Translational Biomedical Informatics 2010: Infrastructure and Scaling – Brian Athey,
PhD; Professor of Biomedical Informatics and Director for Academic Informatics,
University of Michigan Medical School; Chair Designate for Computational Medicine and Bioinformatics, University of Michigan; Associate Director, Michigan Institute for Clinical Health Research; Principal Investigator, National Center for Integrative Biomedical Informatics
Computer Aided Diagnosis in Pathology: Pros & Cons by Dr. Liron PantanowitzCirdan
This presentation looks at the benefits and problems related to computer aided diagnosis in pathology. It was delivered by Dr. Liron Pantanowitz, University of Pittsburgh, USA at the Pathology Horizons conference in Cairns, Australia.
Pathology Horizons is an annual CPD conference organised by Cirdan on the future of pathology. More information on Pathology Horizons can be accessed at www.pathologyhorizons.com
Disrupting the Oncology Care Continuum through AI and Advanced AnalyticsMichael Peters
Having Presented at #SROA18 on the need to move from basic Data and Reporting to Advanced Analytics and Artificial Intelligence, I thought I would share my deck for all.
Big Data Provides Opportunities, Challenges and a Better Future in Health and...Cirdan
This presentation was delivered by Ashraf Mina, NSW Pathology at the Pathology Horizons 2017 Conference in Cairns, Australia.
Pathology Horizons 2017 is an annual CPD conference organised by Cirdan on the future of pathology. You can access more information about the event at www.pathologyhorizons.com
The company was founded in 2010 and is headquartered in Lisburn, Northern Ireland and has additional offices in Canada and Australia.
Cirdan is also responsible for organising Pathology Horizons, an annual and open CPD conference on the future of pathology. For more information visit - www.pathologyhorizons.com
The presentation outlines three fundamental questions: (1) how is medicare doing today?, (2) why is MACRA happening?, and (3) Why is clinical data quality important to you?
An AI-based Decision Platform built using unified data model, incorporating systems biology topics for unit analysis using semi-supervised learning models
Computer Aided Diagnosis in Pathology: Pros & Cons by Dr. Liron PantanowitzCirdan
This presentation looks at the benefits and problems related to computer aided diagnosis in pathology. It was delivered by Dr. Liron Pantanowitz, University of Pittsburgh, USA at the Pathology Horizons conference in Cairns, Australia.
Pathology Horizons is an annual CPD conference organised by Cirdan on the future of pathology. More information on Pathology Horizons can be accessed at www.pathologyhorizons.com
Disrupting the Oncology Care Continuum through AI and Advanced AnalyticsMichael Peters
Having Presented at #SROA18 on the need to move from basic Data and Reporting to Advanced Analytics and Artificial Intelligence, I thought I would share my deck for all.
Big Data Provides Opportunities, Challenges and a Better Future in Health and...Cirdan
This presentation was delivered by Ashraf Mina, NSW Pathology at the Pathology Horizons 2017 Conference in Cairns, Australia.
Pathology Horizons 2017 is an annual CPD conference organised by Cirdan on the future of pathology. You can access more information about the event at www.pathologyhorizons.com
The company was founded in 2010 and is headquartered in Lisburn, Northern Ireland and has additional offices in Canada and Australia.
Cirdan is also responsible for organising Pathology Horizons, an annual and open CPD conference on the future of pathology. For more information visit - www.pathologyhorizons.com
The presentation outlines three fundamental questions: (1) how is medicare doing today?, (2) why is MACRA happening?, and (3) Why is clinical data quality important to you?
An AI-based Decision Platform built using unified data model, incorporating systems biology topics for unit analysis using semi-supervised learning models
Importance of Patient voice helps healthcare providers and facilities offer better healthcare delivery. It helps them understand how their patients truly feel, their needs, expectations, and concerns during every point of the care journey. Analyzing voice of the patient data allows healthcare professionals to leverage the feedback data not only for better operational aspects but also for diagnostic needs. These insights thus gained can help shape strategic treatment plans, even as healthcare providers and decision makers use data-backed information to build the foundation of patient-centric healthcare.
