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Measuring Efficiency of Use in a Web-based EMR Developed for Malawi: Lessons Learned From Performing a Preliminary CogTool Analysis [5 Cr2 1100 Landislewis]
Measuring Efficiency of Use in a Web-based EMR Developed for Malawi: Lessons Learned From Performing a Preliminary CogTool Analysis [5 Cr2 1100 Landislewis]
Measuring Efficiency of Use in a Web-based EMR Developed for Malawi: Lessons Learned From Performing a Preliminary CogTool Analysis [5 Cr2 1100 Landislewis]
Measuring Efficiency of Use in a Web-based EMR Developed for Malawi: Lessons Learned From Performing a Preliminary CogTool Analysis [5 Cr2 1100 Landislewis]
Measuring Efficiency of Use in a Web-based EMR Developed for Malawi: Lessons Learned From Performing a Preliminary CogTool Analysis [5 Cr2 1100 Landislewis]
Measuring Efficiency of Use in a Web-based EMR Developed for Malawi: Lessons Learned From Performing a Preliminary CogTool Analysis [5 Cr2 1100 Landislewis]
Measuring Efficiency of Use in a Web-based EMR Developed for Malawi: Lessons Learned From Performing a Preliminary CogTool Analysis [5 Cr2 1100 Landislewis]
Measuring Efficiency of Use in a Web-based EMR Developed for Malawi: Lessons Learned From Performing a Preliminary CogTool Analysis [5 Cr2 1100 Landislewis]
Measuring Efficiency of Use in a Web-based EMR Developed for Malawi: Lessons Learned From Performing a Preliminary CogTool Analysis [5 Cr2 1100 Landislewis]
Measuring Efficiency of Use in a Web-based EMR Developed for Malawi: Lessons Learned From Performing a Preliminary CogTool Analysis [5 Cr2 1100 Landislewis]
Measuring Efficiency of Use in a Web-based EMR Developed for Malawi: Lessons Learned From Performing a Preliminary CogTool Analysis [5 Cr2 1100 Landislewis]
Measuring Efficiency of Use in a Web-based EMR Developed for Malawi: Lessons Learned From Performing a Preliminary CogTool Analysis [5 Cr2 1100 Landislewis]
Measuring Efficiency of Use in a Web-based EMR Developed for Malawi: Lessons Learned From Performing a Preliminary CogTool Analysis [5 Cr2 1100 Landislewis]
Measuring Efficiency of Use in a Web-based EMR Developed for Malawi: Lessons Learned From Performing a Preliminary CogTool Analysis [5 Cr2 1100 Landislewis]
Measuring Efficiency of Use in a Web-based EMR Developed for Malawi: Lessons Learned From Performing a Preliminary CogTool Analysis [5 Cr2 1100 Landislewis]
Measuring Efficiency of Use in a Web-based EMR Developed for Malawi: Lessons Learned From Performing a Preliminary CogTool Analysis [5 Cr2 1100 Landislewis]
Measuring Efficiency of Use in a Web-based EMR Developed for Malawi: Lessons Learned From Performing a Preliminary CogTool Analysis [5 Cr2 1100 Landislewis]
Measuring Efficiency of Use in a Web-based EMR Developed for Malawi: Lessons Learned From Performing a Preliminary CogTool Analysis [5 Cr2 1100 Landislewis]
Measuring Efficiency of Use in a Web-based EMR Developed for Malawi: Lessons Learned From Performing a Preliminary CogTool Analysis [5 Cr2 1100 Landislewis]
Measuring Efficiency of Use in a Web-based EMR Developed for Malawi: Lessons Learned From Performing a Preliminary CogTool Analysis [5 Cr2 1100 Landislewis]
Measuring Efficiency of Use in a Web-based EMR Developed for Malawi: Lessons Learned From Performing a Preliminary CogTool Analysis [5 Cr2 1100 Landislewis]
Measuring Efficiency of Use in a Web-based EMR Developed for Malawi: Lessons Learned From Performing a Preliminary CogTool Analysis [5 Cr2 1100 Landislewis]
Measuring Efficiency of Use in a Web-based EMR Developed for Malawi: Lessons Learned From Performing a Preliminary CogTool Analysis [5 Cr2 1100 Landislewis]
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Measuring Efficiency of Use in a Web-based EMR Developed for Malawi: Lessons Learned From Performing a Preliminary CogTool Analysis [5 Cr2 1100 Landislewis]

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    • 1. Landis Lewis, Z. et al.: Measuring Efficiency of Use in a Web-based EMR Developed for Malawi: Lessons Learned From Performing a Preliminary CogTool Analysis
      • This slideshow, presented at Medicine 2.0’08 , Sept 4/5 th , 2008, in Toronto, was uploaded on behalf of the presenter by the Medicine 2.0 team
      • Do not miss the next Medicine 2.0 congress on 17/18th Sept 2009 ( www.medicine20congress.com )
      • Order Audio Recordings (mp3) of Medicine 2.0’08 presentations at http://www.medicine20congress.com/mp3.php
    • 2. Measuring Efficiency of Use in a Web-based EMR Developed for Malawi: Lessons Learned From Performing a Preliminary CogTool Analysis Zach Landis Lewis, MLIS Gerald Douglas, MSIS Valerie Monaco, PhD, MHCI University of Pittsburgh Department of Biomedical Informatics
    • 3.  
