Predictive Analytics in Value-Based Healthcare
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Artificial Intelligence in Health Care: the Hope, the Hype, the Promise, the Peril
Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril Michael Matheny, Sonoo Thadaney Israni, Mahnoor Ahmed, and Danielle Whicher, Editors WASHINGTON, DC NAM.EDU PREPUBLICATION COPY - Uncorrected Proofs NATIONAL ACADEMY OF MEDICINE • 500 Fifth Street, NW • WASHINGTON, DC 20001 NOTICE: This publication has undergone peer review according to procedures established by the National Academy of Medicine (NAM). Publication by the NAM worthy of public attention, but does not constitute endorsement of conclusions and recommendationssignifies that it is the by productthe NAM. of The a carefully views presented considered in processthis publication and is a contributionare those of individual contributors and do not represent formal consensus positions of the authors’ organizations; the NAM; or the National Academies of Sciences, Engineering, and Medicine. Library of Congress Cataloging-in-Publication Data to Come Copyright 2019 by the National Academy of Sciences. All rights reserved. Printed in the United States of America. Suggested citation: Matheny, M., S. Thadaney Israni, M. Ahmed, and D. Whicher, Editors. 2019. Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. NAM Special Publication. Washington, DC: National Academy of Medicine. PREPUBLICATION COPY - Uncorrected Proofs “Knowing is not enough; we must apply. Willing is not enough; we must do.” --GOETHE PREPUBLICATION COPY - Uncorrected Proofs ABOUT THE NATIONAL ACADEMY OF MEDICINE The National Academy of Medicine is one of three Academies constituting the Nation- al Academies of Sciences, Engineering, and Medicine (the National Academies). The Na- tional Academies provide independent, objective analysis and advice to the nation and conduct other activities to solve complex problems and inform public policy decisions. -
History of AI at IBM and How IBM Is Leveraging Watson for Intellectual Property
History of AI at IBM and How IBM is Leveraging Watson for Intellectual Property 2019 ECC Conference June 9-11 at Marist College IBM Intellectual Property Management Solutions 1 IBM Intellectual Property Management Solutions © 2017-2019 IBM Corporation Who are We? At IBM for 37 years I currently work in the Technology and Intellectual Property organization, a combination of CHQ and Research. I have worked as an engineer in Procurement, Testing, MLC Packaging, and now T&IP. Currently Lead Architect on IP Advisor with Watson, a Watson based Patent and Intellectual Property Analytics tool. • Master Inventor • Number of patents filed ~ 24+ • Number of submissions in progress ~ 4+ • Consult/Educate outside companies on all things IP (from strategy to commercialization, including IP 101) • Technical background: Semiconductors, Computers, Programming/Software, Tom Fleischman Intellectual Property and Analytics [email protected] Is the manager of the Intellectual Property Management Solutions team in CHQ under the Technology and Intellectual Property group. Current OM for IP Advisor with Watson application, used internally and externally. Past Global Business Services in the PLM and Supply Chain practices. • Number of patents filed – 2 (2018) • Number of submissions in progress - 2 • Consult/Educate outside companies on all things IP (from strategy to commercialization, including IP 101) • Schaumburg SLE Sue Hallen • Technical background: Registered Professional Engineer in Illinois, Structural Engineer by [email protected] degree, lots of software development and implementation for PLM clients 2 IBM Intellectual Property Management Solutions © 2017-2019 IBM Corporation How does IBM define AI? IBM refers to it as Augmented Intelligence…. • Not artificial or meant to replace Human Thinking…augments your work AI Terminology Machine Learning • Provides computers with the ability to continuing learning without being pre-programmed. -
Big Blue in the Bottomless Pit: the Early Years of IBM Chile
Big Blue in the Bottomless Pit: The Early Years of IBM Chile Eden Medina Indiana University In examining the history of IBM in Chile, this article asks how IBM came to dominate Chile’s computer market and, to address this question, emphasizes the importance of studying both IBM corporate strategy and Chilean national history. The article also examines how IBM reproduced its corporate culture in Latin America and used it to accommodate the region’s political and economic changes. Thomas J. Watson Jr. was skeptical when he The history of IBM has been documented first heard his father’s plan to create an from a number of perspectives. Former em- international subsidiary. ‘‘We had endless ployees, management experts, journalists, and opportunityandlittleriskintheUS,’’he historians of business, technology, and com- wrote, ‘‘while it was hard to imagine us getting puting have all made important contributions anywhere abroad. Latin America, for example to our understanding of IBM’s past.3 Some seemed like a bottomless pit.’’1 However, the works have explored company operations senior Watson had a different sense of the outside the US in detail.4 However, most of potential for profit within the world market these studies do not address company activi- and believed that one day IBM’s sales abroad ties in regions of the developing world, such as would surpass its growing domestic business. Latin America.5 Chile, a slender South Amer- In 1949, he created the IBM World Trade ican country bordered by the Pacific Ocean on Corporation to coordinate the company’s one side and the Andean cordillera on the activities outside the US and appointed his other, offers a rich site for studying IBM younger son, Arthur K. -