A brief presentation outlining the concepts of data quality in the context of clinical data, and highlighting the importance of data quality for population health, health analytics, and other secondary uses of clinical data.
Bringing Things Together and Linking to Health Information using openEHRKoray Atalag
My prezo at Medinfo 2015 Conference in the workshop:
Digital Patient Modeling and Clinical Decision Support by Kerstin Denecke, Stefan Kropf, Claire Chalopin, Mario A, Cypko, Yihan Deng, Jan Gaebel, Koray Atalag
Carl Kesselman and I (along with our colleagues Stephan Erberich, Jonathan Silverstein, and Steve Tuecke) participated in an interesting workshop at the Institute of Medicine on July 14, 2009. Along with Patrick Soon-Shiong, we presented our views on how grid technologies can help address the challenges inherent in healthcare data integration.
Researchers and care providers wanted to have access to all of the patients` vitals signs (temperature, blood pressure, heart rate, and respiratory rate) but most of this data wasn?t recorded, only a few readings a day were posted to the patients Electronic Medical Record (EMR). The EMR isn`t meant to store such volume of data, let alone to perform any data mining on it. This session will describe the architecture of the solution that was implemented to collect these vital signs automatically from Bedside Medical Devices (BDMI), and store them into a temporary storage, then load them into a Hadoop cluster. The session will also cover how the team married this vital signs data in the HDFS (Hadoop File System) with the rest of the EMR data for our Principles Investigators (PI) in our research institute to search for correlations between administered medications, diagnosis, and vital signs readings. The session will describe the reasons behind the design decisions that were made, such as using a Cloud Hadoop cluster versus on-premises while maintaining HIPAA.
Importance of Patient voice helps healthcare providers and facilities offer better healthcare delivery. It helps them understand how their patients truly feel, their needs, expectations, and concerns during every point of the care journey. Analyzing voice of the patient data allows healthcare professionals to leverage the feedback data not only for better operational aspects but also for diagnostic needs. These insights thus gained can help shape strategic treatment plans, even as healthcare providers and decision makers use data-backed information to build the foundation of patient-centric healthcare.
A brief presentation outlining the concepts of data quality in the context of clinical data, and highlighting the importance of data quality for population health, health analytics, and other secondary uses of clinical data.
Bringing Things Together and Linking to Health Information using openEHRKoray Atalag
My prezo at Medinfo 2015 Conference in the workshop:
Digital Patient Modeling and Clinical Decision Support by Kerstin Denecke, Stefan Kropf, Claire Chalopin, Mario A, Cypko, Yihan Deng, Jan Gaebel, Koray Atalag
Carl Kesselman and I (along with our colleagues Stephan Erberich, Jonathan Silverstein, and Steve Tuecke) participated in an interesting workshop at the Institute of Medicine on July 14, 2009. Along with Patrick Soon-Shiong, we presented our views on how grid technologies can help address the challenges inherent in healthcare data integration.
Researchers and care providers wanted to have access to all of the patients` vitals signs (temperature, blood pressure, heart rate, and respiratory rate) but most of this data wasn?t recorded, only a few readings a day were posted to the patients Electronic Medical Record (EMR). The EMR isn`t meant to store such volume of data, let alone to perform any data mining on it. This session will describe the architecture of the solution that was implemented to collect these vital signs automatically from Bedside Medical Devices (BDMI), and store them into a temporary storage, then load them into a Hadoop cluster. The session will also cover how the team married this vital signs data in the HDFS (Hadoop File System) with the rest of the EMR data for our Principles Investigators (PI) in our research institute to search for correlations between administered medications, diagnosis, and vital signs readings. The session will describe the reasons behind the design decisions that were made, such as using a Cloud Hadoop cluster versus on-premises while maintaining HIPAA.
tranSMART Community Meeting 5-7 Nov 13 - Session 3: Characterization of the c...David Peyruc
tranSMART Community Meeting 5-7 Nov 13 - Session 3: Characterization of the cell phenotypes involved in metastasis
Characterization of the cell phenotypes involved in metastasis: Using tranSMART to enable high-throughput heterogeneous data integration and analysis
Brian Athey, University of Michigan
Ontology-Driven Clinical Intelligence: A Path from the Biobank to Cross-Disea...Remedy Informatics
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.
