James talksabout hisstory in thefoster homesystemJames talksabout issuesregardingusing digitaltoolsAn exampleof how modelmetrics worksis shownTheproposedarchitectureof the systemis shownDisney castleand dreamsfor theorganizationis presentedJamesdiscusses waysthat we mightaddress issuesusing digitaltoolsMarydescribespotentialimpacts ofthe projectThe groupparticipatesin a poll onfacilitatorsAn exampleof the AERdashboardis presentedThe groupparticipatesin a poll onbarriersMary showshistoricaltrends inadverseeventsMary talksabout thesysteminfrastructureand whereOEAIDD fits inMary showshistorical trendsin adverseevents perparticipants inDDDAn exampleof topfeatures forthe modelsis shownProgress isindicatedthroughweavingrelationshipsJack reviewshow the UHteam isinvolved inthe projectData toWisdomtriangle isshownDifferentassumptionsbuilt into alogic modelis presentedJack presentson how wemight mergedata withclinicaloutcomesGeorge describesthe scope of theproject byidentifyingproblems andsolutionsJack presentson logic modelsand how thiscan addressprogram needsLove betweenprocess focusedevaluations andquantitativemethods ishighlightedThe currentadverse eventmanagementsystem isshownGeorgedescribes whatmachinelearning is witha pipeline figureMary showshistorical trendsin participantsreceivingservices inDDDTable withHoike toNaauao acrossdifferent playersis shownMary reviewsdifferent fivedifferent playersinvolved in theprojectWe learn howallergens andmedicationsmay predictAERsThe futureadverse eventmanagementsystem isshownJack shows thedifferencebetween simplemediation andmoderatedmediationJames talksabout hisstory in thefoster homesystemJames talksabout issuesregardingusing digitaltoolsAn exampleof how modelmetrics worksis shownTheproposedarchitectureof the systemis shownDisney castleand dreamsfor theorganizationis presentedJamesdiscusses waysthat we mightaddress issuesusing digitaltoolsMarydescribespotentialimpacts ofthe projectThe groupparticipatesin a poll onfacilitatorsAn exampleof the AERdashboardis presentedThe groupparticipatesin a poll onbarriersMary showshistoricaltrends inadverseeventsMary talksabout thesysteminfrastructureand whereOEAIDD fits inMary showshistorical trendsin adverseevents perparticipants inDDDAn exampleof topfeatures forthe modelsis shownProgress isindicatedthroughweavingrelationshipsJack reviewshow the UHteam isinvolved inthe projectData toWisdomtriangle isshownDifferentassumptionsbuilt into alogic modelis presentedJack presentson how wemight mergedata withclinicaloutcomesGeorge describesthe scope of theproject byidentifyingproblems andsolutionsJack presentson logic modelsand how thiscan addressprogram needsLove betweenprocess focusedevaluations andquantitativemethods ishighlightedThe currentadverse eventmanagementsystem isshownGeorgedescribes whatmachinelearning is witha pipeline figureMary showshistorical trendsin participantsreceivingservices inDDDTable withHoike toNaauao acrossdifferent playersis shownMary reviewsdifferent fivedifferent playersinvolved in theprojectWe learn howallergens andmedicationsmay predictAERsThe futureadverse eventmanagementsystem isshownJack shows thedifferencebetween simplemediation andmoderatedmediation

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