The Need for a Global Language - SNOMED CT Introduction
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Microsoft Amalga the Unified Intelligence System
m Microsoft Amalga the Unified Intelligence System > Turn information into health intelligence and critical knowledge PG 02 MICROSOFT AMALGA MICROSOFT AMALGA PG 03 Our vision: For more than a decade, Microsoft has invested significant time and resources into understanding the needs of healthcare organizations. We are developing solutions that To improve health encompass both the provider and the consumer to help you achieve your goals from better patient care to improving the financial health of your organization. We believe around the world the issues that Microsoft is best positioned to address focus on healthcare information management—getting the right data in front of the right people in the right way at the right time. That’s why we’re working to speed and improve the capture, manipulation, aggregation, and presentation of healthcare data by offering a family of integrated IT systems for the healthcare enterprise. The Microsoft® Amalga™ Family of Enterprise Health Systems is built on Microsoft technology, offering a comprehensive range of solutions to meet the needs of your health enterprise. Microsoft Amalga Microsoft Amalga, the new version of the product formerly known as Azyxxi, is the Unified Intelligence System that allows hospital enterprises to unlock the power of all their data sitting in clinical, financial, and administrative silos. Without replacing current systems, Amalga offers leading-edge institutions an innovative way to capture, consoli- date, store, access, and quickly present data in meaningful ways. Microsoft Amalga Hospital Information System Microsoft Amalga Hospital Information System (HIS), the new version of Hospital 2000, is a state-of-the-art, integrated hospital information system designed to meet the needs of developing and emerging markets. -
Building the Business Case for SNOMED CT®
Building the Business Case for SNOMED CT® Promoting and Realising SNOMED CT®’s value in enabling high-performing health systems Russell Buchanan Marc Koehn A Gevity Consulting Inc. Company Building the Business Case for SNOMED CT® Promoting and Realising SNOMED CT®’s value in enabling high-performing health systems Copyright © 2014, International Health Terminology Standards Development Organisation (www.ihtsdo.org) Gordon Point Informatics Ltd. (GPi), Gevity Consulting Inc., and IHTSDO recognize all trademarks, registered trademarks and other marks as the property of their respective owners. Gordon Point Informatics Ltd. A Gevity Consulting Inc. Company (Gevity Consulting Inc. was formerly known as Global Village Consulting Inc.) #350 - 375 Water Street Vancouver, British Columbia Canada V6B 5C6 Telephone: +1-604-608-1779 www.gpinformatics.com | www.gevityinc.com ii Acknowledgments The authors would like to gratefully acknowledge a number of contributors without whose advice and support this paper would not have been possible. PROJECT STEERING COMMITTEE MEMBERS Members of the project steering committee provided overall guidance and insights throughout the project: • Liara Tutina, Customer Relations Lead (Asia Pac), IHTSDO • Vivian A. Auld, Senior Specialist for Health Data Standards, National Library of Medicine, USA • Dr. Md Khadzir Sheikh Ahmad, Deputy Director, Health informatic Centre, P&D Division, Ministry of Health, Malaysia • Kate Ebrlll, Head of National Service Operation and Management, National eHealth Transition Authority (NEHTA), Australia • Anna Adelöf, Customer Relations Lead (EMEA) , IHTSDO WORKING GROUP MEMBERS Our working group offered not only ongoing advice and reviews but also provided access to critical materials and contacts: • Liara Tutina, Customer Relations Lead (Asia Pac), IHTSDO • Dr. -
Direct Comparison of MEDCIN and SNOMED CT for Representation Of
Direct Comparison of MEDCIN ® and SNOMED CT ® for Representation of a General Medical Evaluation Template Steven H. Brown MS MD 1,2 , S. Trent Rosenbloom MD MPH 2 , Brent A. Bauer MD 3, Dietli nd Wahner -Roedler MD 3, David A. Froehling, MD, Kent R, Bailey PhD, M ichael J Lincoln MD, Diane Montella MD 1, Elliot M. Fielstein PhD 1,2 Peter L. Elkin MD 3 1. Department of Veterans Affairs 2. Vanderbilt University, Nashville TN 3. Mayo Clinic, Rochester MN Background : Two candidate terminologies to efforts. Usable and functionally complete support entry of ge neral medical data are standard terminologies need to be available to SNOMED CT and MEDCIN . W e compare the systems designers and architects. Two candidate ability of SNOMED CT and MEDCIN to terminologies to support entry of general medical represent concepts and interface terms from a data are SNOMED CT and MED CIN . VA gener al medical examination template. Methods : We parsed the VA general medical SNOMED CT is a reference terminology that evaluation template and mapped the resulting has been recommended for various components expressions into SNOMED CT and MEDCIN . of patient medical record information by the Internists conducted d ouble independent reviews Consolidated Health Informatics Council and the on 864 expressions . Exact concept level matches National Committee on Vital and Health were used to evaluate reference coverage. Exact Statistics. (12) SNOMED CT, licensed for US - term level matches were required for interface wide use by the National Library of Medicine in terms. 2003, was evaluated in 15 M edline indexed Resul ts : Sensitivity of SNOMED CT as a studie s in 2006 . -
