Development of minimum data set and dashboard for monitoring adverse events in radiology departments.

Background: To reduce the risk of errors, patient safety monitoring in the medical imaging department is crucial. Interventions are required and these can be provided as a framework for documenting, reporting, evaluating, and recognizing events that pose a threat to patient safety. The aim of this study was to develop minimum data set and dashboard for monitoring adverse events in radiology departments. Material and methods: This developmental research was conducted in multiple phases, including content determination using the Delphi technique; database designing using SQL Server; user interface (UI) building using PHP; and dashboard evaluation in three aspects: the accuracy of calculating; UI requirements; and usability. Results: This study identified 26 patient safety (PS) performance metrics and 110 PS-related significant data components organized into 14 major groupings as the system contents. The UI was built with three tabs: pre-procedure, intra-procedure, and post-procedure. The evaluation results proved the technical feasibility of the dashboard. Finally, the dashboard's usability was highly rated (76.3 out of 100). Conclusion: The dashboard can be used to supplement datasets to obtain a more accurate picture of the PS condition and to draw attention to characteristics that professionals might otherwise overlook or undervalue.

Extraction frequent patterns in trauma dataset based on automatic generation of minimum support and feature weighting.

PURPOSE: Data mining has been used to help discover Frequent patterns in health data. it is widely used to diagnose and prevent various diseases and to obtain the causes and factors affecting diseases. Therefore, the aim of the present study is to discover frequent patterns in the data of the Kashan Trauma Registry based on a new method. METHODS: We utilized real data from the Kashan Trauma Registry. After pre-processing, frequent patterns and rules were extracted based on the classical Apriori algorithm and the new method. The new method based on the weight of variables and the harmonic mean was presented for the automatic calculation of minimum support with the Python. RESULTS: The results showed that the minimum support generation based on the weighting features is done dynamically and level by level, while in the classic Apriori algorithm considering that only one value is considered for the minimum support manually by the user. Also, the performance of the new method was better compared to the classical Apriori method based on the amount of memory consumption, execution time, the number of frequent patterns found and the generated rules. CONCLUSIONS: This study found that manually determining the minimal support increases execution time and memory usage, which is not cost-effective, especially when the user does not know the dataset's content. In trauma registries and massive healthcare datasets, its ability to uncover common item groups and association rules provides valuable insights. Also, based on the patterns produced in the trauma data, the care of the elderly by their families, education to the general public about encountering patients who have an accident and how to transport them to the hospital, education to motorcyclists to observe safety points in Recommended when using a motorcycle.

Brain Tumor Classification and Segmentation Using Dual-Outputs for U-Net Architecture: O2U-Net.

We propose a modified version of the U-Net architecture for segmenting and classifying brain tumors, introducing another output between down- and up-sampling. Our proposed architecture utilizes two outputs, adding a classification output beside the segmentation output. The central idea is to use fully connected layers to classify each image before applying U-Net's up-sampling operations. This is achieved by utilizing the features extracted during the down-sampling procedure and combining them with fully connected layers for classification. Afterward, the segmented image is generated by U-Net's up-sampling process. Initial tests show competitive results against comparable models with 80.83%, 99.34%, and 77.39% for the dice coefficient, accuracy, and sensitivity, respectively. The tests were conducted on the well-established dataset from Nanfang Hospital, Guangzhou, China, and General Hospital, Tianjin Medical University, China, from 2005 to 2010 containing MRI images of 3064 brain tumors.

Development and Testing Requirements for an Integrated Maternal and Child Health Information System in Iran: A Design Thinking Case Study.

BACKGROUND: Management of child health care can be negatively affected by incomplete recording, low data quality, and lack of data integration of health management information systems to support decision making and public health program needs. Given the importance of identifying key determinants of child health via capturing and integrating accurate and high-quality information, we aim to address this gap through the development and testing requirements for an integrated child health information system. SUBJECTS AND METHODS: A five-phase design thinking approach including empathizing, defining, ideation, prototyping, and testing was applied. We employed observations and interviews with the health workers at the primary health care network to identify end-users' challenges and needs using tools in human-centered design and focus group discussion. Then, a potential solution to the identified problems was developed as an integrated maternal and child health information system (IMCHIS) prototype and tested using Software Quality Requirements and Evaluation Model (SQuaRE) ISO/IEC 25000. RESULTS: IMCHIS was developed as a web-based system with 74 data elements and seven maternal and child health care requirements. The requirements of "child disease" with weight (0.26), "child nutrition" with weight (0.20), and "prenatal care" with weight (0.16) acquired the maximum weight coefficient. In the testing phase, the highest score with the weight coefficient of 0.48 and 0.73 was attributed to efficiency and functionality characteristics, focusing on software capability to fulfill the tasks that meet users' needs. CONCLUSION: Implementing a successful child health care system integrates both maternal and child health care information systems to track the effect of maternal conditions on child health and support managing performance and optimizing service delivery. The highest quality score of IMCHIS in efficiency and functionality characteristics confirms that it owns the capability to identify key determinants of child health.

Development of clinical-guideline-based mobile application and its effect on head CT scan utilization in neurology and neurosurgery departments.

BACKGROUND: There is little evidence regarding the adoption and intention of using mobile apps by health care professionals (HCP) and the effectiveness of using mobile apps among physicians is still unclear. To address this challenge, the current study seeks two objectives: developing and implementing a head CT scan appropriateness criteria mobile app (HAC app), and investigating the effect of HAC app on CT scan order. METHODS: A one arm intervention quasi experimental study with before/after analysis was conducted in neurology & neurosurgery (N&N) departments at the academic hospital. We recruited all residents' encounters to N&N departments with head CT scan to examine the effect of HAC app on residents' CT scan utilization. The main outcome measure was CT scan order per patient for seven months at three points, before the intervention, during the intervention, after cessation of the intervention -post-intervention follow-up. Data for CT scan utilization were collected by reviewing medical records and then analyzed using descriptive statistics, Kruskal-Wallis, and Mann-Whitney tests. A focus group discussion with residents was performed to review and digest residents' experiences during interaction with the HAC app. RESULTS: Sixteen residents participated in this study; a total of 415 N&N encounters with CT scan order, pre-intervention 127 (30.6%), intervention phase 187 (45.1%), and 101 (24.3%) in the post-intervention follow-up phase were included in this study. Although total CT scan utilization was statistically significant during three-time points of the study (P = 0.027), no significant differences were found for CT utilization after cessation of the intervention (P = 1). CONCLUSION: The effect of mobile devices on residents' CT scan ordering behavior remains open to debate since the changes were not long-lasting. Further studies based on real interactive experiences with mobile devices is advisable before it can be recommended for widespread use by HCP.

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.