Informatics Research Seminars · 2016 2030 2016 2030 2016 2030 2016 2030 2016 2030 Australia2 9.6%...
Transcript of Informatics Research Seminars · 2016 2030 2016 2030 2016 2030 2016 2030 2016 2030 Australia2 9.6%...
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Informatics Research Seminars
We ask that all participants mute their microphonesPlease note that the session is being recorded.
Health Care IT Advisor
The Decision MachineAnalytics and the Rise of AI
Carolina Health Informatics ProgramNovember 7th, 2018
Greg KuhnenSenior Research [email protected]
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Sources: “Analytics,” Wikipedia, last modified March 5, 2018, https://en.wikipedia.org/wiki/Analytics; English Oxford Living Dictionaries. Oxford: Oxford University Press, 2018 (also available at https://en.oxforddictionaries.com/); Health Care IT Advisor research and analysis.
AnalyticsThe discovery, interpretation, and communication of meaningful patterns in data.
–Wikipedia
Artificial Intelligence (AI)The theory and development of computer systems able to perform tasks normally requiring human intelligence…
–Oxford English Dictionary
Machine Learning (ML)The field of study that gives computers the ability to learn without being explicitly programmed.
–Arthur Samuel, 1959
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ROAD MAP4
The Data Dilemma1
2 AI and the New Machine Age
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Health Economics: An Unsustainable TrajectoryAnalytics Can Be a Powerful Tool for Bending the Cost Curve
Sources: Cottarelli C, “Macro-Fiscal Implications of Health Care Reform in Advanced and Emerging Economies,” IMF Fiscal Affairs Department, December 2010; McKinsey & Company, “mHealth: A new vision for healthcare,” 2010; Office of Economic Co-operation and development, ‘Health Spending”; Health Care IT Advisor research and analysis.
1) Global Burden of Disease Health Financing Collaborator Network, “Future and potential spending on health 2015-40: development assistance for health, and government, prepaid private, and out-of-pocket health spending in 184 countries,” The Lancet, 389, No. 10083 (2016); 2) According to the Australian Institute of Health and Welfare (AIHW) calculations; 3) New Zealand Ministry of Health projections; 4) GDP = Gross domestic product.
Per Capita Health Care Spending1 Health Expenditures as Percentage of GDP4
2016 20302016 2030
2016 2030 2016 2030
2016 2030
Australia2
9.6% 14.4%
2010 2030
New Zealand3
6.2% 8.9%
10.6%15.9%
11.0%18.0%
FranceCanada
United States United Kingdom
17.2%24.9%
9.7%13.5%
$9,237
$5,234
$4,751
$4,576
$4,032
$4,050
$3,749
$3,096
$12,448
$7,799
$6,437
$5,926
$5,606
$5,496
$5,002
$4,245
United States
Netherlands
Belgium
Canada
Australia
New Zealand
UnitedKingdom
Spain
Health Expenditure per Capita 2014
Health Expenditure per Capita 2030
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Technology Brings a Flood of DataSmartphones, and Genomes, and Sensors, Oh My!
Source: Health Care IT Advisor research and analysis.
1) IoT = Internet of things.2) RTLS = Real-time locating system.3) ROI = Return on investment.
Common Barriers to Adoption of New Sources• Privacy / security concerns• Immature technology• Lack of budget• Unclear benefit / ROI3
• Staff already overwhelmed with data
A Sampling of New Data Sources
Integrated Partners (e.g., social services, cross-continuum care)
Data from Outside Organizations (e.g., social)
Environmental Data (e.g., IoT,1 RTLS,2temperature, air
quality)
Genomes, Proteomes, Microbiomes, Metabolomes
Clinical Surveillance (e.g., public health, outbreak reports)
Remote Patient Monitoring(e.g., bedside monitors,
remote patient monitoring, consumer wearables)
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New Data SourcesMainstream Systems Cover Only a Fraction of Predictive Power
Acquire Data
Sources: Nash DB, “Population Health: Why It Matters,” Essentials in Population Health, Children’s Hospital Association, Thomas Jefferson University, October 2016; Health Care IT Advisor research and analysis.
1) RPM = Remote patient monitoring.
