Risk assessment of national railway infrastructure due to sea-level rise: an application of a methodological framework in Italian coastal railways.

Nowadays, within the built environment, railway infrastructures play a key role to sustain national policies oriented toward promoting sustainable mobility. For this reason, national institutions and infrastructure managers need to increase their awareness in relation to the current and future climate risks on their representative systems. Among climate change impacts, preventing the effects of sea-level rise (SLR) on coastal railway infrastructures is a priority. The first step in the climate change adaptation policy cycle is the development of an ad hoc climate risk assessment. In this view, this research develops a vulnerability and a risk assessment metric to identify the hotspots within a national coastal railway due to the SLR impacts. The proposed methodology required different steps to quantify the SLR projections and the vulnerability characteristics of the assets, in terms of sensitivity and adaptive capacity. The investigated case study is the coastal railway infrastructure in Italy, thanks to an initial approach of co-design participative processes with the national Infrastructure Manager: Rete Ferroviaria Italiana (RFI). The results of this application, although not included in the paper due to confidential reasons imposed by the infrastructure manager - led to a clear identification of the areas and the coastal railway sections which are exposed to high levels of risks and of the places which require priority actions for urgent adaptation in a view of climate proof infrastructures.

Multidimensional analysis of road traffic noise and probable public health hazards in Barisal city corporation, Bangladesh.

Noise pollution is a major challenge in urban contexts all around the world. The study was designed to assess road traffic noise pollution with possible health effects on those living in the study region. The IDW spatial interpolation approach and an ArcGIS-based evaluation were used to map the recorded noise levels in the research region. The noise descriptors including Noise Climate (NC), Traffic Noise Index (TNI), Equivalent Noise Level (Leq), and Noise Pollution Level (NPL) were computed. The required information has been collected through a questionnaire survey and previously published documents. The study reveals that the current noise level is higher than the recommended national threshold at every location. According to the study, the Nathullabad region had the highest level of noise pollution (86.5 dBA), while the Kaunia Abasik area had the lowest level (67.8 dBA). Study findings also show that in the area context, the highest levels of noise pollution are found in commercial areas (82 dBA), followed by industrial areas (80.4 dBA),mixed areas (81.3 dBA), and residential areas (72.7 dBA). The lowest level is found in sensitive areas (72.5 dBA). Statistical analyses, including one-way ANOVA, Tukey HSD post-hoc and LSD post-hoc test results, showed that there was no statistically significant difference (p > 0.05) between the noise pollution levels (NPL) in the morning, noon, and evening shifts. The results showed that 32 % of respondents stated they felt disturbed while working, and 27% of respondents said it was somewhat sensitive for them. As the last step in minimizing noise pollution in the research area, 37 % of respondents reported enforcing the regulations, 31% suggested making hydraulic horns illegally, and 21 % suggested raising public awareness. This study may contribute to academic knowledge and assist decision-makers of government officials in formulating appropriate local strategies to deal with this grave environmental problem.

Enhancing flood mapping through ensemble machine learning in the Gamasyab watershed, Western Iran.

Floods are among the natural hazards that have seen a rapid increase in frequency in recent decades. The damage caused by floods, including human and financial losses, poses a serious threat to human life. This study evaluates two machine learning (ML) techniques for flood susceptibility mapping (FSM) in the Gamasyab watershed in Iran. We utilized random forest (RF), support vector machine (SVM), ensemble models, and a geographic information system (GIS) to predict FSM. The application of these models involved 10 effective factors in flooding, as well as 82 flood locations integrated into the GIS. The SVM and RF models were trained and tested, followed by the implementation of resampling techniques (RT) using bootstrap and subsampling methods in three repetitions. The results highlighted the importance of elevation, slope, and precipitation as primary factors influencing flood occurrence. Additionally, the ensemble model outperformed both the RF and SVM models, achieving an area under the curve (AUC) of 0.9, a correlation coefficient (COR) of 0.79, a true skill statistic (TSS) of 0.83, and a standard deviation (SD) of 0.71 in the test phase. The tested models were adapted to available input data to map the FSM across the study watershed. These findings underscore the potential of integrating an ensemble model with GIS as an effective tool for flood susceptibility mapping.

The challenges and opportunities of continuous data quality improvement for healthcare administration data.

Background: Various data quality issues have prevented healthcare administration data from being fully utilized when dealing with problems ranging from COVID-19 contact tracing to controlling healthcare costs. Objectives: (i) Describe the currently adopted approaches and practices for understanding and improving the quality of healthcare administration data. (ii) Explore the challenges and opportunities to achieve continuous quality improvement for such data. Materials and Methods: We used a qualitative approach to obtain rich contextual data through semi-structured interviews conducted at a state health agency regarding Medicaid claims and reimbursement data. We interviewed all data stewards knowledgeable about the data quality issues experienced at the agency. The qualitative data were analyzed using the Framework method. Results: Sixteen themes emerged from our analysis, collected under 4 categories: (i) Defect characteristics: Data defects showed variability, frequently remained obscure, and led to negative outcomes. Detecting and resolving them was often difficult, and the work required often exceeded the organizational boundaries. (ii) Current process and people issues: The agency adopted primarily ad-hoc, manual approaches to resolving data quality problems leading to work frustration. (iii) Challenges: Communication and lack of knowledge about legacy software systems and the data maintained in them constituted challenges, followed by different standards used by various organizations and vendors, and data verification difficulties. (iv) Opportunities: Training, tool support, and standardization of data definitions emerged as immediate opportunities to improve data quality. Conclusions: Our results can be useful to similar agencies on their journey toward becoming learning health organizations leveraging data assets effectively and efficiently.

