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Dungeons & Dragons Taught Me How to Make Better VPs

Dungeons & Dragons Taught Me How to Make Better VPs. Randomizing Physiological Data to Rapidly Produce 97 Clinically Realistic VPs. Mike Paget, Janet Tworek, Kevin McLaughlin, Bruce Wright University of Calgary Calgary, Alberta, Canada. Overview. Requirements.

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Dungeons & Dragons Taught Me How to Make Better VPs

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  1. Dungeons & Dragons Taught Me How to Make Better VPs Randomizing Physiological Data to Rapidly Produce 97 Clinically Realistic VPs Mike Paget, Janet Tworek, Kevin McLaughlin, Bruce Wright University of Calgary Calgary, Alberta, Canada

  2. Overview

  3. Requirements 1. Create 97 VPs for full year course • Clerkship (Year 3) students • Little SME time • Past experience with templates • Time consuming • Overlooked data • No preexisting cases for UofCcontext

  4. Requirements 2. Clinically accurate values and variety in VP • Names • History • Laboratory • Physical Exam

  5. Solution - Gaming Dungeons and Dragons

  6. Solution - Gaming Dungeon Master’s Guide • Randomized realism • Qualities related to numbers

  7. Solution - Gaming Dungeon Master’s Guide • Randomized realism • Qualities related to numbers • Virtual Patients • Assign text strings to numbers • Treat numeric data as numbers that fall within a pre-specified range.

  8. Solution: Process of Creating VPs

  9. 1a. Create Template for VPs • HxExIxDxRx model of VPs • Set questions

  10. 1b. Create dB tables of VP data

  11. 2a. Create Specific Data Range by Node

  12. 2b. Randomly Assign ID Values in Data Range to specific VPs

  13. 3a. Create report by linking randomized ID values to text fields in appropriate tables

  14. 3b. Convert IDs to actual values & text

  15. Proofing VPs

  16. 4a. Normal Template to SME

  17. 4b. SME records edits

  18. 5. Upload content to OLab • SME time

  19. Multiple cases at once • Insert picture of multiple cases

  20. Results • Time: • 20 mins per VP • 45 mins per side-by-side VPs (2) • Salient negatives, positives/normals • Side-by-side • Contextual cues & patterns by presentation or diagnosis • Complexity of medicine

  21. Result – Clinically accurate content

  22. Conclusions • Education as driver • Realistic VPs = data issue • Leverage gaming, dB experience • Existing software + open source VP software • Multiple clinically accurate VPs • Appropriate personnel use • Complexity of Medicine • Sharing

  23. Conclusions • Gaming “nerd” = VP programming superhero

  24. Thank You Janet Tworek jktworek@ucalgary.ca

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