Realising the potential of Health Data Science: opportunities and challenges ...Paolo Missier
A guest lecture given to a group of healthcare professionals as part of an Information Management course at Newcastle University, on working with healthcare data to generate disease risk prediction models
The Learning Health System: Thinking and Acting Across ScalesPhilip Payne
A Learning Health System (LHS) can be defined as an environment in which knowledge generation processes are embedded into daily clinical practice in order to continually improve the quality, safety, and outcomes of healthcare delivery. While still largely an aspirational goal, the promise of the LHS is a future in which every patient encounter is an opportunity to learn and improve that patient’s care, as well as the care their family and broader community receives. The foundation for building such an LHS can and should be the Electronic Health Record (EHR), which provides the basis for the comprehensive instrumentation and measurement of clinical phenotypes, as well as a means of delivering new evidence at the patient- and population levels. In this presentation, we will explore the ways in which such EHR-derived phenotypes can be combined with complementary data across a spectrum from biomolecules to population level trends, to both generate insights and deliver such knowledge in the right time, place, and format, ultimately improving clinical outcomes and value.
Medical innovation calls for new models for collaborations that facilitates, government, academia and industry.
Barriers to research and ultimate commercialization will be lowered by bringing best practices from industry and academic settings.
Hippocrates platform facilitates early drug development extending from basic research to drug invention and commercialization significantly saving time and money.
The platform is designed in such way to facilitate collaboration amongst stakeholders as well as taking advantage of the vast resources currently available on the web to generate and aggregate content based on the needs of the research of the end-user.
With the upcoming move to ICD-10 Procedure Codes across the world, information flow will reach many new recipients to improve the world's health conditions!
Health research, clinical registries, electronic health records – how do they...Koray Atalag
This is a talk I gave at my own organisation - National Institute for Health Innovation (NIHI) of the University of Auckland on 6 Aug 2014. Abstract as follows:
In this talk I’ll first cover the topic of clinical registry – an invaluable tool for supporting clinical practice but also gaining momentum in research and quality improvement. NIHI has been very active in this space: we have delivered the prestigious and highly successful National Cardiac Registry (ANZACS-QI) together with VIEW research team and also very recently launched the Gestational Diabetes Registry with Counties Manukau DHB & Diabetes Projects Trust. A few others are in likely to come down the line. This is a huge opportunity for health data driven research and NIHI to position itself as ‘the health data steward’ in the country given our independent status and existing IT infrastructure and “good culture” of working with health data . NIHI’s ‘health informatics’ twist in delivering these projects is how we go about defining ‘information’ – using a scientifically credible and robust methodology: openEHR. This is an international (and now national too) standard to non-ambiguously define health information so that they are easy to understand and also are computable. We build software (even automatically in some cases!) using models created by this formalism. I’ll give basics of openEHR approach and then walk you through how to make sense out of all these. Hopefully you may have an idea about its ‘value proposition’ (as business people call) or Science merit as I like to call it ;)
di Riccardo Bellazzi
Università di Pavia
ICS Maugerio Pavia
Slide per l'incontro dal titolo "Big data, machine learning e medicina di precisione."
10 maggio 2018, Milano, Fondazione Giannino Bassetti
Video integrale: https://www.fondazionebassetti.org/it/focus/2018/08/big_data_machine_learning_e_me.html
I gave this talk in the "Presidential Symposium" at the annual meeting of the American Association of Physicists in Medicine, in Annaheim, California. The President of AAPM, Dr. Maryellen Giger, wanted some people to give some visionary talks. She invited (I kid you not) Foster, Gates, and Obama. Fortunately Bill and Barack had other commitments, so I did not need to share the time with them.
