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Precisely Practicing Medicine from
700 Trillion Points of
University of California Health Data
Atul Butte, MD, PhD
Chief Data Scientist, University of California Health (UC Health)
Priscilla Chan and Mark Zuckerberg Distinguished Professor
Director, Bakar Computational Health Sciences Institute, UCSF
atul.butte@ucsf.edu ▪ @atulbutte
Conflicts of Interest
• Scientific founder
– Personalis
– NuMedii
– Carmenta (Progenity)
– Genstruct
• Honoraria for talks
– Lilly
– Pfizer
– Siemens
– Bristol Myers Squibb
– AstraZeneca
– Roche
– Genentech
– Warburg Pincus
– CRG
– AbbVie
– Westat
• Past or present
consultancy
– Personalis
– NuMedii
– Lilly
– Johnson and Johnson
– Roche
– Genstruct
– Tercica
– Ecoeos
– Helix
– Ansh Labs
– uBiome
– Prevendia
– Samsung
– Assay Depot
– Regeneron
– Verinata (Illumina)
– Pathway Diagnostics
– Geisinger Health
– Covance
– Wilson Sonsini Goodrich &
Rosati
– Orrick
– 10X Genomics
– GNS Healthcare
– Gerson Lehman Group
– Coatue Management
• Other corporate
relationships
– Northrop Grumman
– Genentech
– Johnson and Johnson
– Optum
• Shares or Ownership
– NuMedii (major)
– Personalis (major)
– Apple
– Facebook
– Alphabet (Google)
– Microsoft
– Amazon
– Snap
– 10x Genomics
– Illumina
– Nuna Health
– Assay Depot (Scientist.com)
– Vet24seven
– Regeneron
– Sanofi
– Royalty Pharma
– AstraZeneca
– Moderna
– Biogen
– Paraxel
– Sutro
• Speakers’ bureau
– None
• Companies started by
students
– Carmenta
– Serendipity
– Stimulomics
– NunaHealth
– Praedicat
– MyTime
– Flipora
– Tumbl.in
– Polyglot
– Iota Health
– Ongevity Health
How it started…
How it’s going…
Atul Butte's presentation at #AMIA2021 for the Knowledge Discovery and Data Mining Workshop meeting
Faculty Recruitments 2015-2021
Dara
Torgerson
Will
Brown
Alejandro Sweet-
Cordero
Udi
Manber
Julia Adler-
Milstein
Ted
Goldstein
Ashish Raj Marina
Sirota
Julian Hong
Peds DOM,
DPM
Peds DOM DOM, DHM Hem Onc Radiology Peds Rad Onc
Vasilis
Ntranos
Jingjing Li Ben Rosner Franklin
Huang
Jean Feng John
Capra
Yulin Hswen Ryan
Hernandez
Tom
Peterson
Vivek
Rudrapatna
Diabetes
Ctr
Neurol DOM, DHM DOM,
Onc
DEB DEB DEB BTS DOM,
Ortho
DOM, Gastro
computationalhealth.ucsf.edu
AWS & Wynton Shared Compute & Storage
For many types of computing needs
Driving Integrative Research/Precision Medicine
Spanning Basic, Translational, Clinical, Population, ….
