Evaluating information management system in epidemic infectious diseases in Iran.

Health information management is a vital and constructive component of the health system, refers to the process of producing and collecting, organising and storing, analysing, disseminating and using information. The aim of this study was to evaluate the strengths and weaknesses of the information management system in epidemic infectious diseases in Iran, specifically focusing on the registration, reporting, quality, confidentiality, and security of infectious disease data. This assessment was conducted from the perspective of policymakers and experts responsible for data registration and reporting. After examining the processes of registering and reporting infectious disease data and interviewing experts, a researcher-designed questionnaire was prepared to evaluate the infectious disease information management system. To assess the content validity of the Content Validity Index and Content Validity Ratio Index, a questionnaire was utilized. The reliability of the questionnaire was confirmed using Cronbach's alpha. By employing purposeful sampling and adhering to the inclusion criteria, 150 participants were included in the study. Questionnaires were distributed via email, WhatsApp, or Telegram to employees at various levels of Iran's health and treatment systems who were responsible for registering and reporting infectious disease data. The study encompassed 100 participants who successfully concluded the research. The results highlight that the key strength of healthcare data registration lies in its ability to "depict the epidemic curve during outbreaks of infectious diseases." Conversely, a notable weakness was the "insufficient collaboration from non-academic sectors (e.g., clinics, private laboratories) in registering and reporting infectious diseases. The present study's findings suggest that the issue lies not in the framework itself, but rather in the execution and functionality of the strategies. We can cultivate a repository of reliable and beneficial data by incorporating initiatives like training programs, enforcing regulations with consequences for inadequate data documentation, offering both material and motivational rewards, and streamlining all data collection and reporting systems.

Family Information Management in the Context of Inherited Conditions: An Integrative Review.

This review aimed to develop a framework to understand the process of information management in families with inherited conditions. Electronic databases were searched for relevant peer-reviewed articles. Articles were included if they were original research on families affected by any confirmed inherited condition, described how a family accesses, interprets, conveys, and/or uses information about the disease, included the recruitment of more than one family member, and used family as the unit of analysis. Data were analyzed through directed content analysis. Thirty-four articles from 27 studies were analyzed. We propose a framework for family information management consisting of the following domains: contextual influences, family information management behaviors, and family information management outcomes. This proposed framework expands the understanding of how families manage their genetic information in making health care decisions for their affected and at-risk relatives.

Healthcare Providers' Adherence to COVID-19 Prevention and Control Practices in Health Records and Information Management, Ghana.

Background: The impact of contracting coronavirus on healthcare providers (HCPs) affects their ability to combat the infection. The virus can be transmitted through droplets from sneezing, coughing, and yelling, making it essential for HCPs to plan ahead when dealing with patients with respiratory symptoms. The need to assess healthcare providers' perceived adherence to COVID-19 Prevention and Control Practices (PCP) in Health Records and Information Management is vital for optimizing healthcare operations and ensuring the safety of both patients and providers. This study assesses healthcare providers' perceived adherence to COVID-19 PCP in Health Records and Information Management. Subjects and Method. A cross-sectional survey was conducted to collect data from 1268 HCPs working in eight randomly selected hospitals across five regions in Ghana. The survey was carried out from May 15, 2022, to August 13, 2022. Simple random sampling was used to choose these eight facilities from a total of 204 hospitals. Within each facility, HCPs from various departments were selected using simple random sampling. The EpiInfo 7 software's StatCalc tool was used to choose a total sample size of 1268 from an estimated 4482 HCP-PR from the eight hospitals. Compliance with COVID-19 PCP was assessed using a 3-point scale, ranging from one (Yes always) to three (No). Cronbach's alpha reliability coefficient was used to examine the statistical reliability of the variables in the dataset. Cronbach's alpha was 0.73 overall, suggesting strong reliability. Bartlett's test for equal variances was used for comparative analysis of health facility and overall mean COVID-19 PCP in different areas of health facilities. IBM SPSS (version 23) statistical software was used for the data analysis process. Results: A total of 1268 HCP-PR participated in the survey, resulting in a 99.6% response rate. Findings reveal that 760 healthcare professionals who handle patients' records (HCP-PR), constituting 60%, consistently followed COVID-19 protocols in the registration and clinic preparation zones. Another 390 individuals (30.7%) adhered to these protocols occasionally, while 119 (9.4%) failed to comply. Similarly, in the filing area, 739 respondents (58.3%) consistently adhered to COVID-19 protocols, 358 (28.3%) occasionally did so, and 170 (13.4%) did not follow the protocols at all. Regarding handling health records cautiously, 540 participants (42.5%) always did, 448 (35.3%) did so sometimes, and 280 (22.2%) neglected these precautions. Additionally, 520 respondents (41.0%) consistently followed COVID-19 precautions when handling computers and other equipment, 393 (31.0%) did so occasionally, and 355 (28.0%) did not adhere to these precautions. Conclusion: The majority of respondents showed good compliance with COVID-19 protocol in the registration and clinic preparation areas. However, in the filing area, just over four out of every seven respondents consistently adhered to COVID-19 PCP. Additionally, four out of every seven participants did not comply with COVID-19 PCP when handling patients' records. Analysis reveals diverse adherence to COVID-19 PCP, and statistical tests show variable performance, highlighting standout health facilities.

