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.
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.
Cracking the Code: When and How to Validate ICD Algorithms for RWEInsideScientific
The availability of real world (e.g., routinely collected) data has allowed researchers to generate massive amounts of evidence on epidemiology, natural history, disease burden, and drug efficacy. However, very few studies conducted with these data use validated code algorithms to identify the study cohort, exposure, or control variables. Even when algorithms are validated, their performance is often suboptimal. Several research groups and government agencies have offered recommendations for when and how algorithms should be validated and how the results should be reported.
Key learning objectives:
- The majority of studies performed with real world data lack adequate algorithm validation.
- Exposures and outcomes algorithms are often more important to validate than population identification algorithms.
- Positive predictive value, while the most often reported validation statistic, may not be the most useful or important one
- Validation of algorithms for rare conditions requires a different approach than for common ones.
- Medical record review remains the only reliable validation method in most cases and cannot be reliably performed with artificial intelligence techniques.
- Validation of code algorithms using accepted methods improves study quality and increases chance of publication acceptance at higher impact journals.
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.
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.
Cracking the Code: When and How to Validate ICD Algorithms for RWEInsideScientific
The availability of real world (e.g., routinely collected) data has allowed researchers to generate massive amounts of evidence on epidemiology, natural history, disease burden, and drug efficacy. However, very few studies conducted with these data use validated code algorithms to identify the study cohort, exposure, or control variables. Even when algorithms are validated, their performance is often suboptimal. Several research groups and government agencies have offered recommendations for when and how algorithms should be validated and how the results should be reported.
Key learning objectives:
- The majority of studies performed with real world data lack adequate algorithm validation.
- Exposures and outcomes algorithms are often more important to validate than population identification algorithms.
- Positive predictive value, while the most often reported validation statistic, may not be the most useful or important one
- Validation of algorithms for rare conditions requires a different approach than for common ones.
- Medical record review remains the only reliable validation method in most cases and cannot be reliably performed with artificial intelligence techniques.
- Validation of code algorithms using accepted methods improves study quality and increases chance of publication acceptance at higher impact journals.
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
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
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?
Enabling Clinical Data Reuse with openEHR Data Warehouse EnvironmentsLuis Marco Ruiz
Modern medicine needs methods to enable access to data,
captured during health care, for research, surveillance,
decision support and other reuse purposes. Initiatives like the
National Patient Centered Clinical Research Network in the
US and the Electronic Health Records for Clinical Research
in the EU are facilitating the reuse of Electronic Health
Record (EHR) data for clinical research. One of the barriers
for data reuse is the integration and interoperability of
different Healthcare Information Systems (HIS). The reason is
the differences among the HIS information and terminology
models. The use of EHR standards like openEHR can alleviate
these barriers providing a standard, unambiguous,
semantically enriched representation of clinical data to
enable semantic interoperability and data integration. Few
works have been published describing how to drive
proprietary data stored in EHRs into standard openEHR
repositories. This tutorial provides an overview of the key
concepts, tools and techniques necessary to implement an
openEHR-based Data Warehouse (DW) environment to reuse
clinical data. We aim to provide insights into data extraction
from proprietary sources, transformation into openEHR
compliant instances to populate a standard repository and
enable access to it using standard query languages and
services
Problems such as inaccurate diagnoses and poor drug-adherence pose challenges to individual health and safety. These challenges are now being alleviated with big data analytics using personalized drug regimes, follow-up alerts and real-time diagnosis monitoring.
In this paper, learn how predictive analytics is helping healthcare industry with technologies such as Clinical Decision Support, Medical Text Analysis and Electronic Health Record (EHR).
Zeta Research contract research organisation clinical studiesZeta Research
cro4Q is the Zeta Research's CRO (Contract Research Organization) registered at AIFA (Italian Medicines Agency) that develops professional researches in the scientific and statistics field. Since over a decade Zeta Research is offering scientific, technical and statistics consulting services for the medical and clinical sectors.
cro4Q offers complete service that follow experiments, clinical and pharmaceutical researches in the following sectors: medicine, biomedicine, biotechnology and medical devices.
