Synopsis Day 3: 2018 Annual World Medical Innovation Forum Artificial Intelligence April 23–25, 2018 Boston, Massachusetts | Westin Copley Place | Leaders in Pharmaceutical Business Intelligence (LPBI) Group

« Synopsis Day 2: 2018 Annual World Medical Innovation Forum Artificial Intelligence April 23–25, 2018 Boston, Massachusetts | Westin Copley Place

Synopsis Days 1,2,3: 2018 Annual World Medical Innovation Forum Artificial Intelligence April 23–25, 2018 Boston, Massachusetts | Westin Copley Place »

Synopsis Day 3: 2018 Annual World Medical Innovation Forum Artificial Intelligence April 23–25, 2018 Boston, Massachusetts | Westin Copley Place

April 25, 2018 by 2012pharmaceutical

Synopsis Day 3: 2018 Annual World Medical Innovation Forum Artificial Intelligence April 23–25, 2018 Boston, Massachusetts | Westin Copley Place

Reporter: Aviva Lev-Ari, PhD, RN

https://worldmedicalinnovation.org/

Announcement

Founder and Director of LPBI Group will be in attendance covering the event in REAL TIME

All TWEETS from LPBI’s Twitter.com handles at

Wednesday, April 25, 2018

7:00 am – 7:30 am
Nuance Foyer

Reconceiving Medical Devices in an AI Dominated Environment

Medical device companies are focused on developing smaller, faster and smarter devices. New technologies will enhance the function of medical devices throughout patient care. Leveraging AI technology to more effectively interact with patients and inform / facilitate outcomes enables smart devices that can learn and improve performance over time. The nature of AI panel based devices, the challenges inherent in developing them and how such devices can evolve over the next 5 years and beyond will be examined.

Moderator: Pat Fortune, PhD

VP, Market Sectors, Innovation, PHS
Medical devices cover equipment: BP Cuff, MRI, implantable devices are not yet amenable for #AI, #ML
i.e., coronary stents, artificial joints
#AI, #ML are used in the design of medical devices
class of devices that will both collect the data and learn for self modification of the design itself
Prostetics for amputation: will be capable of learning from the movement they are designed by #AI, #ML
Watch – will collect data on clinical condition in a casual mode but will be used for medical decisions in therapy/treatment
Opportunities for #AI in #MedicalDevices
What is becoming to be available
How Patients will be cared for by using #AI

Charles Cadieu, PhD

CEO, Bay Labs
Help Cardiologists in Cardiovascular using deep
@MIT developed human visual system doing visual recognition for decision expert knowledge digitized for brain neurons tested by #deeplearning algorithms
CVD – misditection, interventions tied to monitoring results
Sonography combined with CVD expertise in Echo-cardiography – high quality imagery
Devices and #AI: where the needs are?
Democratization that AI allows – took 50 years

Jonathan Rothberg, PhD

Founder and CEO, Butterfly Network
DNA Sequencing ($1Billion) moving to Devices, chips – Gemone sequence for $1000
Change imaging by putting Ultrasound imaging machine onto ONE chip (>$200) – FDA [investment required was $100 Million in 5 years, MDs, CS, ENG.] approved for 40 application process of approval short
#AI applications: compute Ejection Fraction with an application on iPhone device – detect aneurysm in aorta
Google took rules to train chessmaster – same approach used in Medical devices
In ten years – EMR and Devices – machines can watch a cancer metastasize, learn HOW it does that

Richard Kuntz, MD

SVP, Chief Medical and Scientific Officer, Medtronic
SENSING capability to modulate therapy
deep stimulation for Parkinson
Sensing ICD detect pre-occaring arhythymias
detect hyper glicemia
PRD algorithms
EKG device – looks for Heart FAilure, water in the lungs – this device used to detect early ketoacidosis
Neuromodulation, cardiac monitoring, Diabetes
Artificial Pancreas – three arms clinical trial – Baysian statistic informative Priors – FDA works hard to understand and approve

Jagmeet Singh, MD, PhD

Associate Chief, Cardiology Division, MGH; Professor of Medicine, HMS – Electrophysiology
Disterbances detected by sensors for prediction of Heart Failure – NOT YET integrated to EMR for better care – implentable devices are not yet using #AI
Non-implantable are using #AI vs implantable
arrhythmia – needs collaborations
#AI application alert the MD and the Patient vd Device is smart enough that the DEVICE WILL INTERVENE on their own – Validation of #AI algorithms will define future usage of #AI