Construction and application of procedural pathways combined with information management in nursing staff skills training system.

OBJECTIVE: To explore the application effect of procedural pathways combined with information management in the construction of nursing staff skills training system. METHODS: This was a quasi-experimental study with a control group and an experimental group. A total of 300 newly admitted nurses or nurses who required training within three years of admission were selected as the experimental group, and 267 nurses who were trained in the same hospital during the same period in 2020 were selected as the control group. The experimental group received skills training using a system that combines procedural pathways with information management, while the control group received traditional teaching mode. The outcome measures included theoretical score, operation score, nurse competency, patient satisfaction, and nursing-related adverse events. The data were analyzed using t-test, chi-square test, and rank-sum test. RESULTS: The experimental group had higher scores in theoretical assessment, skills assessment, nurse competency, and patient satisfaction, and lower incidence of nursing-related adverse events than the control group (P < 0.05). CONCLUSION: The strategy of procedural pathways combined with information management provides a new perspective and method for nursing operation skills training, effectively improves clinical nursing quality and ensures patient safety.

Information Management in Hospital Unit Daily Operations: A Descriptive Study With Nurses and Physicians.

Operations management of a hospital unit is a shared activity involving nursing and medical professionals, characterized by suddenly changing situations, constant interruptions, and ad hoc decision-making. Previous studies have explored the informational needs affecting decision-making, but only limited information has been collected regarding factors affecting information management related to the daily operations of hospital units. The aim of this study was to describe the experiences of nursing and medical professionals of information management in the daily operations of hospital units. This qualitative study consists of interviews following the critical incidence technique. Twenty-six nurses and eight physicians working in operational leadership roles in hospital units were interviewed, and the data were subjected to thematic analysis. The data analysis showed that strengths of current systems were organizational operational procedures, general instruments supporting information management, and a digital operations dashboard, whereas opportunities for improvement included the information architecture, quality of information, and technology use. The study findings highlight that despite several decades of efforts to provide solutions to support information management in hospital daily operations, further measures need to be taken in developing and implementing information systems with user-centered strategies and systematic approaches to better support healthcare professionals.

Impact of an ICU bed capacity optimisation method on the average length of stay and average cost of hospitalisation following implementation of China's open policy with respect to COVID-19: a difference-in-differences analysis based on information management system data from a tertiary hospital in southwest China.

