The document discusses the CDC's LabHIT program which aims to facilitate laboratory test ordering and reporting in electronic health records. The program develops and disseminates standardized terminology and code sets to support clinical data capture and interoperability. It also works with various stakeholders to engage in terminology development, promote semantic interoperability, and ensure usability and patient safety. The long-term goal is a single national reference database for recommended vocabulary sets to achieve full-scale interoperability for laboratory data.
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.
What Happens After Your Device is Approved? Collecting Data in the Real WorldMedpace
In this workshop, Medpace will discuss key considerations for generating real-world evidence and how to apply critical insights in order to drive late-stage clinical research. To listen to this presentation, visit https://vimeo.com/168768256
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.
What Happens After Your Device is Approved? Collecting Data in the Real WorldMedpace
In this workshop, Medpace will discuss key considerations for generating real-world evidence and how to apply critical insights in order to drive late-stage clinical research. To listen to this presentation, visit https://vimeo.com/168768256
Patient Centricity: EHR Pillars to Patient CentricityDayOne
AT the DayOne Experts - Next Generation Clinical Trials, Randy Ramin-Wright from Clinerion demonstrated how patient recruitment works in the digital age.
Infographic- Improve Clinical Trial Participation with Mobile AppsDiaspark
Mobile apps are pivotal to patient recruitment & retention in clinical trials, we have done research on changing trends &d challenges in clinical trial patient recruitment & retention.
Investigator-initiated clinical trials: a community perspectiveARDC
Presentation by Miranda Cumpston, ACTA, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Here is the slideset I presented at the 2nd International Conference on Business Analytic and Intelligence (ICBAI). The intent behind the paper/presentation was to bring out awareness on AE analysis and present status of AE data in india. Certainly there are huge potential benefit/insights out of this data if the base is set well. Private health care institutions can reap the benefit (Both financial and patient care) if they invest to define the system for their use.
Big data, RWE and AI in Clinical Trials made simpleHadas Jacoby
Technology is slowly but surely penetrating the healthcare industry in general and the clinical trials sector in particular. New and advanced solutions offer a variety of possibilities aimed to both improving existing processes and creating new and more efficient ones. And on top of all stands the desire to make clinical trials more patient centric.
In all of this, even though some of the technologies have yet to mature enough to meet the high quality standards necessary, it is important to know them and begin imagining the promise they hold for clinical trials.
Patient Centricity: EHR Pillars to Patient CentricityDayOne
AT the DayOne Experts - Next Generation Clinical Trials, Randy Ramin-Wright from Clinerion demonstrated how patient recruitment works in the digital age.
Infographic- Improve Clinical Trial Participation with Mobile AppsDiaspark
Mobile apps are pivotal to patient recruitment & retention in clinical trials, we have done research on changing trends &d challenges in clinical trial patient recruitment & retention.
Investigator-initiated clinical trials: a community perspectiveARDC
Presentation by Miranda Cumpston, ACTA, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Here is the slideset I presented at the 2nd International Conference on Business Analytic and Intelligence (ICBAI). The intent behind the paper/presentation was to bring out awareness on AE analysis and present status of AE data in india. Certainly there are huge potential benefit/insights out of this data if the base is set well. Private health care institutions can reap the benefit (Both financial and patient care) if they invest to define the system for their use.
Big data, RWE and AI in Clinical Trials made simpleHadas Jacoby
Technology is slowly but surely penetrating the healthcare industry in general and the clinical trials sector in particular. New and advanced solutions offer a variety of possibilities aimed to both improving existing processes and creating new and more efficient ones. And on top of all stands the desire to make clinical trials more patient centric.
In all of this, even though some of the technologies have yet to mature enough to meet the high quality standards necessary, it is important to know them and begin imagining the promise they hold for clinical trials.
Integrate RWE into clinical developmentIMSHealthRWES
With greater application of RWE throughout the pharmaceutical
lifecycle, learnings are emerging that offer guidance for
approaches to derive the maximum value. This article captures
the author’s experience at a leading international biotech, with
insights for smoothing RWE assimilation into clinical
development and realizing the benefits it brings.
Challenges and Opportunities Around Integration of Clinical Trials DataCitiusTech
Conducting a Clinical Trial is a complex process, consisting of activities such as protocol preparation, site selection, approval of various authorities, meticulous collection and management of data, analysis and reporting of the data collected
Each activity is benefited from the development of point applications which ease the process of data collection, reporting and decision making. The recent advancements in mobile technologies and connectivity has enabled the generation and exchange of a lot more data than previously anticipated. However, the lack of interoperability and proper planning to leverage this data, still acts as a roadblock in allowing organizations truly harness their data assets. This document will help life sciences IT professionals and decision makers understand challenges and opportunities around clinical data integration
Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...Perficient, Inc.