8:20 am – 8:50 am
NVIDIA Ballroom

Fireside Chat: Seema Verma, Administrator, Centers for Medicare and Medicaid Services

Moderator: Gregg Meyer, MD

Chief Clinical Officer, PHS; Professor of Medicine, HMS; 2018 Forum Co-Chair

Seema Verma

Administrator, Centers for Medicare and Medicaid Services
20% of GDP spent on Health Care taking away from Education and Defense
Mission to deliver INNOVATIONS, Robotics and Precision Medicine – CMS wants patient to get access to innovation
MODERNIZING: Drug Pricing, Value-Based Pricing – Patient in the Center
Price Transparency by Hospital to place online – WHAT cost what
Efficiency: 30% is spent on ADMINISTRATION – Technology needed to drive efficiencies
Patient-over-Paperwork – Advocated by @GreggMeyer, MD

@PHS @HMS
Patients – Own their data –
BLUE Button – claim data from CMS available in Format organizing data
More data @CMS – Dat of MedicCare and MedicAID made available
Overhaul – Patient have their data to give it to researchers and to NIH
EMR – ease of use
#AI @CMS – all is DATA – meaningful format collecting all information in EMR – #AI needed for data analysis Biomarkers that requires actions
Value-based is for addressing efficacies
ACA – and Innovations: Subsidized – Rates went up over 100% happened before 2017 Carriers are opting out – Monopolies in some geographies – concerned over that
MedicAID aged, blind, disabled – more people placed into it – quality of care affected
INNOVATION center @CMS – great leadership, Providers coordinate care, how to include the Patient as beneficiary into the discussion Providers – and values, incentives by different models
Competition – @CMS was price setting – competition in Price setting nor @CMS as sole Price setter

8:50 am – 9:20 am
NVIDIA Ballroom

Fireside Chat: Paying for AI: Thinking Strategically About Reimbursements and Acceptance

Understanding how AI will be absorbed into a highly defined payment system is crucial to determining the rate and breadth that the technology will play in health care in the next decade. Two senior leaders will share their perspectives on how the technology will be paid for and what mechanisms will be used to arbitrate the scope and timing of those payments.

Moderator: Peter Markell

EVP Administration and Finance, CFO and Treasurer, PHS
How #AI will effect use of labor for efficiencies and cost
How the market will operate by use of Data to get provider get a share in the Saving of efficiency
Often the cost is not know
Pace of #AI
Providers and Insurers need to work together of what is paid, disclosure information must be a better way

Patrick Conway, MD

CEO, BCBS of North Carolina – $10Millions
Deep Providers relations – share responsibility with providers
GNS Partnership
Dat analytics, Patiennt Segmentations
Customer experience for HealthCare plans – #AI to be used to engage Patients avatar for Millennials
Manage Providers with Technology for Customer
Zip code and life expectance both go down
Partners a pioneer in monitoring for efficiencies: Coding needed for risk adjustment
Radiology, Pathology, Anestesiology — #AI is more effective and assist MDs – feedback of @AI is instantaneous
DAt Analytics increases very fast
#AI is growing it can BRING COST DOWN – it is a must and existential
Partnership is replacing negotiation

Eric Murphy

CEO, @OptumInsight & Enterprise Growth Officer, @Optum – Data analytics – 5,000 Data Scientists
OptumIQ – subject matter expertise, #AI and analytics – Services offered to clients
Payer-Provider Convergence – no friction is the interest of both sides, Claim administration
right price, right claim

9:20 am – 9:50 am
NVIDIA Ballroom

1:1 Fireside Chat: Vasant Narasimhan, MD, CEO, Novartis

Moderator: Gregg Meyer, MD

Chief Clinical Officer, PHS; Professor of Medicine, HMS; 2018 Forum Co-Chair

Vasant Narasimhan, MD

CEO, Novartis
R&D for transformational efficacies
Novartis is a Data Science 70Biliion Doses per year – efficiencies needed and a Drug developer company
use #AI for 500 Clincial Trials around the World, Monitoring all trials
Data from research, biomarker,
Data Mining phyno type find new signature for drug discovery
Navidia – quantum computing for drug discovery and blockchain used for rolling the clinical trials from discovery
Insurance and Banking – data discovery and mining is extraordinary
Science 27 nurses in Clinical trial, Para Therapeutic, Google Partnership for clinical trials
Real Time monitoring: Study Operation Centers for all the Clinical Trials around the World if a Monitor arrived and what was the quality score
25% of drug discovery affect Drug Prices
App NORA application with all the Clinical Trial workflow, identify Patients by Social Media, patient meets NP, protocol starts
Finding Drug Targets –
Push with regulators: deep minting of medical history to find super responders
Para therapeutics FDA label, Algorithm for ADDICTION, updating algorithm, need to inform regulator, Privacy Data vs mining permission RETHINK cultural transformation Organizational
Gene therapy and Cell therapy
Biosimilar Monoclonal antibody is been deployed in Africa
Finding super responders – Novartis can be a Leader due to data available
Regain Trust of Society in Big Pharma

9:50 am – 10:40 am
NVIDIA Ballroom

Machine Learning in Image Analysis: A Diagnostician’s Best Friend…or Replacement?