OBJECTIVES: Following the implementation of China's open policy with respect to COVID-19 on 7 December 2022, the influx of patients with infectious diseases has surged rapidly, necessitating hospitals to adopt temporary requisition and modification of ward beds to optimise hospital bed capacity and alleviate the burden of overcrowded patients. This study aims to investigate the effect of an intensive care unit (ICU) bed capacity optimisation method on the average length of stay (ALS) and average cost of hospitalisation (ACH) after the open policy of COVID-19 in China. DESIGN AND SETTING: A difference-in-differences (DID) approach is employed to analyse and compare the ALS and ACH of patients in four modified ICUs and eight non-modified ICUs within a tertiary hospital located in southwest China. The analysis spans 2 months before and after the open policy, specifically from 5 October 2022 to 6 December 2022, and 7 December 2022 to 6 February 2023. PARTICIPANTS: We used the daily data extracted from the hospital's information management system for a total of 5944 patients admitted by the outpatient and emergency access during the 2-month periods before and after the release of the open policy in China. RESULTS: The findings indicate that the ICU bed optimisation method implemented by the tertiary hospital led to a significant reduction in ALS (HR -0.6764, 95% CI -1.0328 to -0.3201, p=0.000) and ACH (HR -0.2336, 95% CI -0.4741 to -0.0068, p=0.057) among ICU patients after implementation of the open policy. These results were robust across various sensitivity analyses. However, the effect of the optimisation method exhibits heterogeneity among patients admitted through the outpatient and emergency channels. CONCLUSIONS: This study corroborates a significant positive impact of ICU bed optimisation in mitigating the shortage of medical resources following an epidemic outbreak. The findings hold theoretical and practical implications for identifying effective emergency coordination strategies in managing hospital bed resources during sudden public health emergency events. These insights contribute to the advancement of resource management practices and the promotion of experiences in dealing with public health emergencies.

Transformer with difference convolutional network for lightweight universal boundary detection.

Although deep-learning methods can achieve human-level performance in boundary detection, their improvements mostly rely on larger models and specific datasets, leading to significant computational power consumption. As a fundamental low-level vision task, a single model with fewer parameters to achieve cross-dataset boundary detection merits further investigation. In this study, a lightweight universal boundary detection method was developed based on convolution and a transformer. The network is called a "transformer with difference convolutional network" (TDCN), which implies the introduction of a difference convolutional network rather than a pure transformer. The TDCN structure consists of three parts: convolution, transformer, and head function. First, a convolution network fused with edge operators is used to extract multiscale difference features. These pixel difference features are then fed to the hierarchical transformer as tokens. Considering the intrinsic characteristics of the boundary detection task, a new boundary-aware self-attention structure was designed in the transformer to provide inductive bias. By incorporating the proposed attention loss function, it introduces the direction of the boundary as strongly supervised information to improve the detection ability of the model. Finally, several head functions with multiscale feature inputs were trained using a bidirectional additive strategy. In the experiments, the proposed method achieved competitive performance on multiple public datasets with fewer model parameters. A single model was obtained to realize universal prediction even for different datasets without retraining, demonstrating the effectiveness of the method. The code is available at https://github.com/neulmc/TDCN.

Application study of surgical instruments information management system in sports medicine specialty.

The management of surgical instruments is related to the safety and efficiency of surgical operations, and a surgical instruments information management system (SIIMS) has been developed. The aim of the current study is to explore the application value of the SIIMS in sports medicine specialty. A set of self-developed SIIMS for sports medicine surgeries was applied to the study. The application value of the SIIMS was verified by comparing the safety and efficiency of instrument manipulation before and after its application, with instrument accidents, instrument repair rate, instrument scrap rate and instrument use efficiency as indicators. Through the application of the SIIMS, the incidence of surgical instrument accidents decreased from 3.7 times to 1.8 times (P = 0.02), the number of instrument repair decreased from 7.7 times to 2.9 times (P = 0.00), and the number of scrapped instruments decreased from 5.1 to 2.3 (P = 0.03), when referred to per thousand operations. Before and after the application of the SIIMS, the average instrument use efficiency was 74.0% ± 3.3% and 88.2% ± 4.4%, respectively, with statistically significant difference (P = 0.00). The application of the SIIMS in sports medicine specialty is helpful to the fine management of surgical instruments, improve surgical safety and instrument use efficiency.

