The discovery of clinical insights through effective management and reuse of data requires several conditions to be optimized: Data need to be digital, data need to be structured, and data need to be standardized in terms of metadata and ontology. This presentation describes a bioinformatics system that combines a next-generation biobank management model mapped to applicable international standards and guidelines with a master ontology that controls all input and output and is able to add unique properties to meet the specialized needs of clinicians for cross-disease research.
Ontology-Driven Clinical Intelligence: Removing Data Barriers for Cross-Disci...Remedy Informatics
The presentation describes how Remedy Informatics is advocating and innovating "flexible standardization" through an ontology-driven approach to clinical research. You will see in greater detail how a foundational, standardized Mosaic Ontology can be extended for more specific research applications and even more specific and focused disease research.
Paper presented at the 2012 MLA Quad Chapter meeting in Baltimore, MD, Oct. 13-16. Discusses i2b2 and how it could be used in medical education. And suggests other data if i2b2 not available in your hospital.
Ontology-Driven Clinical Intelligence: Removing Data Barriers for Cross-Disci...Remedy Informatics
The presentation describes how Remedy Informatics is advocating and innovating "flexible standardization" through an ontology-driven approach to clinical research. You will see in greater detail how a foundational, standardized Mosaic Ontology can be extended for more specific research applications and even more specific and focused disease research.
Paper presented at the 2012 MLA Quad Chapter meeting in Baltimore, MD, Oct. 13-16. Discusses i2b2 and how it could be used in medical education. And suggests other data if i2b2 not available in your hospital.
Clinical Research Informatics (CRI) Year-in-Review 2014Peter Embi
Peter Embi's review of notable publications and events in the field of Clinical Research Informatics (CRI) that took place in 2013+. This was presented as the closing keynote presentation of the 2014 AMIA CRI Summit in San Francisco, CA on April 11, 2014.
JALA Deputy Editor-in-Chief Edward Chow, Ph.D., University of Singapore, offers instruction for central message design, journal selection and proper manuscript composition. Originality, citations and the peer review process also are covered. This presentation is from the popular “JALA & JBS Author Workshop: How to Get Your Work Published,” SLAS2014 in San Diego.
Provenance abstraction for implementing security: Learning Health System and ...Vasa Curcin
Discussion of provenance usage in the Learning Health System paradigm, as implemented in the TRANSFoRm project, with focus on security requirements and how they can be addressed using provenance graph abstraction.
tranSMART Community Meeting 5-7 Nov 13 - Session 5: Advancing tranSMART Analy...David Peyruc
tranSMART Community Meeting 5-7 Nov 13 - Session 5: Advancing tranSMART Analytical Capabilities with Knowledge Content
Sirimon Ocharoen, Thomson Reuters
To effectively analyze data in tranSMART, biological analysis/knowledge-based approach is needed. Through a case study, we will demonstrate how system biology content can be integrated in tranSMART to enable functional analysis and biological interpretation. We will also share our experience and user feedbacks from various projects.
An overview of the oncology clinical trials network (CTNeT) which is being implemented throughout Texas.
The non-profit network is a first of its kind and combines the innovative science of Texas cancer centers with the expertise and resources of both academic and community oncologists throughout the state.
To learn more, visit www.ctnet.org
Radiomics Data Management, Computation, and Analysis for QIN F2F 2016Ashish Sharma
Large Scale Data Management Computation and Analysis for Quantitative Imaging Research
Talk at the 2016 QIN Annual Meeting — covers resources developed for the Quantitative Imaging Network. Includes TCIA data curation, APIs, supported data types, as well as co-located computing and systematic phenotyping of imaging biomarkers
Epic EMR to OMOP CDM to Clinical Research Data Mart: an Unmaintained Road or ...Oksana Gologorskaya
Poster we presented at 2017 AMIA Joint Summits on Clinical and Translational Research Informatics.