Quality support and tools are offered to the client: methodological and normative adequacy, protocol design, biostatistical services for protocol (SAP), data management, statistical analysis, medical writing and reporting.
A presentation given at the Duke Margollis Health Policy meeting in 2015 and providing insights into the current challenges related to EHR data quality. Proposes a new approach - OneSource.
Improvement Story session at the 2013 Saskatchewan Health Care Quality Summit. For more information about the summit, visit www.qualitysummit.ca. Follow @QualitySummit on Twitter.
The implementation and on-going enhancement of the eHealth Saskatchewan Clinical Portal to complement existing systems to support improved health care province-wide through electronic access to important clinical information.
Better Health
Kevin Kidney
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
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
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?
Enabling Clinical Data Reuse with openEHR Data Warehouse EnvironmentsLuis Marco Ruiz
Modern medicine needs methods to enable access to data,
captured during health care, for research, surveillance,
decision support and other reuse purposes. Initiatives like the
National Patient Centered Clinical Research Network in the
US and the Electronic Health Records for Clinical Research
in the EU are facilitating the reuse of Electronic Health
Record (EHR) data for clinical research. One of the barriers
for data reuse is the integration and interoperability of
different Healthcare Information Systems (HIS). The reason is
the differences among the HIS information and terminology
models. The use of EHR standards like openEHR can alleviate
these barriers providing a standard, unambiguous,
semantically enriched representation of clinical data to
enable semantic interoperability and data integration. Few
works have been published describing how to drive
proprietary data stored in EHRs into standard openEHR
repositories. This tutorial provides an overview of the key
concepts, tools and techniques necessary to implement an
openEHR-based Data Warehouse (DW) environment to reuse
clinical data. We aim to provide insights into data extraction
from proprietary sources, transformation into openEHR
compliant instances to populate a standard repository and
enable access to it using standard query languages and
services
Problems such as inaccurate diagnoses and poor drug-adherence pose challenges to individual health and safety. These challenges are now being alleviated with big data analytics using personalized drug regimes, follow-up alerts and real-time diagnosis monitoring.
In this paper, learn how predictive analytics is helping healthcare industry with technologies such as Clinical Decision Support, Medical Text Analysis and Electronic Health Record (EHR).
Zeta Research contract research organisation clinical studiesZeta Research
cro4Q is the Zeta Research's CRO (Contract Research Organization) registered at AIFA (Italian Medicines Agency) that develops professional researches in the scientific and statistics field. Since over a decade Zeta Research is offering scientific, technical and statistics consulting services for the medical and clinical sectors.
cro4Q offers complete service that follow experiments, clinical and pharmaceutical researches in the following sectors: medicine, biomedicine, biotechnology and medical devices.
Quality support and tools are offered to the client: methodological and normative adequacy, protocol design, biostatistical services for protocol (SAP), data management, statistical analysis, medical writing and reporting.
A presentation given at the Duke Margollis Health Policy meeting in 2015 and providing insights into the current challenges related to EHR data quality. Proposes a new approach - OneSource.
Improvement Story session at the 2013 Saskatchewan Health Care Quality Summit. For more information about the summit, visit www.qualitysummit.ca. Follow @QualitySummit on Twitter.
The implementation and on-going enhancement of the eHealth Saskatchewan Clinical Portal to complement existing systems to support improved health care province-wide through electronic access to important clinical information.