Similar to Translational Biomedical Informatics 2010: Infrastructure and Scaling (20)
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
Pulmonary Thromboembolism - etilogy, types, medical- Surgical and nursing man...VarunMahajani
Disruption of blood supply to lung alveoli due to blockage of one or more pulmonary blood vessels is called as Pulmonary thromboembolism. In this presentation we will discuss its causes, types and its management in depth.
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
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.
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
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
These lecture slides, by Dr Sidra Arshad, offer a quick overview of physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar leads (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
Report Back from SGO 2024: What’s the Latest in Cervical Cancer?bkling
Are you curious about what’s new in cervical cancer research or unsure what the findings mean? Join Dr. Emily Ko, a gynecologic oncologist at Penn Medicine, to learn about the latest updates from the Society of Gynecologic Oncology (SGO) 2024 Annual Meeting on Women’s Cancer. Dr. Ko will discuss what the research presented at the conference means for you and answer your questions about the new developments.
Report Back from SGO 2024: What’s the Latest in Cervical Cancer?
Translational Biomedical Informatics 2010: Infrastructure and Scaling
1. “Translational Biomedical Informatics 2010: Infrastructure and Scaling”Brian Athey and the NCIBI and CTSA Teamsbleu@mich.edu and NCIBI.org Center for Computational Medicine and Bioinformatics Michigan Institute for Clinical and Health Research University of Michigan
4. Effective linkages with better environmental, dietary, and behavioral datasets for eco-genetic analyses
5. Credible privacy and confidentiality protections in research and clinical care
6. Breakthrough tests, vaccines, drugs, behaviors, and regulatory actions to reduce health risks and cost-effectively treat patients globally.Omenn and Athey, 2010
8. Disease Specific View: Prostate Cancer ProgressionIntegration Strategy Chris Maher, Jim Cavalcoli, and Gil Omenn
9. Models we Must Consider (with IT Implications elaborated upon) Eric E. Schadt “Molecular networks as sensors and drivers of common human diseases”. (2009). Nature 461, 218-223. doi:10.1038/nature08454
13. Scope of Applications in CTSA InformaticsWe must focus on the elements in Red **Interoperability with Institutional EHR Systems Clinical transaction systems Clinical Data Repository (CDR) De-identification/Honest Brokering Tools to Facilitate Extracting/Downloading Data Software tools CTSI Portals, Research Networks, and Resource Directories **Clinical Trial/Study Databases **Genomic, Proteomic, and Metabolomic High-Throughput Data Repositories and Analysis Tools Clinical Imaging Data Repositories and Analysis Tools **An Institutional Specimen Tracking System--Biorepository A CTSA Core Lab LIMS (Laboratory Information Management System) Population/Public Health Databases & Informatics Needs **Standards to promote Interoperation within and between CTSA sites Informatics Teaching & Training (Interface with CTSA Education Program) Biomedical Informatics Research in Support of C&T Research Faculty, Staff, and Administrative Structure for Biomedical Informatics **Indicates MICHR/CTSA Priorities CTSA Informatics Consortium Operations Committee Bill Hersh (OSHU) and Brian Athey (UMICH), co-chairs. 2007
34. Biomedical Informatics View of ITData Warehouse 101 13 Operational Management (Historical. e.g. quality, billing, reporting etc.) Patient Care (Electronic Health Record) Biomedical Research External Organizations External Organizations Population Research Multiple Clinical Systems Trials Research Warehouse -De-Identification -Consents -Identity Management -Vocabulary Mapping Clinical Data Warehouse Clinical Data Repository Quality Reports Comparative Effectiveness Research Administration Systems ‘Omics Repository
