• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Panel Discussion:  Government Initiatives and Opportunities
 

Panel Discussion: Government Initiatives and Opportunities

on

  • 384 views

Thursday, October 25, 1990 ...

Thursday, October 25, 1990
Panel Discussion: Government Initiatives and Opportunities

Moderator: Bill Riley, PhD – Chief, Science of Research and Technology Branch, NCI

Panelists: Misha Pavel, PhD – Program Director, NSF Bakul Patel, MS – Policy Advisor, Office of the Center Director, CDRH, FDA Kim Tyrell-Knot, JD – Partner, Epstein Becker & Green

Statistics

Views

Total Views
384
Views on SlideShare
383
Embed Views
1

Actions

Likes
0
Downloads
1
Comments
0

1 Embed 1

http://profiles.sc-ctsi.org 1

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment
  • Same two slides for last 3 years.
  • NSF program thrusts
  • The proposed projects should focus transformative advancements in healthcare delivery and/or improvements in wellbeing, but must include advancements in one or more fundamental scientific areas as indicated in this slide.
  • Context: Elevated blood pressure in the context of high-energy activity vs quiet as measured by the accelerometers in the watch and phone.Calibration of “like” measuremetns is a pre-requisite for interoperability
  • http://www.starstore.com/acatalog/iceberg-poster.jpg

Panel Discussion:  Government Initiatives and Opportunities Panel Discussion: Government Initiatives and Opportunities Presentation Transcript

