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New Computationally-Enabled Theoretical Models to Support Health BC&M What about personal models versus general models? How can you imagine developing and evaluating a model that could be used in a broad population and then adapting that model dynamically to individuals. Ross Hammond James Lester Nillo Saranummi Brigitte Piniewski Vicente Traver Scribe: Jimi Huh Breakout group summary 1 New Computationally-Enabled Theoretical Models to Support Health BC&M Problem area #1 • Challenge/barrier: Current scarcity of individuallevel, context-aware data – Makes construction and testing of individual-level models very difficult • Bold step: Create a community data commons – linking citizens with their co-occurrence data – Not HSS population-level data, which is not translatable to individuals – NOAA-like model in health, complete with IP-free zone and supporting multiple, diverse business models Breakout group summary 2 New Computationally-Enabled Theoretical Models to Support Health BC&M Problem area #2 • Challenge/barrier: Continue to build models focused on health outcomes per se rather understanding principles of mass participation that can produce health as by-product • Bold step: Think broader, e.g., free parking, micro-loans in developing countries lent to groups of people (social pressures and contract mechanism leveraging social dynamics) Breakout group summary 3 New Computationally-Enabled Theoretical Models to Support Health BC&M Problem area #3 • Challenge/barrier: Absence of pre-clinical data and starting after problem arises, e.g., obesity • Bold step: Collaboration to collect large volume of co-occurrence data on an on-going, e.g., students providing data, whether or not they suffer from a particular condition Breakout group summary 4 New Computationally-Enabled Theoretical Models to Support Health BC&M Problem area #4 • Challenge/barrier: Context of phenomena matters, but equally important, temporal sequences matter to capture path dependence • Bold step: Two approaches: 1) Data streams and data sharing to capture sequences longitudinally (L-EMA), 2) Process mining – inferring context-dependent knowledge, e.g., prior habits; 3) Consider pathway dependence in phenomena which can impact intervention selection Breakout group summary 5 New Computationally-Enabled Theoretical Models to Support Health BC&M Problem area #5 • Challenge/barrier: 1) Previously dominant “received” constructs for research design, e.g., causality, bell curve, emphasis on average, correlational analysis, 2) Data collected for pragmatic reasons, and questions raised based on whatever data is available • Bold step: 1) Adopt a greater tolerance for nontraditional approaches to modeling phenomena being studied, 2) iteration between model formation and data collection (iterative model refinement, e.g., MIDAS) Breakout group summary 6