35. Data Warehouse 102 Syntax Alignment Collection Aggregation Entity Alignment Source Data Clinical Data Repository HSDW Patient Data Aggregation Operational Reporting systems EMAR EMR Billing Meds - Insulin EMAR Diagnosis – ICD9 Labs Billing Data Warehouse Environment Lab – A1C>9 Mapping/Pointers Claims Patient Diagnosis(part of the patient subject) Labs Ontologies, Coding, Integration Velos Integration Domain Alignment (complete semantic and syntactic alignment)
36. Architecture today Privacy Walls Patient Care Systems Biomedical Research Reporting IRB HSDW HIPAA ICU Systems Financial Reporting Cohort Discovery Registration/ ADT OR Systems Public Reporting Patient Scheduling Hospital EMR Population Research Quality Data Warehouse (New) Departmental Applications Comparative Effectiveness Research Abstraction Transcription Health Services Research Clinical Data Repository (CDR) CPT Billing ICD-9 Non-Cancer Research Claims EMPI CommonRule Patient Health Record Cancer Research Genomic Research Imaging Ambulatory EMR Patient Recruitment Consent Clinical Trial Management System High Throughput ‘Omics LIS Lab Systems Tissue Bank Clinical Reporting Clinical Transactions Research Activities Research Transactions Coding Last Modified 5-14-2010
37. High Level Conceptual Diagram—Future State Research Interface with the Clinical Record Common Specimen Identifier Services Terminology mapping systems Transactional Research Systems Analytic Research Systems Laboratory Research/ ‘Omics’ Clinical POC systems Patient , Participant & Provider Facing Systems Epic Legacy Epic Systems (Ambulatory Care, Emerg. Med, etc.) CareLink/ Eclipsys Ambulatory Research EDC Systems Careweb, Carelink - Eclipsys, etc. Research AdministrationSystems Research Adminis-trative Systems Clinical Research Electronic Data Capture Systems Next-Gen Sequencing Laboratory and Shared Facility Data Capture Disease-Specific Disease & Site specific data marts Emergency Med. Patient & Participant Portals, Health Information Exchanges, etc. Pharmacy Revenue Cycle RedCAP ChIP-Seq Demographics Patient & Participant Portal(s) Assay-Specific ClickCommerce (IRB) Pathology Velos Organ Systems Metabolomics Scheduling Radiology Population-Risks OpenCLinica eThority (billing) Proteomics Nursing Docs Qual/Outcomes Biorepositories Health Information Exchange Epic Clarity Research Data Warehouse CDR Note: Use of specific technologies not implied at this time Honest Broker & Security Infrastructure Messaging Bus & ETL Services Terminology Resources (ICD-10, caDSR, SNOMED, etc.) External Resources (PubMed, GenBank, KEGG, GO, etc.) Data Sharing with External Collaborators Health Sciences Library Resources International Cohort Discovery & Data Mining Genomics data workbench(es) caBIG CTSAs TCGA
38. High Level Conceptual Diagram Research Interface with the Clinical Record Common Specimen Identifier Services Terminology mapping systems Transactional Research Systems Analytic Research Systems Laboratory Research/ ‘Omics’ Clinical POC systems Patient , Participant & Provider Facing Systems Epic Legacy CareLink/ Eclipsys Ambulatory Research EDC Systems Research AdministrationSystems Next-Gen Sequencing Disease-Specific Emergency Med. Pharmacy Revenue Cycle RedCAP ChIP-Seq Demographics Patient & Participant Portal(s) Assay-Specific ClickCommerce (IRB) Pathology Velos Organ Systems Metabolomics Scheduling Radiology Population-Risks OpenCLinica eThority (billing) Proteomics Nursing Docs Qual/Outcomes Biorepositories Health Information Exchange Epic Clarity Research Data Warehouse CDR Note: Use of specific technologies not implied at this time Honest Broker & Security Infrastructure Messaging Bus & ETL Services Terminology Resources (ICD-10, caDSR, SNOMED, etc.) External Resources (PubMed, GenBank, KEGG, GO, etc.) Data Sharing with External Collaborators Health Sciences Library Resources International Cohort Discovery & Data Mining Genomics data workbench(es) caBIG CTSAs TCGA
