James talksabout issuesregardingusing digitaltoolsThe futureadverse eventmanagementsystem isshownGeorgedescribes whatmachinelearning is witha pipeline figureJack presentson how wemight mergedata withclinicaloutcomesThe groupparticipatesin a poll onbarriersGeorge describesthe scope of theproject byidentifyingproblems andsolutionsThe groupparticipatesin a poll onfacilitatorsMary showshistoricaltrends inadverseeventsProgress isindicatedthroughweavingrelationshipsMary showshistorical trendsin participantsreceivingservices inDDDMarydescribespotentialimpacts ofthe projectTable withHoike toNaauao acrossdifferent playersis shownAn exampleof how modelmetrics worksis shownJack shows thedifferencebetween simplemediation andmoderatedmediationJack reviewshow the UHteam isinvolved inthe projectMary talksabout thesysteminfrastructureand whereOEAIDD fits inJamesdiscusses waysthat we mightaddress issuesusing digitaltoolsAn exampleof topfeatures forthe modelsis shownDisney castleand dreamsfor theorganizationis presentedData toWisdomtriangle isshownJack presentson logic modelsand how thiscan addressprogram needsAn exampleof the AERdashboardis presentedTheproposedarchitectureof the systemis shownJames talksabout hisstory in thefoster homesystemThe currentadverse eventmanagementsystem isshownMary reviewsdifferent fivedifferent playersinvolved in theprojectMary showshistorical trendsin adverseevents perparticipants inDDDWe learn howallergens andmedicationsmay predictAERsLove betweenprocess focusedevaluations andquantitativemethods ishighlightedDifferentassumptionsbuilt into alogic modelis presentedJames talksabout issuesregardingusing digitaltoolsThe futureadverse eventmanagementsystem isshownGeorgedescribes whatmachinelearning is witha pipeline figureJack presentson how wemight mergedata withclinicaloutcomesThe groupparticipatesin a poll onbarriersGeorge describesthe scope of theproject byidentifyingproblems andsolutionsThe groupparticipatesin a poll onfacilitatorsMary showshistoricaltrends inadverseeventsProgress isindicatedthroughweavingrelationshipsMary showshistorical trendsin participantsreceivingservices inDDDMarydescribespotentialimpacts ofthe projectTable withHoike toNaauao acrossdifferent playersis shownAn exampleof how modelmetrics worksis shownJack shows thedifferencebetween simplemediation andmoderatedmediationJack reviewshow the UHteam isinvolved inthe projectMary talksabout thesysteminfrastructureand whereOEAIDD fits inJamesdiscusses waysthat we mightaddress issuesusing digitaltoolsAn exampleof topfeatures forthe modelsis shownDisney castleand dreamsfor theorganizationis presentedData toWisdomtriangle isshownJack presentson logic modelsand how thiscan addressprogram needsAn exampleof the AERdashboardis presentedTheproposedarchitectureof the systemis shownJames talksabout hisstory in thefoster homesystemThe currentadverse eventmanagementsystem isshownMary reviewsdifferent fivedifferent playersinvolved in theprojectMary showshistorical trendsin adverseevents perparticipants inDDDWe learn howallergens andmedicationsmay predictAERsLove betweenprocess focusedevaluations andquantitativemethods ishighlightedDifferentassumptionsbuilt 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. James talks about issues regarding using digital tools
  2. The future adverse event management system is shown
  3. George describes what machine learning is with a pipeline figure
  4. Jack presents on how we might merge data with clinical outcomes
  5. The group participates in a poll on barriers
  6. George describes the scope of the project by identifying problems and solutions
  7. The group participates in a poll on facilitators
  8. Mary shows historical trends in adverse events
  9. Progress is indicated through weaving relationships
  10. Mary shows historical trends in participants receiving services in DDD
  11. Mary describes potential impacts of the project
  12. Table with Hoike to Naauao across different players is shown
  13. An example of how model metrics works is shown
  14. Jack shows the difference between simple mediation and moderated mediation
  15. Jack reviews how the UH team is involved in the project
  16. Mary talks about the system infrastructure and where OEAIDD fits in
  17. James discusses ways that we might address issues using digital tools
  18. An example of top features for the models is shown
  19. Disney castle and dreams for the organization is presented
  20. Data to Wisdom triangle is shown
  21. Jack presents on logic models and how this can address program needs
  22. An example of the AER dashboard is presented
  23. The proposed architecture of the system is shown
  24. James talks about his story in the foster home system
  25. The current adverse event management system is shown
  26. Mary reviews different five different players involved in the project
  27. Mary shows historical trends in adverse events per participants in DDD
  28. We learn how allergens and medications may predict AERs
  29. Love between process focused evaluations and quantitative methods is highlighted
  30. Different assumptions built into a logic model is presented