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
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Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
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4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
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Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
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Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
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See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
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Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
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5. Introduction to Apache Kafka and S3
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6. Viewing Kafka Messages in the Data Lake
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7. What is Prometheus?
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8. Monitoring Application Metrics with Prometheus
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9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
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11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
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2. Connected Diagnostics Opportunity
Improve
Linkage to Care
Reduce Loss to
Follow Up
Improve
Surveillance Reduce
Transcription
Errors
Monitor
Quality
Assurance
Reduce Stock
Outs
Improve
Training & HR
Diagnostics Data
3. Challenge - Fragmented Data Collection
Dx1 Portal Dx2 Portal Dx3 .. No portal
Dx1 Dx2 Dx3
4. Aggregated View Across Diagnostics
DX2
DX1
DX3
DX2 Portal
DX1 Portal
?
Useful DX1 info
Useful DX2 info
5. CDX: Connected Diagnostics Initiative
CDX Platform:
CDX
Initiative:
• Technology & Services
• Transmit & store data
• Multiple diseases, manufacturers
• Open source
• Secure, PII privacy
• Commitment to standards
• Manufacturer engagement
• Policy maker engagement
• Design Advisory Board
• Ecosystem of field support vendors
• Evaluation and Research
InSTEDD specializes in designing open
source, scalable technology tools to solve
critical health, safety and development issues.
FIND’s expertise is in the domain of diagnostic
development and delivery, particularly for
infectious diseases.
6. CDX Platform
Accelerates connecting
new devices by
manufacturers & 3rd
parties
Device Software
Open & standards-
based protocols
for 2 way
communication
between devices
and servers.
See dxapi.org
Protocols
Software that can
run in the cloud or
your own servers.
Aggregates device,
result, error and
other information
Servers &
Services
Ecosystem of
ehealth & mhealth
tools:
EMR, ERP,
TB Dashboards,
HIMS,
mobile alerts, etc.
3rd Party
Applications
etc….
7. Trained resources for eHealth interventions
CDX Initiative Areas of Effort
Hospitals
& Labs
Health postCommunity
health
worker
eHealth policy & strategy
Evidence of eHealth impact
Standards & Interoperability
Centralised data collection, sharing & access
A
B
D
C
E
National health
programs
Global policy
& surveillance
18. Connectivity & Roles in the Ecosystem
• Centralized, multivendor data systems (EMR,
ERP, etc) are best served by ISVs, not device
manufacturers
• Quick integration of secure data streams makes
system adoption easier
• Different roles mean different data needs for
institutions and manufacturers
• Manufacturers: system heuristics, usage, errors
• Institution: EMRs, epidemiology, logistics
• CDX allows for quick, uniform, contextual large
scale diagnostic system data collection for an
institution
19. Data Streams in a CDX Ecosystem
GeneXpert harvests data and
Transmits to RemoteXpert
CDX local server
receives data and
stores it in
institution EMR
Tests run on
lab devices –
cross labs and
geos
Heterogeneous
devices transmit
data in CDX format
Institution X data
Institution, NGO users
log into their servers
and use aggregated regional
EMR and logistical data
RemoteXpert receives data and
securely stores it, generates
machine health analysis and
alerts
CDX client transmits
formatted data
from GX to institution servers
RemoteXpertGeneXpert
CDX
20. CDX Cepheid Support
• Cepheid supports integration with many different
LIS, EMR, and multi-disciplined ecosystem tools
• Secure, structured integration with all data
systems is vital
• Quicker, structured integration of
comprehensive diagnostics data (not just PCR)
frees up resources, accelerates care
• Cepheid will make available open source clients
for it’s systems to securely and robustly support
data transmission in the CDX format to support
FIND’s initiative
21. Anita Suresh
Worldwide Mktg Manager, TB Diagnostics
BD Diagnostics
“BD's vision is to enable fully
integrated and connected
informatics solutions for our
diagnostics customers.
In TB, this would include real-
time reporting, data aggregation
and surveillance tools, bringing
together diagnostic results
across multiple platforms at
multiple health settings.
