Jack reviewshow the UHteam isinvolved inthe projectTable withHoike toNaauao acrossdifferent playersis shownJames talksabout hisstory in thefoster homesystemThe currentadverse eventmanagementsystem isshownThe futureadverse eventmanagementsystem isshownThe groupparticipatesin a poll onfacilitatorsGeorgedescribes whatmachinelearning is witha pipeline figureData toWisdomtriangle isshownLove betweenprocess focusedevaluations andquantitativemethods ishighlightedJamesdiscusses waysthat we mightaddress issuesusing digitaltoolsJack presentson logic modelsand how thiscan addressprogram needsMarydescribespotentialimpacts ofthe projectAn exampleof how modelmetrics worksis shownAn exampleof the AERdashboardis presentedDifferentassumptionsbuilt into alogic modelis presentedJack presentson how wemight mergedata withclinicaloutcomesAn exampleof topfeatures forthe modelsis shownDisney castleand dreamsfor theorganizationis presentedThe groupparticipatesin a poll onbarriersTheproposedarchitectureof the systemis shownMary showshistorical trendsin participantsreceivingservices inDDDGeorge describesthe scope of theproject byidentifyingproblems andsolutionsJames talksabout issuesregardingusing digitaltoolsMary showshistoricaltrends inadverseeventsProgress isindicatedthroughweavingrelationshipsMary showshistorical trendsin adverseevents perparticipants inDDDMary reviewsdifferent fivedifferent playersinvolved in theprojectWe learn howallergens andmedicationsmay predictAERsJack shows thedifferencebetween simplemediation andmoderatedmediationMary talksabout thesysteminfrastructureand whereOEAIDD fits inJack reviewshow the UHteam isinvolved inthe projectTable withHoike toNaauao acrossdifferent playersis shownJames talksabout hisstory in thefoster homesystemThe currentadverse eventmanagementsystem isshownThe futureadverse eventmanagementsystem isshownThe groupparticipatesin a poll onfacilitatorsGeorgedescribes whatmachinelearning is witha pipeline figureData toWisdomtriangle isshownLove betweenprocess focusedevaluations andquantitativemethods ishighlightedJamesdiscusses waysthat we mightaddress issuesusing digitaltoolsJack presentson logic modelsand how thiscan addressprogram needsMarydescribespotentialimpacts ofthe projectAn exampleof how modelmetrics worksis shownAn exampleof the AERdashboardis presentedDifferentassumptionsbuilt into alogic modelis presentedJack presentson how wemight mergedata withclinicaloutcomesAn exampleof topfeatures forthe modelsis shownDisney castleand dreamsfor theorganizationis presentedThe groupparticipatesin a poll onbarriersTheproposedarchitectureof the systemis shownMary showshistorical trendsin participantsreceivingservices inDDDGeorge describesthe scope of theproject byidentifyingproblems andsolutionsJames talksabout issuesregardingusing digitaltoolsMary showshistoricaltrends inadverseeventsProgress isindicatedthroughweavingrelationshipsMary showshistorical trendsin adverseevents perparticipants inDDDMary reviewsdifferent fivedifferent playersinvolved in theprojectWe learn howallergens andmedicationsmay predictAERsJack shows thedifferencebetween simplemediation andmoderatedmediationMary talksabout thesysteminfrastructureand whereOEAIDD fits in

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