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Many international radiology societies, including American College of Radiologists (ACR), have established guidelines for optimum forms and contents of medical imaging reports to ensure high quality and to guarantee the satisfaction of both the referring physician and the patient. Therefore, this study aims to analyze the criteria of magnetic resonance imaging (MRI) reports in Jordan according to the standards of the ACR.

This cross-sectional study was conducted in early January 2021 for two weeks. An invitation letter was sent to 85 MRI centers of various health-care sectors in Jordan to participate in the study. Each invitee was requested to send at least ten different MRI reports. The study used a questionnaire containing the checklist of the latest edition 2020 of ACR’s practice parameter to communicate the diagnostic imaging results and the demographic information of the participating MRI centers. Seven basic elements were assessed for content-related quality of MRI reports, which are administrative data, patient demographics, clinical history, imaging procedures, clinical symptoms, imaging observations and impressions. Statistical analyses were used to evaluate the data.

Forty-one MRI centers participated in the study with 386 different MRI exam reports. The majority (92%) of the reports were computer-generated. Free texted unstructured reports and head-structured reports had an almost equal percentage of around 40%. Exam and radiologist demography as well as exam findings criteria were 100% available in all reports. The percentage of exam conclusion, and exam description and techniques were 2% and 4.9%, respectively (N = 368). There was a positive association between computer-generated reports and the presence of picture archiving and communication systems (PACS)/health information systems r = 0.443.

Structured and free text unstructured reporting were the common types of MRI exam reports in Jordan. Handwriting exam reporting existed in few MRI centers, particularly in those that had no PACS and radiology information systems.

Mumbai needs to be transformed into a world-class city as stated in the 2005–2025 development plan of Municipal Corporation. For this initiative, hospital management information system (HMIS) has to be implemented across 400+ health facilities in the city.

A case study methodology was adopted to study HMIS implementation. Wave 1 of Phase 1 implementation of HMIS is carried out as a pilot project at Film City’s Hospital, Mumbai, which “go-live” on 21st June 2018. The work for hardware and software implementation was awarded to HardSystems and Solutions Limited and SoftSolutions India Private Limited, respectively, through e-tender.

Provision of inadequate quantity of hardware, slowness of network or system, non-satisfactory training after observation confirmation and sign-off process, lack of data entry operators, mismatch in numbering systems in blood bank and many other challenges concerned with the specific departments had become a major impediment in the efforts to maximize number of patients registered into HMIS.

Even after providing many clinical and managerial benefits, being the first cloud-based centrally located HMIS in any of the hospitals in the city, it imposes a major challenge for the management in terms of resistance of employees toward technology and need for the adoption of theoretical models for implementing change for the overall organizational development.

To the best of the authors’ knowledge, no other teaching case study is conducted to study the HMIS implementation in large-scale public health-care services. This is a dummy case study for teaching exercises. The identity of the stakeholders, organizations and events has been masked to maintain confidentiality.

This paper aims to propose lean-based interventions that address the main causes of emergency department overcrowding. Emergency department overcrowding (EDOC) and increased length of stay (LOS) have been key global issues for more than 20 years, as they have serious repercussions. No measurements have been done to assess the situation nationally. Expanding emergency departments (EDs) and adding more beds have never succeeded in eliminating wastes and targeting the root causes of the problem.

This paper is a quantitative analytical applied research. The paper used direct observation for seven days to collect patient flow data on ED patients at a secondary care hospital in Kuwait. It calculated wait times and services to identify the major causes of EDOC and increased LOS.

Around one-third of the ED design capacity was used by 12 per cent of the patients who stayed >6 h each. The wasted waiting time represents 56.2 per cent of the aggregated LOS, which puts lean management (LM) on the top of the process reengineering approaches suitable for improving overcrowding by reducing waste. Guided by the LM concepts, the paper proposes solutions that fall into three themes. The selected solutions address the vital few causes of the EDOC and prolonged EDLOS.

This paper is the first study of its kind in Kuwait, and one of the most outstanding studies in the Gulf region, in terms of the number of the daily ED visits and the comprehensive multi-level proposed interventions.