The Evolution of Ibm Research Looking Back at 50 Years of Scientific Achievements and Innovations
FEATURES THE EVOLUTION OF IBM RESEARCH LOOKING BACK AT 50 YEARS OF SCIENTIFIC ACHIEVEMENTS AND INNOVATIONS l Chris Sciacca and Christophe Rossel – IBM Research – Zurich, Switzerland – DOI: 10.1051/epn/2014201 By the mid-1950s IBM had established laboratories in New York City and in San Jose, California, with San Jose being the first one apart from headquarters. This provided considerable freedom to the scientists and with its success IBM executives gained the confidence they needed to look beyond the United States for a third lab. The choice wasn’t easy, but Switzerland was eventually selected based on the same blend of talent, skills and academia that IBM uses today — most recently for its decision to open new labs in Ireland, Brazil and Australia. 16 EPN 45/2 Article available at http://www.europhysicsnews.org or http://dx.doi.org/10.1051/epn/2014201 THE evolution OF IBM RESEARCH FEATURES he Computing-Tabulating-Recording Com- sorting and disseminating information was going to pany (C-T-R), the precursor to IBM, was be a big business, requiring investment in research founded on 16 June 1911. It was initially a and development. Tmerger of three manufacturing businesses, He began hiring the country’s top engineers, led which were eventually molded into the $100 billion in- by one of world’s most prolific inventors at the time: novator in technology, science, management and culture James Wares Bryce. Bryce was given the task to in- known as IBM. vent and build the best tabulating, sorting and key- With the success of C-T-R after World War I came punch machines. -
Integration of System for Research in Health with Computation in Cloud Using Artificial Intelligence
MOJ Proteomics & Bioinformatics Research Article Open Access Integration of system for research in health with computation in cloud using artificial intelligence Abstract Volume 7 Issue 4 - 2018 Currently the use of information technology is often applied in the health area. With regard 1 to scientific research, we have the SINPE© Integrated System of Electronic Protocols. Carlos Henrique Kuretzki, José Simão de 2 3 The purpose of this tool is to support the researcher in this area who until now did not Paula Pinto, José Claudio Vianna have statistical tests. The main objective of this work is to provide SINPE© users with the 1Professor of the Program for Analysis and Development of improvement in health data analysis through the use of cognitive computing technologies. Systems and Computer Engineering, Federal University, Brazil 2Professor of the Information Management Program, Federal Keywords: artificial intelligence, cloud computing, information systems University, Brazil 3Professor of the Computer Engineering, Universidade Positivo, Brazil Correspondence: Carlos Henrique Kuretzki, Professor of the Program for Analysis and Development of Systems and Computer Engineering, Federal University, Brazil, Email [email protected] Received: June 29, 2018 | Published: August 08, 2018 Introduction more quickly.4 IBM Watson is a cognitive computing technology and have been used to investigate the biological and health sciences. The health sector is one of the largest in most countries and can It is based on medical literature, patents, genetic, and chemical and significantly benefit from high‒quality, real‒time and location‒ pharmacological data.5 The IBM’s Watson platform combines some independent data. However, many healthcare professionals are capabilities, building a complete cognitive system, which is being not familiar with information technology solutions, business and called the new era of computing. -
Data Transfer Options and Requirements
Data Transfer Options and Requirements Original date 25 Jan 2005 Revision date 21 Aug 2018 Proprietary and Confidential Data Transfer Options and Requirements Summary of changes Date Short description of changes September 2016 Updated name of submission tool to SDSS. August 2018 Rebranded. Contacting support Support is available through the following link: https://www.ibm.com/software/support/watsonhealth/truven_support.html © Copyright IBM Corporation 2018 IBM Confidential 2 Data Transfer Options and Requirements Contents Summary of changes .................................................................................................................................... 2 Contacting support ........................................................................................................................... 2 Contents ........................................................................................................................................................ 3 Overview ....................................................................................................................................................... 5 Electronic data transfer methods ..................................................................................................... 5 Data suppliers ..................................................................................................................... 5 Data recipients ................................................................................................................... -