Managing Non-DICOM Images Within an Enterprise Imaging Solution Agenda • Enterprise Imaging / Non-DICOM Market Trends
Managing Non-DICOM Images within an Enterprise Imaging Solution Agenda • Enterprise Imaging / Non-DICOM Market Trends • Managing Non-DICOM Images in the Enterprise • Merge’s Non-DICOM Solution Options • Questions? Enterprise Imaging / Non-DICOM Trends 3 Interoperability Market Drivers M&A / Consolidation Enterprise Image Management Clinical and Financial Economies of Scale Comprehensive Image Record Connecting Providers EHR Optimization Patient Centric Care Across Sites Unified Patient Image Record 4 Providers Generate a Flood of Data Digital Clinical Objects Specialty MPEG, JPEG Other Most Common Devices DICOM A/V, WAV , PDF Clinical Anesthesia In Room C-Arm, X-ray, Anesthetic, Record Keeping Reports Cardiology CVMR, CVCT, Cath, CVUS, CVECG / Holter / Stress / Pace Dermatology Photos, dermatological Reports Emergency Medicine X-ray, CT, ECG, Triage Reports Endocrinology SPECT / CT, PET / CT, Physician Reports, Voice Dictation Files Family Medicine Physician Notes, Reports Gastroenterology Barium X-ray, CT, NM, Endoscopes, US, Reports, Voice Dictation Files Hematology HT Reports, Voice Dictation Files ICU Medicine In Dept C-Arm, X-ray, Patient CIS Flow Chart Reports Nephrology US and MRI Angiography, Scintigraphy (Nuc Med) Reports Voice Neurology Renal Scans, SPECT/CT, PET/CT, Physician Reports, Renal Grams, Voice iConnect™ Solution Nuclear Medicine Stored in a VNA US, Physician Reports, Voice Dictation Files Obstetrics X-ray, CT, US, Reports, Voice Dictation Files, Fetal Strips, Reports Oncology -
Advancing Standards for Precision Medicine
Advancing Standards for Precision Medicine FINAL REPORT Prepared by: Audacious Inquiry on behalf of the Office of the National Coordinator for Health Information Technology under Contract No. HHSM-500-2017-000101 Task Order No. HHSP23320100013U January 2021 ONC Advancing Standards for Precision Medicine Table of Contents Executive Summary ...................................................................................................................................... 5 Standards Development and Demonstration Projects ............................................................................ 5 Mobile Health, Sensors, and Wearables ........................................................................................... 5 Social Determinants of Health (SDOH) ............................................................................................. 5 Findings and Lessons Learned .......................................................................................................... 6 Recommendations ........................................................................................................................................ 6 Introduction ................................................................................................................................................... 7 Background ................................................................................................................................................... 7 Project Purpose, Goals, and Objectives .................................................................................................. -
Intro to HL7 and DICOM
Working in an Integrated Digital Healthcare Enterprise 2004 Introduction and Update to HL7 and DICOM Herman Oosterwijk, Adjunct faculty, University of North Texas President OTech Inc. [email protected] www.otechimg.com DICOM/HL7 slide 1 Working in an Integrated Digital Healthcare Enterprise 2004 Agenda: ! What is HL7 ! What’s new in HL7 ! What is DICOM ! What’s new in DICOM ! Conclusion, Q and A DICOM/HL7 slide 2 DICOM/HL7 what’s new? Herman Oosterwijk www.otechimg.com page 1 Working in an Integrated Digital Healthcare Enterprise 2004 What is HL7: ! A pragmatic, simple protocol to exchange information dealing with, for example: ! Patient registration ! Orders (clinical, radiology, laboratory, etc.) ! results and observations ! queries, e.g. for patient demographics ! finance for billing purposes ! master files and indexes ! document control, such as approval status ! scheduling and logistics DICOM/HL7 slide 3 Working in an Integrated Digital Healthcare Enterprise 2004 Information Workflow example: Broker System Ordering, Scheduling Modality Worklist Mgt HL7 Storage Archive Storage Cmt Reporting Retrieve Performed Procedure Viewing Step DICOM/HL7 slide 4 DICOM/HL7 what’s new? Herman Oosterwijk www.otechimg.com page 2 Working in an Integrated Digital Healthcare Enterprise 2004 DICOM vs HL7 ! Scope is Imaging ! Scope beyond Imaging ! Protocol is mainly ! Protocol is Event driven, Client/Server i.e. unsolicited Events ! Based on Object ! Object Oriented in v 3.0 Oriented principles ! Attributes encoded ! Attributes: text strings ! Conformance -
