Usability Comparison of a mHealth Application for Pre-anesthesia Evaluation Before and After Re-design of User Interface
Trial Snapshot
- Phase
- Not Applicable
- Status
- Completed
- Enrollment
- 30
- Locations
- 1
- Primary Endpoint
- task complete time
Study Overview
Brief Summary
Pre-anesthesia assessment is an important part of anesthesiologists daily routine. A thorough assessment leads to an impeccable anesthesia plan. In the past, the department of anesthesiologist in NTUH (National Taiwan University Hospital) utilizes paper assessment form. As government policy leans towards digitization, the department of anesthesiologist in NTUH collaborates with the information technology office to develop an electronic pre-anesthesia assessment system called EVAN, which is an iOS based application (App). EVAN includes an anesthesia patient list, patient EMR (electronic medical record) and pre-anesthesia assessment form. EVAN was online since March, 2018.
This is an observational research to investigate the user experience and improve the user interface of EVAN.
This research subjects include anesthesiologists from NTUH. This research has three parts: user tasks, questionnaires, and a semi-structured interview. First, the research assistant will record how the user use the App to complete the tasks, which includes video recording and App monitoring. After the tasks is completed, the subject will write a questionnaire. Then the principal investigator will give an interview for feedbacks from the users.
Study Design
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Prospective
Eligibility Criteria
- Ages
- 20 Years to 70 Years (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- Yes
Inclusion Criteria
- •anesthesiologists in NTUH
Exclusion Criteria
- Not provided
Outcomes
Primary Outcomes
task complete time
Time Frame: the duration the user needs to complete the tasks, usually within 10 minutes
the time duration the user needs to complete the user tasks
Secondary Outcomes
- questionnaire grading(after the user questionnaire completion, usually within 1 minutes)