scholarly journals Usability testing of radiotherapy systems as a medical device evaluation tool to inform hospital procurement decision-making

2021 ◽  
Vol 104 (3) ◽  
pp. 003685042110361
Author(s):  
Mingyin Jiang ◽  
Xuancheng Tu ◽  
Wanchao Xiao ◽  
Jinhui Tang ◽  
Qiang Li ◽  
...  
Keyword(s):  
Decision Making ◽  
User Experience ◽  
Completion Time ◽  
Difficulty Level ◽  
Task Completion ◽  
User Performance ◽  
Evaluation Tool ◽  
Task Completion Time ◽  
Study Results ◽  
Selection Of

Purpose: Poor usability designs of radiotherapy systems can contribute to use errors and adverse events. Therefore, we evaluated the usability of two radiotherapy systems through radiation therapists’ performance, workload, and experience that can inform hospital procurement decision-making about the selection of appropriate radiotherapy system for radiation therapist use. Methods: We performed a comparative usability study for two radiotherapy systems through user testing. Thirty radiation therapists participated in our study, in which four typical operational tasks were performed in two tested radiotherapy systems. User performance was measured by task completion time and completion difficulty level. User workloads were measured by perceived and physiological workload using NASA-TLX questionnaires and eye motion data. User experience was measured by the USE questionnaire. Results: Significantly less task completion time and an easier task completion difficulty level were shown with the Varian Trilogy than with the XHA600E. The study results suggest that higher perceived and physiological workloads were experienced with the XHA600E than with the Varian Trilogy. Radiation therapists reported better user experience with the Varian Trilogy than with the XHA600E. Five paired t-tests regarding user performance, user workload, and user experience between the Varian Trilogy and the XHA600E were performed, showing that the Varian Trilogy radiotherapy system has a better usability design than the XHA600E radiotherapy system. Conclusions: Based on study results, we confirmed that the Varian Trilogy radiotherapy system has a better usability design than the XHA600E radiotherapy system. Furthermore, the study results provide valuable evidence for hospital procurement decision-making regarding the selection of a suitable radiotherapy system for radiation therapists to use.

scholarly journals Analysis of user experience during interaction with selected CMS platforms

2021 ◽  
Vol 21 ◽  
pp. 336-343
Author(s):  
Michał Miszczak ◽  
Mariusz Dzieńkowski
Keyword(s):  
Eye Tracking ◽  
User Experience ◽  
Completion Time ◽  
Task Completion ◽  
Task Completion Time ◽  
Tracking Technique

The purpose of this study was assessing user experience while working with two popular CMS systems: WordPress and PrestaShop. The evaluation was done using a questionnaire and an eye tracking technique. Average task completion time, the number of fixations, the percentage of correctly completed tasks and the SUS index were used for comparisons. On the basis of the obtained results which, were collected during and after the users' interaction with a given system, it is difficult to clearly state which CMS proved to be better.

scholarly journals Integration of biosensor to a window-based control system for user emotion detection to static and dynamic visual contents of webpages

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fatima M. Isiaka ◽  
Awwal Adamu ◽  
Zainab Adamu
Keyword(s):  
Control System ◽  
Eye Movement ◽  
User Experience ◽  
Completion Time ◽  
Dynamic Control ◽  
Task Completion ◽  
Time Interval ◽  
Task Completion Time ◽  
Content Type ◽  
Dynamic Control System