Information Commons
SPOKE Knowledge Network
Reflecting how human biology really works - pathways
Inquiry Tools and Talent
Clinical
Notes
Radiology
Images
Info
Extract
Structured
Clinical
Data
35+ biomed
reference
databases
Genomic
profiles
Clinical
Commons
Imaging
Commons
Omics
Commons
Information Commons
The Research Environment For Precision Medicine
Nearly 200 researchers onboarded
Clinical Notes De-identification
State of the art algorithm developed at UCSF
25,000 manually annotated notes for training and testing
75 million notes de-identified in development environment
3rd party vendor certifying data de-identification process
ü
ü
Collaboration with Privacy and IRB on making the data
available upon completion of certification
ü
Beau Norgeot
github.com/beaunorgeot/philter_ucsf
ü
ü
Atul Butte's presentation at #AMIA2021 for the Knowledge Discovery and Data Mining Workshop meeting
Post-approval safety
Updating side effect rates
Discovering novel side effects
Supporting regulatory approval
Single-arm experimental trials
”Digital approvals”
Biosimilar development
Informing clinical trials design
Better patient selection
Trimming the trials: more efficient data collection
Continually establishing efficacy
Assessing the efficacy-effectiveness gap
Searching for efficacy in specific populations
Effect modifiers and precision medicine
Long-term, post trial outcomes
Comparative effectiveness
Integrating costs with comparative effectiveness
Understanding effects of pharmacy practices on healthcare utilization
Studying novel on-label pharmaceuticals versus older off-label drugs
Studying the practice of medicine
Quality of practice, medical errors
Standardizing care and care delivery
The effect of payors on medical care
Are new-generation diagnostics improving outcomes?
Data driven decision support
Clinical-Decision Support: the Provider Perspective
Clinical-Decision Support: the Patient Perspective
Clinical-Decision Support: the Community Perspective
Twenty-one uses for Real World Data
Vivek Rudrapatna
bit.ly/jci_RWD
Atul Butte's presentation at #AMIA2021 for the Knowledge Discovery and Data Mining Workshop meeting
Over 100 now approved just counting radiology!
http://bit.ly/acr_ai
Why is AI back in biomedicine?
• Great hardware from video gamers
• Software libraries with novel machine learning methods
• More big data sets to train with
– Molecular data, genomics on more people, more cases
– Electronic health records
• Hard unsolved questions need answers
– Patient predictions
– Efficient drug discovery
– Population modeling
The United States is spending $billions on electronic
health records, and too few are using any of this data
University of California
• 10 campuses and 3 national labs
• ~200,000 employees, ~250,000 students/yr
UC Health
• 20 health professional schools (6 med schools)
• Train half the medical students and residents in
California
• ~$2 billion NIH funding
• $13+ billion clinical operating revenue
• 5000 faculty physicians, 12000 nurses
• UCSF and UCLA are in US News top 10
• 5 NCI Comprehensive Cancer Centers, 5 NIH CTSA
• IRB reliance, centralized contracting
UC Health Data Analytics Platform
Combining healthcare data from across the
six University of California medical schools and systems
Data Warehouse
• Combined EHR data from UCSF, UCLA, UC Irvine, UC Davis,
UC San Diego, and UC Riverside
• Central database built using OMOP (not Epic) as a data backend
– First Epic installation was January 2012
– Structured data from 2012 to the present day
– 7.0 million patients with “modern” data
– 220M encounters, 560M procedures, 798M med orders,
722M diagnosis codes, 2.1B lab tests and vital signs
