The currentadverse eventmanagementsystem isshownMary showshistorical trendsin adverseevents perparticipants inDDDData toWisdomtriangle isshownAn exampleof topfeatures forthe modelsis shownAn exampleof how modelmetrics worksis shownDifferentassumptionsbuilt into alogic modelis presentedAn exampleof the AERdashboardis presentedMary talksabout thesysteminfrastructureand whereOEAIDD fits inMary reviewsdifferent fivedifferent playersinvolved in theprojectJamesdiscusses waysthat we mightaddress issuesusing digitaltoolsGeorge describesthe scope of theproject byidentifyingproblems andsolutionsWe learn howallergens andmedicationsmay predictAERsMary showshistorical trendsin participantsreceivingservices inDDDJack shows thedifferencebetween simplemediation andmoderatedmediationMary showshistoricaltrends inadverseeventsThe groupparticipatesin a poll onfacilitatorsTheproposedarchitectureof the systemis shownThe groupparticipatesin a poll onbarriersJack presentson how wemight mergedata withclinicaloutcomesJames talksabout issuesregardingusing digitaltoolsJack reviewshow the UHteam isinvolved inthe projectThe futureadverse eventmanagementsystem isshownProgress isindicatedthroughweavingrelationshipsMarydescribespotentialimpacts ofthe projectJames talksabout hisstory in thefoster homesystemTable withHoike toNaauao acrossdifferent playersis shownDisney castleand dreamsfor theorganizationis presentedGeorgedescribes whatmachinelearning is witha pipeline figureLove betweenprocess focusedevaluations andquantitativemethods ishighlightedJack presentson logic modelsand how thiscan addressprogram needsThe currentadverse eventmanagementsystem isshownMary showshistorical trendsin adverseevents perparticipants inDDDData toWisdomtriangle isshownAn exampleof topfeatures forthe modelsis shownAn exampleof how modelmetrics worksis shownDifferentassumptionsbuilt into alogic modelis presentedAn exampleof the AERdashboardis presentedMary talksabout thesysteminfrastructureand whereOEAIDD fits inMary reviewsdifferent fivedifferent playersinvolved in theprojectJamesdiscusses waysthat we mightaddress issuesusing digitaltoolsGeorge describesthe scope of theproject byidentifyingproblems andsolutionsWe learn howallergens andmedicationsmay predictAERsMary showshistorical trendsin participantsreceivingservices inDDDJack shows thedifferencebetween simplemediation andmoderatedmediationMary showshistoricaltrends inadverseeventsThe groupparticipatesin a poll onfacilitatorsTheproposedarchitectureof the systemis shownThe groupparticipatesin a poll onbarriersJack presentson how wemight mergedata withclinicaloutcomesJames talksabout issuesregardingusing digitaltoolsJack reviewshow the UHteam isinvolved inthe projectThe futureadverse eventmanagementsystem isshownProgress isindicatedthroughweavingrelationshipsMarydescribespotentialimpacts ofthe projectJames talksabout hisstory in thefoster homesystemTable withHoike toNaauao acrossdifferent playersis shownDisney castleand dreamsfor theorganizationis presentedGeorgedescribes whatmachinelearning is witha pipeline figureLove betweenprocess focusedevaluations andquantitativemethods ishighlightedJack presentson logic modelsand how thiscan addressprogram needs

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