Purpose Basic capturing of emotion on user experience of web applications and browsing is important in many ways. Quite often, online user experience is studied via tangible measures such as task completion time, surveys and comprehensive tests from which data attributes are generated. Prediction of users’ emotion and behaviour in some of these cases depends mostly on task completion time and number of clicks per given time interval. However, such approaches are generally subjective and rely heavily on distributional assumptions making the results prone to recording errors. This paper aims to propose a novel method – a window dynamic control system – that addresses the foregoing issues. Design/methodology/approach Primary data were obtained from laboratory experiments during which 44 volunteers had their synchronized physiological readings – skin conductance response, skin temperature, eye movement behaviour and users activity attributes taken by biosensors. The window-based dynamic control system (PHYCOB I) is integrated to the biosensor which collects secondary data attributes from these synchronized physiological readings and uses them for two purposes: for detection of both optimal emotional responses and users’ stress levels. The method’s novelty derives from its ability to integrate physiological readings and eye movement records to identify hidden correlates on a webpage. Findings The results from the analyses show that the control system detects basic emotions and outperforms other conventional models in terms of both accuracy and reliability, when subjected to model comparison – that is, the average recoverable natural structures for the three models with respect to accuracy and reliability are more consistent within the window-based control system environment than with the conventional methods. Research limitations/implications Graphical simulation and an example scenario are only provided for the control’s system design. Originality/value The novelty of the proposed model is its strained resistance to overfitting and its ability to automatically assess user emotion while dealing with specific web contents. The procedure can be used to predict which contents of webpages cause stress-induced emotions to users.

Effects of Automation Type on Human Performance in Proofreading Tasks

2018 ◽  
Vol 62 (1) ◽  
pp. 1140-1140 ◽  
Author(s):  
Heejin Jeong ◽  
Jangwoon Park ◽  
Jaehyun Park ◽  
Byung Cheol Lee
Keyword(s):  
Task Performance ◽  
Skill Acquisition ◽  
Completion Time ◽  
Human Performance ◽  
Task Completion ◽  
Strong Positive Correlation ◽  
Task Completion Time ◽  
Perceived Trust ◽  
Mental Demand ◽  
Study Results

Automation is ubiquitous and indispensable in modern working environments. It is adopted and used in not only advanced industrial- and technology-oriented operations, but also ordinary home or office computational functions. In general, automated systems aim to improve overall work efficiency and productivity of labor-intensive tasks by decreasing the risk of errors, and cognitive and physical workloads. The systems offer the support for diverse decision-making processes as well. However, the benefits of automation are not consistently achieved and depend on the types and features of automation (Onnasch, Wickens, Li, & Manzey, 2014; Parasuraman, Sheridan, & Wickens, 2000). Possible negative side effects have been reported. Sometimes, automation may lead to multi-tasking environments, which allows operators to be distractive with several tasks. It ultimately prolongs task completion time and causes to neglect monitoring and follow-up steps of the pre-processing tasks (Endsley, 1996). Furthermore, the operators who excessively depend on automation are easily deteriorated in skill acquisition, which is necessary for the emergency or manual operations. Thus, inconsistent performance in automation is a major issue in successful adoption and trust in automation (Jeong, Park, Park, & Lee, 2017). This paper presents an experimental study that investigates the main features and causes of the inconsistency in task performance in different types of automation. Automated proofreading tasks were used in this study, which is one of the most common types of automation we experience in daily life. Based on the similar algorithm of the auto-correct function in Microsoft Word, a custom-built program of five proofreading tasks, including one non-automated and four automated proofreading tasks, were developed using Visual Studio 2015 C#. In the non-automated task used as a reference for individual difference, participants were asked to manually find a typographical error in a sentence. In the automated tasks, auto-correcting functions are provided in two levels (i.e., low and high) of automation and two statuses (i.e., routine and failure of automation). The type of automation is defined as the combinations of a status and a level. Participants identified typographical errors by only an underlined word at the low-level automation, whereas an underlined word with a possible substituting word was given at the high-level. Additionally, in the routine automation status, a correct substituting word is provided. On the other hand, a grammatically incorrect word is given in the failed automation status. Nineteen participants (11 females and 8 males; age mean = 33.8, standard deviation = 19.1) took part in this study. Results of statistical analyses show a clear advantage in high-routine automation, in terms of both task completion time and accuracy. While task performances of high & routine automation types are quite obvious in both task completion time and accuracy, those in the failed automation types are mixed and indistinguishable. Different levels and statues of failed automation do not much influence task performance. Moreover, task completion time and mental demand are strongly correlated, and the accuracy rate and perceived trust show a strong positive correlation. The approaches and outcomes of the current study can provide some insights into the human-automation interaction systems that support human performance and safety, such as in-vehicle warning systems and automated vehicle controls.

scholarly journals The usability of ventilator maintenance user interface: A comparative evaluation of user task performance, workload, and user experience