Diagnostic imaging is among the clinical fields receiving the greatest attention in the early stages of AI in healthcare. Even in this initial phase it appears that the technology may have profound effects on one of the most resource intensive fields in medicine. Panelists will consider the broad implications as well as topics such as how will role of radiologists evolve? Will AI tools ever become advanced enough to make decisions autonomously within the clinical workflow?

Moderator: Giles Boland, MD

Chair, Department of Radiology, BWH; Philip H. Cook Professor of Radiology, HMS
Diagnostic Imaging and Medicine is challenged by #AI
#AI is different than other transformational technological REVOLUTIONS
How to avoid unneeded scanns

Andrew Beck, MD, PhD

CEO, PathAI
Digital Pathology, information processing from Tissue to treatment
#AI is a friend not a Foe
#AI will bring value to Medicine by utilizing tools for Patients
Information Processing across specialties is for the future – Pathology and Radiology cooperation

Dorin Comaniciu, PhD

VP, Medical Imaging Technology, Siemens Healthineers
#AI more data , better performance, optimism to apply #AI for diagnosis, intervention
Precision, predicting
Population health, outcomes
Deliver systems performing per specification
incidentals: #AI efficiency, pushing forward Precision Medicine
#AI is the only technology to improve efficiency in Radiology #DeepReasoning does predictions intelligent management of Data
Data integration is a very difficult: Validation, for trust and it is not all rule based

Eyal Gura

Co-Founder and Chairman, Zebra Medical Vision
30 #AI Products for Memograms, ultrasounds,
#AI will help Radiology
Triage by #AI brain bleed, lung colapse, aneurysm

Karim Karti

CEO, GE Healthcare Imaging
#AI is a Transformational Technology
Google DeepMind beat Chest player: Computer teach itself to play anf played against itself in few hours and computer played against best Computer, better than human
GE build Scanners: build Intelligent Scanners want to do it like NVIDIA
Simulation of CAT Labs – can avoid sending a Patient to the Lab, significantly lower the cost
GE is building platforms that integrate data inputs from multiple sources

Constance Lehman, MD, PhD

Chief, Breast Imaging Division, MGH; Professor of Radiology, HMS
Improving the Patient experience
#AI as a friend, #AI are tools that Radiologist are excited to have
Is surgery needed @MGH is based on #ML #DeepLearning applications to #BreatCancer
#AI to be used to find the Patients that need surgery and will benefit from it – help to find the 2% for surgery

10:40 am – 10:50 am
Nuance Foyer

Morning Break

10:50 am – 11:20 am
NVIDIA Ballroom

1:1 Fireside Chat: John Kelly, PhD, SVP, Cognitive Solutions and Research, IBM

Moderator: James Noga

VP and CIO, PHS
Bias in #ML #AI
lessons from WATSON #ML #AI

John Kelly, PhD

SVP, Cognitive Solutions and Research, IBM
#ML #AI – @IBM in the 50s
@IBM in 90s Built first Computer to play Chess and win
2005 – WATSON – different paradigm leverage #ML #AI – intrigued by the data
2010 – #ML #AI is very big again @IBM
Killer App #ML #AI in Healthcare is

DRUG DISCOVERY: molecule, drug target, pathways
with Mayo Clinic: MATCHING Patients to Clinical Trials using #ML #AI
with Memorial Sloan Kettering: WATSON in Oncology for MDs consultation

Avoidance BIAS by #ML #AI usage in supervised knowledge WHAT DATA is used in a source for bias avoidance
Financial Services is @IBM bedrock where scaling was learned Call Center Assistance using #ML #AI
Oil & Gas – WATSON trained deployment to all platform in Northshore
Health Care workflow: #ML #AI for false positive and negative
Blood sugar control – prediction of Hyperglicemia
#AI #ML Implementation @IBM best sources of knowledge as Experts has been used: WHAT DATA YOU CHOOSE, Who choose it is annotated
WATSON – Oncology MDs worked on User Interface for sharing WATSON results for Patient decision making – Experience of Doctor-Patient relations has been transformed
Opportunities in HC for #AI #ML – we are just at the beginning, compute power in 5 years or 10 years there will not be a decision making, not been made using #AI #ML

