UCSF INFORMATICS DAY10 June 2014 1
UCSF INFORMATICS DAY:
INNOVATIVE INFORMATICS AT THE UCSF BREAST CARE CENTER
10 June 201...
UCSF INFORMATICS DAY10 June 2014 2
Outline
 The UCSF Breast Care Center
o Leadership
o Mission and Vision
o Key Programs
...
UCSF INFORMATICS DAY10 June 2014 3
UCSF Breast Care Center Leadership
Laura Esserman, M.D., M.B.A.
Director, UCSF Carol Fr...
UCSF INFORMATICS DAY10 June 2014 4
Mission and Vision for the UCSF’s BCC
• Total Quality Management (TQM)
• Improve outcom...
UCSF INFORMATICS DAY10 June 2014 5
EM
R
EHR
Patient
reported data
Clinical trial
management
& adaptation
Clinical trial
ma...
UCSF INFORMATICS DAY10 June 2014 6
Achieving the Promise of Total Quality Management:
Requires lotsof coordinationat each ...
UCSF INFORMATICS DAY10 June 2014 7UCSF INFORMATICS DAY10 June 2014 7
I-SPY 2 CLINICAL TRIAL
A Replicable Model for
Acceler...
UCSF INFORMATICS DAY10 June 2014 8
What problem are we solving?
 New oncology drugs take 10-
15 years to reach patients
...
UCSF INFORMATICS DAY10 June 2014 9
I-SPY’s Primary Aim: Accelerate Pace of
Progress
Key Goals include:
Implement efficien...
UCSF INFORMATICS DAY10 June 2014 10
I-SPY’s Acceleration of
Knowledge Turns
Promising
qualifying
biomarker
I-SPY 2 TRIAL
a...
UCSF INFORMATICS DAY10 June 2014 11
22 Participating Trial Sites, Expanding to Canada
Screening40+ patientsper month
UCSF INFORMATICS DAY10 June 2014 12
Clinical Trial Data Capture – Advances with
TRANSCEND
 An integrated modular platform...
UCSF INFORMATICS DAY10 June 2014 13
TRANSCEND Platform
Data Flow
Key Features of TRANSCEND
 Scalability with Salesforce, ...
UCSF INFORMATICS DAY10 June 2014 14
Advantages of Adaptive Design
 Learn if the drug works better or worse than you think...
UCSF INFORMATICS DAY10 June 2014 15
I-SPY 2 Major Accomplishments
 Demonstrated that endpoints work better by subtype
 E...
UCSF INFORMATICS DAY10 June 2014 16
I-SPY 2/3 Partnership Opportunities
Collaboration with other therapeutic areas to
pro...
UCSF INFORMATICS DAY10 June 2014 17
R&D, Product to Implementation: Linear Inefficiency
UCSF INFORMATICS DAY10 June 2014 18
...TO A MODEL THAT LEARNS BY RE-USE OF INFORMATION
Source: Buetow, BIO
• New biomarker...
UCSF INFORMATICS DAY10 June 2014 19
Athena
On mission to save lives by transforming how we deliver
care today, learn from ...
UCSF INFORMATICS DAY10 June 2014 20
Athena
Platform
Patient
Web services
(algorithms, risk
models, thresholds)
Athena Brea...
UCSF INFORMATICS DAY10 June 2014 21
Athena Breast Health Network
On a mission to achieve transformational change
Capture ...
UCSF INFORMATICS DAY10 June 2014 22
Automated delivery and patient submission of
targeted electronic intake forms based on...
UCSF INFORMATICS DAY10 June 2014 23
Athena Technology Platform
!
!
!
Health!Ques+onnaire!System!
!
!
!
!
!
!
!
Shared!Ques...
UCSF INFORMATICS DAY10 June 2014 24
Increasing efficiency, reducing costs and
improving care
Athena
Platform
Referral
thre...
UCSF INFORMATICS DAY10 June 2014 25
Athena
Platform
Patient
Web services
(algorithms, risk
models, thresholds)
Personalize...
UCSF INFORMATICS DAY10 June 2014 26
Strength In Numbers and Team Science
Dr. Barbara Parker, UCSD Dr. Robert Cardiff, UCD
...