Better Health
Kevin Kidney
Healthstory Enabling The Emr Dictation To Clinical DataNick van Terheyden
EHRs are database centric while medical records are document centric. The conventional wisdom is that documents are bad and discrete data is good. Historically, clinicians have resisted efforts to establish structured data standards for dictated reports. This lack of an industry-wide standard for report content and format confounds interoperability efforts. For nearly two decades, information system specialists have attempted to impose new documentation methods that are more suited to database management but do not meet the needs of the practicing physician. Achieving physician buy-in for electronic record systems that do not accommodate narrative documentation methods such as dictation and transcription has proven to be quite difficult for many EHR vendors
The Health Story Project (formerly the CDA4CDT initiative Clinical Document Architecture for Common Data Types) is an alliance of organizations that have been working together with HL7 for nearly two years to develop and publish data standards for electronic clinical documents. The initiative is based on Clinical Document Architecture (CDA) - a balloted HL7 document markup standard that specifies the structure and semantics of a clinical document for the purpose of exchange. Document templates for the most commonly dictated report types (H&P, Consult, Operative Note, etc) specify required and optional headings. Templates are developed based on prevailing practice and establish consensus on content and format
Healthstory Enabling The Emr - Dictation To Clinical DataNick van Terheyden
EHRs are database centric while medical records are document centric. The conventional wisdom is that documents are bad and discrete data is good. Historically, clinicians have resisted efforts to establish structured data standards for dictated reports. This lack of an industry-wide standard for report content and format confounds interoperability efforts. For nearly two decades, information system specialists have attempted to impose new documentation methods that are more suited to database management but do not meet the needs of the practicing physician. Achieving physician buy-in for electronic record systems that do not accommodate narrative documentation methods such as dictation and transcription has proven to be quite difficult for many EHR vendors.
The Health Story Project (formerly the CDA4CDT initiative Clinical Document Architecture for Common Data Types) is an alliance of organizations that have been working together with HL7 for nearly two years to develop and publish data standards for electronic clinical documents. The initiative is based on Clinical Document Architecture (CDA) - a balloted HL7 document markup standard that specifies the structure and semantics of a clinical document for the purpose of exchange. Document templates for the most commonly dictated report types (H&P, Consult, Operative Note, etc) specify required and optional headings. Templates are developed based on prevailing practice and establish consensus on content and format
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.
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.
Building Data Driven Workflows in HIM: More than just an EHRJenniferTen22
You'll gain a deeper understanding of EHR’s data demands and clinical intelligence limitations by understanding how NLP harmonizes clinical information, structured and unstructured.
Registry Participation 101: A Step-by-Step Guide to What You Really Need to K...Wellbe
– Is your hospital contemplating joining a registry but you don’t know where to begin?
– Do the acronyms CJR, QCDR, and PROMs cause you angst?
– Have you heard that registry participation can count towards quality programs but you don’t understand the connection?
– Are you a surgeon needing a registry to meet Meaningful Use requirements?
– Are you in one of the 67 geographical areas mandated by the CMS’s Comprehensive Care for Joint Replacement (CJR) program?
– Is your hospital considering a patient-reported outcome measure (PROMs) program and you want to know more about what that entails?
If so, the American Joint Replacement Registry (AJRR) will walk you through everything you need to know about participating in a registry. This session will focus on best practices from over 4,500 surgeons and 675+ hospitals who have successfully implemented and engaged with the data from over 400,000 hip and knee replacement procedures. AJRR will help you to debunk the myth that submitting private health information is complicated, time consuming, and that it takes hundreds of man-hours to participate in a registry.
You’ll also learn how:
• Registry participation can support mandated quality programs – including Meaningful Use, CJR, and PQRS
• To implementing a PROM system in your hospital – what to look out for when starting and helpful tips from current users on what they have learned
• Not all data elements are mandatory – what are the different levels, what does the national registry require, and what is optional
About the Speakers:
Joe Greene is currently the Program Manager of Outreach and Development for the University of Wisconsin Hospital and Clinics in the Department of Orthopedics and Rehabilitation. In this role, Joe coordinates business and philanthropic development activities for the UW Hospital department and University of Wisconsin Department of Orthopedics and Rehabilitation. He represents the needs of all orthopedic subspecialties and has worked for the UW since 1991 when he initiated his career there as an athletic trainer and clinician. He has worked in management and administration across the Department since 1997.
In addition to his role with the UW Hospital, Joe also is the CEO and Owner of OrthoVise. OrthoVise is an Orthopedic advisory firm that assists orthopedic practices of all types with operational and business development needs. His experiences have allowed him and his advisors the opportunity to consult formally with orthopedic practices since 2010. He has particular areas of interest that include Orthopedic and Sports Medicine Program Business Development, Service Line Development, Health Information Technology and EMR Operational Optimization for Orthopedics, Innovative Service Delivery Implementation, Smart Staffing, and Workflow Enhancement.
Joe will be joined by AJRR staff who are experts in guiding individual surgeons and hospital orthopaedic service line directors through the process.