11:20 am – 12:10 pm
NVIDIA Ballroom

Illuminating the Path to Clinician Empowerment

The sacred exchange between patient and clinician at the heart of medicine is increasingly under duress driven by a range of factors. Increasing clinician burnout is recognized as among the many negative consequences of this trend. Panelists will discuss how AI may improve the quality of the patient encounter, clinician workflow and ultimately clinician quality of life. Panelist will discuss how the new technology can meet these objectives when earlier information based technologies may have exacerbated the challenge.

Moderator: Sree Chaguturu, MD

VP, Population Health Management, PHS
#AI #ML as a tool in workflow to bring back Trust, Humanity due to clinicians burnout correlated with malpractice, turnover, suicide and alcohol, recognition at Partners
Actionable solutions: social psychological need
eliminate sources of burnout

Peter Durlach

SVP, New Business Development, Healthcare Division, Nuance
#AI #ML Product Dragon: move from dictation to data analytics, first time right – the documentation of the encounter with the Patient will be done by #AI #ML
Medication reconciliation progress done Alaxia
Talk to a device on the wall – Send a document to so and so
inject intelligence into the workflow: Image classification, deploy technology into the workflow: prioritization a stack of films for Radiologist to look first at acuity based ranking of films

Dennis Schmuland, MD

Chief Health Strategy Officer, US Health & Life Sciences, Microsoft
Building blocks for academics and clinitiVIsion Search knowledgespeech
#ML #AI everywhere for the office
using high definition cameras for triaging condition of the Patient used for screening
reduce ratinopathy treatment cost – image go to Retina specialist and go directly to the chart, MD is paid for the test

Punit Soni

CEO, Robin AI – ex-Google
#ML Doctor-Patient interaction Models
Patterns of medical documentation – ontologies of content

David Ting, MD

Chief Medical Information Officer, MGPO
Administration Burden causes burn-out of Clinicians
Data capture for retrieval
Project @MGH: multiple departments: capturing the sources of MDs burn-out: Journey of data
computer voice recognition – speak to computer have computer do work for you
120 offices implemented LIVE scribes, 150 virtual scribes
Chart capture by #ML #AI – robotics solutions
ROI – increased productivity: see More patients, Acuity is up? intangible ROI – Job satisfaction of physicians
central scheduling and ordering
clinicians are trained in apprantainship model hard to change the workflow

12:10 pm – 1:10 pm
NVIDIA Ballroom

Disruptive Dozen: 12 AI Technologies That Will Reinvent Care

The culture of innovation throughout Partners HealthCare naturally fosters robust discussions about new “disruptive” technologies and which ones will have the biggest impact on health care. The Disruptive Dozen was created to identify and rank the technologies that Partners faculty feel will break through over the next decade to significantly improve health care. This year, the Disruptive Dozen focuses on relevant advances and opportunities in artificial intelligence (AI).

Moderator: Katherine Andriole, PhD

Director of Research Strategy and Operations, MGH & BWH CCDS; Associate Professor, Radiology, HMS

Moderator: Keith Dreyer, DO, PhD

Chief Data Science Officer, PHS; Vice Chairman, Radiology, MGH; Associate Professor, Radiology, HMS

TOP THREE DISTRUPTIVE #AI TECHNOLOGIES

#3 #DistruptiveDozen @OmarArnaout @HMS Can Personal Devices Improve Your Health?

#2 #DistruptiveDozen @HadiShafiee @BWH @HMS A Picture is Worth a Thousand Words

#1 #DistruptiveDozen @BrandonWestover @MGH @HMS #AI at the #Bedside

1:10 pm – 1:15 pm
NVIDIA Ballroom

Last Look

Keith Dreyer, DO, PhD

Chief Data Science Officer, PHS; Vice Chairman, Radiology, MGH; Associate Professor, Radiology, HMS
Interdisciplinary
Adoption is challenging
Journey will be long, continue to be positions with successes along the way

Anne Klibanski, MD

Chief Academic Officer, PHS; Laurie Carrol Guthart Professor of Medicine, Academic Dean for Partners, HMS; 2018 Forum Co-Chair
THANK YOU – SW writing SW – NAVIDIA, CEO
Contest Biobank Disease Challenge
Next year – Neuroscience & AI – major advances since the first Neuroscience

Gregg Meyer, MD