UCSF INFORMATICS DAY10 June 2014 27
Athena wouldn’t be possible without many of you
 UCSF IT Teams (Radiology,
Interface,...
UCSF INFORMATICS DAY10 June 2014 28
“Never doubt that a small group of committed people can
change the world. Indeed it is...
UCSF INFORMATICS DAY10 June 2014 29
Athena UCSF Interfaces
*Athena platform collects discrete data; opportunities to integ...
UCSF INFORMATICS DAY10 June 2014 30
Data in,
Knowledge out
Capture patient stories and
preferences, tumor biology, clinica...
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UCSF Informatics Day 2014 - Sue Dubman and Alexandra Solomon, "Innovative informatics at the UCSF Breast Care Center"

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UCSF Informatics Day 2014 - Sue Dubman and Alexandra Solomon, "Innovative informatics at the UCSF Breast Care Center"

  1. 1. UCSF INFORMATICS DAY10 June 2014 1 UCSF INFORMATICS DAY: INNOVATIVE INFORMATICS AT THE UCSF BREAST CARE CENTER 10 June 2014 Sue Dubman, Director IT & Informatics Carol Franc Buck Breast Care Center Alexandra Solomon, Athena & Applied Genomics Program Manager
  2. 2. UCSF INFORMATICS DAY10 June 2014 2 Outline  The UCSF Breast Care Center o Leadership o Mission and Vision o Key Programs o Organizational Framework  UCSF Breast Care Center Integrated Platform  Use Cases o I-SPY2: Accelerating new treatments to market o Athena: Integrating research and care  Q&A
  3. 3. UCSF INFORMATICS DAY10 June 2014 3 UCSF Breast Care Center Leadership Laura Esserman, M.D., M.B.A. Director, UCSF Carol Franc Buck Breast Care Center Internationally-recognized Surgeon and Breast Cancer Oncology Specialist Visionary behind Informatics-enabled research and care programs Identified by San Francisco Chronicle as a “Bay Area Person to Watch” in 2014 “A force of nature” Laura Van’t Veer, Ph.D. World renowned Molecular Biologist Called by Cancer World, the “Person Behind Personalized Medicine” Inventor of MammaPrint, a diagnostic test that foretells the risk of recurrence for breast cancer patients Winner, 2014 EU Prize for Women Innovators Co-PI with Dr. Laura Esserman on one of key BCC Programs enabled by Informatics
  4. 4. UCSF INFORMATICS DAY10 June 2014 4 Mission and Vision for the UCSF’s BCC • Total Quality Management (TQM) • Improve outcomes and quality of life for Breast Cancer Patients • While reducing overall costs Mission • Transform • The way we do research • The way care is provided • How patients, providers, payers and other stakeholders interact with each other and the health care system Vision
  5. 5. UCSF INFORMATICS DAY10 June 2014 5 EM R EHR Patient reported data Clinical trial management & adaptation Clinical trial matching Individualized Connected Care Clinician entered and verified UCSF Breast Care Integrated Platform
  6. 6. UCSF INFORMATICS DAY10 June 2014 6 Achieving the Promise of Total Quality Management: Requires lotsof coordinationat each juncturethroughdevelopment, discovery and delivery, from benchto bedsideand back
  7. 7. UCSF INFORMATICS DAY10 June 2014 7UCSF INFORMATICS DAY10 June 2014 7 I-SPY 2 CLINICAL TRIAL A Replicable Model for Accelerating Drug Development and Approval
  8. 8. UCSF INFORMATICS DAY10 June 2014 8 What problem are we solving?  New oncology drugs take 10- 15 years to reach patients  Price tag is $1+ billion  Absence of innovation in trial design/data collection tools  Cancer is a subset of diseases  Blockbuster approach won’t work Current path is UN-SUSTAINABLE
  9. 9. UCSF INFORMATICS DAY10 June 2014 9 I-SPY’s Primary Aim: Accelerate Pace of Progress Key Goals include: Implement efficient trial designs with use of biomarkers and/or surrogate endpoints to drive knowledge turns  Increase therapeutic agents tested with a standing trial and extensive network of clinical sites  Integrate the processes of clinical care and research, both technologically and culturally with team approach
  10. 10. UCSF INFORMATICS DAY10 June 2014 10 I-SPY’s Acceleration of Knowledge Turns Promising qualifying biomarker I-SPY 2 TRIAL amendment approved Continuous enrollment Drug graduates or is dropped Full Approval Accelerated Approval for Agent/ Approval for biomarker/PMA SCREENING PHASE Adapts on drugs (~60 patients) Agent Enters New agent/combination qualifies and is approved for I-SPY 2 pCR not confirmed BIOMARKER PHASE CONFIRMATORY PHASE Adapts on biomarker (~300 patients)