– “From Tylenol to CAR-T cells…”
– California OSHPD data, pathology and radiology text elements, death index
– Claims data from our self-funded plans now included
– Continually harmonizing elements
• Quality and performance dashboards
The University of California has an
incredible view of the medical system
3.1 million hemoglobin A1c measurements
All University of California academic medical centers provide
health data to patients through FHIR (and Apple Health)
bit.ly/ucappleh
Many operational teams within UC Health now using and
benefitting from the UC Health Data Warehouse, saving $millions
Central tools to
improve quality
of care
Decreasing specific
unnecessary
inpatient drug use
Managing costs
in our self-
funded health
plans
Centralized
population health
management
Decile 1 (Least Disadvantaged)
Decile 10 (Most Disadvantaged)
Suppressed Value
California - 2015 ADI State Rankings
• Estimate socioeconomic status based on
income, education, employment, housing
quality at the neighborhood level
• Use with race, ethnicity, gender, age to
identify health disparities
• Central geocoding capability to link 9-digit
zip code for addresses
• We have now geocoded our UC-wide
primary care population
• ADI is significantly associated with adverse
health outcomes in our patients, in
addition to race, ethnicity, and age
Social Determinants of Health: Area Deprivation Index (ADI)
San Francisco Bay Area Sacramento Area
San Diego Area
Riverside Area
Los Angeles Area
Orange County Area
Source: American Diabetes Association Standards of Medical Care in Diabetes (2016)
26,822 Type 2 Diabetes
Patients at UCSF since EHR
Adoption in 2012
Tom Peterson
bit.ly/uc_dc_5
26,822 Type 2 Diabetes
Patients at UCSF since EHR
Adoption in 2012
Tom Peterson
bit.ly/uc_dc_5
26,822 Type 2 Diabetes
Patients at UCSF since EHR
Adoption in 2012
Tom Peterson
bit.ly/uc_dc_5
26,822 Type 2 Diabetes
Patients at UCSF since EHR
Adoption in 2012
Tom Peterson
bit.ly/uc_dc_5
Tom Peterson
bit.ly/uc_dc_5
bit.ly/2NKc6dz
bit.ly/2NKda13
Beau Norgeot
bit.ly/jamaRA
Predicting the future state of a patient with
Rheumatoid Arthritis
Using the UC Covid Research Data Set
(UC CORDS)
Rohit Vashisht
bit.ly/3EB7jpL
Beau Norgeot
bit.ly/DLHCare
A New Deep Learning Healthcare System
AI/ML lessons I’ve learned over 25 years
• Get the question right; solve problems that health care professionals need solved, don't guess
– And verify good questions and good unmet needs with more than one doc
– Build a great diagnostic vs. understanding the biology
• Perfectly lassoed variables may miss the big picture biology
– Medical professionals (and biologists) really love explanations over black boxes
• Watch out for input limiting models
– Patients might not type in the right codes for their symptoms
– They barely enter their own race/ethnicity
– And docs?
• Learn what IRB, HIPAA, BAA are. Learn what ICD-10 and CPT codes are. CLIA and CAP.
– And learn patience.
– Not all of us are cloud allergic.
• Not everything needs deep learning
• Having all data on everyone is super rare: genomics, images, and longitudinal EHR data
• Data integration and harmonization can happen if there is a business reason for it
• $1 trillion dollars is spent on Medicare and Medicaid. Would your parents would use it?
• Health care inefficiency is not about friction, it’s about resistance to change
Collaborators
• Elizabeth Thomson, Patrick Dunn, Morgan
Crafts / Northrop Grumman
• Quan Chen, Anupama Gururaj, Dawei Lin /
NIAID
• Andrei Goga / UCSF Oncology
• Mallar Bhattacharya / UCSF Pulmonary
• Minnie Sarwal / UCSF Nephrology
• Geoffrey Gurtner / UCSF Surgery
• Roberta Diaz Brinton / Arizona
• Carol Bult / Jackson Labs