In this research data delivery project, we explored a less traveled path of building a clinical data mart for a registry study on kidney transplant patients, based on the institutional instance of the EMR data, translated into the OMOP (Observational Medical Outcomes Partnership) common data model.
Leveraging Medical Health Record Data for Identifying Research Study Particip...SC CTSI at USC and CHLA
Date: May 2, 2018
Speakers: Juan Espinoza, MD, FAAP, Assistant Professor of Clinical Pediatrics, Keck School of Medicine of USC, Physician and Director of Clinical Research Informatics, Children’s Hospital Los Angeles; and Mark Abajian, Applications Lead, Clinical Research Informatics, SC CTSI
Overview: This webinar will highlight three applications available at USC, CHLA and LA County DHS that assist researchers with identifying prospective study participants.
Study recruitment remains one of the major challenges to successful clinical and translational science. Medical health record data provide a new source for identifying prospective study participants that fit the eligibility criteria. The speakers will introduce three applications (TriNetX, i2b2, and SHRINE) and provide a step-by-step guide for accessing and using this type of data.
Using real-world evidence to investigate clinical research questionsKarin Verspoor
Adoption of electronic health records to document extensive clinical information brings with it the opportunity to utilise that information to support clinical research, and ultimately to support clinical decision making. In this talk, I discuss both these opportunities and the challenges that we face when working with real-world clinical data, and introduce some of the strategies that we are adopting to make this data more usable, and to extract more value from it. I specifically discuss the use of natural language processing to transform clinical documentation into structured data for this purpose.
Clinical Research Informatics (CRI) Year-in-Review 2014Peter Embi
Peter Embi's review of notable publications and events in the field of Clinical Research Informatics (CRI) that took place in 2013+. This was presented as the closing keynote presentation of the 2014 AMIA CRI Summit in San Francisco, CA on April 11, 2014.
JALA Deputy Editor-in-Chief Edward Chow, Ph.D., University of Singapore, offers instruction for central message design, journal selection and proper manuscript composition. Originality, citations and the peer review process also are covered. This presentation is from the popular “JALA & JBS Author Workshop: How to Get Your Work Published,” SLAS2014 in San Diego.
Provenance abstraction for implementing security: Learning Health System and ...Vasa Curcin
Discussion of provenance usage in the Learning Health System paradigm, as implemented in the TRANSFoRm project, with focus on security requirements and how they can be addressed using provenance graph abstraction.
tranSMART Community Meeting 5-7 Nov 13 - Session 5: Advancing tranSMART Analy...David Peyruc
tranSMART Community Meeting 5-7 Nov 13 - Session 5: Advancing tranSMART Analytical Capabilities with Knowledge Content
Sirimon Ocharoen, Thomson Reuters
To effectively analyze data in tranSMART, biological analysis/knowledge-based approach is needed. Through a case study, we will demonstrate how system biology content can be integrated in tranSMART to enable functional analysis and biological interpretation. We will also share our experience and user feedbacks from various projects.
An overview of the oncology clinical trials network (CTNeT) which is being implemented throughout Texas.
The non-profit network is a first of its kind and combines the innovative science of Texas cancer centers with the expertise and resources of both academic and community oncologists throughout the state.
To learn more, visit www.ctnet.org
Radiomics Data Management, Computation, and Analysis for QIN F2F 2016Ashish Sharma
Large Scale Data Management Computation and Analysis for Quantitative Imaging Research
Talk at the 2016 QIN Annual Meeting — covers resources developed for the Quantitative Imaging Network. Includes TCIA data curation, APIs, supported data types, as well as co-located computing and systematic phenotyping of imaging biomarkers
Epic EMR to OMOP CDM to Clinical Research Data Mart: an Unmaintained Road or ...Oksana Gologorskaya
Poster we presented at 2017 AMIA Joint Summits on Clinical and Translational Research Informatics.