The average academic research organization (ARO) and hospital has many systems that house patient-related information, such as patient records and genomic data. Combining data from a variety of sources in an ongoing manner can enable complex and meaningful querying, reporting and analysis for the purposes of improving patient safety and care, boosting operational efficiency, and supporting personalized medicine initiatives.
In this webinar, Perficient’s Mike Grossman, a director of clinical data warehousing and analytics, and Martin Sizemore, a healthcare strategist, discussed:
-How AROs and hospitals can benefit from a systematic approach to combining data from diverse systems and utilizing a suite of data extraction, reporting, and analytical tools, in order to support a wide variety of needs and requests
-Examples of proposed solutions to real-life challenges AROs and hospitals often encounter
The Pistoia Alliance is examining the challenges of the Faster Safe Companion Diagnostics (CDx) by Aligning Discovery & Clinical Data in the Regulatory Domain.
The slides discuss whether the data standards used in the research environment be aligned better with the data standards used in the regulated environment? If so, the time and cost of the development of NGS-based CDx could be reduced.
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
Strengthening Health Systems through the application of Wireless TechnologyOPS Colombia
Presentación realizada por el Dr. Trishan Panch, de Harvard School of Public Health, el 20 de Septiembre en OPS Colombia, en el espacio de intercambio sobre e-health.
El Dr. Panch, participa, con el auspicio de esta Representación, como conferencista en el IV Congreso Colombiano de Bioingeniería e Ingeniería Biomédica que se realizará en Barranquilla del 21 al 24 de septiembre del 2011.
A crucial stage in clinical research is clinical data management CDM , which produces high quality, reliable, and statistically sound data from clinical trials. This results in a significantly shorter period of time between drug development and marketing. Team members of CDM are laboriously involved in all stages of clinical trials right from commencement to completion. They should be able to sustain the quality standards set by CDM processes by having sufficient process expertise. colorful procedures in CDM including Case Report Form CRF designing, CRF reflection, database designing, data entry, data confirmation, distinction operation, medical coding, data birth, and database locking are assessed for quality at regular intervals during a trial. In the present script, theres an increased demand to ameliorate the CDM norms to meet the nonsupervisory conditions and stay ahead of the competition by means of brisk commercialization of products. With the perpetration of nonsupervisory biddable data operation tools, the CDM platoon can meet these demands. also, its getting obligatory for companies to submit the data electronically. CDM professionals should meet applicable prospects and set norms for data quality and also have the drive to acclimatize to the fleetly changing technology. This composition highlights the processes involved and provides the anthology an overview of the tools and norms espoused as well as the places and liabilities in CDM. Syed Shahnawaz Quadri | Syeda Saniya Ifteqar | Syed Shafa Raoof "Data Management in Clinical Research" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-2 , April 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd55050.pdf Paper URL: https://www.ijtsrd.com.com/pharmacy/other/55050/data-management-in-clinical-research/syed-shahnawaz-quadri
A presentation about the role of informatics standards in facilitating electronic data interchange, and a framework for service-oriented semantic interoperability among data systems.
NASSCOM CoE IoT spearheaded a high-level industry roundtable to discuss firsthand the challenges & opportunities in India’s clinical trial industry and how technology can accelerate development
6/29/2016 library.ahima.org/PB/DataStandards#appxA
http://library.ahima.org/PB/DataStandards#appxA 1/20
Data Standards, Data Quality, and Interoperability (2013
update)
Remove from myBoK
Editor's note: This update replaces the 2007 practice brief "Data Standards, Data Quality, and Interoperability."
Data quality and consistency are critical to ensuring patient safety, communicating delivery of health services, coordinating
care, and healthcare reporting. Assessing the quality and consistency of data requires data standards. This practice brief
provides health information management (HIM) professionals with a clear understanding of data standards as a tool to
enable interoperability and promote data quality.
The online version of this practice brief [...] is accompanied by an appendix that provides HIM professionals with a list of
standards to reference in data dictionary development, electronic health records, the exchange of health information, and
general data management processes to ensure information integrity and reliability. Evaluation of data validity, reliability,
completeness, and timeliness are accomplished through a combination of human and machine processes in healthcare, and
the list of data standard sources is a helpful reference guide when more detailed information is required.
Data Standards and Regulatory Framework
Data standards are "documented agreements on representations, formats, and definitions of common data. Data standards
provide a method to codify invalid, meaningful, comprehensive, and actionable ways, information captured in the course of
doing business." Rules to describe how the data is recorded to ensure consistency across multiple sources is another way to
think of data standards. Without data standards and data quality, the future of interoperability is bleak. Data fields and the
content of those fields need to be standardized.