EMR(s)
Lab Systems
Pharmacy Systems
Imaging
Medical Claims
Prescription Drug Claims
Billing and Supply Chain
Scheduling Systems
Medical Care Data
10%
Sensors / RPM1
mHealth Apps
Patient-Reported Outcomes
Genetics
Socioeconomic Data
Patient Questionnaires
Human Biology
Data20%
Lifestyle and
Behavior Data50%
Social and Environmental
Data20%
Data Sources by Influence on Health Outcomes
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Cognitive Overload and Clinician Burnout
Sources: Millett D, Exclusive: “Three GPs a day seeking help for burnout, official data show,” GP, Jan 2018; Health Care IT Advisor research and analysis.
1) Obermeyer Z, Lee T, “Lost in Thought – The Limits of the Human Mind and the Future of Medicine,” N Engl J Med; 377, no. 13; (2017):1209-1211.
Tim
e R
equi
red
“Medical thinking has become vastly more complex, mirroring changes in our patients, our health care system, and medical science. The complexity of medicine now exceeds the capacity of the human mind.1”
Ziad Obermeyer, M.D. and Thomas Lee, M.D.
Number of Considerations for Decision Making
Significant opportunity for AI and analytics
Augmented Intelligence Reduces Cognitive Burden
Reliance on heuristics, rules of thumb, “good-enough”
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Data Power the Decision Machine
Source: Health Care IT Advisor research and analysis.
Four Levels of Analytic Capabilities
Data
Inform
Recommend
Decision Automation
Decision Support
DescriptiveWhat happened?
How many of our patients were readmitted last month?
What is likely to happen?Is this patient likely to readmit? What do we expect seasonally?
PrescriptiveRecommend or decide the best action.
What should we doto address this patient’s risks?
PredictiveDiagnosticWhy did it happen?
Rates spiked; was there a change in case mix? Demographics?
Decide
Hum
an
Dec
isio
n
Ana
lytic
s
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ROAD MAP10
The Data Dilemma1
2 AI and the New Machine Age
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AI: Are We There Yet?And if Not, When?
Source: Health Care IT Advisor research and analysis.
We May Be at the Start of an Explosive Growth in AI Capabilities
Incremental Growth:AI continues to make small advances, delivering value in niche applications.
AI Winters: AI has already gone through past phases of hype and troughs of disillusionment.
Exponential Growth: AI rapidly gains capability and becomes a mainstream part of most digital systems.
Unpredictable Timing: Some advances never seem to arrive (conversational systems), while others take off unexpectedly (smartphones).
60s 70s 80s 90s 2000 2010 2020 203050s 2040 2050
?
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Fuel for the AI Fire
Source: Health Care IT Advisor research and analysis.
1) EMR = Electronic medical record. 2) GPU = Graphics processing unit.3) TPU = Tensor processing unit.
AlgorithmsData• Internet Everywhere
• Broad EMR1 Adoption
• Wearables
• Real-Time Location
• IoT Sensors
• Genomics, Proteomics, Microbiome
• Automated Feature Extraction
• Cluster Compute Platforms (e.g., Spark, Hadoop)
• Deep Learning
• Off-the-Shelf Libraries for Common Tasks• Faster Processors
• Specialised Processors (GPUs,2 TPUs3)
• Inexpensive Storage
• Elastic Cloud Computing
Hardware
Combinatorial Advances DeliveringRadically Better Models
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The AI Winter Is Thawing
Source: Health Care IT Advisor research and analysis.
1) NASA = National Aeronautics and Space Administration.
Microsoft and Alibaba outscore humans in a Stanford reading comprehension test 2018
Google DeepMind’s AlphaZero convincingly leads at Chess, Shogi, Go 2017
Skype launches real-time voice translation 2014
Mass market voice assistants (Apple Siri, Google Now, Microsoft Cortana) 2012
IBM Watson conclusively beats Jeopardy game show champions 2011
NASA’s1 Mars Rovers operate semi-autonomously 2004
IBM’s Deep Blue beats world chess champion Gary Kasparov 1997
1950 Alan Turing proposes the “Turing Test” for conversational systems
1956 The term “Artificial Intelligence” is coined
1970s First AI Winter
1981 Digital Equipment Corporation order expert saves company $40M per year
Timeline of AI Successes
From Toys to Tools
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DeepMind’s AlphaZero
Novice to Superhuman in Just Four HoursDeepMind’s AlphaZero Uses ‘Zero Human Knowledge’
Sources: Vincent J, “DeepMind’s AI became a superhuman chess player in a few hours, just for fun,” The Verge, December 6, 2017; Gershgorn D, “DeepMind wants to find the next miracle material—experts just don’t know they’ll pull it off,” Quartz, October 25, 2017; Health Care IT Advisor research and analysis.