  • Smart Health and Wellbeing Misha Pavel National Science Foundation Computer & Information Science & Engineering Directorate Information and Intelligent Systems Division & Oregon Health and Science University Department of Biomedical EngineeringAny opinion, finding, and conclusions or recommendations expressed in thismaterial; are those of the author and do not necessarily reflect the views of the National Science Foundation 1
  • Information and communication technologies are poised to be key components in transforming healthcare … preventing the onset of diseases, improving diagnoses and treatments,enhancing the quality of health care delivery,and empowering us to participate in our own health and well-being
  • Related Federal Plans and Reports • PCAST: Realizing the Full Potential of Health Information Technology to Improve Healthcare for Americans: The Path Forward, Dec 2010 • PCAST: Designing a Digital Future: Federally Funded Research and Development in Networking and Information Technology, Dec 2010 • NRC: IOM Learning Health System Series of Reports • ONC: Federal Health Information Technology Strategic Plan 2011-2015, Office of the National Coordinator for Health Information Technology 2011. • National Prevention Strategy, National Prevention Council, U.S. Department of Health and Human Services, Office of the Surgeon General, 2011 • HHS: National Strategy for Quality Improvement in Health Care, U.S. Department of Health and Human Services, Interagency Working Group on Health Care Quality, 2011. • NSTC: Trustworthy Cyberspace: Strategic Plan for the Federal Cybersecurity Research and Development Program, NSTC, 2011.
  • New Vision Patient-Centered Frameworkfor Health and Wellness Environment Payers Employers Privacy Legal Self-care Physio Sensors Inference Assessment Patient User Interfaces Family Physical Function Activity Sensors Caregiver Cognitive Function Chronic Disease Coach Socialization Mobile Sensors Clinician Devices EHR, PHR Mobile Health NIT: Networks, DB, API Software, EHR, PHR 4
  • Some Research Challenges in Health• Safe and secure distributed home healthcare ▫ Fool-proof application even under cognitive decline• Mental health and cognitive decline• Behavior modification ▫ Obesity, smoking, exercise• Networked wireless cooperative medical devices• Assistive robots and prosthetics• Augmented human: ▫ Integrated sensory, cognitive and mobility assist
  • Smart Health Research Thrusts Digital Health Information • Continuous accrual and integration of EHR, pharma and clinical Infrastructure research data in a distributed but federated system Informatics and • A foundation for evidence-based, patient-centric practice & Infrastructure research • Cognitive support systems spanning clinical to lay decision Data to Knowledge to Decision making Reasoning under uncertainty • Data mining, machine learning, discovery from massive longitudinal and individual data Empowered Individuals • New models of distributed and home-centered healthcare provision Energized, enabled, educated • Technologies that aide in modifying self and group behavior Sensors, Devices, and Robotics • Assistive technologies embodying computational intelligence Sensor-based actuation • Medical devices, co-robots, cognitive orthotics, rehab coaches
  • Causes of Premature Mortality 10% Medical Care Deficiency Genetic 30% Environmental 40% Exposure 5% BehavioralSocial Circumstances 15% 7
  • 8
  • Smart Health and Wellbeing Program Proposals• Have the potential to transform healthcare delivery and/or improve quality of life• Advances in at least one core scientific area ▫ Engineering, e.g., sensor technology, signal processing, optimization, complex systems analysis, etc. ▫ Computer Science and Engineering, new inference algorithm, mathematical modeling, ▫ Social, Behavioral and Economics, e.g. behavior change, psychology, social psychology, systems science, and others
  • Proposal Review Criteria  Intellectual Merit  Broader Impacts  SHB Programmatic CriteriaReturn
  • Intellectual Merit Criteria ▫ To what extent does the proposal suggest and explore creative, original and potentially transformative concepts? ▫ How important is theSystem and youradvancing knowledge NSF’s Panelist proposed activity to Reviews and understanding within its own field and/or across fields? ▫ What will be the significant contribution of the project to the research and knowledge base of the field? ▫ How well conceived and organized is the proposed activity? ▫ How well qualified is the team to conduct the proposed activity? ▫ Is there sufficient access to resources; equipment, facilities, requested support (budget)?Return
  • Standard NSF Evaluation Criteria: Broader Impacts• Implicit – new knowledge, field, benefits to society• Explicit – societal impact, technology transfer, results dissemination• Integration of Research and Education – teaching, training, and learning ▫ Development of curriculum ▫ Development of education experiences through student involvement in emerging research and technology areas ▫• Broadening participation of underrepresented groups including gender, ethnicity, disability, geographic, etc. Return 12
  • SHB Requirements • Impact on a key health and wellbeing problem • Significance of the fundamental contribution to engineering, computer and information sciences, or social, behavioral and economic sciences. • Collaboration Plan – Demonstrate that the participating investigators will work synergistically to accomplish the project objectives • Data Management Plan – The definition of “data” may include, but not limited to data, publications, samples, physical collections, software and models • Postdoctoral Training – A description of the mentoring activities for the postdocs including collaboration with researchers from diverse backgrounds and disciplines
  • Suggestions for Proposers• Find the most appropriate directorate and program• Look at the current and past funded projects• Become a reviewer• Write for the reviewers including the Project Summary• Focus on the innovative, transformative aspects of your proposal• Double-check that your proposal has ALL the parts required by the solicitation• Have your colleagues read it• Pay attention to details, e.g. speling spelling• Submit it to FastLane 14
  • Useful Website: www.nsf.gov15
  • A examples of currently SHB-funded projects• Predictive modeling and patient-centered detection and prediction ▫ Novel Computational Techniques for Cardiovascular Risk Stratification mHealth ▫ Assistive Cloudlet-based Mobile Computing for the Cognitively Impaired ▫ Self-care Management: Patient-Centered Diabetic Wound Care Using Smart Phones ▫ From the Ground Up -- Mobile Tools for Grassroots Programs in Public Health)• Inference of activities and states ▫ Quantitative Observational Practice in Family Studies: The case of reactivity• Robotics, co-robots, smart prosthetics and orthotics ▫ Socially Assistive Human-Machine Interaction for Improved Compliance and Health Outcomes ▫ An Assistive, Robotic Table [ART] Promoting Independent Living• Social computing – empowering individuals, coaching ▫ Matchmaking for health: Facilitating Mentoring in Peer Health Communities through Social Matching• Monitoring and Inference – home care ▫ Crafting a Human-Centric Environment to Support Human Health Needs (Cook, WU) ▫ Computational Algorithms for Predictive Health Assessment Multi-Patient Fall-Risk Monitoring in Health Care Facilities
  • Currently Funded Wireless by NSFOrganizations SBE OISE IIS IIP ECCS DUE DMS DBI CNS CMMI CCF CBET 0 5 10 15 20 25 30 35 4017
  • Currently Funded mHealth by NSF Organizations SESOISE OCI OCE IIS IIPECCS DUEDMR DEB CNSCMMI CCFCBET BCS ANT AGS 0 5 10 15 20 25 30 35 4018
  • Sample of Organizational Challenges • Organizing transdisciplinary teams ▫ Learn each others language ▫ Acculturation: Merging technology and clinical/behavioral domains • Recognizing innovative ideas ▫ Experts are frequently wrong in their prediction ▫ Few of us like transformative ideas when they see them for the first time • Finding good problems and focus areas ▫ Important health and wellbeing problem ▫ Contribution to fundamental science 19
  • Sample of Scientific Challenges Ahead • Raw data processing and cleaning ▫ Harmonizing ▫ Synchronizing ▫ Maintaining data integrity • Computational Predictive Modeling ▫ Relating observable to variables of interest ▫ Maximizing statistical efficiency ▫ Modeling individuals • Analytic issues ▫ Missing data ▫ Big data ▫ Detecting anomalies ▫ Rapidly changing technology 20
  • Extraction of Knowledge and MeaningHarmonizing/Coherence: Invariant Decisions Decisions Decisions Decisions Decisions NIT (ICT) Network Layer, Databases, EHR, PHR, XHR Adaptation, Calibration & Fusion Transform Transform Transform Transform Heterogeneous Sources/Sensors
  • Multiscale Modeling: From sensors to brain functionshould include behavioral and cognitive factors • Unobtrusive Sensory measurement of gait Motor characteristics Cognition Perception • Model relationship Inference between the sensoryof Brain Function inputs and gait characteristics Gait • Infer sensory- motor, perceptual and cognitive functions Inference of Gait Parameters Sensors 22
  • Missing Data: Missing Not at Random Weight Monitoring: Congestive Heart Failure Patient Subject 2 174 Philips 172 HealthBuddy 170 168 Weight [lb] 166 164 162 160 158 156 154 110 120 130 140 150 160 170 180 190 Time [Days]23
  • Modeling Individuals not Populations S1 S2 S3 S4 Number of Errors Speed of Dialing [#/second]24
  • Take home messagesHealthcare needs a disruptive changeWireless Technology is likely to play a key role but …• We need to build transdisciplinary teams to focus on key health/wellbeing problems• There us a need for more/new science• Computational modeling is needed to ▫ Connect observable measures to aspects of health ▫ Predict and detect ▫ Use data efficiently 25
  • 26Thank You