Treatment and Differential Diagnosis Insights for the Physician's
Treatment and differential diagnosis insights for the physician’s consideration in the moments that matter most The role of medical imaging in global health systems is literally fundamental. Like labs, medical images are used at one point or another in almost every high cost, high value episode of care. Echocardiograms, CT scans, mammograms, and x-rays, for example, “atlas” the body and help chart a course forward for a patient’s care team. Imaging precision has improved as a result of technological advancements and breakthroughs in related medical research. Those advancements also bring with them exponential growth in medical imaging data. The capabilities referenced throughout this document are in the research and development phase and are not available for any use, commercial or non-commercial. Any statements and claims related to the capabilities referenced are aspirational only. There were roughly 800 million multi-slice exams performed in the United States in 2015 alone. Those studies generated approximately 60 billion medical images. At those volumes, each of the roughly 31,000 radiologists in the U.S. would have to view an image every two seconds of every working day for an entire year in order to extract potentially life-saving information from a handful of images hidden in a sea of data. 31K 800MM 60B radiologists exams medical images What’s worse, medical images remain largely disconnected from the rest of the relevant data (lab results, patient-similar cases, medical research) inside medical records (and beyond them), making it difficult for physicians to place medical imaging in the context of patient histories that may unlock clues to previously unconsidered treatment pathways. -
Cell Broadband Engine Spencer Dennis Nicholas Barlow the Cell Processor
Cell Broadband Engine Spencer Dennis Nicholas Barlow The Cell Processor ◦ Objective: “[to bring] supercomputer power to everyday life” ◦ Bridge the gap between conventional CPU’s and high performance GPU’s History Original patent application in 2002 Generations ◦ 90 nm - 2005 ◦ 65 nm - 2007 (PowerXCell 8i) ◦ 45 nm - 2009 Cost $400 Million to develop Team of 400 engineers STI Design Center ◦ Sony ◦ Toshiba ◦ IBM Design PS3 Employed as CPU ◦ Clocked at 3.2 GHz ◦ theoretical maximum performance of 23.04 GFLOPS Utilized alongside NVIDIA RSX 'Reality Synthesizer' GPU ◦ Complimented graphical performance ◦ 8 Synergistic Processing Elements (SPE) ◦ Single Dual Issue Power Processing Element (PPE) ◦ Memory IO Controller (MIC) ◦ Element Interconnect Bus (EIB) ◦ Memory IO Controller (MIC) ◦ Bus Interface Controller (BIC) Architecture Overview SPU/SPE Synergistic Processing Unit/Element SXU - Synergistic Execution Unit LS - Local Store SMF - Synergistic Memory Frontend EIB - Element Interconnect Bus PPE - Power Processing Element MIC - Memory IO Controller BIC - Bus Interface Controller Synergistic Processing Element (SPE) 128-bit dual-issue SIMD dataflow ○ “Single Instruction Multiple Data” ○ Optimized for data-level parallelism ○ Designed for vectorized floating point calculations. ◦ Workhorses of the Processor ◦ Handle most of the computational workload ◦ Each contains its own Instruction + Data Memory ◦ “Local Store” ▫ Embedded SRAM SPE Continued Responsible for governing SPEs ◦ “Extensions” of the PPE Shares main memory with SPE ◦ can initiate -
Cheryl Watson's
Cheryl Watson’s 1998, No. 6 TUNING Letter A PRACTICAL JOURNAL OF S/390 TUNING AND MEASUREMENT ADVICE Inside this issue... This Issue: My heart attack is important news, at least from my point of view. See page 2. But I'm feeling really great these days, thanks to A Note From Cheryl & Tom .2 modern medicine. Management Issues #20 ......3 Class Schedule .....................4 Upgrading a processor is the focus of this issue. How to size, S/390 News how to understand the difference between speed and capacity, and GRS Star .............................5 how to avoid typical problems after an upgrade are all covered MXG & CA-MICS.................5 starting on page 14. CMOS & Compression ......5 Y2K ......................................6 Java and Component Broker are featured in two articles pro- Java .....................................6 vided by Glenn Anderson of IBM Education (page 6). Component Broker ............7 LE.........................................9 IBM is now recommending that multi-system sites be at WSC Flashes ....................10 OS/390 R5 before freezing systems for Y2K. See WSC Flash The Net..............................11 98044 on page 10 for this important item. Pubs & APARs .................12 Focus: Processor Upgrades A known integrity exposure in ISPF has existed for the five Sizing Processors............14 years since ISPF V4, but new customers keep running into the Migration Issues...............23 problem. See page 29. Upgrade Problems...........26 Cheryl's Updates New Web links and important new manuals and books are Most Common Q&As.......28 listed in our S/390 News starting on page 12. TCP/IP................................28 ISPF Exposure..................29 Don't go to OS/390 R5 without checking with your TCP/IP WLM Update .....................30 vendors or you could be in serious trouble. -