Standards development organizations (SDOs) address a variety of aspects of health information and informatics. For
example, the American Society for Testing and Materials (ASTM) and Health Level Seven (HL7) target clinical data
standards. Insurance and remittance standards are a focus of the Accredited Standards Committee (ASC) X12. Standards to
transmit diagnostic images are developed through Digital Imaging and Communications in Medicine (DICOM). The
National Council for Prescription Drug Programs (NCPDP) represents pharmacy messages.
The Institute of Electrical and Electronics Engineers (IEEE), HL7, ASTM, and others develop data models and
frameworks. See the table on page 65 for a breakdown of regulatory agencies responsible for working with the American
National Standards Institute (ANSI) to drive data standards to achieve interoperability.
The AHIMA Leadership Model states that HIM professionals should serve as the leaders in healthcare organizations and in
their professional community for ensuring that data content standards are identified, understood, implemented, a.
6/29/2016 library.ahima.org/PB/DataStandards#appxA
http://library.ahima.org/PB/DataStandards#appxA 1/20
Data Standards, Data Quality, and Interoperability (2013
update)
Remove from myBoK
Editor's note: This update replaces the 2007 practice brief "Data Standards, Data Quality, and Interoperability."
Data quality and consistency are critical to ensuring patient safety, communicating delivery of health services, coordinating
care, and healthcare reporting. Assessing the quality and consistency of data requires data standards. This practice brief
provides health information management (HIM) professionals with a clear understanding of data standards as a tool to
enable interoperability and promote data quality.
The online version of this practice brief [...] is accompanied by an appendix that provides HIM professionals with a list of
standards to reference in data dictionary development, electronic health records, the exchange of health information, and
general data management processes to ensure information integrity and reliability. Evaluation of data validity, reliability,
completeness, and timeliness are accomplished through a combination of human and machine processes in healthcare, and
the list of data standard sources is a helpful reference guide when more detailed information is required.
Data Standards and Regulatory Framework
Data standards are "documented agreements on representations, formats, and definitions of common data. Data standards
provide a method to codify invalid, meaningful, comprehensive, and actionable ways, information captured in the course of
doing business." Rules to describe how the data is recorded to ensure consistency across multiple sources is another way to
think of data standards. Without data standards and data quality, the future of interoperability is bleak. Data fields and the
content of those fields need to be standardized.
Standards development organizations (SDOs) address a variety of aspects of health information and informatics. For
example, the American Society for Testing and Materials (ASTM) and Health Level Seven (HL7) target clinical data
standards. Insurance and remittance standards are a focus of the Accredited Standards Committee (ASC) X12. Standards to
transmit diagnostic images are developed through Digital Imaging and Communications in Medicine (DICOM). The
National Council for Prescription Drug Programs (NCPDP) represents pharmacy messages.
The Institute of Electrical and Electronics Engineers (IEEE), HL7, ASTM, and others develop data models and
frameworks. See the table on page 65 for a breakdown of regulatory agencies responsible for working with the American
National Standards Institute (ANSI) to drive data standards to achieve interoperability.
The AHIMA Leadership Model states that HIM professionals should serve as the leaders in healthcare organizations and in
their professional community for ensuring that data content standards are identified, understood, implemented, a ...
Microsoft: A Waking Giant In Healthcare Analytics and Big DataHealth Catalyst
In 2005, Northwestern Memorial Healthcare embarked upon a strategic Enterprise Data Warehousing (EDW) initiative with the Microsoft technology platform as the foundation. Dale Sanders was CIO at Northwestern and led the development of Northwestern’s Microsoft-based EDW. At that time, Microsoft as an EDW platform was not en vogue and there were many who doubted the success of the Northwestern project. While other organizations were spending millions of dollars and years developing EDW’s and analytics on other platforms, Northwestern achieved great and rapid value at a fraction of the cost of the more typical technology platforms. Now, there are more healthcare data warehouses built around Microsoft products than any other vendor. The risky bet on Microsoft in 2005 paid off.
Ten years ago, critics didn’t believe that Microsoft could scale in the second generation of relational data warehouses, but they did. More recently, many of these same pundits have criticized Microsoft for missing the technology wave du jour in cloud offerings, mobile technology, and big data. But, once again, Microsoft has been quietly reengineering its culture and products, and as a result, they now offer the best value and most visionary platform for cloud services, big data, and analytics in healthcare.
In this context, Dale will talk about:
His up and down journey with Microsoft as an Air Force and healthcare CIO, and why he is now more bullish on Microsoft like never before
A quick review of the Healthcare Analytics Adoption Model and Closed Loop Analytics in healthcare, and how Microsoft products relate to both
The rise of highly specialized, cloud-based analytic services and their value to healthcare organizations’ analytics strategies
Microsoft’s transformation from a closed-system, desktop PC company to an open-system consumer and business infrastructure company
The current transition period of enterprise data warehouses between the decline of relational databases and the rise of non-relational databases, and the new Microsoft products, notably Azure and the Analytic Platform System (APS), that bridge the transition of skills and technology while still integrating with core products like Office, Active Directory, and System Center
Microsoft’s strategy with its PowerX product line, and geospatial analysis and machine learning visualization tools
A hybrid approach to data management is emerging in healthcare as organizations recognize the value of an enterprise data warehouse in combination with a data lake.