The purpose of this paper is to review the prevalence and risk factors of work-related musculoskeletal disorders (WMSDs) among healthcare workers (HCWs) in order to ascertain the occupation with the highest susceptibility to WMSD in the health sector. This paper will also review the effective interventions which have been used to prevent WMSDs among HCWs.

This study is a literature review of 11 papers related to the prevalence and risk factors of WMSDs and 12 papers about the interventions being used to prevent WMSDs among HCWs. The papers were retrieved from respectable databases such as PubMed, Science Direct, Google Scholar and E-Thesis.

Nurses belong to the major group of HCWs who had the highest prevalence of WMSDs compared with other health professionals and other hospital workers. Although there are several interventions being commonly used to prevent WMSD risk factors, some interventions were unsuccessful in the prevention of WMSDs in healthcare tasks. Therefore, it is necessary that future research focuses on the tasks of HCWs that are WMSD risk factors and tries to innovate or redesign ergonomic workstations to prevent those risk factors.

The expected benefit of this study is to motivate ergonomists to provide appropriate and innovative interventions to ensure health and safety for nurses and other HCWs.

Proposes using the analytic concept of genre of organizational communication to study the organizational consequences of implementing clinical information systems and shifting from paper‐based to electronic patient records in clinical practices. By focusing research attention on interpersonal communication and social interaction issues not addressed in medical informatics research, this approach contributes to the understanding of organizational and institutional issues that implementing such systems may entail. The paper develops an example drawn from an in‐depth case study of a computerized order entry system to illustrate the insights this approach may provide.

Successful organisations harness the talents of all their members but especially their most skilled and creative people. In a hospital the most highly skilled staff are professionals working in the “operating core”, providing services directly to users. The existence of a “dual hierarchy” in health care organisations often separates this group from institutional management. A hospital′s success depends, however, on the integration of professional expertise and general management. Professionals and managers must inhabit each other′s worlds. These lessons are applied in a NHS Hospital Unit of Management.

The purpose of this study is to propose a framework for extracting medical information from the Web using domain ontologies. Patient–Doctor conversations have become prevalent on the Web. For instance, solutions like HealthTap or AskTheDoctors allow patients to ask doctors health-related questions. However, most online health-care consumers still struggle to express their questions efficiently due mainly to the expert/layman language and knowledge discrepancy. Extracting information from these layman descriptions, which typically lack expert terminology, is challenging. This hinders the efficiency of the underlying applications such as information retrieval. Herein, an ontology-driven approach is proposed, which aims at extracting information from such sparse descriptions using a meta-model.

A meta-model is designed to bridge the gap between the vocabulary of the medical experts and the consumers of the health services. The meta-model is mapped with SNOMED-CT to access the comprehensive medical vocabulary, as well as with WordNet to improve the coverage of layman terms during information extraction. To assess the potential of the approach, an information extraction prototype based on syntactical patterns is implemented.

The evaluation of the approach on the gold standard corpus defined in Task1 of ShARe CLEF 2013 showed promising results, an F-score of 0.79 for recognizing medical concepts in real-life medical documents.

The originality of the proposed approach lies in the way information is extracted. The context defined through a meta-model proved to be efficient for the task of information extraction, especially from layman descriptions.

This study aims to analyze a conspicuous corpus of literature related to the field of technology-based intensive care research and to develop an architecture model of the future smart intensive care unit (ICU).

Papers related to the topics of electronic health record (EHR), big data, data flow and clinical decision support in ICUs were investigated. These concepts have been analyzed in combination with secondary use of data, prediction models, data standardization and interoperability challenges. Based on the findings, an architecture model evaluated using MIMIC III is proposed.

Research identified issues regarding implementation of systems, data sources, interoperability, management of big data and free text produced in ICUs and lack of accuracy of prediction models. ICU should be treated as part of a greater system, able to intercommunicate with other entities.

