Mary talksabout thesysteminfrastructureand whereOEAIDD fits inJames talksabout issuesregardingusing digitaltoolsMary showshistorical trendsin participantsreceivingservices inDDDJack shows thedifferencebetween simplemediation andmoderatedmediationDifferentassumptionsbuilt into alogic modelis presentedLove betweenprocess focusedevaluations andquantitativemethods ishighlightedJames talksabout hisstory in thefoster homesystemGeorgedescribes whatmachinelearning is witha pipeline figureMarydescribespotentialimpacts ofthe projectTheproposedarchitectureof the systemis shownDisney castleand dreamsfor theorganizationis presentedAn exampleof the AERdashboardis presentedThe groupparticipatesin a poll onbarriersThe currentadverse eventmanagementsystem isshownGeorge describesthe scope of theproject byidentifyingproblems andsolutionsMary showshistoricaltrends inadverseeventsProgress isindicatedthroughweavingrelationshipsThe groupparticipatesin a poll onfacilitatorsTable withHoike toNaauao acrossdifferent playersis shownThe futureadverse eventmanagementsystem isshownWe learn howallergens andmedicationsmay predictAERsJack presentson how wemight mergedata withclinicaloutcomesData toWisdomtriangle isshownAn exampleof how modelmetrics worksis shownJack presentson logic modelsand how thiscan addressprogram needsMary reviewsdifferent fivedifferent playersinvolved in theprojectAn exampleof topfeatures forthe modelsis shownJamesdiscusses waysthat we mightaddress issuesusing digitaltoolsJack reviewshow the UHteam isinvolved inthe projectMary showshistorical trendsin adverseevents perparticipants inDDDMary talksabout thesysteminfrastructureand whereOEAIDD fits inJames talksabout issuesregardingusing digitaltoolsMary showshistorical trendsin participantsreceivingservices inDDDJack shows thedifferencebetween simplemediation andmoderatedmediationDifferentassumptionsbuilt into alogic modelis presentedLove betweenprocess focusedevaluations andquantitativemethods ishighlightedJames talksabout hisstory in thefoster homesystemGeorgedescribes whatmachinelearning is witha pipeline figureMarydescribespotentialimpacts ofthe projectTheproposedarchitectureof the systemis shownDisney castleand dreamsfor theorganizationis presentedAn exampleof the AERdashboardis presentedThe groupparticipatesin a poll onbarriersThe currentadverse eventmanagementsystem isshownGeorge describesthe scope of theproject byidentifyingproblems andsolutionsMary showshistoricaltrends inadverseeventsProgress isindicatedthroughweavingrelationshipsThe groupparticipatesin a poll onfacilitatorsTable withHoike toNaauao acrossdifferent playersis shownThe futureadverse eventmanagementsystem isshownWe learn howallergens andmedicationsmay predictAERsJack presentson how wemight mergedata withclinicaloutcomesData toWisdomtriangle isshownAn exampleof how modelmetrics worksis shownJack presentson logic modelsand how thiscan addressprogram needsMary reviewsdifferent fivedifferent playersinvolved in theprojectAn exampleof topfeatures forthe modelsis shownJamesdiscusses waysthat we mightaddress issuesusing digitaltoolsJack reviewshow the UHteam isinvolved inthe projectMary showshistorical trendsin adverseevents perparticipants inDDD

OEAIDD Data Party BINGO - Call List

(Print) Use this randomly generated list as your call list when playing the game. There is no need to say the BINGO column name. Place some kind of mark (like an X, a checkmark, a dot, tally mark, etc) on each cell as you announce it, to keep track. You can also cut out each item, place them in a bag and pull words from the bag.


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  1. Mary talks about the system infrastructure and where OEAIDD fits in
  2. James talks about issues regarding using digital tools
  3. Mary shows historical trends in participants receiving services in DDD
  4. Jack shows the difference between simple mediation and moderated mediation
  5. Different assumptions built into a logic model is presented
  6. Love between process focused evaluations and quantitative methods is highlighted
  7. James talks about his story in the foster home system
  8. George describes what machine learning is with a pipeline figure
  9. Mary describes potential impacts of the project
  10. The proposed architecture of the system is shown
  11. Disney castle and dreams for the organization is presented
  12. An example of the AER dashboard is presented
  13. The group participates in a poll on barriers
  14. The current adverse event management system is shown
  15. George describes the scope of the project by identifying problems and solutions
  16. Mary shows historical trends in adverse events
  17. Progress is indicated through weaving relationships
  18. The group participates in a poll on facilitators
  19. Table with Hoike to Naauao across different players is shown
  20. The future adverse event management system is shown
  21. We learn how allergens and medications may predict AERs
  22. Jack presents on how we might merge data with clinical outcomes
  23. Data to Wisdom triangle is shown
  24. An example of how model metrics works is shown
  25. Jack presents on logic models and how this can address program needs
  26. Mary reviews different five different players involved in the project
  27. An example of top features for the models is shown
  28. James discusses ways that we might address issues using digital tools
  29. Jack reviews how the UH team is involved in the project
  30. Mary shows historical trends in adverse events per participants in DDD