By not using this human data, by not using human features or human expertise in any fashion, we’ve actually rem
oved the constraints of human knowledge. It’s able to therefore create knowledge for itself.”
• AI product of Google subsidiary, DeepMind
• Provided no prior knowledge of the game; given only basic game rules and an objective (win)
• Learns through self-play at an accelerated pace (‘reinforced learning’); searches 80 thousand positions per second
• Variants of AlphaZero also defeated world champion Go and Shogi systems
AlphaZero outperformed chess world-champion, Stockfish,1 in just 4 hours (300k steps)
By not using this human data, by not using human features or human expertise in any fashion, we’ve actually removed the constraints of human knowledge. It’s able to therefore create knowledge for itself.”
David Silver, Lead ProgrammerDEEPMIND
1) Stockfish is the 2016 Top Chess Engine Championship (TCEC) world-champion.
AlphaZero Chess Performance
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Need a Radiologist?
There’s an App for That
Sources: Kubota T, “Stanford algorithm can diagnose pneumonia better than radiologists,” Stanford News, Nov 2017; Health Care IT Advisor research and analysis.
1) Stanford University Medical Center.2) NIH = National Institutes of Health.
Case in Brief: Stanford University1
CheXNet Algorithm
• Algorithm developed by researchers at Stanford University in the US can diagnose 14 common pathologies in chest x-rays
• Trained on ChestX-ray14, a public data set released by the NIH2 containing 112,120 frontal-view chest x-ray images labelled with the 14 possible pathologies
• Outperforms previous models from the same data set for all 14 conditions and diagnoses pneumonia at an accuracy exceeding the performance of four control radiologists
• Produces heatmaps that visualize the areas of an image most indicative of disease
Development of CheXNet
Sept. 26, 2017
ChestX-ray14 data set released along with a preliminary algorithm that could detect the labelled conditions
≈ One week later
CheXNet could diagnose 10 of the 14 pathologies more accurately than all previous algorithms
≈ One month later
CheXNet surpassed best published results for all 14 pathologies and outperformed Stanford radiologists in detecting pneumonia“I treat the model as a lazy resident. They’re not dumb, they’ll
just use every trick they can find to avoid doing hard work.”
Dr. Matthew Lungren, RadiologistStanford University Medical Center
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Lessons from Stanford’s CheXNet
AI Is Effective on Narrow Tasks
Recent advances in AI have focused on solving narrowly defined tasks. Eachsub-problem (e.g., pneumonia) is a model.
Handle “Normal” Explicitly
Identifying normal conditions versus anomalies can help recognize when human review may be needed.
Explainable1 AI (XAI)
Models that can be interpreted, at least partially, are easier to trust, deploy, and govern. Explainability is an area of rapid progress in AI research.
1) Also known as interpretable AI.
Training Requires a Teacher or Referee
Machine learning needs to be told what answers are correct or what actions produce positive outcomes. Well-curated data are critical to the learning process.
Source: Health Care IT Advisor research and analysis.
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The AI Value PropositionAI Excels at Decisions That Are Dull, Detailed, and Dear (Expensive)
Three Key Areas of Benefit
Dear:Provide speed, capacity, 24x7 availability, and consistency at a fraction of the cost
Although these are well known benefits of automation, when you apply this to cognitive and complex tasks, it’s a new ball game.
Detailed:Evaluate a vastly broader and deeper set of data to improve decision quality
The complexity of health care decision making is growing due to new data from genetics, IoT devices, wearables, better interoperability, and the growing number of drugs and treatments.
Unlimited capacity, instant response, disruptive economics
Perform at super-human levels, exceeding experts
Serves as an assistant(watches your back)
Dull:Free human decision makers to focus on more challenging “top of license” activities
Reduce effort spent on repetitive tasks, process large amounts of complex information, provide continuous monitoring and notifications, and ensure “stuff” doesn’t “fall through the cracks.”
Source: Health Care IT Advisor research and analysis.
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Where to Start, Where We’re Going
1) Narrow (Task, Weak) AI focuses on narrowly defined problems, such as predicting a patient’s risk of developing sepsis.2) General (Strong) AI attempts to match the flexibility and versatility of human intelligence.
