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

2. Overview 97 VPs Clinically relevant Requirements Gaming experience dB design Solution 20 mins SME time per VP 97 clinically relevant VPs Results

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 UofC context

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

5. Solution - Gaming

6. 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 All content areas Ranges for normal results 1. VP Template Table for each text field ID number per row 2. Create Data Tables Randomized normal values Variety of text responses 3. Prepared Normal Template Change key nodes Approve -, + 4. SME Proofread Complete content SME approved 5. Case ready for upload

9. 1a. Create Template for VPs NodePatient Response Any diseases run in the family? Lab: Anti Hepatitis A Virus - Total Lab: Chemistry Na – Sodium Knee exam: Do you have any pets? H x E x I x D x R x model of VPs Set questions

10. 1b. Create dB tables of VP data IDPet 1No, I don’t like animals 2I have 3 dogs 3I have a cat I took in 4Just a goldfish 5I have a dog, but I think I might be allergic IDPositive or Negative 1Positive 2Negative IDDiseases that run in the family 1Cancer 2Stupidity 3Heart attacks 4Lung cancer 5Arthritis 6Accident prone 7Asthma 8Strokes 9Dementia 10Pneumonia

11. 2a. Create Specific Data Range by Node NodeLowHighAdditional Text Any diseases run in the family? 110Not applicable Lab: Anti Hepatitis A Virus - Total 12 Lab: Chemistry Na – Sodium133145 Normal range between 133 to 145 mmol/L Knee exam:11 Do you have any pets?15

12. 2b. Randomly Assign ID Values in Data Range to specific VPs NodePatient 1Patient 2Patient 3Additional Text Any diseases run in the family? 381 Lab: Anti Hepatitis A Virus - Total 112 Lab: Chemistry Na – Sodium 137139141 Normal range between 133 to 145 mmol/L Knee exam: Active movement: Flexion 140 degrees, Ext…. Active movement: Flexion 140 degrees, Ext…. Active movement: Flexion 140 degrees, Ext…. Do you have any pets? 352

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

14. 3b. Convert IDs to actual values & text NodePatient 1Patient 2Patient 3Additional Text Any diseases run in the family? Heart attacks StrokesCancer Lab: Anti Hepatitis A Virus - Total Positive Negative Lab: Chemistry Na – Sodium 137139141 Normal range between 133 to 145 mmol/L Knee exam: Active movement: Flexion 140 degrees, Ext…. Active movement: Flexion 140 degrees, Ext…. Active movement: Flexion 140 degrees, Ext…. Do you have any pets? I have a cat I took in I have a dog, but I think I might be allergic I have 3 dogs

15. Proofing VPs All content areas Ranges for normal results 1. VP Template Table for each text field ID number per row 2. Create Data Tables Randomized normal values Variety of text responses 3. Prepared Normal Template Change key nodes Approve -, + 4. SME Proofread Complete content SME approved 5. Case ready for upload

16. 4a. Normal Template to SME

17. 4b. SME records edits NodeJohn SmithAdditional Text Any diseases run in the family? Kidney disease in a few relatives Lab: Anti Hepatitis A Virus - Total Positive Lab: Chemistry Na – Sodium 160 Normal range between 133 to 145 mmol/L Knee exam: Active movement: Flexion 140 degrees, Ext…. Do you have any pets? I have a dog, but I think I might be allergic

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