In this SlideShare, we discuss data lakes in healthcare and we:
Provide an overview of a Hadoop-based data lake architecture and integration platform, and its application in machine learning, predictive modeling, and data discovery
Discuss several key use cases driving the adoption of data lakes for both providers and health plans
Discuss available data storage forms and the required tools for a data lake environment
Detail best practices for conducting data lake assessments and review key implementation considerations for healthcare
1. www.cdc.gov | Contact CDC at: 1-800-CDC-INFO or www.cdc.gov/info
The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.
Division of Laboratory Systems LabHIT Team: Megan E. Sawchuk, BS, MT(ASCP), MariBeth Gagnon, MS CT(ASCP)HTL, Nancy E. Cornish, MD, FCAP,
Ira M. Lubin, PhD, FACMG, Graylin Mitchell, MPH, MT, Sonya Strider, PhD, MT(ASCP), Manjula Gama Ralalage, MBBS, MSc
Facilitating Laboratory Test Ordering and Reporting in the Electronic Health Record
Division of Laboratory Systems
Objectives: Quality Clinical Data Capture
and Achieving Meaningful Use
Develop and disseminate code sets to support quality clinical data capture,
thereby enabling electronic health record data to be used to inform decision
making for individuals, healthcare providers, researchers and public health.
Methods
aLOINC® - Model Example Project
Laboratory Test Order Codes Initiative for Top Ordered Tests
SNOMED CT® - Model Example Project
Specimen attribute coding to fully define the tested specimen
Vision: Achieving Interoperability
“Stretch Goal”
Support full-scale interoperability for laboratory data by providing a
single national reference database of recommended vocabulary sets
with mapping of test systems to code systems.
Building World-Class Local, Regional & National Surveillance Capability
Goal: Patient Safety & Data Integrity
Laboratory data can be distributed rapidly and to numerous users in
the healthcare system. The goal of interoperability is to ensure data
integrity, or trust, in every electronic transmission, thereby also
ensuring laboratory data is provided in a manner to support patient
safety and optimized healthcare decision making.
LabHIT’s Central Role
Bringing diverse stakeholders together
Results
• aLOINC® order codes will be included in Regenstrief’s LOINC Mapping Assistant
(RELMA®) tool.
• SNOMED CT® codes are completed and available to fully define specimens.
• Terminology work is resource intensive and voluminous, requiring a long term
commitment of resources.
• The long term iterative process to develop health IT terminology can result in
duplicative efforts over time and from different stakeholders, nationally and
internationally.
• An incremental approach is needed to continue moving each code set toward the
vision.
Next Steps
ENGAGEMENT
• Continue promoting health IT opportunities via LabHIT email subscriber database:
• Relevant federal grant opportunities (CDC, ONC, AHRQ)
• Association of Pathology Informatics (API) Technical Member category to
groom next generation laboratory science SMEs
• Comment periods on proposed regulations, guidelines, and requests for
information
• Facilitate participation of stakeholders with standards organizations, including
clinical SMEs, software vendors, manufacturers, and professional organizations
INTEROPERABILITY
• Promote semantic interoperability workshops and guidance for laboratory testing
systems in collaboration with FDA, NLM, CMS and ONC
• Support participation and leadership of private industry test system manufacturer
funded organizations with an interest in interoperability
• Identify mechanisms to distribute standardized vocabulary for specimen test
ordering to EHR vendors, e.g. relational database
• Serve as SMEs on numerous HL7 Initiatives related to laboratory standards
• Promote use of the CLIA standard level LRI Validation Suite by EHR vendors
• Propose a centralized laboratory LOINC® and SNOMED CT® coding process
USABILITY & SAFETY
• Participate on HL7 Laboratory Functional Behaviors Guide Workgroup
• Support NIST’s 2016 Usability Workshop, “The Role of Standards in Preventing &
Mitigating Health IT Patient Safety Risks”
• Evaluate health IT related patient safety events reported to the FDA
• Create laboratory safety checklist for assessment of laboratory data display
Use of trade names, commercial sources or private organizations is for identification only and does not imply endorsement by the U.S. Department of Health and Human Services and/or CDC.
Poster presented at the 2016 Public Health Informatics Conference, Atlanta GA (August 22, 2016) and the CDC 5th Global Health Lab Forum, Atlanta GA (September 12, 2016).
For more information: visit www.cdc.gov/labhit or email LabHIT@cdc.gov
Regulatory and Accreditation
Organizations
Professional Societies
Vendors Health IT Clinical Laboratories
LabHIT
Clinical and Public Health
Laboratory Reporting