• Alejandro Sweet-Cordero, Julien Sage /
Pediatric Oncology
• Takashi Kadowaki, Momoko Horikoshi, Kazuo
Hara, Hiroshi Ohtsu / U Tokyo
• Kyoko Toda, Satoru Yamada, Junichiro Irie /
Kitasato Univ and Hospital
• Shiro Maeda / RIKEN
• David Stevenson, Gary Shaw / Stanford
Neonatology
• Mark Davis, C. Garrison Fathman / Stanford
Immunology
• Russ Altman, Steve Quake / Stanford
Bioengineering
• Euan Ashley, Joseph Wu / Stanford Cardiology
• Mike Snyder, Carlos Bustamante, Anne Brunet
/ Stanford Genetics
• Jay Pasricha / Stanford Gastroenterology
• Rob Tibshirani, Brad Efron / Stanford Statistics
• Hannah Valantine, Kiran Khush/ Stanford
Cardiology
• Mark Musen, Nigam Shah / National Center for
Biomedical Ontology
• Sam So, Ken Weinberg, David Miklos /
Stanford Oncology
The Team
Atul Butte
Carrie Byington
Cora Han
Pagan Morris
Monte Ratzlaff
Mike Kilpatrick
Lisa Dahm
Ayan Patel
Aiden Barin
Chaya Mohn
Ray Pablo
Tim Hayes
David Gonzalez
Rob Follett
Teju Yardi
Nadya Balabanova
Ellen Pollack (UCLA)
Chris Longhurst (UCSD)
Scott Joslyn (UCI)
Joe Bengfort (UCSF)
Ashish Atreja (UCD)
Tom Andriola (UCI)
The CIO Team
Kent Anderson
Steve Covington
Hemanth Tatiparthi
Ranjit Singh
Jodi Nygaard
Jeffrey Sterett
Ralph Perrin
Thomas Ami
David Merrill
Dan Phillips
Melody Hill
Kathy Pickell
Leanie Mayor
Neaktisia Lee
Albert Duntugan
Andrew Weaver
Yael Berkovich
Bill Lazarus
Edgar Tijerino
Jay Shah
Vajra Kasturi
Bill Cinnater
Josh Pelino
Shital Pandya
Priya Sreedharan
Josh Glandorf
Jennifer Holland
Peter Ryan
Andy Lucas
Kirk Kurashige
Calvin Fong
Rick Larsen
Nelson Lee
Brian Chan
Oksana Gologorskaya
Sandy Stall
Thomas Huray
Jason Woll
Support
Admin and Tech Staff
• Andrew Jan
• Mounira Kenaani
• Pam Allarde
• Boris Oskotsky
• Tyler Fogal
• University of California, San Francisco
• Priscilla Chan and Mark Zuckerberg
• Barbara and Gerson Bakar Foundation
• NIH: NIAID, NLM, NIGMS, NCI, NHLBI, OD; NIDDK, NHGRI, NIA, NCATS, NICHD
• Intervalien Foundation, Leon Lowenstein Foundation
• March of Dimes, Juvenile Diabetes Research Foundation
• California Governor’s Office of Planning and Research
• Howard Hughes Medical Institute, California Institute for Regenerative Medicine
• Hewlett Packard, L’Oreal, Progenity, Genentech
• Scleroderma Research Foundation
• Clayville Research Fund, PhRMA Foundation, Stanford Cancer Center, Bio-X, SPARK
• Tarangini Deshpande
• Kimayani Butte
• Carrie Byington, Talmadge King, Mark Laret
• Jack Stobo, Sam Hawgood, Keith Yamamoto
• Isaac Kohane
Atul Butte's presentation at #AMIA2021 for the Knowledge Discovery and Data Mining Workshop meeting

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Atul Butte's presentation at #AMIA2021 for the Knowledge Discovery and Data Mining Workshop meeting

  • 1. Precisely Practicing Medicine from 700 Trillion Points of University of California Health Data Atul Butte, MD, PhD Chief Data Scientist, University of California Health (UC Health) Priscilla Chan and Mark Zuckerberg Distinguished Professor Director, Bakar Computational Health Sciences Institute, UCSF atul.butte@ucsf.edu ▪ @atulbutte
  • 2. Conflicts of Interest • Scientific founder – Personalis – NuMedii – Carmenta (Progenity) – Genstruct • Honoraria for talks – Lilly – Pfizer – Siemens – Bristol Myers Squibb – AstraZeneca – Roche – Genentech – Warburg Pincus – CRG – AbbVie – Westat • Past or present consultancy – Personalis – NuMedii – Lilly – Johnson and Johnson – Roche – Genstruct – Tercica – Ecoeos – Helix – Ansh Labs – uBiome – Prevendia – Samsung – Assay Depot – Regeneron – Verinata (Illumina) – Pathway Diagnostics – Geisinger Health – Covance – Wilson Sonsini Goodrich & Rosati – Orrick – 10X Genomics – GNS Healthcare – Gerson Lehman