2020 ◽  
Vol 103 (4) ◽  
pp. 003685042096288
Author(s):  
Mingyin Jiang ◽  
Dongjie Sun ◽  
Qiang Li ◽  
Daoxiong Wang
Keyword(s):  
User Interface ◽  
Task Performance ◽  
User Experience ◽  
Error Rate ◽  
Completion Time ◽  
Human Error ◽  
Task Completion ◽  
Performance Task ◽  
Task Completion Time ◽  
Significant Difference

Poor usability designed of ventilator user interface can easily lead to human error. In this study, we evaluated the usability design of ventilator maintenance user interface and identified problems related to the usability of user interface that could easily cause human error. Sixteen respiratory therapists participated in this usability study. The usability of the ventilator maintenance user interface was evaluated by participants’ task performance (task completion time, task error rate), physiological workload (eye-fixation duration) and perceived workload (NASA-TLX), and user experience (questionnaire). For task performance, task completion time and task error rate showed significant differences. For task completion time, significant difference was found when conducting ventilator self-test ( p < 0.001), replace the breathing circuit ( p = 0.047), and check battery status ( p = 0.005). For task error rate, the three ventilators showed significant difference ( p = 0.012), and the Serov I showed a significantly higher task error rate than the Boaray 5000D ( p = 0.031). For workload, the Serov I was associated with higher physiological and perceived workloads than other ventilators ( p < 0.05). For user experience, the Boaray 5000D received better scores among the ventilators in terms of ease to maintain, friendly to maintain, and willingness to use ( p < 0.05, respectively). Our study adds available literature for usability evaluation of ventilator maintenance user interface. The results indicate that the maintenance user interface of the Boaray 5000D performed better than the other two tested ventilators. Moreover, the study results also proved that eye-fixation duration can be a reliable tool for evaluating the usability of ventilator user interface.

Foot Interaction Concepts to Support Radiological Interventions

i-com ◽  
2018 ◽  
Vol 17 (1) ◽  
pp. 3-13 ◽  
Author(s):  
Benjamin Hatscher ◽  
Maria Luz ◽  
Christian Hansen
Keyword(s):  
Operating Room ◽  
User Experience ◽  
Completion Time ◽  
Medical Image ◽  
Image Data ◽  
Task Completion ◽  
Task Completion Time ◽  
Interaction Task ◽  
Medical Image Data ◽  
Better Than

AbstractDuring neuroradiological interventions, physicians need to interact with medical image data, which cannot be done while the hands are occupied. We propose foot input concepts with one degree of freedom, which matches a common interaction task in the operating room. We conducted a study to compare our concepts in regards to task completion time, subjective workload and user experience. Relative input performed significantly better than absolute or rate-based input. Our findings may enable more effective computer interactions in the operating room and similar domains where the hands are not available.

Selection of manipulator system for multiple-goal task by evaluating task completion time and cost with computational time constraints

2013 ◽  
Vol 27 (4) ◽  
pp. 233-245 ◽  
Author(s):  
Yanjiang Huang ◽  
Lounell B. Gueta ◽  
Ryosuke Chiba ◽  
Tamio Arai ◽  
Tsuyoshi Ueyama ◽  
...  
Keyword(s):  
Completion Time ◽  
Time Constraints ◽  
Computational Time ◽  
Task Completion ◽  
Task Completion Time ◽  
Multiple Goal ◽  
Selection Of

scholarly journals Evaluating the Impact of the Grading and Assessment of Predictive Tools Framework on Clinicians and Health Care Professionals’ Decisions in Selecting Clinical Predictive Tools: Randomized Controlled Trial

10.2196/15770 ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. e15770
Author(s):  
Mohamed Khalifa ◽  
Farah Magrabi ◽  
Blanca Gallego Luxan
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Health Care Professionals ◽  
Completion Time ◽  
Decisional Conflict ◽  
Task Completion ◽  
Evidence Based ◽  
Task Completion Time ◽  
Predictive Tools ◽  
The Impact