In this research data delivery project, we explored a less traveled path of building a clinical data mart for a registry study on kidney transplant patients, based on the institutional instance of the EMR data, translated into the OMOP (Observational Medical Outcomes Partnership) common data model.
Leveraging Medical Health Record Data for Identifying Research Study Particip...SC CTSI at USC and CHLA
Date: May 2, 2018
Speakers: Juan Espinoza, MD, FAAP, Assistant Professor of Clinical Pediatrics, Keck School of Medicine of USC, Physician and Director of Clinical Research Informatics, Children’s Hospital Los Angeles; and Mark Abajian, Applications Lead, Clinical Research Informatics, SC CTSI
Overview: This webinar will highlight three applications available at USC, CHLA and LA County DHS that assist researchers with identifying prospective study participants.
Study recruitment remains one of the major challenges to successful clinical and translational science. Medical health record data provide a new source for identifying prospective study participants that fit the eligibility criteria. The speakers will introduce three applications (TriNetX, i2b2, and SHRINE) and provide a step-by-step guide for accessing and using this type of data.
Using real-world evidence to investigate clinical research questionsKarin Verspoor
Adoption of electronic health records to document extensive clinical information brings with it the opportunity to utilise that information to support clinical research, and ultimately to support clinical decision making. In this talk, I discuss both these opportunities and the challenges that we face when working with real-world clinical data, and introduce some of the strategies that we are adopting to make this data more usable, and to extract more value from it. I specifically discuss the use of natural language processing to transform clinical documentation into structured data for this purpose.
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
Pine Biotech - a company that merges big -omics data analysis with clinical care and precision applications for Real World Evidence: research & development of new targets and therapeutics, stratified clinical trials, and development of biomarkers for early detection and companion diagnostics. We want to improve patient outcomes and provide tools for researchers and clinicians to have an impact on healthcare.
Next generation electronic medical records and search a test implementation i...lucenerevolution
Presented by David Piraino, Chief Imaging Information Officer, Imaging Institute Cleveland Clinic, Cleveland Clinic
& Daniel Palmer, Chief Imaging Information Officer, Imaging Institute Cleveland Clinic, Cleveland Clinic
Most patient specifc medical information is document oriented with varying amounts of associated meta-data. Most of pateint medical information is textual and semi-structured. Electronic Medical Record Systems (EMR) are not optimized to present the textual information to users in the most understandable ways. Present EMRs show information to the user in a reverse time oriented patient specific manner only. This talk discribes the construction and use of Solr search technologies to provide relevant historical information at the point of care while intepreting radiology images.
Radiology reports over a 4 year period were extracted from our Radiology Information System (RIS) and passed through a text processing engine to extract the results, impression, exam description, location, history, and date. Fifteen cases reported during clinical practice were used as test cases to determine if ""similar"" historical cases were found . The results were evaluated by the number of searches that returned any result in less than 3 seconds and the number of cases that illustrated the questioned diagnosis in the top 10 results returned as determined by a bone and joint radiologist. Also methods to better optimize the search results were reviewed.
An average of 7.8 out of the 10 highest rated reports showed a similar case highly related to the present case. The best search showed 10 out of 10 cases that were good examples and the lowest match search showed 2 out of 10 cases that were good examples.The talk will highlight this specific use case and the issues and advances of using Solr search technology in medicine with focus on point of care applications.
Dr. Dennis Wang discusses possible ways to enable ML methods to be more powerful for discovery and to reduce ambiguity within translational medicine, allowing data-informed decision-making to deliver the next generation of diagnostics and therapeutics to patients quicker, at lowered costs, and at scale.
The talk by Dr. Dennis Wang was followed by a panel discussion with Mr. Albert Wang, M. Eng., Head, IT Business Partner, Translational Research & Technologies, Bristol-Myers Squibb.