Group – Coatue Management • Other corporate relationships – Northrop Grumman – Genentech – Johnson and Johnson – Optum • Shares or Ownership – NuMedii (major) – Personalis (major) – Apple – Facebook – Alphabet (Google) – Microsoft – Amazon – Snap – 10x Genomics – Illumina – Nuna Health – Assay Depot (Scientist.com) – Vet24seven – Regeneron – Sanofi – Royalty Pharma – AstraZeneca – Moderna – Biogen – Paraxel – Sutro • Speakers’ bureau – None • Companies started by students – Carmenta – Serendipity – Stimulomics – NunaHealth – Praedicat – MyTime – Flipora – Tumbl.in – Polyglot – Iota Health – Ongevity Health
  • 3. How it started… How it’s going…
  • 5. Faculty Recruitments 2015-2021 Dara Torgerson Will Brown Alejandro Sweet- Cordero Udi Manber Julia Adler- Milstein Ted Goldstein Ashish Raj Marina Sirota Julian Hong Peds DOM, DPM Peds DOM DOM, DHM Hem Onc Radiology Peds Rad Onc Vasilis Ntranos Jingjing Li Ben Rosner Franklin Huang Jean Feng John Capra Yulin Hswen Ryan Hernandez Tom Peterson Vivek Rudrapatna Diabetes Ctr Neurol DOM, DHM DOM, Onc DEB DEB DEB BTS DOM, Ortho DOM, Gastro
  • 7. AWS & Wynton Shared Compute & Storage For many types of computing needs Driving Integrative Research/Precision Medicine Spanning Basic, Translational, Clinical, Population, …. Information Commons SPOKE Knowledge Network Reflecting how human biology really works - pathways Inquiry Tools and Talent Clinical Notes Radiology Images Info Extract Structured Clinical Data 35+ biomed reference databases Genomic profiles Clinical Commons Imaging Commons Omics Commons Information Commons The Research Environment For Precision Medicine Nearly 200 researchers onboarded
  • 8. Clinical Notes De-identification State of the art algorithm developed at UCSF 25,000 manually annotated notes for training and testing 75 million notes de-identified in development environment 3rd party vendor certifying data de-identification process ü ü Collaboration with Privacy and IRB on making the data available upon completion of certification ü Beau Norgeot github.com/beaunorgeot/philter_ucsf ü ü
  • 10. Post-approval safety Updating side effect rates Discovering novel side effects Supporting regulatory approval Single-arm experimental trials ”Digital approvals” Biosimilar development Informing clinical trials design Better patient selection Trimming the trials: more efficient data collection Continually establishing efficacy Assessing the efficacy-effectiveness gap Searching for efficacy in specific populations Effect modifiers and precision medicine Long-term, post trial outcomes Comparative effectiveness Integrating costs with comparative effectiveness Understanding effects of pharmacy practices on healthcare utilization Studying novel on-label pharmaceuticals versus older off-label drugs Studying the practice of medicine Quality of practice, medical errors Standardizing care and care delivery The effect of payors on medical care Are new-generation diagnostics improving outcomes? Data driven decision support Clinical-Decision Support: the Provider Perspective Clinical-Decision Support: the Patient Perspective Clinical-Decision Support: the Community Perspective Twenty-one uses for Real World Data Vivek Rudrapatna bit.ly/jci_RWD
  • 12. Over 100 now approved just counting radiology! http://bit.ly/acr_ai
  • 13. Why is AI back in biomedicine? • Great hardware from video gamers • Software libraries with novel machine learning methods • More big data sets to train with – Molecular data, genomics on more people, more cases – Electronic health records • Hard unsolved questions need answers – Patient predictions – Efficient drug discovery – Population modeling