The research examines the current needs of ICUs in interoperability and data management. As environment changes dynamically, continuous assessment and evaluation of the model with other ICU databases is required.

The proposed model improves ICUs interoperability in national health system, ICU staff intercommunication, remote access and decision support. Its modular approach ensures that ICUs can have their own particularities and specialisms while ICU functions provide ongoing expertise and training to upgrade its staff.

The purpose of this study is to investigate and demonstrate the advancements achieved in the field of chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Typical convolutional neural networks (CNNs) are unable to capture both local and global contextual information effectively and apply a uniform operation to all pixels in an image. To address this, we propose an innovative approach that integrates a dynamic convolution operation at the encoder stage, improving image encoding quality and disease detection. In addition, a decoder based on the gated recurrent unit (GRU) is used for language modeling, and an attention network is incorporated to enhance consistency. This novel combination allows for improved feature extraction, mimicking the expertise of radiologists by selectively focusing on important areas and producing coherent captions with valuable clinical information.

In this study, we have presented a new report generation approach that utilizes dynamic convolution applied Resnet-101 (DyCNN) as an encoder (Verelst and Tuytelaars, 2019) and GRU as a decoder (Dey and Salemt, 2017; Pan et al., 2020), along with an attention network (see Figure 1). This integration innovatively extends the capabilities of image encoding and sequential caption generation, representing a shift from conventional CNN architectures. With its ability to dynamically adapt receptive fields, the DyCNN excels at capturing features of varying scales within the CXR images. This dynamic adaptability significantly enhances the granularity of feature extraction, enabling precise representation of localized abnormalities and structural intricacies. By incorporating this flexibility into the encoding process, our model can distil meaningful and contextually rich features from the radiographic data. While the attention mechanism enables the model to selectively focus on different regions of the image during caption generation. The attention mechanism enhances the report generation process by allowing the model to assign different importance weights to different regions of the image, mimicking human perception. In parallel, the GRU-based decoder adds a critical dimension to the process by ensuring a smooth, sequential generation of captions.

The findings of this study highlight the significant advancements achieved in chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Experiments conducted using the IU-Chest X-ray datasets showed that the proposed model outperformed other state-of-the-art approaches. The model achieved notable scores, including a BLEU_1 score of 0.591, a BLEU_2 score of 0.347, a BLEU_3 score of 0.277 and a BLEU_4 score of 0.155. These results highlight the efficiency and efficacy of the model in producing precise radiology reports, enhancing image interpretation and clinical decision-making.

This work is the first of its kind, which employs DyCNN as an encoder to extract features from CXR images. In addition, GRU as the decoder for language modeling was utilized and the attention mechanisms into the model architecture were incorporated.

Meeting the patients' requirements as customers of the health care sector is crucially important as a social responsibility. According to the resource constraints, only an efficient utilisation of health services can provide that purpose. This study aims to develop a quantitative assessment framework for radiology centres as a vital section in healthcare to translate the patients' requirements into service quality specifications. This would help to achieve quality improvement by emphasising the voice of customers.

A literature review is conducted to specify the service quality criteria and the patients' requirements related to healthcare and hospitals. Based on the experts' opinions, these criteria and requirements are later customised for the radiology centres. Moreover, the requirements are categorised into five dimensions of SERVQUAL. The interrelations between service elements are also determined through expert group consensus using Pearson correlation. Afterwards, by applying the QFD method, the relations between the requirements and criteria are explored. Additionally, a customer satisfaction survey is executed in Tehran public hospitals to prioritise these requirements and provide an importance-satisfaction analysis.

Based on the result of the case study, service elements are prioritised for improvement, and practical suggestions are provided using the Delphi technique for quality improvement. In addition, a cause-and-effect diagram is presented to highlight the improvement area and provide enhancement suggestions.

This study is the first empirical attempt to benefit from the VOC in evaluating and enhancing the quality of service delivered to radiology patients. In doing so, the study applies a hybrid approach of QFD and SERVQUAL as well as other tools to highlight the improvement area and provide enhancement suggestions. The findings can be readily used by the practitioners.