1. Hippocrates Archive John Brennan (jbren@wam.umd.edu) Hyunyoung Song (hsong@gmail.com) Nima Negahban (nimacn@gmail.com)

2. Introduction Patient Data Management Application Patient Data Management Application –Inherited application “Pattern Finder” –Pattern finder was using fake data –Conducted usability studies to make recommendations on the interface

3. Motivation Twenty percent of the nations medical spending is on managing patient data Twenty percent of the nations medical spending is on managing patient data –Paper Systems Potential to be an invaluable tool for doctors and researchers Potential to be an invaluable tool for doctors and researchers

4. Previous Works Lifeline Lifeline –One of the first applications in this field –Very good at displaying large sets of data –Lacked the ability to make complex queries

5. Previous Works KNAVE KNAVE –Robust query system –Poor Visualization System

6. Pattern Finder Medical History Search Application built by Jerry Fails, Amy Karlson, Layla Shahamet in Spring 2005 CMSC838S term project Medical History Search Application built by Jerry Fails, Amy Karlson, Layla Shahamet in Spring 2005 CMSC838S term project System Spec System Spec –C# program on top of.NET Framework –Piccolo api for visualization

8. Dataset Test - Students Original Data Original Data –Medication –Tests –Visits Values Values –Imaginary values Test - Doctors National Washington Hospital Data Set National Washington Hospital Data Set –Only tests (blood test) –Real values with reference level

9. Usability Study: 6 students 3 scenarios with 3 tasks each 3 scenarios with 3 tasks each Participants used Pattern Finder with its Participants used Pattern Finder with its default (phony) data Tasks created to test several key Tasks created to test several key fundamentals of the application –Understanding events –Creating simple and complex queries using each o f the tools provided Our study gave more focus to the query Our study gave more focus to the query construction than visualization understanding

10. Observation 3 tier menu hierarchy 3 tier menu hierarchy Understanding full query when more Understanding full query when more than 3 events have been created Creating time spans between events Creating time spans between events Difficulty querying medication changes Difficulty querying medication changes (relative values feature) (relative values feature) User Troubles

11. Post-Test Result Used Shneiderman’s QUIS model (1-9 semantically anchored radio buttons)

12. Recommendation Create text query window to display Create text query window to display full query user is generating Change editable span text to NumericUpDo wn controls Change editable span text to NumericUpDo wn controls Change “relative values” text wording to “inc reasing/decreasing values” Change “relative values” text wording to “inc reasing/decreasing values” Those using custom data for the system sho uld use care when developing the event dro p down menu hierarchy (naming and place ment) Those using custom data for the system sho uld use care when developing the event dro p down menu hierarchy (naming and place ment)

14. Usability Test : 2 Doctors No need for scenarios No need for scenarios 6 Concrete Tasks 6 Concrete Tasks –Desc: one from each query type –Measurement: Difficulty and Usefulness Pilot Profile Pilot Profile –Researcher who practiced Neurology 15 years ago => Researcher point of view –Gynecology and Obstetrics doctor who practiced over 30 years => Pure doctor point of view

15. Task 1 – E (Event) Task: Find Patients who have had 3 WBC (White Blood Cell) measurement.

16. Task 2 – E-FT (Event - Fixed Time span) Task: Find Patients whose HGB (Hemoglobin) measurement was between 10 ~ 14 Gm/dl (normal person reference level) but 2 day later it increased over 14 Gm/dl.

17. Task 3 – E-VT (Event – Variable Time span) Task: Find Patients whose HGB measurement was under 14 Gm/dl, but within 3 days it decreased under 14 Gm/dl.

18. Task 4 – E*-VT (Multiple Event – Variable Time span) Task: Find Patients whose WBC measurement is above 8.50 K/ul and BASO (basophilia ) lab test after 2 days

19. Task: Find patients w ho had 5 or mo re HGB within 3 days followed b y MCH measur ement. Task 5 - E*WC-VT (Multiple Event with Window Constraint – Variable Time span)

20. Task 6 – ( E*WC)F-VT (Multiple Event with Functional Window Constraint – Variable Time span) Task: Find patients who had 2 or more HGB test within 2 days that are less than the reference level (10~14 Gm/dl) within a range of 3 Gm/dl.

21. Results Components Components –Most Intuitive (1)(7) –Confusing but useful (2)(3)(6) –Not much useful (4)(5)

22. Results QueryTypeDifficultyUsefulness PracticeResearch E(1+1)/251 E-FT(3+4)/2=3.582 E-VT(3+5)/2=488 E*-VT(3+5)/2=477 E*WC-VT(9+9)/2=927 (E*WC)F-VT(9+9)/2=947

23. Conclusion Visualization Visualization –Combinatorial display of results are redundant Query Power Query Power –3 rd, 4 th query are the most useful –Consumes exponential time when query becomes complicated Medical Use Medical Use –Potential for Patient monitoring system in ER

24. Futurework Need for centralized patient data Need for centralized patient data –Data from emergency room was only result of blood, urine and cerebral spinal fluid result for a month period. Add various filters (currently the system has just two) to the search system Add various filters (currently the system has just two) to the search system

25. Special Thanks To.. Professor Ben Shneiderman Professor Ben Shneiderman Professor James Reggia, Doctor Yong Song for the Usability Study Professor James Reggia, Doctor Yong Song for the Usability Study Doctor Mike Gillam for providing the emergency room patient dataset and useful information Doctor Mike Gillam for providing the emergency room patient dataset and useful information

26. The End John Brennan (jbren@wam.umd.edu) John Brennan (jbren@wam.umd.edu)jbren@wam.umd.edu Hyunyoung Song (hsong@cs.umd.edu) Hyunyoung Song (hsong@cs.umd.edu)hsong@cs.umd.edu Nima Negaban (nimacn@gmail.com) Nima Negaban (nimacn@gmail.com)