  • 14. The United States is spending $billions on electronic health records, and too few are using any of this data
  • 15. University of California • 10 campuses and 3 national labs • ~200,000 employees, ~250,000 students/yr UC Health • 20 health professional schools (6 med schools) • Train half the medical students and residents in California • ~$2 billion NIH funding • $13+ billion clinical operating revenue • 5000 faculty physicians, 12000 nurses • UCSF and UCLA are in US News top 10 • 5 NCI Comprehensive Cancer Centers, 5 NIH CTSA • IRB reliance, centralized contracting
  • 16. UC Health Data Analytics Platform Combining healthcare data from across the six University of California medical schools and systems Data Warehouse
  • 17. • Combined EHR data from UCSF, UCLA, UC Irvine, UC Davis, UC San Diego, and UC Riverside • Central database built using OMOP (not Epic) as a data backend – First Epic installation was January 2012 – Structured data from 2012 to the present day – 7.0 million patients with “modern” data – 220M encounters, 560M procedures, 798M med orders, 722M diagnosis codes, 2.1B lab tests and vital signs – “From Tylenol to CAR-T cells…” – California OSHPD data, pathology and radiology text elements, death index – Claims data from our self-funded plans now included – Continually harmonizing elements • Quality and performance dashboards The University of California has an incredible view of the medical system
  • 18. 3.1 million hemoglobin A1c measurements
  • 19. All University of California academic medical centers provide health data to patients through FHIR (and Apple Health) bit.ly/ucappleh
  • 20. Many operational teams within UC Health now using and benefitting from the UC Health Data Warehouse, saving $millions Central tools to improve quality of care Decreasing specific unnecessary inpatient drug use Managing costs in our self- funded health plans Centralized population health management
  • 21. Decile 1 (Least Disadvantaged) Decile 10 (Most Disadvantaged) Suppressed Value California - 2015 ADI State Rankings • Estimate socioeconomic status based on income, education, employment, housing quality at the neighborhood level • Use with race, ethnicity, gender, age to identify health disparities • Central geocoding capability to link 9-digit zip code for addresses • We have now geocoded our UC-wide primary care population • ADI is significantly associated with adverse health outcomes in our patients, in addition to race, ethnicity, and age Social Determinants of Health: Area Deprivation Index (ADI)
  • 22. San Francisco Bay Area Sacramento Area San Diego Area Riverside Area Los Angeles Area Orange County Area
  • 23. Source: American Diabetes Association Standards of Medical Care in Diabetes (2016)
  • 24. 26,822 Type 2 Diabetes Patients at UCSF since EHR Adoption in 2012 Tom Peterson bit.ly/uc_dc_5
  • 25. 26,822 Type 2 Diabetes Patients at UCSF since EHR Adoption in 2012 Tom Peterson bit.ly/uc_dc_5
  • 26. 26,822 Type 2 Diabetes Patients at UCSF since EHR Adoption in 2012 Tom Peterson bit.ly/uc_dc_5
  • 27. 26,822 Type 2 Diabetes Patients at UCSF since EHR Adoption in 2012 Tom Peterson bit.ly/uc_dc_5
  • 30. Beau Norgeot bit.ly/jamaRA Predicting the future state of a patient with Rheumatoid Arthritis
  • 31. Using the UC Covid Research Data Set (UC CORDS) Rohit Vashisht bit.ly/3EB7jpL
  • 32. Beau Norgeot bit.ly/DLHCare A New Deep Learning Healthcare System
