Differentassumptionsbuilt into alogic modelis presentedAn exampleof the AERdashboardis presentedJack reviewshow the UHteam isinvolved inthe projectThe groupparticipatesin a poll onbarriersTable withHoike toNaauao acrossdifferent playersis shownMary reviewsdifferent fivedifferent playersinvolved in theprojectAn exampleof topfeatures forthe modelsis shownMary showshistorical trendsin participantsreceivingservices inDDDTheproposedarchitectureof the systemis shownAn exampleof how modelmetrics worksis shownLove betweenprocess focusedevaluations andquantitativemethods ishighlightedGeorge describesthe scope of theproject byidentifyingproblems andsolutionsGeorgedescribes whatmachinelearning is witha pipeline figureJamesdiscusses waysthat we mightaddress issuesusing digitaltoolsJames talksabout hisstory in thefoster homesystemWe learn howallergens andmedicationsmay predictAERsMarydescribespotentialimpacts ofthe projectThe currentadverse eventmanagementsystem isshownJames talksabout issuesregardingusing digitaltoolsThe futureadverse eventmanagementsystem isshownProgress isindicatedthroughweavingrelationshipsJack presentson logic modelsand how thiscan addressprogram needsJack shows thedifferencebetween simplemediation andmoderatedmediationData toWisdomtriangle isshownMary talksabout thesysteminfrastructureand whereOEAIDD fits inMary showshistorical trendsin adverseevents perparticipants inDDDThe groupparticipatesin a poll onfacilitatorsDisney castleand dreamsfor theorganizationis presentedMary showshistoricaltrends inadverseeventsJack presentson how wemight mergedata withclinicaloutcomesDifferentassumptionsbuilt into alogic modelis presentedAn exampleof the AERdashboardis presentedJack reviewshow the UHteam isinvolved inthe projectThe groupparticipatesin a poll onbarriersTable withHoike toNaauao acrossdifferent playersis shownMary reviewsdifferent fivedifferent playersinvolved in theprojectAn exampleof topfeatures forthe modelsis shownMary showshistorical trendsin participantsreceivingservices inDDDTheproposedarchitectureof the systemis shownAn exampleof how modelmetrics worksis shownLove betweenprocess focusedevaluations andquantitativemethods ishighlightedGeorge describesthe scope of theproject byidentifyingproblems andsolutionsGeorgedescribes whatmachinelearning is witha pipeline figureJamesdiscusses waysthat we mightaddress issuesusing digitaltoolsJames talksabout hisstory in thefoster homesystemWe learn howallergens andmedicationsmay predictAERsMarydescribespotentialimpacts ofthe projectThe currentadverse eventmanagementsystem isshownJames talksabout issuesregardingusing digitaltoolsThe futureadverse eventmanagementsystem isshownProgress isindicatedthroughweavingrelationshipsJack presentson logic modelsand how thiscan addressprogram needsJack shows thedifferencebetween simplemediation andmoderatedmediationData toWisdomtriangle isshownMary talksabout thesysteminfrastructureand whereOEAIDD fits inMary showshistorical trendsin adverseevents perparticipants inDDDThe groupparticipatesin a poll onfacilitatorsDisney castleand dreamsfor theorganizationis presentedMary showshistoricaltrends inadverseeventsJack presentson how wemight mergedata withclinicaloutcomes

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.


1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
  1. Different assumptions built into a logic model is presented
  2. An example of the AER dashboard is presented
  3. Jack reviews how the UH team is involved in the project
  4. The group participates in a poll on barriers
  5. Table with Hoike to Naauao across different players is shown
  6. Mary reviews different five different players involved in the project
  7. An example of top features for the models is shown
  8. Mary shows historical trends in participants receiving services in DDD
  9. The proposed architecture of the system is shown
  10. An example of how model metrics works is shown
  11. Love between process focused evaluations and quantitative methods is highlighted
  12. George describes the scope of the project by identifying problems and solutions
  13. George describes what machine learning is with a pipeline figure
  14. James discusses ways that we might address issues using digital tools
  15. James talks about his story in the foster home system
  16. We learn how allergens and medications may predict AERs
  17. Mary describes potential impacts of the project
  18. The current adverse event management system is shown
  19. James talks about issues regarding using digital tools
  20. The future adverse event management system is shown
  21. Progress is indicated through weaving relationships
  22. Jack presents on logic models and how this can address program needs
  23. Jack shows the difference between simple mediation and moderated mediation
  24. Data to Wisdom triangle is shown
  25. Mary talks about the system infrastructure and where OEAIDD fits in
  26. Mary shows historical trends in adverse events per participants in DDD
  27. The group participates in a poll on facilitators
  28. Disney castle and dreams for the organization is presented
  29. Mary shows historical trends in adverse events
  30. Jack presents on how we might merge data with clinical outcomes