Factors influencing the selection of a picture archiving and communication system: A qualitative study

Extended QUALIFLEX method for electronic music acoustic quality evaluation based on the picture fuzzy multiple attribute group decision making

In the past, different useful extensions of fuzzy sets were established by the researchers to manage the vagueness and uncertainty in various practical problems. Usually, the real numbers are utilized to express the decision information, but it is noted that the description of attributes using picture fuzzy sets (PFSs) proves to be more appropriate. As a powerful decision tool, PFSs provides more decision information that requires the application of some specific situations more types of response of human ideas: yes, contain, no, reject. QUALIFLEX (qualitative flexible multiple criteria method), is one of the well-known outranking methods to solve the multiple attribute group decision making (MAGDM) problems with crisp numbers. The QUALIFLEX method can perfectly address the complex MAGDM problems where a lot of attributes are utilized to assess a limited number of alternatives. The electronic music acoustic quality evaluation is a classical MAGDM. This paper proposes and utilizes the QUALIFLEX to develop the picture fuzzy QUALIFLEX(PF-QUALIFLEX) method for MAGDM. The current study is mainly devoted to explore and extend the measurement of alternatives and ranking according to the QUALIFLEX under the background of PFSs. Furthermore, an example to evaluate the electronic music acoustic quality is handled through the proposed method to substantiate the extended approach.

Intelligent Algorithm-Based Picture Archiving and Communication System of MRI Images and Radiology Information System-Based Medical Informatization

Objective

The study aimed to explore the application value of picture archiving and communication system (PCAS) of MRI images based on radial basis function (RBF) neural network algorithm combined with the radiology information system (RIS).

Methods

551 patients who required MRI examination in a hospital from May 2016 to May 2021 were selected as research subjects. Patients were divided into two groups according to their own wishes. Those who were willing to use the RBF neural network algorithm-based PCAS of MRI images combined with RIS were set as the combined group, involving a total of 278 cases; those who were unwilling were set as the regular group, involving a total of 273 cases. The RBF neural network algorithm-based PCAS of MRI images combined with RIS was trained and tested for classification performance and then used for comparison analysis.

Result

The actual output (0.031259–0.038515) of all test samples was almost the same as the target output (0.000000) (P > 0.05). In the first 50,000 learnings, the iteration error of the RBF neural network dropped rapidly and finally stabilized at 0.038. The classification accuracy of the RBF neural network algorithm-based PCAS of MRI images combined with RIS for the head was 94.28%, that of abdomen was 97.22%, and it was 93.10% for knee joint, showing no statistically significant differences (P > 0.05), and the total classification accuracy was as high as 95%. The time spent in the examination in the combined group was about 2 hours, and that in the regular group was about 4 hours (P > 0.05). The satisfaction of the combined group (96.76%) was significantly higher than that of the control group (46.89%) (P > 0.05).

Conclusion

The RBF neural network has good classification performance for MRI images. To incorporate intelligent algorithms into the medical information system can optimize the system. RBF has good application prospects in the medical information system, and it is worthy of continuous exploration.

Usability evaluation of selected picture archiving and communication systems at the national level: Analysis of users' viewpoints

BACKGROUND AND OBJECTIVES

Picture Archiving and Communication System (PACS) can be considered as one of the most important information systems used in healthcare facilities and its usability problems can lead to delays in the acceptance of information systems, increased medical errors and also user dissatisfaction. The primary purpose of this study was to evaluate the usability of PACS from the perspective of its main users.

METHODS

This study used a combination of qualitative and quantitative methods and was carried out in 2019 where the research community consisted of PACS in five selected companies in Iran. The statistical sample included 200 individuals using the PACS in several hospitals across the country. Moreover, the sample was selected using the multistage random method. The data were then collected using the standard Computer System Usability Questionnaire (CSUQ) consisting of 5 sections and 19 items. Finally, the data were analyzed by SPSS software, version 18 using both descriptive and inferential methods. Content analysis was done for the qualitative data sets.

