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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 4/5th, 2008, in Toronto, was uploaded on behalf of the presenter by the Medicine 2.0 team
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Measuring Efficiency of Use in a Web-based EMR Developed for Malawi: Lessons Learned From Performing a Preliminary CogTool Analysis
Zach Landis Lewis, MLISGerald Douglas, MSISValerie Monaco, PhD, MHCI
University of PittsburghDepartment of Biomedical Informatics
Pennsylvania Malawi
Area119,282 km2 118,484 km2
Population
(2007 est.)12.4 million 13.6 million
Background: Malawi
Life expectancy at birth in years
77.8 43.5
Number of people living with HIV/AIDS
18,000
(0.15%)900,000 (14.2%)
Background: Baobab Anti-Retroviral Therapy system (BART)
Objective
Our research objective is to determine how efficiently novice users complete tasks using the touchscreen interface of the EMR.
Methods
1. Predict skilled task performanceo Select taskso Use CogTool software application to generate prediction
2. Measure novice task performanceo Collect timestamp data from user interface events
(e.g. pressing a button )o Repeat each task three times
3. Compare prediction with results of novice performance
Methods: CogTool
Q: What is predictive human performance modeling?A: A method for predicting how long a skilled user will take to complete a task
• 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
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
Methods: CogTool
Methods: CogTool
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”
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”
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”.
Results: CogTool
1. Selected 31 routinely performed tasks in BART2. Used CogTool to predict skilled task performance3. Predicted performance times in seconds for each task
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
Results: Comparison of CogTool Prediction with Novice Performance
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
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
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
Acknowledgements
The National Institutes of Health and the National Library of Medicine, USA - Grant # 5T15LM007059-22 for funding this research
Bonnie John, PhDThe CogTool Project - http://www.cs.cmu.edu/~bej/cogtool/Greg Cooper, MD, PhDMike McKayJoe Rauch, DDSYolanda DiBucciMargaret Henry