Clinical trial data wants to be free: Lessons from the ImmPort Immunology Dat...Barry Smith
Presentation to the Clinical and Research Ethics Seminar, Clinical and Translational Science Center, Buffalo, January 21, 2014
https://immport.niaid.nih.gov/
http://youtu.be/booqxkpvJMg
Leveraging Text Classification Strategies for Clinical and Public Health Appl...Karin Verspoor
Human-generated text is a critical component of recorded clinical data, yet remains an under-utilised resource in clinical informatics applications due to minimal standards for sharing of unstructured data as well as concerns about patient privacy. Where we can access and analyse clinical text, we find that it provides a hugely valuable resource. In this talk, I will describe two projects where we have used text classification as the basis for addressing a clinical objective: (1) a syndromic surveillance project where the task is the monitoring of health and social media data sources for changes that indicate the onset of disease outbreaks, and (2) the analysis of hospital records to enable retrieval of specific disease cases, for monitoring of the hospital case mix as well as for construction of patient cohorts for clinical research studies. I will end by briefly discussing the huge potential for clinical text analysis to support changing the way modern medicine is practised.
Forum on Personalized Medicine: Challenges for the next decadeJoaquin Dopazo
Bioinformatics and Big Data in the era of Personalized Medicine
10th Anniversary Instituto Roche Forum on Personalized Medicine: Challenges for the next decade.
Santiago de Compostela (Spain), September 25th 2014
Similar to Ontology-Driven Clinical Intelligence: A Path from the Biobank to Cross-Disease Research (20)
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
CDSCO and Phamacovigilance {Regulatory body in India}NEHA GUPTA
The Central Drugs Standard Control Organization (CDSCO) is India's national regulatory body for pharmaceuticals and medical devices. Operating under the Directorate General of Health Services, Ministry of Health & Family Welfare, Government of India, the CDSCO is responsible for approving new drugs, conducting clinical trials, setting standards for drugs, controlling the quality of imported drugs, and coordinating the activities of State Drug Control Organizations by providing expert advice.
Pharmacovigilance, on the other hand, is the science and activities related to the detection, assessment, understanding, and prevention of adverse effects or any other drug-related problems. The primary aim of pharmacovigilance is to ensure the safety and efficacy of medicines, thereby protecting public health.
In India, pharmacovigilance activities are monitored by the Pharmacovigilance Programme of India (PvPI), which works closely with CDSCO to collect, analyze, and act upon data regarding adverse drug reactions (ADRs). Together, they play a critical role in ensuring that the benefits of drugs outweigh their risks, maintaining high standards of patient safety, and promoting the rational use of medicines.
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
The Gram stain is a fundamental technique in microbiology used to classify bacteria based on their cell wall structure. It provides a quick and simple method to distinguish between Gram-positive and Gram-negative bacteria, which have different susceptibilities to antibiotics
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
Basavarajeeyam is an important text for ayurvedic physician belonging to andhra pradehs. It is a popular compendium in various parts of our country as well as in andhra pradesh. The content of the text was presented in sanskrit and telugu language (Bilingual). One of the most famous book in ayurvedic pharmaceutics and therapeutics. This book contains 25 chapters called as prakaranas. Many rasaoushadis were explained, pioneer of dhatu druti, nadi pareeksha, mutra pareeksha etc. Belongs to the period of 15-16 century. New diseases like upadamsha, phiranga rogas are explained.
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
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Maxilla, Mandible & Hyoid Bone & Clinical Correlations by Dr. RIG.pptx
Ontology-Driven Clinical Intelligence: A Path from the Biobank to Cross-Disease Research
1. Ontology-Driven Clinical Intelligence
A Path from the Biobank to Cross-Disease Research
Bruce Pharr | Vice President, Bioinformatics Systems
Molecular Medicine Tri-Conference | February 11, 2014
1
2. Data Barriers to Clinical Research
Critical Data is Dispersed in Separate Systems
Disease A
Disease B
Considering the vast stores of clinical data available to potential
investigators, the actual amount of clinical research performed has
been quite modest. At many medical centers, the data are dispersed in
separate systems that have evolved independently of one another.