Background While selecting predictive tools for implementation in clinical practice or for recommendation in clinical guidelines, clinicians and health care professionals are challenged with an overwhelming number of tools. Many of these tools have never been implemented or evaluated for comparative effectiveness. To overcome this challenge, the authors developed and validated an evidence-based framework for grading and assessment of predictive tools (the GRASP framework). This framework was based on the critical appraisal of the published evidence on such tools. Objective The aim of the study was to examine the impact of using the GRASP framework on clinicians’ and health care professionals’ decisions in selecting clinical predictive tools. Methods A controlled experiment was conducted through a web-based survey. Participants were randomized to either review the derivation publications, such as studies describing the development of the predictive tools, on common traumatic brain injury predictive tools (control group) or to review an evidence-based summary, where each tool had been graded and assessed using the GRASP framework (intervention group). Participants in both groups were asked to select the best tool based on the greatest validation or implementation. A wide group of international clinicians and health care professionals were invited to participate in the survey. Task completion time, rate of correct decisions, rate of objective versus subjective decisions, and level of decisional conflict were measured. Results We received a total of 194 valid responses. In comparison with not using GRASP, using the framework significantly increased correct decisions by 64%, from 53.7% to 88.1% (88.1/53.7=1.64; t193=8.53; P<.001); increased objective decision making by 32%, from 62% (3.11/5) to 82% (4.10/5; t189=9.24; P<.001); decreased subjective decision making based on guessing by 20%, from 49% (2.48/5) to 39% (1.98/5; t188=−5.47; P<.001); and decreased prior knowledge or experience by 8%, from 71% (3.55/5) to 65% (3.27/5; t187=−2.99; P=.003). Using GRASP significantly decreased decisional conflict and increased the confidence and satisfaction of participants with their decisions by 11%, from 71% (3.55/5) to 79% (3.96/5; t188=4.27; P<.001), and by 13%, from 70% (3.54/5) to 79% (3.99/5; t188=4.89; P<.001), respectively. Using GRASP decreased the task completion time, on the 90th percentile, by 52%, from 12.4 to 6.4 min (t193=−0.87; P=.38). The average System Usability Scale of the GRASP framework was very good: 72.5% and 88% (108/122) of the participants found the GRASP useful. Conclusions Using GRASP has positively supported and significantly improved evidence-based decision making. It has increased the accuracy and efficiency of selecting predictive tools. GRASP is not meant to be prescriptive; it represents a high-level approach and an effective, evidence-based, and comprehensive yet simple and feasible method to evaluate, compare, and select clinical predictive tools.

Emotion detection on webpages using biosensors integrated to a window-based dynamic control system

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fatima Isiaka ◽  
Salihu Aish Abdulkarim ◽  
Kassim Mwitondi ◽  
Zainab Adamu
Keyword(s):  
Control System ◽  
Eye Movement ◽  
User Experience ◽  
Completion Time ◽  
Dynamic Control ◽  
Task Completion ◽  
Task Completion Time ◽  
Content Type ◽  
Stress Levels ◽  
Dynamic Control System

PurposeDetecting emotion on user experience of web applications and browsing is important in many ways. Web designers and developers find such approach quite useful in enhancing navigational features of webpages, and biomedical personnel regularly use computer simulations to monitor and control the behaviour of patients. On the other hand, law enforcement agents rely on human physiological functions to determine the likelihood of falsehood in interrogations. Quite often, online user experience is studied via tangible measures such as task completion time, surveys and comprehensive tests from which data attributes are generated. Prediction of users' emotion and behaviour in some of these cases depends mostly on task completion time and number of clicks per given time interval. However, such approaches are generally subjective and rely heavily on distributional assumptions making the results prone to recording errors.Design/methodology/approachThe authors propose a novel method-a window dynamic control system that addresses the foregoing issues. Primary data were obtained from laboratory experiments during which forty-four volunteers had their synchronised physiological readings, skin conductance response (SCR), skin temperature (ST), eye movement behaviour and users’ activity attributes taken using biosensors. The window-based dynamic control system (PHYCOB I) is integrated to the biosensor which collects secondary data attributes from these synchronised physiological readings and uses them for two purposes. For both detection of optimal emotional responses and users' stress levels. The method's novelty derives from its ability to integrate physiological readings and eye movement records to identify hidden correlates on a webpage.FindingsResults show that the control system detects basic emotions and outperforms other conventional models in terms of both accuracy and reliability, when subjected to model comparison that is, the average recoverable natural structures for the three models with respect to accuracy and reliability are more consistent within the window-based control system environment than with the conventional methods.Research limitations/implicationsThe paper is limited to using a window control system to detect emotions on webpages, while integrated to biosensors and eye-tracker.Originality/valueThe originality of the proposed model is its resistance to overfitting and its ability to automatically assess human emotion (stress levels) while dealing with specific web contents. The latter is particularly important in that it can be used to predict which contents of webpages cause stress-induced emotions to users when involved in online activities.