Nar
row
(Tas
k, W
eak)
AI1
Clinical Decisions
Administrative Decisions
Gen
eral
(Stro
ng) A
I2
Full Diagnosisand Treatment Plan
Clinical Chatbots
RadiologyInterpretation
Acute ClinicalRisk
Recruiting
Safety Risk SmartMonitoring
Medication Dosing
AdministrativeChatbots (Concierge)
Precision Engagement
Precision Medicine
Ambient Documentation
Adaptive UserInterfaces
WorklistPrioritization
Inevitable with Time
Today’s Opportunity (growing)
The Computer Will See You Now
The Uber-ization ofHealth Care
An AI Application Landscape
Capacity / StaffingManagement
Population Health Risk
The Self-DrivingHospitalInformation
Security
Targeted Marketing
Source: Health Care IT Advisor research and analysis.
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The Self-Driving HospitalCombine Predictive Models with Front-Line Activation
Sources: “Virtual Air Traffic Control for Emergency Departments,” Qventus, https://qventus.com/content/uploads/2017/03/QVE-ED-Solution-Brief.pdf; Health Care IT Advisor research and analysis.
1) EVS = Environmental services.2) LWBS = Left without being seen.3) LOS = Length of stay.
Case in Brief: Mercy Hospital Fort Smith
• 336-bed acute care hospital in Fort Smith, AR
• Partnered with Qventus to improve emergency department (ED) patient flow and patient satisfaction
• Analytics applies ML algorithms to anticipate potential capacity shortfalls, process bottlenecks
• Application sends real-time notifications (nudges) to care teams via their mobile phones to trigger specific interventions
• Emergency department results:– LWBS2 rates dropped 30%
– Door-to-doc time reduced by 20%
– 24-minute reduction in average LOS3
– Annual case capacity increased by 6%
See that a radiology order is delayed for a patient within a few days of discharge and notify radiology to prioritize the reading
Activate more EVS1 staff when high churn of beds is anticipated
Send an early warning for difficult patient placements at discharge
Prioritize inpatient transports depending on expected bed needs
Sample “Decision Recipes”
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Applicants Like You Liked This Job
Predictive Models Reduce Turnover, Improve Engagement
Case in Brief: MultiCare Health System
• Non-profit system based in Tacoma, WA with 16,000 employees
• Partnered with Arena, a cloud-based HR solution, to predict the likelihood that a candidate will be retained in a role
• Arena’s machine learning algorithms evaluate applicant submissions, digital interactions, and externally sourced data on the organization (e.g., employer review sites)
• Achieved a 40% reduction in RN turnover at 180 days and a 28% reduction in overall turnover at 180 days
MultiCare’s Hiring Process
Reduced recruiting expenses
More predictable staffing levels
More experienced staff
Realized Benefits
Algorithms predict the likelihood the applicant will be retained and engaged in the target role and send that information to the recruiter
Applicant completes a 15-20 minute assessment process on Arena’s platform
Recruiters determine whether or not to send the candidate along to hiring managers
Hiring managers use Arena’s recommendation as an optional factor in the hiring decision
Source: Health Care IT Advisor research and analysis.
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Advanced Readmission Prediction at UNC1
“Simpler Isn’t Always Better, Sometimes Better Is Better.”
Case in Brief: UNC Hospitals• 853-bed academic medical center based Chapel Hill, NC• 77,000 annual ED visits and 30,000 annual surgeries• Post-discharge care coordination driven by a LACE-like2 readmissions model; UNC’s
Center for Innovation sought more powerful alternatives.• Partnered with Forecast Health to develop the Modern SDH3 model, resulting in
significant improvements in readmission prediction• 6 weeks from initial data access to proposed model
0%
20%
40%
60%
80%
BOOST LACE PARR Modern SDH Model
Readmissions Successfully Predicted4
5 6
1) UNC = The University of North Carolina.2) LACE = Length of stay, acuity, co-morbidities, and
emergency department visits.3) SDH = Social determinants of health.
Source: Advisory Board interviews and analysis.
4) Percentage of readmissions successfully predicted when the top 20% of predicted risk scores are selected. Metrics are representative of model performance, not specific to UNC.
5) BOOST = Better outcomes by optimizing safe transitions.6) PARR = Patient s at risk of readmission.