39. High Level Conceptual Diagram Research Interface with the Clinical Record Common Specimen Identifier Services Data from structured clinical research activities, including phase 1, 2, and 3 clinical trials and various domain specific registries is fed via ETL or real time processes to the research data warehouse Terminology mapping systems Transactional Research Systems Analytic Research Systems Laboratory Research/ ‘Omics’ Clinical POC systems Patient , Participant & Provider Facing Systems Epic Legacy CareLink/ Eclipsys Ambulatory Research EDC Systems Research AdministrationSystems Next-Gen Sequencing Disease-Specific Emergency Med. Pharmacy Revenue Cycle RedCAP ChIP-Seq Demographics Patient & Participant Portal(s) Assay-Specific ClickCommerce (IRB) Pathology Velos Organ Systems Metabolomics Scheduling Radiology Population-Risks OpenCLinica eThority (billing) Proteomics Nursing Docs Qual/Outcomes Biorepositories Health Information Exchange Epic Clarity Research Data Warehouse CDR Note: Use of specific technologies not implied at this time Honest Broker & Security Infrastructure Messaging Bus & ETL Services Terminology Resources (ICD-10, caDSR, SNOMED, etc.) External Resources (PubMed, GenBank, KEGG, GO, etc.) Health Sciences Library Resources Data Sharing with External Collaborators International Cohort Discovery & Data Mining Genomics data workbench(es) caBIG CTSAs TCGA
40. High Level Conceptual Diagram Research Interface with the Clinical Record Common Specimen Identifier Services Terminology mapping systems Transactional Research Systems Analytic Research Systems Laboratory Research/ ‘Omics’ Clinical POC systems Patient , Participant & Provider Facing Systems Data capture from biorepository, laboratory, core facility support systems feeds data to the Enterprise Research Data Warehouse. This allows us to capture data about cellular and molecular-scale phenotypes, and to integrate genomics and other ‘omic’-scale data. Epic Legacy CareLink/ Eclipsys Ambulatory Research EDC Systems Research AdministrationSystems Next-Gen Sequencing Disease-Specific Emergency Med. Pharmacy Revenue Cycle RedCAP ChIP-Seq Demographics Patient & Participant Portal(s) Assay-Specific ClickCommerce (IRB) Pathology Velos Organ Systems Metabolomics Scheduling Radiology Population-Risks OpenCLinica eThority (billing) Proteomics Nursing Docs Qual/Outcomes Biorepositories Health Information Exchange Epic Clarity Research Data Warehouse CDR Note: Use of specific technologies not implied at this time Honest Broker & Security Infrastructure Messaging Bus & ETL Services Terminology Resources (ICD-10, caDSR, SNOMED, etc.) External Resources (PubMed, GenBank, KEGG, GO, etc.) Health Sciences Library Resources Data Sharing with External Collaborators International Cohort Discovery & Data Mining Genomics data workbench(es) caBIG CTSAs TCGA
41. High Level Conceptual Diagram Research Interface with the Clinical Record Research administration systems provide research billing data to the enterprise data warehouse. Integration of clinical and research billing is now possible at several levels Common Specimen Identifier Services Terminology mapping systems Transactional Research Systems Analytic Research Systems Laboratory Research/ ‘Omics’ Clinical POC systems Patient , Participant & Provider Facing Systems Epic Legacy CareLink/ Eclipsys Ambulatory Research EDC Systems Research AdministrationSystems Next-Gen Sequencing Disease-Specific Emergency Med. Pharmacy Revenue Cycle RedCAP ChIP-Seq Demographics Patient & Participant Portal(s) Assay-Specific ClickCommerce (IRB) Pathology Velos Organ Systems Metabolomics Scheduling Radiology Population-Risks OpenCLinica eThority (billing) Proteomics Nursing Docs Qual/Outcomes Biorepositories Health Information Exchange Epic Clarity Research Data Warehouse CDR Note: Use of specific technologies not implied at this time Honest Broker & Security Infrastructure Messaging Bus & ETL Services Terminology Resources (ICD-10, caDSR, SNOMED, etc.) External Resources (PubMed, GenBank, KEGG, GO, etc.) Health Sciences Library Resources Data Sharing with External Collaborators International Cohort Discovery & Data Mining Genomics data workbench(es) caBIG CTSAs TCGA