Cybersecurity on a budget: Evaluating security and performance of open-source SIEM solutions for SMEs.

The proliferation of cyber threats necessitates robust security measures to safeguard critical assets and data in today's evolving digital landscape. Small and Medium Enterprises (SMEs), which are the backbone of the global economy are particularly vulnerable to these threats due to inadequate protection for critical and sensitive information, budgetary constraints, and lack of cybersecurity expertise and personnel. Security Information and Event Management (SIEM) systems have emerged as pivotal tools for monitoring, detecting, and responding to security incidents. While proprietary SIEM solutions have historically dominated the market, open-source SIEM systems have gained prominence for their accessibility and cost-effectiveness for SMEs. This article presents a comprehensive study focusing on the evaluation of open-source SIEM systems. The research investigates the capabilities of these open-source solutions in addressing modern security challenges and compliance with regulatory requirements. Performance aspects are explored through empirical testing in simulated enterprise-grade SME network environments to assess resource utilization, and real-time data processing capabilities. By providing a rigorous assessment of the security and performance features of open-source SIEM systems, this research offers valuable insights to cybersecurity practitioners, organizations seeking cost-effective security solutions, and the broader academic community. The findings shed light on the strengths and limitations of these systems, aiding decision-makers in selecting the most suitable SIEM solution for their specific requirements while enhancing the cybersecurity posture of SMEs.

South Africa's oceans and coastal and information management system towards improved ocean access, protection, and governance.

The Oceans and Coastal Information Management System (OCIMS) was launched by the South African Government in 2015 to support the development and governance of the South African ocean economy. The OCIMS has established knowledge tools for marine spatial planning, maritime domain awareness, search and rescue, water quality and harmful algal bloom monitoring. Those tools are used daily by stakeholders across government departments, industry, and civil society. Unlike many other operational oceanographic and coastal systems around the world, the OCIMS was designed from its inception using inputs from stakeholders. Continuous engagements between developers and stakeholders have ensured that the system remains fit for purpose. The OCIMS is both locally relevant and globally cognizant. Developments are undertaken to ensure inter-operability with other systems in the world and promote the exchange and discovery of data. The OCIMS project was able to leverage co-funding and the sharing of data and expertise through partnerships across the public and private sectors. These partnerships have been essential to the success of OCIMS and would not have been possible without continued engagements and the sustained funding provided by the South African national government. The development pathway followed to establish the OCIMS could benefit other countries looking to implement their own operational ocean and coastal system knowledge platform.

Impact of the Narcotics Information Management System on Opioid Use Among Outpatients With Musculoskeletal and Connective Tissue Disorders: Quasi-Experimental Study Using Interrupted Time Series.

BACKGROUND: Opioids have traditionally been used to manage acute or terminal pain. However, their prolonged use has the potential for abuse, misuse, and addiction. South Korea introduced a new health care IT system named the Narcotics Information Management System (NIMS) with the objective of managing all aspects of opioid use, including manufacturing, distribution, sales, disposal, etc. OBJECTIVE: This study aimed to assess the impact of NIMS on opioid use. METHODS: We conducted an analysis using national claims data from 45,582 patients diagnosed with musculoskeletal and connective tissue disorders between 2016 and 2020. Our approach included using an interrupted time-series analysis and constructing segmented regression models. Within these models, we considered the primary intervention to be the implementation of NIMS, while we treated the COVID-19 outbreak as the secondary event. To comprehensively assess inappropriate opioid use, we examined 4 key indicators, as established in previous studies: (1) the proportion of patients on high-dose opioid treatment, (2) the proportion of patients receiving opioid prescriptions from multiple providers, (3) the overlap rate of opioid prescriptions per patient, and (4) the naloxone use rate among opioid users. RESULTS: During the study period, there was a general trend of increasing opioid use. After the implementation of NIMS, significant increases were observed in the trend of the proportion of patients on high-dose opioid treatment (coefficient=0.0271; P=.01) and in the level of the proportion of patients receiving opioid prescriptions from multiple providers (coefficient=0.6252; P=.004). An abrupt decline was seen in the level of the naloxone use rate among opioid users (coefficient=-0.2968; P=.04). While these changes were statistically significant, their clinical significance appears to be minor. No significant changes were observed after both the implementation of NIMS and the COVID-19 outbreak. CONCLUSIONS: This study suggests that, in its current form, the NIMS may not have brought significant improvements to the identified indicators of opioid overuse and misuse. Additionally, the COVID-19 outbreak exhibited no significant influence on opioid use patterns. The absence of real-time monitoring feature within the NIMS could be a key contributing factor. Further exploration and enhancements are needed to maximize the NIMS' impact on curbing inappropriate opioid use.