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Driving the Decision Machine
1Acquire Data
• Ensure all models have
a sponsor and regular
evaluation against goals
• All models require
tune-ups as the
environment changes.
Monitor Performance and Revisit
42Train or Refine
the Model
• The more the better,
but…
• High-quality, well-
governed data pay
enormous dividends
• Diverse, cross-continuum
sources improve
predictions and
robustness of models
• Decide what features
are important (e.g.,
explainability)
• Apply practical and
ethical constraints
• Evaluate under
realistic conditions
(silent mode, real-time,
incomplete data)
• Embed insights directly
into applications at key
decision points
• Reengineer processes
as warranted
• Consider the “5 rights”
of decision support
3Incorporate Models
into Workflow
Hint: The Analytics Is the Easy Part
Begin with:• Explicit leadership agreement on outcome goals, expected benefit mechanisms
• Participation from empowered representatives of impacted process
• External experience—learn from others
Source: Health Care IT Advisor research and analysis.
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Implement a single, coherent management platform across all enterprise data
Strengths: Highly flexible, consistent data management, best positioning for advanced analytics
Weaknesses: Longer time-to-value, larger staff investment
Application AnalyticsUse analytic capabilities bundled with primary systems of record
Strengths: Strong, out-of-the-box data integration and ability to embed analytics into workflows
Weaknesses: Difficulty integrating data from adjacent domains or competitors’ systems in mixed environments
Point SolutionsAcquire solutions targeting specific problem domains (e.g., population health, cost accounting, clinical quality)
Strengths: Deep domain knowledge, rapid time to value
Weaknesses: Narrow focus on individual domains, limited ability to stretch into adjacent areas for more advanced analysis
Choose Your BI Architecture DeliberatelyPick a Core Approach, But Mix in Others as Appropriate
Acquire Data
Enterprise Data Platform
On Premise or in the Cloud?Analytics workloads are especially suited to cloud deployment. Elastic compute resources can “surge” to handle difficult tasks, then rapidly scale down when demand falls.
Source: Health Care IT Advisor research and analysis.
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Evolution of the Data WarehouseData Lakes Deliver, But Avoid the Data Swamp
Acquire Data
1) PACS = Picture archiving and communication system.
EMRs
Claims
Laboratory
PACS1
Finance
Transactional Systems Data Lake Data
WarehouseCore Reports
Data Marts
ExplorationDiagnosticsHypothesis Generation
AcquisitionDiscoveryTraceability
Data Capture GovernanceReconciliationQualityEnrichment
Ad-hoc Analytic Tools
DeliveryMeasurementAccountability
Systems
Activities
Promotion
Enrichment
Source: Health Care IT Advisor research and analysis.
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How Machines LearnSimilar Methods, Differing Goals
Build the Model
Source: Advisory Board research and analysis.
Input Data• The more the better• Labeled with key indicators
Adjust the Model• Automated trial and error• Cross-breeding successful
candidates
Repeat
Supervised Learning
Evaluate models against the “correct” answersprovided with the data. The learning process focuses on minimizing errors.
Unsupervised Learning
Evaluate models against an abstract algorithmic goal. Example: Divide this population of diabetics into a handful of subpopulations that have similar future risk profiles.
Reinforcement Learning
Evaluate models by giving a reward for desirable outcomes and a penalty for negative outcomes. Models are trained to maximize their total rewards over time.
Ready for Review
Evaluate Performance• Against what goal?• Forces explicit
tradeoffs (e.g., costvs. quality)
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Workflow Is Key to Adoption
Incorporate into Workflow
The Right Information
To the Right Person
In the Right Form
By the Right Channel
At the Right Time
Case in Brief: Mayo Breast OncologyClinic Trials Recruitment
• Up to 100 concurrent drug therapy clinical trials are available to Mayo Clinic breast cancer patients, making it challenging to consistently identify candidates.
• New clinical trial matching technology developed in collaboration with IBM Watson Health evaluates charts to identify eligible patients.
• Launched in January 2016 as a point-of-care solution for oncology providers; initial adoption was low due to time pressures and fell to zero by February.
• Rework by health systems engineers resulted in a July 2016 relaunch; screening moved to occur in advance of patient visits so clinicians know of trial matches before opening a patient’s chart.
• New process enrolls significantly more patients in clinical trials than the prior manual process; success led to additional coordinator funding.
The Five Rights of Clinical Decision Support
Source: Health Care IT Advisor research and analysis.