Source: Obstacles and Approaches to Clinical Database Research: Experience at the University of California, San Francisco
3. Removing the Data Barriers
Structured Digital Data with Standardized Metadata and Ontology
Disease A
Disease B
The discovery of scientific insights through
effective management and reuse of data
requires several conditions to be optimized:
• Data need to be digital;
• Data need to be structured;
• Data need to be standardized in terms of metadata and ontology.
Source: Anne E. Thessen and David J. Patterson, Data issues in life sciences, PMC (NIH/NLM) (November 28, 2011).
4. Ontology-Driven Clinical Intelligence
Structured Data with Standardized Metadata and Ontology
New Patient
Biobank
Lab Test & Analysis
Disease
Registry
Pre-analytical Data
Analytical Data
Mosaic™ Ontology-Based Platform
Legacy Data
Patient
Data
Legacy Disease Database
Patient
Data
5. Ontology-Driven Clinical Intelligence
Remedy Informatics Architecture
Patient
Data
New
Data
Patient
Data
Remedy Bioinformatics
RemedyAMH™
Biobank Management Informatics
Aggregate, Map & Harmonize
Legacy
Data
Mosaic Builder Applications
Patient
Data
Content and Registry Development
Mosaic Engine
Functional Layers: Physical, Data Model, Information Model, Ontology, Representation Model
Mosaic™ Platform
Remedy Informatics
Disease
Registry
6. Next-Gen Biobank
A Path from the Biobank to Cross-Disease Research
Patient
Data
New
Data
Remedy Bioinformatics
Biobank Management Informatics
Remedy Informatics
7. Biobank Growth and Upgrade Cycle
Drivers for Next-Gen Biobanks
Growth
33% of all biobanks have been installed since the early 2000s (HGP)
•
•
•
Increase in population genetics studies
Personalized medicine
Genetic information in food safety, forensics and disease surveillance
Upgrade
The Cancer Genome Atlas (TCGA) project (2006-8) exposed deficiencies
•
Many biobank managers didn’t know exactly what was in their freezers
•
Some specimens were unfit for analysis
•
•
Others had been obtained from patients without adequate consent
The rate of unacceptable shipments from some institutions was 99%
Source:
The
Future
of
Biobanking,
Laboratory
Focus,
January
2013
8. Next-Gen Biobank Management
Best Practices Model Mapped to Applicable Global Standards
Patient
Biobank
Manage all information about:
1. Specimens,
2. Patients, and
3. Operations throughout:
• Collection
• Processing
• Storage and Inventory
• Distribution
9. Best Practices
Biobank Management Informatics Requirements
•
•
•
•
•
•
•
•
•
•
•
•
•
Metadata
Entity Types
Sample Acquisition
Sample and Data Management
Sample Retention and Distribution
Support of Laboratory Processes
User Management
Search
Presentation of Entities
Printing
Reports and Audits
Non-functional Requirements
External Interface Requirements
10. Best Practices
Applicable International Standards and Guidelines
ISBER
International Society for Biological and Environmental Repositories. Best Practices for Repositories:
Collection, Storage, Retrieval, and Distribution of Biological Materials for Research.
NCI National Cancer Institute. First-generation guidelines for NCI-supported Biorepositories.
BAP Biorepository Accreditation Program (BAP) Checklist – College of American Pathologists (CAP)
21 CFR Part 11 US FDA – Guidelines on electronic records and electronic signatures.
45 CFR § 164.514 US HHS – Other requirements relating to uses and disclosures of protected health information.
ISO 15189 Medical laboratories – Particular requirements for quality and competence.
ISO 17025 General requirements for the competence of testing and calibration laboratories.
MoReq2 European Commission. Model Requirements for the management of electronic records.
OECD Best Practice Guidelines for biological resource centres.