RESULTS

It was indicated that ease of use, as a category, was not significantly different from the perspective of various PACS users. However, the ANOVA test revealed that there were significant differences in terms of information quality, user interface quality, overall user satisfaction and usability of PACS from the users' perspectives. Furthermore, content analysis of users' comments showed that speeding up the image processing and frequent system failures were amongst the most positive and negative aspects of the PACS, respectively.

CONCLUSIONS

According to the perspective of the users of the investigated PACS in Iran, the usability of these PACS had a favorable status regarding ease of use while provided lower information quality. Generally, based on the users' viewpoints, the PACS from Company B were the most usable while the PACS provided by Company D were the least usable. It is suggested that the information quality and user interface of systems be improved by using appropriate analysis and needs assessment of the end users.

Promises of artificial intelligence in neuroradiology: a systematic technographic review

PURPOSE

To conduct a systematic review of the possibilities of artificial intelligence (AI) in neuroradiology by performing an objective, systematic assessment of available applications. To analyse the potential impacts of AI applications on the work of neuroradiologists.

METHODS

We identified AI applications offered on the market during the period 2017-2019. We systematically collected and structured information in a relational database and coded for the characteristics of the applications, their functionalities for the radiology workflow and their potential impacts in terms of 'supporting', 'extending' and 'replacing' radiology tasks.

RESULTS

We identified 37 AI applications in the domain of neuroradiology from 27 vendors, together offering 111 functionalities. The majority of functionalities 'support' radiologists, especially for the detection and interpretation of image findings. The second-largest group of functionalities 'extends' the possibilities of radiologists by providing quantitative information about pathological findings. A small but noticeable portion of functionalities seek to 'replace' certain radiology tasks.

CONCLUSION

Artificial intelligence in neuroradiology is not only in the stage of development and testing but also available for clinical practice. The majority of functionalities support radiologists or extend their tasks. None of the applications can replace the entire radiology profession, but a few applications can do so for a limited set of tasks. Scientific validation of the AI products is more limited than the regulatory approval.

Challenges of Implementing Picture Archiving and Communication System in Multiple Hospitals: Perspectives of Involved Staff and Users

Today, despite the advantages of the PACS system, its implementation in some healthcare organizations faces many challenges. One of the important factors in the successful implementation of a PACS system is identifying and prioritizing the challenges from the perspectives of involved staff and user of this system. Therefore, the aim of this study was to determine and compare the challenges of implementing PACS from perspectives these users in educational hospitals. This study was conducted on all IT and medical equipment staff, and radiology residents (n = 140) in Kerman University of Medical Sciences (KUMS) and Shiraz University of Medical Sciences (SUMS) in 2016. The data were collected through two researcher-made questionnaires. Their validity was approved by radiologists, IT staff, and medical informatics specialists and their reliability through calculation of Cronbach's Alpha (0.969 and 0.795). We used Multivariate Analysis of Variance (MANOVA) to compare the scores given by three groups of participants in the challenges and Univariate Analysis of Variance (ANOVA) to compare the scores in two universities. The participants believed that technical challenges were more important than other challenges (x̄=3.74, SD = 0.7). IT experts (x̄=3.87, SD = 1) and radiology residents (x̄=3.95, SD = 0.9) gave the higher scores to the "shortage of high quality monitors" factor and medical equipment experts (x̄=4.26, SD = 0.87) to the "low speed of communication networks" factor among all technical challenges. The mean scores given to technical (x̄=76.1, SD = 13.5) and managerial (x̄=16, SD = 5.9) challenges in SUMS were more than the scores of the same challenges in KUMS (x̄=69.9, SD = 15.7) and (x̄=11.9, SD = 6.4) (p < 0.05). The technical challenges are the most common challenges to PACS implementation, and different universities experience different levels of technical challenges. Eliminating implementation challenges can reduce the risk of failure in the utilization process. Based on the results of this study, providing necessary infrastructures such as appropriate monitors and upgraded IT equipment can prevent many of the PACS implementation challenges.