scholarly journals Evaluating the Impact of the Grading and Assessment of Predictive Tools Framework on Clinicians and Health Care Professionals’ Decisions in Selecting Clinical Predictive Tools: Randomized Controlled Trial (Preprint)

2019 ◽  
Author(s):  
Mohamed Khalifa ◽  
Farah Magrabi ◽  
Blanca Gallego Luxan
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Health Care Professionals ◽  
Completion Time ◽  
Decisional Conflict ◽  
Task Completion ◽  
Evidence Based ◽  
Task Completion Time ◽  
Predictive Tools ◽  
The Impact

BACKGROUND While selecting predictive tools for implementation in clinical practice or for recommendation in clinical guidelines, clinicians and health care professionals are challenged with an overwhelming number of tools. Many of these tools have never been implemented or evaluated for comparative effectiveness. To overcome this challenge, the authors developed and validated an evidence-based framework for grading and assessment of predictive tools (the GRASP framework). This framework was based on the critical appraisal of the published evidence on such tools. OBJECTIVE The aim of the study was to examine the impact of using the GRASP framework on clinicians’ and health care professionals’ decisions in selecting clinical predictive tools. METHODS A controlled experiment was conducted through a web-based survey. Participants were randomized to either review the derivation publications, such as studies describing the development of the predictive tools, on common traumatic brain injury predictive tools (control group) or to review an evidence-based summary, where each tool had been graded and assessed using the GRASP framework (intervention group). Participants in both groups were asked to select the best tool based on the greatest validation or implementation. A wide group of international clinicians and health care professionals were invited to participate in the survey. Task completion time, rate of correct decisions, rate of objective versus subjective decisions, and level of decisional conflict were measured. RESULTS We received a total of 194 valid responses. In comparison with not using GRASP, using the framework significantly increased correct decisions by 64%, from 53.7% to 88.1% (88.1/53.7=1.64; <i>t<sub>193</sub></i>=8.53; <i>P</i>&lt;.001); increased objective decision making by 32%, from 62% (3.11/5) to 82% (4.10/5; <i>t<sub>189</sub></i>=9.24; <i>P</i>&lt;.001); decreased subjective decision making based on guessing by 20%, from 49% (2.48/5) to 39% (1.98/5; <i>t<sub>188</sub></i>=−5.47; <i>P</i>&lt;.001); and decreased prior knowledge or experience by 8%, from 71% (3.55/5) to 65% (3.27/5; <i>t<sub>187</sub></i>=−2.99; <i>P</i>=.003). Using GRASP significantly decreased decisional conflict and increased the confidence and satisfaction of participants with their decisions by 11%, from 71% (3.55/5) to 79% (3.96/5; <i>t<sub>188</sub></i>=4.27; <i>P</i>&lt;.001), and by 13%, from 70% (3.54/5) to 79% (3.99/5; <i>t<sub>188</sub></i>=4.89; <i>P</i>&lt;.001), respectively. Using GRASP decreased the task completion time, on the 90th percentile, by 52%, from 12.4 to 6.4 min (<i>t<sub>193</sub></i>=−0.87; <i>P</i>=.38). The average System Usability Scale of the GRASP framework was very good: 72.5% and 88% (108/122) of the participants found the GRASP useful. CONCLUSIONS Using GRASP has positively supported and significantly improved evidence-based decision making. It has increased the accuracy and efficiency of selecting predictive tools. GRASP is not meant to be prescriptive; it represents a high-level approach and an effective, evidence-based, and comprehensive yet simple and feasible method to evaluate, compare, and select clinical predictive tools.

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