  • 33. AI/ML lessons I’ve learned over 25 years • Get the question right; solve problems that health care professionals need solved, don't guess – And verify good questions and good unmet needs with more than one doc – Build a great diagnostic vs. understanding the biology • Perfectly lassoed variables may miss the big picture biology – Medical professionals (and biologists) really love explanations over black boxes • Watch out for input limiting models – Patients might not type in the right codes for their symptoms – They barely enter their own race/ethnicity – And docs? • Learn what IRB, HIPAA, BAA are. Learn what ICD-10 and CPT codes are. CLIA and CAP. – And learn patience. – Not all of us are cloud allergic. • Not everything needs deep learning • Having all data on everyone is super rare: genomics, images, and longitudinal EHR data • Data integration and harmonization can happen if there is a business reason for it • $1 trillion dollars is spent on Medicare and Medicaid. Would your parents would use it? • Health care inefficiency is not about friction, it’s about resistance to change
  • 34. Collaborators • Elizabeth Thomson, Patrick Dunn, Morgan Crafts / Northrop Grumman • Quan Chen, Anupama Gururaj, Dawei Lin / NIAID • Andrei Goga / UCSF Oncology • Mallar Bhattacharya / UCSF Pulmonary • Minnie Sarwal / UCSF Nephrology • Geoffrey Gurtner / UCSF Surgery • Roberta Diaz Brinton / Arizona • Carol Bult / Jackson Labs • Alejandro Sweet-Cordero, Julien Sage / Pediatric Oncology • Takashi Kadowaki, Momoko Horikoshi, Kazuo Hara, Hiroshi Ohtsu / U Tokyo • Kyoko Toda, Satoru Yamada, Junichiro Irie / Kitasato Univ and Hospital • Shiro Maeda / RIKEN • David Stevenson, Gary Shaw / Stanford Neonatology • Mark Davis, C. Garrison Fathman / Stanford Immunology • Russ Altman, Steve Quake / Stanford Bioengineering • Euan Ashley, Joseph Wu / Stanford Cardiology • Mike Snyder, Carlos Bustamante, Anne Brunet / Stanford Genetics • Jay Pasricha / Stanford Gastroenterology • Rob Tibshirani, Brad Efron / Stanford Statistics • Hannah Valantine, Kiran Khush/ Stanford Cardiology • Mark Musen, Nigam Shah / National Center for Biomedical Ontology • Sam So, Ken Weinberg, David Miklos / Stanford Oncology
  • 35. The Team Atul Butte Carrie Byington Cora Han Pagan Morris Monte Ratzlaff Mike Kilpatrick Lisa Dahm Ayan Patel Aiden Barin Chaya Mohn Ray Pablo Tim Hayes David Gonzalez Rob Follett Teju Yardi Nadya Balabanova Ellen Pollack (UCLA) Chris Longhurst (UCSD) Scott Joslyn (UCI) Joe Bengfort (UCSF) Ashish Atreja (UCD) Tom Andriola (UCI) The CIO Team Kent Anderson Steve Covington Hemanth Tatiparthi Ranjit Singh Jodi Nygaard Jeffrey Sterett Ralph Perrin Thomas Ami David Merrill Dan Phillips Melody Hill Kathy Pickell Leanie Mayor Neaktisia Lee Albert Duntugan Andrew Weaver Yael Berkovich Bill Lazarus Edgar Tijerino Jay Shah Vajra Kasturi Bill Cinnater Josh Pelino Shital Pandya Priya Sreedharan Josh Glandorf Jennifer Holland Peter Ryan Andy Lucas Kirk Kurashige Calvin Fong Rick Larsen Nelson Lee Brian Chan Oksana Gologorskaya Sandy Stall Thomas Huray Jason Woll
  • 36. Support Admin and Tech Staff • Andrew Jan • Mounira Kenaani • Pam Allarde • Boris Oskotsky • Tyler Fogal • University of California, San Francisco • Priscilla Chan and Mark Zuckerberg • Barbara and Gerson Bakar Foundation • NIH: NIAID, NLM, NIGMS, NCI, NHLBI, OD; NIDDK, NHGRI, NIA, NCATS, NICHD • Intervalien Foundation, Leon Lowenstein Foundation • March of Dimes, Juvenile Diabetes Research Foundation • California Governor’s Office of Planning and Research • Howard Hughes Medical Institute, California Institute for Regenerative Medicine • Hewlett Packard, L’Oreal, Progenity, Genentech • Scleroderma Research Foundation • Clayville Research Fund, PhRMA Foundation, Stanford Cancer Center, Bio-X, SPARK • Tarangini Deshpande • Kimayani Butte • Carrie Byington, Talmadge King, Mark Laret • Jack Stobo, Sam Hawgood, Keith Yamamoto • Isaac Kohane