The groupparticipatesin a poll onbarriersMary showshistorical trendsin adverseevents perparticipants inDDDProgress isindicatedthroughweavingrelationshipsMary showshistorical trendsin participantsreceivingservices inDDDData toWisdomtriangle isshownJack shows thedifferencebetween simplemediation andmoderatedmediationLove betweenprocess focusedevaluations andquantitativemethods ishighlightedTable withHoike toNaauao acrossdifferent playersis shownJames talksabout hisstory in thefoster homesystemAn exampleof the AERdashboardis presentedTheproposedarchitectureof the systemis shownAn exampleof topfeatures forthe modelsis shownJack presentson logic modelsand how thiscan addressprogram needsAn exampleof how modelmetrics worksis shownThe futureadverse eventmanagementsystem isshownMary reviewsdifferent fivedifferent playersinvolved in theprojectThe currentadverse eventmanagementsystem isshownMarydescribespotentialimpacts ofthe projectJack presentson how wemight mergedata withclinicaloutcomesJack reviewshow the UHteam isinvolved inthe projectThe groupparticipatesin a poll onfacilitatorsWe learn howallergens andmedicationsmay predictAERsJamesdiscusses waysthat we mightaddress issuesusing digitaltoolsMary showshistoricaltrends inadverseeventsGeorge describesthe scope of theproject byidentifyingproblems andsolutionsGeorgedescribes whatmachinelearning is witha pipeline figureJames talksabout issuesregardingusing digitaltoolsMary talksabout thesysteminfrastructureand whereOEAIDD fits inDisney castleand dreamsfor theorganizationis presentedDifferentassumptionsbuilt into alogic modelis presentedThe groupparticipatesin a poll onbarriersMary showshistorical trendsin adverseevents perparticipants inDDDProgress isindicatedthroughweavingrelationshipsMary showshistorical trendsin participantsreceivingservices inDDDData toWisdomtriangle isshownJack shows thedifferencebetween simplemediation andmoderatedmediationLove betweenprocess focusedevaluations andquantitativemethods ishighlightedTable withHoike toNaauao acrossdifferent playersis shownJames talksabout hisstory in thefoster homesystemAn exampleof the AERdashboardis presentedTheproposedarchitectureof the systemis shownAn exampleof topfeatures forthe modelsis shownJack presentson logic modelsand how thiscan addressprogram needsAn exampleof how modelmetrics worksis shownThe futureadverse eventmanagementsystem isshownMary reviewsdifferent fivedifferent playersinvolved in theprojectThe currentadverse eventmanagementsystem isshownMarydescribespotentialimpacts ofthe projectJack presentson how wemight mergedata withclinicaloutcomesJack reviewshow the UHteam isinvolved inthe projectThe groupparticipatesin a poll onfacilitatorsWe learn howallergens andmedicationsmay predictAERsJamesdiscusses waysthat we mightaddress issuesusing digitaltoolsMary showshistoricaltrends inadverseeventsGeorge describesthe scope of theproject byidentifyingproblems andsolutionsGeorgedescribes whatmachinelearning is witha pipeline figureJames talksabout issuesregardingusing digitaltoolsMary talksabout thesysteminfrastructureand whereOEAIDD fits inDisney castleand dreamsfor theorganizationis presentedDifferentassumptionsbuilt into alogic modelis presented

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