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Objectives: Symptomatic treatment is an essential component in the overall treatment of multiple sclerosis (MS). However, knowledge in this regard is confusing and scattered. Physicians also have challenges in choosing symptomatic treatment based on the patient’s condition. To share, update, and reuse this knowledge, the aim of this study was to provide an ontology for MS symptomatic treatment.Methods: The Symptomatic Treatment of Multiple Sclerosis Ontology (STMSO) was developed according to Ontology Development 101 and a guideline for developing good ontologies in the biomedical domain. We obtained knowledge and rules through a systematic review and entered this knowledge in the form of classes and subclasses in the ontology. We then mapped the ontology using the Basic Formal Ontology (BFO) and Ontology for General Medical Sciences (OGMS) as reference ontologies. The ontology was built using Protégé Editor in the Web Ontology Language format. Finally, an evaluation was done by experts using criterion-based approaches in terms of accuracy, clarity, consistency, and completeness.Results: The knowledge extraction phase identified 110 articles related to the ontology in the form of 626 classes, 40 object properties, and 139 rules. Five general classes included “patient,” “symptoms,” “pharmacological treatment,” “treatment plan,” and “measurement index.” The evaluation in terms of standards for biomedical ontology showed that STMSO was accurate, clear, consistent, and complete.Conclusions: STMSO is the first comprehensive semantic representation of the symptomatic treatment of MS and provides a major step toward the development of intelligent clinical decision support systems for symptomatic MS treatment.

Objectives Users attitude toward a picture archiving and communication system (PACS) and their interaction with this system are among the most important factors that influence its acceptance. This study aimed to augment the user's interaction with the user's perspective to select a usable PACS among three systems available on the market. Methods We augmented the think aloud (TA) usability evaluation method with the Post-Study System Usability Questionnaire (PSSUQ) to compare user interaction problems of three PACS user interfaces. Four radiologists and four internist physicians participated in this study. Usability characteristics including efficiency, effectiveness, learnability, error, and satisfaction were used to assess the usability of each PACS. Results There was a significant difference in efficiency (p = 0.01), effectiveness (p = 0.005), learnability (p = 0.001), and satisfaction (p = 0.009). However, no significant difference in the number of errors (p = 0.18), mouse clicks and keystrokes (p = 0.12), and the number of usability problems (p = 0.6) were observed among the three PACS systems studied. Conclusions This study showed that applying the proposed approach to augment TA with the user's perspective addresses almost all of the theoretical aspects of usability and can be employed to select the most usable PACS.

Objective: There are several user-based and expert-based usability evaluation methods that may perform differently according to the context in which they are used. The objective of this study was to compare 2 expert-based methods, heuristic evaluation (HE) and cognitive walkthrough (CW), for evaluating usability of health care information systems. Materials and methods: Five evaluators independently evaluated a medical office management system using HE and CW. We compared the 2 methods in terms of the number of identified usability problems, their severity, and the coverage of each method. Results: In total, 156 problems were identified using the 2 methods. HE identified a significantly higher number of problems related to the “satisfaction” attribute (P = .002). The number of problems identified using CW concerning the “learnability” attribute was significantly higher than those identified using HE (P = .005). There was no significant difference between the number of problems identified by HE, based on different usability attributes (P = .232). Results of CW showed a significant difference between the number of problems related to usability attributes (P Conclusion: This study showed that HE and CW do not differ significantly in terms of the number of usability problems identified, but they differ based on the severity of problems and the coverage of some usability attributes. The results suggest that CW would be the preferred method for evaluating systems intended for novice users and HE for users who have experience with similar systems. However, more studies are needed to support this finding.