Watson Machine Learning for Z/OS — Jamar Smith Data Scientist, North America Z Hybrid Cloud [email protected]
Watson Machine Learning for z/OS — Jamar Smith Data Scientist, North America z Hybrid Cloud [email protected] 1 Goal Demonstrate the value of enterprise analytics on the IBM Z platform. 2 Agenda Enterprise Analytics Strategy Machine Learning Overview Value of Analytics in Place IBM Cloud Pak 4 Data 3 Enterprise Analytics Strategy 4 Current trends in analytics The need for Pervasive Analytics is increasing in almost every industry Real time or near real time analytic results are necessary Need to leverage all relevant data sources available for insight Ease demands on highly sought-after analytic skill base Embrace rapid pace of innovation 5 Data gravity key to enterprise analytics Performance matters Core transactional for variety of data on systems of record are and off IBM Z on IBM Z Predominance of data Real-time / near real- originates on IBM Z, time insights are z/OS (transactions, valuable member info, …) Data volume is large, distilling data Data Security / data privacy provides operational needs to be preserved efficiencies Gravity Podcast: http://www.ibmbigdatahub.com/podcast/making-data-simple-what-data-gravity 6 IBM Z Analytics Keep your data in place – a different approach to enterprise analytics • Keep data in place for analytics • Keep data in place, encrypted and secure • Minimize latency, cost and complexity of data movement • Transform data on platform • Improve data quality and governance • Apply the same resiliency to analytics as your operational applications • Combine insight from structured & unstructured data from z and non-z data sources • Leverage existing people, processes and infrastructure 7 Machine Learning Overview 8 What is Machine Learning? Computers that learn without being explicitly programmed. -
IBM Pricing Index New.Xlsx
Appendix C Pricing Index DIR-TSO-3996 International Business Machines Corporation (IBM) DIR Customer Product Category Product Description Discount % off MSRP * IBM POWER SYSTEM POWER-SYSTEM Hardware Rack 26.00% POWER-SYSTEM Hardware Hardware Console 15.00% POWER-SYSTEM Hardware Scale Out and Enterprise Servers 15.00% POWER SYSTEM Software p: AIX, Linux and System Software, Compilers, HMC 26.00% POWER SYSTEM Software i: System software 18.00% IBM z Systems Mainframe Class Servers IBM z Systems Family of Servers IBM Mainframe Product Family 0.75% IBM Z Systems Mainframe Class Servers - Software z Software z OTC (One Time Charge) and MLC (Monthly License Charge 15.00% Exclusions - DB2 QMF for z/OS, VUE products and zIPLA S&S are excluded from this discounting schedule HARDWARE - IBM Storage Solutions Storage Solutions Big Data, Flash, Hybrid,Tape and SAN 25.00% IBM SYSTEM STOARGE SOFTWARE NON Passport Advantage System Storage Software not in PPA 15.00% Other IBM Systems Solutions IBM Systems Solutions Other Systems - Hardware / Software 0.75% SOFTWARE IBM Passport Advantage (PPA) IBM Passport Advantage software Passport Advantage Software - Perpetual 15.00% IBM Passport Advantage software Passport Advantage Software - Perpetual - Education Entities ONLY 60.00% IBM Passport Advantage software Software as a Service (SaaS)* 3.00% IBM Passport Advantage software Software Monthly License Charge 2.00% * excluding those SaaS products for which IBM has not established Relative Selling Price (RSP) IBM Watson Health Solutions Watson Health IBM Watson -
An Introduction to the Cell Broadband Engine Architecture
IBM Systems & Technology Group An Introduction to the Cell Broadband Engine Architecture Owen Callanan IBM Ireland, Dublin Software Lab. Email: [email protected] 1 Cell Programming Workshop 5/7/2009 © 2009 IBM Corporation IBM Systems & Technology Group Agenda About IBM Ireland development Cell Introduction Cell Architecture Programming the Cell – SPE programming – Controlling data movement – Tips and tricks Trademarks – Cell Broadband Engine and Cell Broadband Engine Architecture are trademarks of Sony Computer Entertainment, Inc. 2 Cell Programming Workshop 5/7/2009 © 2009 IBM Corporation IBM Systems & Technology Group Introduction - Development in IBM Ireland Lotus Software Research Tivoli Industry Assets & Models RFID Center of Competence High Performance Computing 3 Cell Programming Workshop 5/7/2009 © 2009 IBM Corporation IBM Systems & Technology Group Introduction – HPC in IBM Dublin Lab Blue Gene team Deep Computing Visualization (DCV) Dynamic Application Virtualization (DAV) Other areas also – E.g. RDMA communications systems 4 Cell Programming Workshop 5/7/2009 © 2009 IBM Corporation IBM Systems & Technology Group Cell Introduction 5 Cell Programming Workshop 5/7/2009 © 2009 IBM Corporation IBM Systems & Technology Group Cell Broadband Engine History IBM, SCEI/Sony, Toshiba Alliance formed in 2000 Design Center opens March 2001 ~$400M Investment, 5 years, 600 people February 7, 2005: First technical disclosures January 12, 2006: Alliance extended 5 additional years YKT, EFK, BURLINGTON, ENDICOTT ROCHESTER