  11. 11. UCSF INFORMATICS DAY10 June 2014 11 22 Participating Trial Sites, Expanding to Canada Screening40+ patientsper month
  12. 12. UCSF INFORMATICS DAY10 June 2014 12 Clinical Trial Data Capture – Advances with TRANSCEND  An integrated modular platform to support adaptive clinical trials like I-SPY 2 TRIAL  Structured, coded eCRFs with source documents attached to CRF in Electronic Data Capture system o Enable real-time, remote source data verification within EDC  Randomization as an automated web service o Using data that has been source data verified  Combining evaluation of drugs and biomarkers together o Scientists need access to data early and in an integrated fashion (one stop shopping) • Clinical, Pathology, Imaging data along with biomarker data of various types (microarray, sequencing, etc.)
  13. 13. UCSF INFORMATICS DAY10 June 2014 13 TRANSCEND Platform Data Flow Key Features of TRANSCEND  Scalability with Salesforce, cloud-based environment  Modular, can securely integrate with other applications as well RE App Quartz Scheduler THE Force on SalesForce (Electronic Data Capture) Data Coordinating Center Study Sites Case Report Forms Agendia caIntegrator (Role based access to data) caArray Research Labs Automated Manual Integration Engine (MirthConnect) SMART Randomization Engine -Single Sign On -Pulls data from caArray NBIA (Imaging with AIM tool) Reporting (Pentaho)
  14. 14. UCSF INFORMATICS DAY10 June 2014 14 Advantages of Adaptive Design  Learn if the drug works better or worse than you think, as the trial progresses  Act early o Drop drugs quickly if they are ineffective or harmful o Graduate sooner if they are clearly beneficial  Learn, for each drug, which biomarkers are optimal  Phase 2 conclusions will be more accurate, better treatment of patients in the trial  Follow on 3 trials can be smaller (usually)
  15. 15. UCSF INFORMATICS DAY10 June 2014 15 I-SPY 2 Major Accomplishments  Demonstrated that endpoints work better by subtype  Enlisted multiple pharma companies into same trial  Developed I SPY 2 infrastructure o IT systems to support adaptive learning o New methods to distribute credit  Accelerated Approval guidance issued by FDA  Next Step: I-SPY 3 international confirmatory trial
  16. 16. UCSF INFORMATICS DAY10 June 2014 16 I-SPY 2/3 Partnership Opportunities Collaboration with other therapeutic areas to propagate I-SPY methodology o Assist in setting up research networks o Sharing of systems & technologies Seeking partners for I-SPY 3 Trial o Investors o Research networks o Delivery partners
  17. 17. UCSF INFORMATICS DAY10 June 2014 17 R&D, Product to Implementation: Linear Inefficiency
  18. 18. UCSF INFORMATICS DAY10 June 2014 18 ...TO A MODEL THAT LEARNS BY RE-USE OF INFORMATION Source: Buetow, BIO • New biomarkers identified • Validation conducted in silico against clinical outcomes Discovery • Drug candidates targeted to defined sub- groups • Clinical trials enriched with appropriate sub-group • Clinical trial recruitment from “standing” cohort of pre-enrolled volunteers • NDA includes genomic data Product Development • All patients genotyped • Clinical trial protocols used as treatment plans, following regulatory approval • Clinical decision support tools assist physician by comparing the patient vs.. the Outcomes Database • Each patient’s outcome de-identified and fed back to the database Clinical Care • All clinical outcomes captured and fed to Outcomes Database • “Sentinel” function sounds alarm for safety or opportunity for expanded indications Outcomes and Surveillance • Outcomes linked to molecular profiles • Algorithms identify linkages and trends • New hypothesis generated Analysis and Learning A Learning Health Care System
  19. 19. UCSF INFORMATICS DAY10 June 2014 19 Athena On mission to save lives by transforming how we deliver care today, learn from our patients, create life-changing science, and improve prevention and treatment options today and tomorrow.