    • 4. Background: Malawi 12.4 million 119,282 km 2 Pennsylvania 13.6 million Population (2007 est.) 118,484 km 2 Area Malawi 43.5 77.8 Life expectancy at birth in years 900,000 (14.2%) 18,000 (0.15%) Number of people living with HIV/AIDS
    • 5.  
    • 6. Background: Baobab Anti-Retroviral Therapy system (BART)
    • 7.     
      •          
    • 8. Objective
      • Our research objective is to determine how efficiently novice users complete tasks using the touchscreen interface of the EMR.
    • 9. Methods
        • Predict skilled task performance
          • Select tasks
          • Use CogTool software application to generate prediction
      •   2. Measure novice task performance
          • Collect timestamp data from user interface events (e.g. pressing a button )
          • Repeat each task three times
      •   3. Compare prediction with results of novice performance
    • 10. Methods: CogTool
      • validated and used in the field of Human Computer Interaction
      • over 100 papers validating or using human performance modeling for evaluation or design of interfaces
      Q: What is predictive human performance modeling? A: A method for predicting how long a skilled user will take to complete a task
        • Examples of real-world applications:
        • - Web pages and browsers
        • - Telephone operator workstations
        • - Space operations database system
        • - Television control system
        • - Intelligent tutoring system
        • - IRS office automation system
        • - Police in vehicle systems
        • - Firefox tab feature
    • 11.     
      •          
    • 12. Methods: CogTool
    • 13. Methods: CogTool
    • 14. Methods: CogTool The five clusters of colored bars represent all the button presses required to perform this task, separated by thinking time. “ 5” “ 8” “ .” “ 3” “ Next”
    • 15. Methods: CogTool This is the final hand movement operator for pressing the button labeled “5”. This pane shows a close-up view of a sequence of cognitive resources being used. Here we see the activities for pressing the button labeled “5”
    • 16. Methods: CogTool This is a “trace” of production rules fired by the ACT-R production rule system during the task performance The highlighted production rules correspond with cognitive activities occurring while a user is pressing the button labeled “5”.
    • 17. Results: CogTool
        • Selected 31 routinely performed tasks in BART
        • Used CogTool to predict skilled task performance
        • Predicted performance times in seconds for each task
    • 18. Results: Novice Performance
        • Rate of errors:
        • Errors are any deviation from the optimal sequence of steps required to complete a task
        • 77% (286) of task performances were error-free and were compared with CogTool predictions
        • 4 of the 31 tasks were performed without error by all subjects on all repetitions
    • 19. Results: Comparison of CogTool Prediction with Novice Performance
    • 20. Discussion
      • 1. CogTool allowed us to rapidly generate predictions of skilled performance
      • 2. Novice subjects demonstrated a low error rate
      •   3. Novices performed faster than CogTool predictions on average :
        • Tasks were modeled independently, but users interleaved some tasks
        • CogTool's assumptions for inserting "Think" events may not be applicable for wizard format interfaces
    • 21. Discussion, continued
      •   4. Unexpected findings:
        • Pittsburgh subjects occasionally used more than one hand to manipulate the interface – (but we haven’t observed that in Malawi… yet)
        • Communication time varied greatly between tasks, sometimes resulting in prolonged dialog rather than a single question and answer
    • 22. Future Work
      • 1. Update the CogTool model to reflect current, more sophisticated understanding of tasks and user actions - We are working with the CogTool team to be able to adjust the models and CogTool itself to fit the assumptions to our tasks and users
      • 2. Characterize the use of the system in a real-world setting
        • Collect anonymized user interface event data in Malawi from a representative group of users
        • Measure system use by novices and skilled users
    • 23. Acknowledgements
        • The National Institutes of Health and the National Library of Medicine, USA
        • - Grant # 5T15LM007059-22 for funding this research
        • Bonnie John, PhD
        • The CogTool Project - http://www.cs.cmu.edu/~bej/cogtool/
        • Greg Cooper, MD, PhD
        • Mike McKay
        • Joe Rauch, DDS
        • Yolanda DiBucci
        • Margaret Henry

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