BD is partnering with the
Connected Diagnostics Initiative
to achieve these goals while
allowing institutions to customize
their solutions to drive clinical
impact. ”
23. Global tuberculosis report 2015 (WHO/HTM/TB/2015.22). Geneva, World Health Organization. Geneva,
World Health Organization; 2015. www.who.int/tb/publications/global_report/en/
MDR−TB cases and additional rifampicin−resistant TB cases detected (orange) compared
with TB cases enrolled on MDR−TB treatment (blue), global trend and trend in 27 high MDR−TB
burden countries, 2009−−2014
24. Uses of TB Laboratory Information
1) The requisition, receipt, scheduling and performance of
tests
2) The collection and management of samples, and the chain
of custody
3) The distribution of test results to clinicians
4) Inventory
5) General laboratory reporting (including billing and contracts)
6) Workload statistics and laboratory performance
7) Surveillance of TB and drug-resistance
8) Human resources development, including training
9) Quality control and external quality assessment processes
10) Laboratory biosafety measures
11) Others (specific to an individual laboratory)
25. Laboratory Informatics
• Strengthening TB laboratory informatics important
- Laboratory confirmation desirable in both TB/DR-TB
patients
- Case detection data (TB & DR-TB) are often sourced
directly from laboratories
- As diagnostics become more advanced even in low
resource settings, weaknesses in information
management become more pronounced
- Better informatics offer other potential to improve care
and laboratory performance.
26. WHO/ERS. Digital health for the End TB Strategy: an agenda for action. (WHO/HTM/TB/2015.21).
Geneva, World Health Organization; 2015.
“Connected diagnostics” : a logical
first step in the development of TB
laboratory informatics
27. Extensive Existing & Planned Clients
• Increasing
coverage of
common ehealth &
mhealth solutions
• From self-contained
to national scale
• Promiscuous
interoperability a
core goal
28. 2016 Efforts
• CDX Advisory Board
• Additional Manufacturer Support
• Implementations
• Research & Evidence
CHRIS:
We all want better data for … outcomes, lab management, national supply chain etc
Good data seems expensive and hard and slow; most of us work with bad data
Opportunity and promise of good data is made real by dx connectivity
The potential benefits of connected diagnostics are potentially being hampered by fragmented platforms for data collection, storage and access
Today data is either routed to manufacturer systems, 3rd party systems or is not routed anywhere at all
Effort is required to centrally collect data from different systems and harmonise format
What we need is aggregation of data from all diagnostics regardless of location, disease or manufacturing origin
We think manufacturers can and should ALSO invest in solutions that help manage the devices more efficiently in ways unique to needs of each platform
To make this oppty a reality FIND and InSTEDD founded CDX
Geneva and silicon valley
Great tech, market saviness with manufacturers, understnding of global needs and role of policy and funders
(ed)
Thanks chris il lspeak about the platform a bit – the goal is to simplify and accelerate making dtaa get to where it needs to
It has A B C D
Obviously technology is only one piece of the puzzle, chris will tell the initiative activities…
Technology is only is only a piece of the puzzle. To make the vision a reality we need to address other areas of policy, strategy, evidence, standards and training including ownership and capacity building at the local level. Back over to you ed. Tell us more about the platform.
<chris>
To support the initiative we are planning on the release of v1.0 of the platform early 2016. Cloud or on premise support. We will be seeking to aid with implementation subject to funds and planning but the platform is open source and freely available to use as part of your existing or planned ehealth projects.
Already have a range of TB dx available but lets hear from one of them to tell us why they are supporting CDx. Like to invite Mike Turnlund to the stage, not only do cepheid provide an important dx for the fight against TB but are also a pioneer in the area of connectivity
This slide shows how the detection of rifampicin resistant TB – which is a laboratory diagnosis – fluctuates from year to year between the countries which have the highest MDR-TB burden in the world. Many of these fluctuations often represent instabilities and inefficiencies of laboratory-based reporting. In some countries the number of cases detected is persistently lower than the number of cases started on treatment, implying incomplete coverage of reporting from the labs.