HETT Conference Olympic Central 2014 Integrating Healthcare DeliveryElmar Flamme
Integrating Healthcare Delivery through the Innovative Use of Information & Technology - A user story from behind the CONTENT covered mountains and the deep
BIG DATA forest
Enabling Clinical Data Reuse with openEHR Data Warehouse EnvironmentsLuis Marco Ruiz
Databases for Clinical Information Systems are difficult to
design and implement, especially when the design should be
compliant with a formal specification or standard. The
openEHR specifications offer a very expressive and generic
model for clinical data structures, allowing semantic
interoperability and compatibility with other standards like
HL7 CDA, FHIR, and ASTM CCR. But openEHR is not only
for data modeling, it specifies an EHR Computational
Platform designed to create highly modifiable future-proof
EHR systems, and to support long term economically viable
projects, with a knowledge-oriented approach that is
independent from specific technologies. Software Developers
find a great complexity in designing openEHR compliant
databases since the specifications do not include any
guidelines in that area. The authors of this tutorial are
developers that had to overcome these challenges. This
tutorial will expose different requirements, design principles,
technologies, techniques and main challenges of implementing
an openEHR-based Clinical Database, with examples and
lessons learned to help designers and developers to overcome the challenges more easily
Speech Understanding Dictation To Clinical Data - TEPR 2009Nick van Terheyden
Speech Understanding automatically converts the spoken work into structured and encoded clinical data that provides access to relevant diagnostic support, evidence based medicine and real time alerts.
Unlocking the data tucked away in the vast mountain of documents produced as part of delivering care to patients is possible today with Speech Understanding, the next generation of speech recognition technology that not only improves the overall efficiency of the documentation process by producing higher quality, more accurate clinical data but also produces structured encoded clinical data that can populate EMR’s that are crying out for high quality input. This information is encoded using the HL7’s Clinical Document Architecture (CDA) and Common Document Types (CDA4CDT).
With knowledge of the meaning the output from Speech Understanding is now able to identify concepts, organize documents into meaningful categories and create a semantically interoperable document .
As the author of “Big Data in Healthcare Hype and Hope,” Dr. Feldman has interviewed over 180 emerging tech and healthcare companies, always asking, “How can your new approach help patients?” Her research shows that data, as an enabling tool, has the power to give us critical new insights into not only what causes disease, but what comprises normal. Despite this promise, few patients have reaped the benefits of personalized medicine. A panel of leading big data innovators will discuss the evolving health data ecosystem and how big data is being leveraged for research, discovery, clinical trials, genomics, and cancer care. Case studies and real-life examples of what’s working, what’s not working, and how we can help speed up progress to get patients the right care at the right time will be explored and debated.
• Bonnie Feldman, DDS, MBA - Chief Growth Officer, @DrBonnie360
• Colin Hill - CEO, GNS Healthcare
• Jonathan Hirsch - Founder & President, Syapse
• Andrew Kasarskis, PhD - Co-Director, Icahn Institute for Genomics & Multiscale Biology; Associate Professor, Genetics & Genomic Studies, Icaahn School of Medicine at Mt. Sinai
• William King - CEO, Zephyr Health
New York eHealth Collaborative Digital Health Conference
November 18, 2014
An overview of big data in clinical research. Discussion of big data related to real world evidence (RWE), wearable sensor data (IoT), and clinical genomics. Introduces the use of map-reduce infrastructure for big data in biomedicine.
Independent forces on the biomedical ecosystem is causing a convergence of care, quality measurement, and clinical research at the point of care. The presentation outlines some of the informatics implications of this convergence.