Development of the Clinical Specimen Information Management System for Multicenter Clinical Studies.

Some multicenter clinical studies require the acquisition of clinical specimens from patients, and the centralized management and analysis of clinical specimens at a research institution. In such cases, it is necessary to manage clinical specimens with anonymized patient information. In addition, clinical specimens need to be managed in connection with clinical information in clinical studies. In this study, we have developed a clinical specimen information management system that works with electronic data capture system for efficient specimen information management and the system workflow has verified at Osaka University Hospital. In addition, by combining this system with medical image collection system that we have developed previously, the integrated management of clinical information, medical image, and clinical specimen information will become possible. This specimen information management system may be expected to provide the platform for integrated analysis utilizing clinical information, medical image, and data from clinical specimens in multicenter clinical studies.

Learning deep representation and discriminative features for clustering of multi-layer networks.

The multi-layer network consists of the interactions between different layers, where each layer of the network is depicted as a graph, providing a comprehensive way to model the underlying complex systems. The layer-specific modules of multi-layer networks are critical to understanding the structure and function of the system. However, existing methods fail to characterize and balance the connectivity and specificity of layer-specific modules in networks because of the complicated inter- and intra-coupling of various layers. To address the above issues, a joint learning graph clustering algorithm (DRDF) for detecting layer-specific modules in multi-layer networks is proposed, which simultaneously learns the deep representation and discriminative features. Specifically, DRDF learns the deep representation with deep nonnegative matrix factorization, where the high-order topology of the multi-layer network is gradually and precisely characterized. Moreover, it addresses the specificity of modules with discriminative feature learning, where the intra-class compactness and inter-class separation of pseudo-labels of clusters are explored as self-supervised information, thereby providing a more accurate method to explicitly model the specificity of the multi-layer network. Finally, DRDF balances the connectivity and specificity of layer-specific modules with joint learning, where the overall objective of the graph clustering algorithm and optimization rules are derived. The experiments on ten multi-layer networks showed that DRDF not only outperforms eight baselines on graph clustering but also enhances the robustness of algorithms.

A Conditional Process Analysis of Emerging Adults' Motivated Information Management with Parents, Family Conversation Orientation, and Intentions to Vaccinate for COVID-19.

Using the theory of motivated information management (TMIM), this study tested the effect of emerging adults' uncertainty discrepancy about COVID-19 vaccines on their intentions to vaccinate. In March and April of 2021, 424 emerging adult children reported on the likelihood of seeking or avoiding information from a parent about COVID-19 vaccines in response to their uncertainty discrepancy and negative emotions related to the vaccines. Results supported the direct and indirect effects specified by the TMIM. Moreover, the indirect effects of uncertainty discrepancy on intentions to vaccinate via the TMIM's explanatory mechanisms were conditioned by family conversation orientation. Consequently, the family communication environment may alter motivated information management in parent-child relationships.

Joint estimation of pose, depth, and optical flow with a competition-cooperation transformer network.