42. High Level Conceptual Diagram Research Interface with the Clinical Record Common Specimen Identifier Services Terminology mapping systems Epic/Clarity clinical data warehouse captures clinical phenotypes and time series care events. Data from Clarity and legacy CDR is fed to Research Data Warehouse for analytic, clinical research, and translation research support. Transactional Research Systems Analytic Research Systems Laboratory Research/ ‘Omics’ Clinical POC systems Patient , Participant & Provider Facing Systems Epic Legacy CareLink/ Eclipsys Ambulatory Research EDC Systems Research AdministrationSystems Next-Gen Sequencing Disease-Specific Emergency Med. Pharmacy Revenue Cycle RedCAP ChIP-Seq Demographics Patient & Participant Portal(s) Assay-Specific ClickCommerce (IRB) Pathology Velos Organ Systems Metabolomics Scheduling Radiology Population-Risks OpenCLinica eThority (billing) Proteomics Nursing Docs Qual/Outcomes Biorepositories Health Information Exchange Epic Clarity Research Data Warehouse CDR Note: Use of specific technologies not implied at this time Honest Broker & Security Infrastructure Messaging Bus & ETL Services Terminology Resources (ICD-10, caDSR, SNOMED, etc.) External Resources (PubMed, GenBank, KEGG, GO, etc.) Health Sciences Library Resources Data Sharing with External Collaborators International Cohort Discovery & Data Mining Genomics data workbench(es) caBIG CTSAs TCGA
43. High Level Conceptual Diagram Research Interface with the Clinical Record Common Specimen Identifier Services Terminology mapping systems Transactional Research Systems Analytic Research Systems Laboratory Research/ ‘Omics’ Clinical POC systems Patient , Participant & Provider Facing Systems Epic Legacy Example Scientific Use case: “Show me all HER2+ Br Ca patients who have received Herceptin, are consented for research use of tissue, and who have banked DNA and needle aspirates” CareLink/ Eclipsys Ambulatory Research EDC Systems Research AdministrationSystems Next-Gen Sequencing Disease-Specific Emergency Med. Pharmacy Revenue Cycle RedCAP ChIP-Seq Demographics Patient & Participant Portal(s) Assay-Specific ClickCommerce (IRB) Pathology Velos Organ Systems Metabolomics Scheduling Radiology Population-Risks OpenCLinica eThority (billing) Proteomics Nursing Docs Qual/Outcomes Biorepositories Health Information Exchange Epic Clarity Research Data Warehouse CDR Note: Use of specific technologies not implied at this time Honest Broker & Security Infrastructure Messaging Bus & ETL Services Terminology Resources (ICD-10, caDSR, SNOMED, etc.) External Resources (PubMed, GenBank, KEGG, GO, etc.) Data Sharing with External Collaborators Health Sciences Library Resources International Cohort Discovery & Data Mining Genomics data workbench(es) caBIG CTSAs TCGA
44. Ex: “Show me all HER2+ Br Ca patients who have received Herceptin…” High Level Conceptual Diagram Research Interface with the Clinical Record Common Specimen Identifier Services Terminology mapping systems Transactional Research Systems Analytic Research Systems Laboratory Research/ ‘Omics’ Clinical POC systems Patient , Participant & Provider Facing Systems Epic Legacy CareLink/ Eclipsys Ambulatory Research EDC Systems Research AdministrationSystems Next-Gen Sequencing Disease-Specific Emergency Med. Pharmacy Revenue Cycle RedCAP ChIP-Seq Demographics Patient & Participant