Modern society is highly dependent on the provisioning of clean water, healthy and plentiful food, breathable air, and prompt intervention to curtail disease outbreaks. The public health system is critical in supporting these activities. Today’s information technology provides public health practitioners key capabilities in maintaining the health of the population. This lecture will provide a basic foundation of knowledge about public health practice for clinical informaticians, and highlight specialized information systems and data standards used in public health today. We will explore the existing public health informatics infrastructure including surveillance systems, the process of electronic laboratory reporting (ELR) of notifiable diseases, vital statistics systems, and the critical importance of GIS systems in the public health
Quantum computing is an emerging new theory of computation based on the principles of quantum mechanics. It is the basis for a fundamentally new information processing model that is garnering increasing attention in the media and from commercial information technology companies. In certain computing tasks, it can theoretically arrive at a solution more efficiently than classical computers. In this session, we explore the basic principles behind quantum computing, including qubit superposition and entanglement -- the basis for quantum parallelism. We explore quantum logic gates as an abstracted representation of underlying hardware and discuss a simple quantum gate circuit that demonstrates parallelism. We also review the current state of the technology and what has been demonstrated compared to what is theoretically predicted. Current trends in the quantum computing industry will be presented along with proposed possible uses in biomedical informatics.
A brief overview of a 2017 project to integrate EHRs and EDRS systems to improve vital event data collection, as well as transmission of the vital event data using HL7.
An introduction to quantum computing, its history and evolution from concept to commercial quantum computer, and an overview of relevant use in biomedical informatics and medice
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
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
- 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
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.
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
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.
The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
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
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
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.
The OneSource Initiative: An Approach to Structured Sourcing of Key Clinical Data (for personalized care)
1. THE ONESOURCE INITIATIVE: AN APPROACH
TO STRUCTURED SOURCING OF KEY
CLINICAL DATA (FOR PERSONALIZED CARE)
Michael Hogarth, MD, FACP, FACMI
Professor, Internal Medicine
Professor and Vice Chair, Dept. of Pathology and Laboratory Medicine
http://www.hogarth.org
mahogarth@ucdavis.edu
2. Tailoring Care to Biology, Patient Preference,
and Clinical Performance
Personalized Medicine is the art of . . . .
3. Two Similar People Are Not . . .
Veronica
Age 51, 1 cm tumor
Screen detected
Works for Walmart
Post menopausal
Grade 1 ER+ tumor
No family history
Kim
Age 51, 1 cm tumor
Found on self exam
Self employed consultant
Recently divorced, single
mom
BRCA carrier
Pre menopausal
Grade 3 triple negative
tumor
Positive nodes
4. Key Elements to Provision Personalized Care
• Learning Healthcare Systems
– Systems that can evaluate care and continue to improve it
• Ability to adapt care based on data
– Personal level
– Systems level
• Precision Medicine:
– understand the disease and the host’s biology;
• To be more “precise” on disease and host response (precision
medicine!)
6. But… Sourcing clinical data has not changed with EHR use
- we continue to be burdened by “manually abstracting data”
6
“Where is that ER/PR Result?”
“Where is that outside MRI?”
“Did the path show invasion?”
”Where is that MammaPrint report?” post-EHR
1907 – ~today
(pre-EHR)
“Where is that ER/PR Result?”
“Where is that outside MRI?”
“Did the path show invasion?”
”Where is that MammaPrint report?”
8. Peeking inside a HIMSS Stage-7 EHR’s data
75% of records
have unknown race?
Nobody is older than 85?
(1) Only have dx for pts. admitted after
1984?
(2) Someone is pre-admitted for 2020....
35 million procedures are “unknown” type?
We have a procedure for someone
To be admitted 12 years from now
Only 659,000 records have a diagnosis
(UCDHS repository profiling 2014)
11. We need better data –
but can we do it?
Survey of 845 primary care providers
“48min loss of free time per clinic day per
physician”
information finding
takes time because
notes are bloated
and “new” or “key”
data is hard to find...
I don’t have time, so
I will cut & paste...
12. The EHR “clinical documentation conundrum”
o The Electronic Documentation Conundrum – “I have met the
enemy and it is I...”
• We EHR-using physicians generate large, verbose, narrative notes that
include auto-inserted unnecessary text (lab values, Rad reports) leading
to very poor readability
(the “note bloat” phenomenon)
• We physicians spend significant time foraging for key pieces of data in
our “bloated notes” which negatively impacts clinical care and physician
productivity
o The extent of productivity loss in US practices implementing EHRs
• A survey of 9 family practice physicians at 1 academic medical center.