Rec(2006)4
Council of Europe, Committee of Ministers. Recommendation of the Committee of Ministers to
member states on research on biological materials of human origin.
11. Mosaic Ontology
Purpose-Specific Structured Data Model
1. Predefined, Standardized Terminology
2. Domain-Specific Mapped Relationships
3. Permissible Values and Validation Rules
Patient
Data
Legacy
Data
RemedyAMH™
Aggregate, Map & Harmonize
Mosaic Builder Applications
Patient
Data
Content and Registry Development
Mosaic Engine
Functional Layers: Physical, Data Model, Information Model, Ontology, Representation Model
Mosaic Platform
Remedy Informatics
Disease
Registry
12. Mosaic Ontology
Predefined, Standardized Terminology
Lab Result
LOINC
Subject
Units
High End of Normal
Low End of Normal
Confidentiality
Validation Status
Validator
Supplier of Data
LOINC Medical Laboratory and Clinical Observations
13. Mosaic Ontology
Predefined, Standardized Terminology
Disorder
SNOMED CT
Assertion
Subject
Severity
Stage
Response to Treatment
Active State
Onset Date
Resolved State
First Diagnosed Date
Confidentiality
Source
Date of Entry
Validation Status
Validator
Supplier of Data
LOINC Medical Laboratory and Clinical Observations
SNOMED CT Clinical Codes, Terms, Synonyms and Definitions
14. Mosaic Ontology
Predefined, Standardized Terminology
LOINC Medical Laboratory and Clinical Observations
SNOMED CT Clinical Codes, Terms, Synonyms and Definitions
ICD Disease Classifications
Gene Ontology Gene Product Characteristics and Annotation
RxNorm Clinical Drug Classifications
CDISC Clinical Protocol, Analysis and Reporting
15. Mosaic Ontology
Domain-Specific Mapped Relationships
Lab Result
Disorder
Procedure
LOINC
SNOMED
SNOMED
Subject
Response to Tx
Cause
Subject
Units
High End of Normal
Assertion
Evidence for
Severity
Subject
Operator
Indication
Facility
Low End of Normal
Stage
Confidentiality
Response to Treatment
Validation Status
Active State
Intent
Onset Date
Confidentiality
Resolved State
Source
First Diagnosed Date
Date of Entry
Confidentiality
Validation Status
Source
Validator
Date of Entry
Supplier of Data
Validator
Supplier of Data
Has Result
Validation Status
Validator
Supplier of Data
Start-Stop Time
Contraindication
Urgency Status
16. Mosaic Ontology
Permissible Value and Validation Rules
Disorder
Procedure
SNOMED
SNOMED
Assertion
Subject
Mild
Subject
Operator
Moderate
Severity
Facility
Severe
Stage
Screening
Start-Stop Time
Response to Treatment
Diagnostic
Urgency Status
Active State
Prevention
Intent
Onset Date
Therapeutic
Confidentiality
Resolved State
Palliation
Source
First Diagnosed Date
End-of-Life
Date of Entry
Confidentiality
Validation Status
Source
Validator
Date of Entry
Supplier of Data
Validation Status
Validator
Supplier of Data
19. Remedy Informatics
• Founded in 2003, privately held.
• U.S. headquarters in Salt Lake City, Utah. Development offices in
Menlo Park, California.
• Satellite offices in London, England; Sao Paulo, Brazil; and Munich,
Germany.
• More than 120 employees.
• Strategic partnerships with Merck and IMS.
• Developed proprietary Mosaic Platform, an ontology-driven clinical
intelligence system scalable to any size enterprise.
• Delivered more than 120 registries to wide range of leading life sciences
research and healthcare delivery organizations.
20. Thanks! – Questions?
Bruce Pharr
Vice President, Bioinformatics Systems
bruce.pharr@remedyinformatics.com
Remedy Informatics
www.remedyinformatics.com
Booth 406