Portal(s) Assay-Specific ClickCommerce (IRB) Pathology Velos Organ Systems Metabolomics Scheduling Radiology Population-Risks OpenCLinica eThority (billing) Proteomics Nursing Docs Qual/Outcomes Biorepositories Health Information Exchange Epic Clarity Research Data Warehouse CDR Addressing this use case may require data from all of the systems shown in red… Honest Broker & Security Infrastructure Messaging Bus & ETL Services Terminology Resources (ICD-10, caDSR, SNOMED, etc.) External Resources (PubMed, GenBank, KEGG, GO, etc.) Data Sharing with External Collaborators Health Sciences Library Resources International Cohort Discovery & Data Mining Genomics data workbench(es) caBIG CTSAs TCGA
45. Ex: “Show me all HER2+ Br Ca patients who have received Herceptin…” High Level Conceptual Diagram Research Interface with the Clinical Record Common Specimen Identifier Services Terminology mapping systems Transactional Research Systems Analytic Research Systems Laboratory Research/ ‘Omics’ Clinical POC systems Patient , Participant & Provider Facing Systems Epic Legacy CareLink/ Eclipsys Ambulatory Research EDC Systems Research AdministrationSystems Next-Gen Sequencing Disease-Specific Emergency Med. Pharmacy Revenue Cycle RedCAP ChIP-Seq Demographics Patient & Participant Portal(s) Assay-Specific ClickCommerce (IRB) Pathology Velos Organ Systems Metabolomics Scheduling Radiology Population-Risks OpenCLinica eThority (billing) Proteomics Nursing Docs Qual/Outcomes Biorepositories Health Information Exchange Epic Clarity Research Data Warehouse CDR Ideally, however, the data has been stored in the enterprise research data warehouse, and only the following systems come into play in real time to answer the investigators query. Honest Broker & Security Infrastructure Messaging Bus & ETL Services Terminology Resources (ICD-10, caDSR, SNOMED, etc.) External Resources (PubMed, GenBank, KEGG, GO, etc.) Data Sharing with External Collaborators Health Sciences Library Resources International Cohort Discovery & Data Mining Genomics data workbench(es) caBIG CTSAs TCGA
46. NCIBI/i2b2 Demo Scenario April 2009 … in patient data… Diagnostic Categories Drag/Drop … in Term Navigator… ICD9 to Gene Plugin (new) … in other contexts… ICD9 to Gene Service (new) NCIBI Databases Related genes
48. HIT-Enabled Health ReformAchieving Meaningful Use 2009 2011 2013 2015 HIT-Enabled Health Reform Meaningful Use Criteria HITECH Policies 2011 Meaningful Use Criteria (Capture/share data) 2013 Meaningful Use Criteria (Advanced care processes with decision support) 2015 Meaningful Use Criteria (Improved Outcomes) 27
49. The “other” informatics… Consumer Health Informatics Bioinformatics Clinical Informatics Courtesy, Larry An
50. Sweet Spot Science Bioinformatics Consumer Health Informatics Personalized Clinical Trial Recuitment Tailored Biobank Consent Remote Patient Monitoring for Protocol and Medication Adherence Cohort Discovery and Data Collection Cancer Survivorship Comparative-Effectiveness Research Clinical Informatics Courtesy, Larry An
51. Sweet Spot Science Bioinformatics Consumer Health Informatics Personalized Clinical Trial Recruitment Eligibility Analysis (MICHR, Bioinformatics) MCancer Survey (CCOG, Team Leads 1-7) Clinical trial awareness, offers Registry interest Culturally Sensitive Online Clinical Trial Education (Go-Miami, Hawley)Courtesy, Larry An Clinical Informatics
52. Special Thanks NCIBI Program Officer (PO) – Dr. Karen Skinner, NIDA NCIBI Lead Science Officer (LSO) – Dr. Jane Ye, NLM Director of Bioinformatics and Computational Biology Dr. German Cavelier, NIMH; NCIBI Science Officer Dr. Peter Lyster, NIGMS; Center for Bioinformatics and Computational Biology Director, Center for Bioinformatics and Computational Biology, NIGMS; Dr. Karin Remington Elaine Collier, NCRR NIGMS/NIDA U54-DA-0215191 UL-1RR024986/NCRR CTSA
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