Providers had 2+ years of experience with an EHRs
average 46 min of free time list per clinic day per physician
• Survey of 410 Internists (JAMA 2014- McDonald)
average of 42 min loss of free time per clinic day per physician
(1) http://www.redwoodmednet.org/projects/events/20130725/rwmn_20130725_mcdonald_v2.pdf
(2) McDonald, McDonald. Arch Intern Med. 2012. Feb 13;172(3):285-7
16. INSPIRE’s Vision
“Improve the acquisition and
exchange of patient data in high
impact conditions in order to support
care coordination, practice
improvement, and longitudinal
disease registries”
17. INSPIRE Approach to Data Capture
Providers fill out “electronic forms”
(templates) at key points of care
Ask providers to enter structured data
for key data elements only
Data is used for structured
sharing/exchange
(caCCD, CAP eCC, CDC-CDA)
18. A breast cancer “e-checklist”
(xml form/template rendered by system)
19. The idea is not new….
The CAP Electronic checklists XML Specification (eCC XML) c2013
20. Data Element Selection for Checklist
Clinical Data
Critical
Clinical
Data
Clinical Trial
Data
ASCO
Data
• Clinical Dataset captured on all
patients
• Identify subset that is critical
for decision making, reporting
– Elements vetted by over 50
clinicians across the UC
Medical System for clinical
and research importance
– Re-vetted by 50 clinicians for
functionality, adoption and
workflow
• Compare against Community
Data Standards
– ASCO, CAP, Cancer Registry,
NCI CTEP Common Data
Elements (for Clinical Trials)
21. INSPIRE Demonstration Project OneSource
‘capturing and exchanging key clinical data for care coordination in high impact conditions’
EHR
SDC
XML
Dynamic Form for Data Capture
(XML-driven and questionnaire like
with skip/branch, etc…. Rendered *within* EHR)
EHR 1
EHR 2
State Central
Cancer Registry
Community
HIE
Repository
ASCO/HL7
BTPS
ASCO/HL7
BTPS
ASCO/HL7
BTPS
ASCO/HL7
BTPS
23. Key Components of the
OneSource Framework
1) A data element core set for high impact
condition
2) OneSource forms delivered to EHR as
Structured Data Capture XML (SDC-XML)
3) OneSource form is loaded and rendered by
the primary system (EHR, CTMS, mobile, etc..)
4) OneSource forms can be co-authored by
clinicians on a single shared form viewable in
the primary system
23
27. OneSource: What are we really talking about?
• Fundamentally changing the documentation style
and data sourcing in EHRs!!
– Value based payment models will provide an
opportunity to think differently about
documentation
– Value-based documentation vs. volume-based
documentation
• “Clinical Data Checklists”
– A co-authored structured data documentation “tab”
in the EHR record
28. Will OneSource “clinical data checklists” just
cause more clinician angst?
• No, because OneSource forms have shared
authorship, which will decrease documentation time
• No, because OneSource data forms put key data in
one place in the chart – making it EASIER to find!
• No, because the clinical checklist has real value to
the clinician
– The effort is rewarded if clinicians document this way for
all patients with high impact conditions
– OneSource for “key data” – makes it EASIER to provide
good care
29. ONC SDC and IHE RFD –
“Remote Structured Forms”
RFD
SDC Additions
CRD DSC BFDR …
ONCSDC
Athena
core data
elements
30.
31. OneSource I-SPY2 Pilot – QoL ePatient; Clinical Data Checklist,
Structured Pathologist forms, Structured Radiologist form
Patient
Data
Checklist
Library
Store
Select
Pre-populate
Load
Submit
Archive
Import
32. The relevance of OneSource
in Clinical Trials
• ~25% of the cost of an investigational drug
phase III clinical trial is in the *verification* of
source data against what is being recorded in
the CRF and reported to FDA
• FDA eSource guidance allows data to be
directly submitted from EHR into a “eCRF”
with appropriate audit trail/controls
33. Clinical Trial Costs Breakdown
Operational costs of a trial are 80% of the cost of a
trial
30% of operational costs are source verification
In a $100M Phase III trial – source verification = $24M
34. OneSource and FDA eSource
FDA– “source data should be attributable, legible,
contemporaneous, original, and accurate (ALCOA)”
When using CRFs, best practice is for a first pass data
entry followed by a second pass or verification step by
an independent operator
FDA eSource guidance – Final Guidance, Jan 29, 2014
To encourage direct transmission of data from EHR record
to the eCRF
To encourage eCRFs vs. “paper”
If one sources data according to eSource guidance, source
verification/validation is not required
35.