Estimating depth, ego-motion, and optical flow from consecutive frames is a critical task in robot navigation and has received significant attention in recent years. In this study, we propose PDF-Former, an unsupervised joint estimation network comprising a full transformer-based framework, as well as a competition and cooperation mechanism. The transformer framework captures global feature dependencies and is customized for different task types, thereby improving the performance of sequential tasks. The competition and cooperation mechanisms enable the network to obtain additional supervisory information at different training stages. Specifically, the competition mechanism is implemented early in training to achieve iterative optimization of 6 DOF poses (rotation and translation information from the target image to the two reference images), the depth of target image, and optical flow (from the target image to the two reference images) estimation in a competitive manner. In contrast, the cooperation mechanism is implemented later in training to facilitate the transmission of results among the three networks and mutually optimize the estimation results. We conducted experiments on the KITTI dataset, and the results indicate that PDF-Former has significant potential to enhance the accuracy and robustness of sequential tasks in robot navigation.

Health literacy and the role of social support in different age groups: results of a German cross-sectional survey.

BACKGROUND: Scholars demand more focus on context-related factors of health literacy as the management of health information is seen as a social practice. One prominent factor is social support that is expected to be particularly relevant for persons vulnerable for low health literacy. It was shown that health literacy can differ across the life span and especially older people have been demonstrated to be vulnerable for low health literacy. Therefore, health literacy and the relation of social support on health literacy in different age groups should be investigated. METHODS: In a German nationwide survey 2,151 adults were interviewed face-to-face. General comprehensive health literacy was measured with the HLS19-Q47 which differentiates single steps of health information management - access, understand, appraise, and apply. Social support was measured with the Oslo 3 Social Support Scale. Bivariate and multivariate analyses were performed for all respondents and for five age groups. RESULTS: Health literacy is relatively low in all age groups but particularly low among old-old people (76 + years). Also, the youngest adults (18-29 years) have slightly lower health literacy than middle-aged adults. On average, health literacy is higher among people with higher social support but this association varies between age groups. It tends to be quite strong among younger adults (18-45 years) and young-old persons (65-75 years) but is weak among older middle-aged (46-64 years) and old-old persons. The association also differs between steps of information management. It is stronger for accessing and applying information but there are differences in age groups as well. CONCLUSIONS: Social support is a relevant aspect to improve individuals' health literacy and therefore should be addressed in interventions. However, it is necessary to differentiate between age groups. While both young adults and particularly old-old persons are challenged by health information management, young adults can strongly profit from social support whereas it can barely compensate the low health literacy of old-old persons. In addition, different challenges in information management steps in different age groups need to be considered when designing health literacy interventions. Thus, target group specific services and programs are needed.

Comprehensive information management system for a medical research cohort biobank based on quality by design.

BACKGROUND: With the development of big health and big data, cohort research has become a medical research hotspot. As an important repository of human genetic resources, biobanks must adapt to the requirements of large-scale and efficient operation. Thus, biobanks urgently need to design and build a legal, convenient, and efficient information management system. METHODS: This study applies the concept of "quality by design" to build a comprehensive biobank information management system based on the analysis of user requirements, legal and regulatory risks, and industry-standard requirements. The system integrates the management of scientific research projects, biological specimens, clinical information, quality control, and multi-dimensional information query and development. After 10 months of its operation, the comprehensive management system was evaluated through statistical analysis of the efficiency of the construction of the pregnancy-birth cohort and the quality of genetic resources. RESULTS: Since the system's launch, the statistics on cohort construction efficiency show that the enrollment rate of eligible pregnant women has increased, and the rate of missing volunteers has dropped. The time needed to establish a 1000-person cohort (with complete biological samples and clinical information in early, middle, and late pregnancy) was reduced, and the effective tracking rate of the samples was 77.42%. The error rate of the deep cryogenic refrigerator decreased, with a clinical information integrity rate of 96.47%. CONCLUSIONS: The comprehensive biobank information management system constructed with the "quality by design" concept is well suited to meet the requirements of medical research. This study provides a solution for designing a comprehensive information system for medical institutions' biobanks.