  20. 20. UCSF INFORMATICS DAY10 June 2014 20 Athena Platform Patient Web services (algorithms, risk models, thresholds) Athena Breast Health Network Information Exchange Breast Health Specialist Supportive cancer services; primary providers; care team Patient
  21. 21. UCSF INFORMATICS DAY10 June 2014 21 Athena Breast Health Network On a mission to achieve transformational change Capture patient stories and preferences, tumor biology, clinical performance Deliver personalized prevention, screening, and treatment Deliver better care tomorrow by enabling care to be an engine for discovery and improvement Data in – knowledge outA UC-wide and affiliate program
  22. 22. UCSF INFORMATICS DAY10 June 2014 22 Automated delivery and patient submission of targeted electronic intake forms based on appointment Athena Platform Patient Web services (algorithms, risk models, thresholds) Real-time assessment of risk, provider and specialist communication, referral to personalized resources and services Breast Health Specialist Supportive cancer services; primary providers; care team Patient
  23. 23. UCSF INFORMATICS DAY10 June 2014 23 Athena Technology Platform ! ! ! Health!Ques+onnaire!System! ! ! ! ! ! ! ! Shared!Ques+on! Bank! ! Contextual! Answer! Repository! Questionnaire Designer Statistical Analysis Questionnaire Distribution and Data Collection Clinical Decision Support Clinic Workflow Triggers EMR Integration Process Improvement Dashboards Customized Consumer Portal
  24. 24. UCSF INFORMATICS DAY10 June 2014 24 Increasing efficiency, reducing costs and improving care Athena Platform Referral thresholds met Pended in APeX for clinician sign off Psycho- oncology Peer Support Social W ork Smoking Cessation Genetic Counseling W eight Management Onco-fertility Supportive Care Services Patient Revenue for services Better care for patients
  25. 25. UCSF INFORMATICS DAY10 June 2014 25 Athena Platform Patient Web services (algorithms, risk models, thresholds) Personalized Screening “one size does not fit all” Breast Health Specialist Supportive cancer services; primary providers; care team Patient Genomic Profiling Breast Density
  26. 26. UCSF INFORMATICS DAY10 June 2014 26 Strength In Numbers and Team Science Dr. Barbara Parker, UCSD Dr. Robert Cardiff, UCD Dr. Hoda Anton-Culver, UCI Dr. Laura van ‘t Veer , UCSF
  27. 27. UCSF INFORMATICS DAY10 June 2014 27 Athena wouldn’t be possible without many of you  UCSF IT Teams (Radiology, Interface, Clinical Inpatient, HIMSS, Leadership)  Athena IT Team and Program Management Office (PMO)  Athena UCSF Site team  UCDavis Mirth Interface Team  Molecular Biologists  Epidemiologists  Radiologists  Genetic Counselors / Breast Health Specialist  Geneticists  Surgeons  Radiation Oncologists  Oncologists  Anthropologists  Statisticians  Social Workers  Psycho-Oncologists  Psychologists  Nutrition and exercise specialists  Pathologists  Reconstructive surgeons  Mammography Technologists  Administrators and Leadership  Clinical Coordinators  Laboratory Technicians  Clinical staff  Call center staff  Patients
  28. 28. UCSF INFORMATICS DAY10 June 2014 28 “Never doubt that a small group of committed people can change the world. Indeed it is the only thing that ever has.” -Margaret Mead “The greater danger for most of us lies not in setting our aim too high and falling short; but in setting our aim too low, and achieving our mark.” - Michelangelo The Challenges are Hard but...
  29. 29. UCSF INFORMATICS DAY10 June 2014 29 Athena UCSF Interfaces *Athena platform collects discrete data; opportunities to integrate discrete data mapped to health history etc. back into Epic.
  30. 30. UCSF INFORMATICS DAY10 June 2014 30 Data in, Knowledge out Capture patient stories and preferences, tumor biology, clinical performance Deliver personalized prevention, screening, and treatment Deliver better care tomorrow by enabling care to be an engine for discovery and improvement

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