(DICOM) Part 16: Content Mapping Resource
PS 3.16-2001 Digital Imaging and Communications in Medicine (DICOM) Part 16: Content Mapping Resource Published by National Electrical Manufacturers Association 1300 N. 17th Street Rosslyn, Virginia 22209 USA © Copyright 2001 by the National Electrical Manufacturers Association. All rights including translation into other languages, reserved under the Universal Copyright Convention, the Berne Convention or the Protection of Literacy and Artistic Works, and the International and Pan American Copyright Conventions. - Standard - PS 3.16 - 2001 Page 2 CONTENTS CONTENTS..........................................................................................................................................................................2 FOREWORD........................................................................................................................................................................4 1.....Scope and field of application...................................................................................................................................5 2.....Normative references .................................................................................................................................................5 BI-RADS Terminology and Nomenclature...................................................................................................5 MQCM 1999 Terminology and Nomenclature................................................................................................5 MQSA Terminology and Nomenclature...........................................................................................................5 -
Standard Nursing Terminologies: a Landscape Analysis
Standard Nursing Terminologies: A Landscape Analysis MBL Technologies, Clinovations, Contract # GS35F0475X Task Order # HHSP2332015004726 May 15, 2017 Table of Contents I. Introduction ....................................................................................................... 4 II. Background ........................................................................................................ 4 III. Landscape Analysis Approach ............................................................................. 6 IV. Summary of Background Data ............................................................................ 7 V. Findings.............................................................................................................. 8 A. Reference Terminologies .....................................................................................................8 1. SNOMED CT ................................................................................................................................... 8 2. Logical Observation Identifiers Names and Codes (LOINC) ........................................................ 10 B. Interface Terminologies .................................................................................................... 11 1. Clinical Care Classification (CCC) System .................................................................................... 11 2. International Classification for Nursing Practice (ICNP) ............................................................. 12 3. NANDA International -
Cooperating Standards in Healthcare GS1 Standards and Other Standards Cooperating in Clinical Treatment Scenarios
GS1 identifiers Other standards The Global Language of Business GTIN (Global Trade Item Number) SNOMED CT (Systematized Nomenclature of Products such as medicinal products, medical Medicine / Clinical Terms) devices, medical consumables, vaccines, blood It is the most comprehensive and precise clinical derivatives and raw materials at all product and health terminology product in the world, owned and packaging levels (e.g. unit of use, primary distributed around the world by SNOMED Cooperating standards packaging, retail unit, inner pack, case and pallet). International. SNOMED CT has been developed Attributes such as batch/lot number and expiry date collaboratively to ensure it meets the diverse needs in healthcare can provide additional traceability information. and expectations of clinicians worldwide and is now Individual trade item instance(s) can be identified accepted as a common global language for health GS1 standards and other standards by combining the GTIN with a serial number, which is terms. Improved health records, clinical decisions cooperating in clinical treatment scenarios mandated by an increasing number of regulations. and analysis, leading to higher quality, consistency and safety in healthcare delivery benefit from GLN (Global Location Number) SNOMED CT. www.snomed.org Locations: Theatres, Patient rooms, Wards, DICOM (Digital Imaging and Communications in Pharmacies, imprest/Store rooms, Pathology, Medicine) Radiology, Distribution centres, Manufacturing sites, It is the international standard to transmit, store, -
Working with the DICOM Data Standard in R
