Difference between revisions of "Complementary methods of system usability evaluation: surveys and observations during software design and development cycles"
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Clinicians resist being forced to change established workflows, long training times and excessive time spent completing data entry rather than with the patient. | Clinicians resist being forced to change established workflows, long training times and excessive time spent completing data entry rather than with the patient. | ||
| − | Usability of the system often has a direct relationship with error rates, clinical productivity, user fatigue and satisfaction all of which can impact user adoption. | + | Usability of the system often has a direct relationship with error rates, clinical productivity, user fatigue and satisfaction all of which can impact user adoption.<Ref name =’Sittig’> Sittig, D.F. and Stead, W.W. Computer-based physician order entry: the state of the art. J Am Med Inform Assoc. 1994; 1: 108–123 < /Ref> |
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The objective of this study was to compare data from four usability evaluation methods and assess how useful they were in the software development process of the SmartForms function of an EHR. | The objective of this study was to compare data from four usability evaluation methods and assess how useful they were in the software development process of the SmartForms function of an EHR. | ||
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== Methods == | == Methods == | ||
Revision as of 03:53, 12 November 2015
This is a review of an article titled Complementary methods of system usability evaluation: surveys and observations during software design and development cycles by Horsky et al.
Background
Studies estimate up to 40% of systems are either abandoned or fail to meet business requirements and usability of information systems has a significant impact on the adoption of Electronic Health Records (EHRs).
Clinicians resist being forced to change established workflows, long training times and excessive time spent completing data entry rather than with the patient.
Usability of the system often has a direct relationship with error rates, clinical productivity, user fatigue and satisfaction all of which can impact user adoption.[1]
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