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Who Watches the Machines?Treat Predictive and Prescriptive Models Like Junior Staff
Monitor Performance and Revisit
Governance is focused on quantifiable target
outcomes
Automated processes need human oversight
“Circuit breaker” rules can limit potential harm
Periodically evaluate processes
against goals
If you aren’t listening to what your data tells you about the present, you’re not ready to use it to predict the future.
Source: Health Care IT Advisor research and analysis.
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Advisors to Our Work
With Sincere AppreciationThe Health Care IT Advisor program would like to express deep gratitude to
the individuals and organizations that shared their insights, analysis, and time
with us. The research team would especially like to recognize the following
contributors for being particularly generous with their time and expertise.
Arena.IOBaltimore, Maryland
Michael FinnMichael Rosenbaum
AthenahealthWatertown, Massachusetts
Girish Venkatachaliah
AyasdiPalo Alto, California
Jonathan Symonds
Children’s Hospital of Eastern OntarioOttawa, Ontario, Canada
Christina Honeywell
CollibraNew York, New York
Chris CooperDan Scholler
Dignity Health San Francisco, California
Dr. Gurmeet Sran
Duke University Health SystemDurham, North Carolina
Dr. Mark Sendak
Forecast Health Durham, North Carolina
Michael Cousins
Geisinger Health SystemDanville, Pennsylvania
Kirk Hanson
Health Catalyst Salt Lake City, Utah
Dale SandersLevi Thatcher
HealthDataVizWaltham, Massachusetts
Sandy Lawson Kathy Rowell
Intermountain Healthcare Salt Lake City, Utah
Dr. Nathan DeanJeffrey Ferraro Lonny Northrup
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Advisors to Our Work
With Sincere AppreciationThe Health Care IT Advisor program would like to express deep gratitude to the individuals and organizations that shared their insights, analysis, and time with us. The research team would especially like to recognize the following contributors for being particularly generous with their time and expertise.
Jewish General Hospital Montréal, Québec, Canada Dr. Lawrence RosenbergPhil Troy
Mayo ClinicRochester, MinnesotaDr. Tufia Haddad
Memorial Sloan Kettering Cancer CenterNew York, New YorkAri CarolineIsaac Wagner
Microsoft Seattle, WashingtonJohn DoyleTom Lawry Dennis Schmuland
New York PresbyterianNew York, New YorkDr. Peter FleischutDr. David Tsay
PaxataRedwood City, CaliforniaNenshad BardoliwallaJayanta Bhowmik
Pieces Technologies Dallas, TexasDr. Ruben AmarasinghamShannon Kmak
QventusLos Altos, CaliforniaMudit GargVenkat Mocherla
Royal Free London NHS Foundation TrustSurrey, London, UKGlenn Winteringham
SalesforceSan Francisco, CaliforniaJayesh Govindarajan
Stanford UniversityMedical CenterStanford, CaliforniaDr. Matthew LungrenDr. Nigam Shah
Taunton and Somerset NHS Foundation TrustSomerset, England, UKAndrew Forrest
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Advisors to Our Work
With Sincere AppreciationThe Health Care IT Advisor program would like to express deep gratitude to
the individuals and organizations that shared their insights, analysis, and time
with us. The research team would especially like to recognize the following
contributors for being particularly generous with their time and expertise.
The MetroHealth SystemCleveland, Ohio
Dr. Robert FerguesonDr. David Kaelber
The University of Chicago MedicineChicago, Illinois
Mike Wall
University Hospital BirminghamBirmingham, England, UK
Steven ChiltonPaul JenningsBarnaby Waters
University Hospitals of LeicesterLeicester, England, UK
Andrew Carruthers
University of CambridgeCambridge, England, UK
Pietro Lio
University of LeedsLeeds, England, UK
Owen Johnson
University of North Carolina Healthcare Chapel Hill, North Carolina
Jason Burke
University of Pittsburg Medical Center Pittsburgh, Pennsylvania
Terri MikolDon RiefnerDr. Rasu Shreshta
Washington University Medical CenterSt. Louis, Missouri
Dr. Brad Racette
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Health Care IT Advisor
Research DirectorGreg [email protected]
Research TeamJacqueline Beltejar
Bethany JonesNouran Ragaban
Sophie Ranen
Andrew Rebhan
Program LeadershipJim Adams
Design ConsultantKevin Matovich