Working with the DICOM Data Standard in R Brandon Whitcher Volker J. Schmid Pfizer Worldwide R&D Ludwig-Maximilians Universit¨at Munchen¨ Andrew Thornton Cardiff University Abstract The package oro.dicom facilitates the interaction with and manipulation of medical imaging data that conform to the DICOM standard. DICOM data, from a single file or single directory or directory tree, may be uploaded into R using basic data structures: a data frame for the header information and a matrix for the image data. A list structure is used to organize multiple DICOM files. The conversion from DICOM to ANALYZE/NIfTI is straightforward using the capabilities of oro.dicom and oro.nifti. Keywords: export, imaging, import, medical, visualization. 1. Introduction Medical imaging is well established in both the clinical and research areas with numerous equipment manufacturers supplying a wide variety of modalities. The DICOM (Digital Imag- ing and Communications in Medicine; http://medical.nema.org) standard was developed from earlier standards and released in 1993. It is the data format for clinical imaging equip- ment and a variety of other devices whose complete specification is beyond the scope of this paper. All major manufacturers of medical imaging equipment (e.g., GE, Siemens, Philips) have so-called DICOM conformance statements that explicitly state how their hardware im- plements DICOM. The DICOM standard provides interoperability across hardware, but was not designed to facilitate efficient data manipulation and image processing. Hence, additional data formats have been developed over the years to accommodate data analysis and image processing. The material presented here provides users with a method of interacting with DICOM files in R (R Development Core Team 2010). -
Identifying Clinical Terms in Medical Text Using Ontology-Guided Machine Learning
JMIR MEDICAL INFORMATICS Arbabi et al Original Paper Identifying Clinical Terms in Medical Text Using Ontology-Guided Machine Learning Aryan Arbabi1,2, MSc; David R Adams3, MD, PhD; Sanja Fidler1, PhD; Michael Brudno1,2, PhD 1Department of Computer Science, University of Toronto, Toronto, ON, Canada 2Centre for Computational Medicine, Hospital for Sick Children, Toronto, ON, Canada 3Section on Human Biochemical Genetics, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States Corresponding Author: Michael Brudno, PhD Department of Computer Science University of Toronto 10 King©s College Road, SF 3303 Toronto, ON, M5S 3G4 Canada Phone: 1 4169782589 Email: [email protected] Abstract Background: Automatic recognition of medical concepts in unstructured text is an important component of many clinical and research applications, and its accuracy has a large impact on electronic health record analysis. The mining of medical concepts is complicated by the broad use of synonyms and nonstandard terms in medical documents. Objective: We present a machine learning model for concept recognition in large unstructured text, which optimizes the use of ontological structures and can identify previously unobserved synonyms for concepts in the ontology. Methods: We present a neural dictionary model that can be used to predict if a phrase is synonymous to a concept in a reference ontology. Our model, called the Neural Concept Recognizer (NCR), uses a convolutional neural network to encode input phrases and then rank medical concepts based on the similarity in that space. It uses the hierarchical structure provided by the biomedical ontology as an implicit prior embedding to better learn embedding of various terms. -
Imaging Reports Using HL7 Clinical Document Architecture Page 2
PS3.20 DICOM PS3.20 2021d - Imaging Reports using HL7 Clinical Document Architecture Page 2 PS3.20: DICOM PS3.20 2021d - Imaging Reports using HL7 Clinical Document Architecture Copyright © 2021 NEMA A DICOM® publication - Standard - DICOM PS3.20 2021d - Imaging Reports using HL7 Clinical Document Architecture Page 3 Table of Contents Notice and Disclaimer ........................................................................................................................................... 13 Foreword ............................................................................................................................................................ 15 1. Scope and Field of Application ............................................................................................................................. 17 2. Normative and Informative References .................................................................................................................. 19 3. Definitions ....................................................................................................................................................... 21 4. Symbols and Abbreviations ................................................................................................................................. 23 5. Conventions ..................................................................................................................................................... 25 5.1. Template Metadata ....................................................................................................................................