36. Our Quest for High Quality Structured Data Does
not End with the EHR
• EHRs are an authoritative source of clinical care data
• BUT, EHRs are NOT the only source of the needed patient
data, now and even more so in the future
• A single source of truth is constructed from multiple
authoritative data sources:
Data
Source A
Data
Source D
Data
Source C
Data
Source B
VIEW
OF THE
TRUTH
37. Patient as a Source of Data -- Engaging Patients
Implementing Electronic Patient Reported Data (ePRI)
Athena Breast Health Network Screening Cohort
- 5 UC med centers, Sanford
- To date: 90,000+ questionnaires of women undergoing screening mammograms
- Automated risk models as a web service
- Composite 15yr risk of breast cancer provided to PCP
- Risk report fully integrated with EHR record
- High-risk referred to genetic counseling
38. 38
100,000 women
① Screening/Prev:
- ePRI
- breast cancer risk
② Dx/Rx/Survivorship
- EHR data, ePRI
- structured data
- comparative eff
- clin trials matching
One Longitudinal and Four Point-in-Time Schemas
The OneSource System lets a user complete a checklist containing a patient’s health history. The checklist data are represented in five domain models: SDC, HQS, FHIR, ODM and TRANSCEND.
The FHIR data model stores longitudinal health histories. In contrast, the other four schemas only support point-in-time data. The data flow from checklist selection to submission follows:
Structured Data Capture Schema (SDC)
The Office of the National Coordinator SDC standard specifies how forms can be exchanged between systems. SDC specifies both how forms are exchanged and the information in each exchange.
OneSource conforms to the IHE SDC schema and supports the following SDC Actors:
Form Filler completes a form for a particular patient
Form Processor provides an empty form and collects a completed form
Form Archiver archives a completed form
Health Questionnaire System Schema (HQS)
The Athena Breast Health Network collects patient questionnaires for breast cancer prevention and treatment. Athena HQS employs its own schema to represent the questions, answer choices and skip logic in each questionnaire. An Athena component named the Q-Engine (“Questionnaire Engine”) determines the question sequence from the skip logic.
The OneSource Form Filler leverages the Athena Q-Engine technology to render its checklists. Each checklist is translated from the SDC schema to the HQS schema. Completed checklists are translated from the HQS schema back to the SDC schema.
HL7 Fast Healthcare Interoperability Resources Schema (FHIR)
The HL7 FHIR data model represents longitudinal patient health history in a set of context-aware resources.
The OneSource Form Processor persists patient histories using the FHIR data model. Each new checklist pre-populates previously answered questions from the FHIR model. The pre-populated checklist is transformed into the SDC schema. Later, the completed checklist is translated from the SDC schema back to the FHIR model and persisted in the OneSource Form Processor.
The FHIR data model in the OneSource Form Processor is represented by a set of Salesforce custom objects. These FHIR custom objects are provided by the Salesforce Health Cloud app.
CDISC Operational Data Model Schema (ODM)
The CDISC ODM standard specifies how a clinical research Case Report Form (CRF) is exchanged between Electronic Data Capture (EDC) systems.
The OneSource Form Archiver persists each completed checklist as an immutable document. Checklists that represent clinical research are translated from the SDC schema into the ODM schema. ODM data elements are annotated with variable names from the Clinical Data Acquisition Standards Harmonization (CDASH) standard. Each therapeutic area has its own set of CDASH variable names.
I-SPY 2 TRANSCEND Schema
The TRANSCEND EDC system collects health information of participants in the I-SPY 2 clinical trial. This information is persisted in Salesforce relational custom objects that represent CRFs.
The TRANSCEND EDC system will populate its CRFs from OneSource checklists. TRANSCEND transforms each completed checklist sent by the OneSource Form Archiver from the ODM schema to the TRANSCEND schema. It then stores the checklist data into TRANSCEND CRF custom objects.