GloCAB cropland field boundary dataset.

This dataset consists of 190,832 manually-digitized cropland field boundaries, with associated attributes, within Brazil, Ukraine, United States of America, Canada, and Russia. Specifically, 22 regions of various sizes (74km2 - 38,000km2) spanning 5 countries were digitized over a range of predominant crop types over different time periods. These field boundaries were drawn over 20 m Sentinel-2 imagery. This field boundary dataset is a byproduct of a larger effort to map cropland burned area (Global Cropland Area Burned: GloCAB product [1]), however, it has several benefits beyond its original intent, including as a training dataset for machine-learning field size analyses, or a dataset to derive cropland field characteristics across different predominant crop types and geographies.

The Capacitated Maximal Covering Location Problem Improves Access to Stroke Treatment: A Cross-Sectional Simulation Study.

Disparities in accessing advanced stroke treatment have been recognized as a policy challenge in multiple countries, including Japan, necessitating priority solutions. Nevertheless, more practical healthcare policies must be implemented due to the limited availability of healthcare staff and financial resources in most nations. This study aimed to evaluate the supply and demand balance of mechanical thrombectomy (MT) and identify areas with high priority for enhancing stroke centers. The target area of this study was Hokkaido, Japan. We adopted the capacitated maximal covering location problem (CMCLP) to propose an optimal allocation without increasing the number of medical facilities. Four realistic scenarios with varying levels of total MT supply capacity for Primary stroke centers and assuming a range of 90 minutes by car from the center were created and simulated. From scenarios 1 to 4, the coverage increased by approximately 53% to 85%, scenarios 2 and 3 had 5% oversupply, and scenario 4 had an oversupply of approximately 20%. When the supply capacity cap was eliminated and 8 PSCs received 31 or more patients, they became priority enhancement targets. The CMCLP estimates demand coverage considering the supply and demand balance and indicates areas and facilities where MT supply capacity enhancement is a priority.

Who is (not) vaccinated? A proposal for a comprehensive immunization information system.

The role of immunization in public health is crucial, offering widespread protection against infectious diseases and underpinning societal well-being. However, achieving optimal vaccination coverage is impeded by vaccine hesitancy, a significant challenge that necessitates comprehensive strategies to understand and mitigate its effects. We propose the integration of Population Health Management principles with Immunization Information Systems (IISs) to address vaccine hesitancy more effectively. Our approach leverages systematic health determinants analysis to identify at-risk populations and tailor interventions, thereby promoting vaccination coverage and public health responses. We call for the development of an enhanced version of the Italian National Vaccination Registry, which aims to facilitate real-time tracking of individuals' vaccination status while improving data accuracy and interoperability among healthcare systems. This registry is designed to overcome current barriers by ensuring robust data protection, addressing cultural and organizational challenges, and integrating behavioral insights to foster informed public health campaigns. Our proposal aligns with the Italian National Vaccination Prevention Plan 2023-2025 and emphasizes proactive, evidence-based strategies to increase vaccination uptake and contrast the spread of vaccine-preventable diseases. The ultimate goal is to establish a data-driven, ethically sound framework that enhances public health outcomes and addresses the complexities of vaccine hesitancy within the Italian context and beyond.

Assessing the integration of refugee health data into national health information systems in Jordan, Lebanon, and Uganda.

BACKGROUND: With the increasing number of protracted refugee crises globally, it is essential to ensure strong national health information systems (HIS) in displacement settings that include refugee-sensitive data and disaggregation by refugee status. This multi-country study aims to assess the degree of integration of refugee health data into national HIS in Jordan, Lebanon, and Uganda and identify the strengths and weaknesses of their national HIS in terms of collecting and reporting on refugee-related health indicators. METHODS: The study employs a comparative country analysis approach using a three-phase framework. The first phase involved reviewing 4120 indicators compiled from global health organizations, followed by a multi-stage refinement process, resulting in 45 indicators distributed across five themes. The second phase consisted of selecting relevant criteria from the literature, including data sources, annual reporting, disaggregation by refugee status, refugee population adjustments, accuracy, and consistency. The third phase involved assessing data availability and quality of the selected indicators against these criteria. RESULTS: Our analysis uncovered significant challenges in assessing the health status of refugees in Jordan, Lebanon, and Uganda, primarily stemming from limitations in the available health data and indicators. Specifically, we identified significant issues including incomplete local data collection with reliance on international data sources, fragmented data collection from various entities leading to discrepancies, and a lack of distinction between refugees and host populations in most indicators. These limitations hinder accurate comparisons and analyses. In light of these findings, a set of actionable recommendations was proposed to guide policymakers in the three countries to improve the integration of refugee health data into their national HIS ultimately enhancing refugees' well-being and access to healthcare services. CONCLUSION: The current status of refugee-related health data in Jordan, Lebanon, and Uganda indicates the need for improved data collection and reporting practices, disaggregation by refugee status and better integration of refugee health data into national HIS to capture the health status and needs of refugees in host countries. Key improvement strategies include establishing a centralized authority for consistent and efficient data management, fostering transparent and inclusive data governance, and strengthening workforce capacity to manage refugee health data effectively.

Pilot implementation MitraTB application to strengthening district based public private mix tuberculosis control in Purwakarta district, Indonesia: A mixed-method study.

BACKGROUND: District-based public private mix (DPPM) tuberculosis in Purwakarta district was strengthened by the MitraTB application. This research is aimed to explore perception of user about MitraTB application and measure their perception of this application in dimensions; design, usefulness, ease of use, and acceptance. METHODS: This study was exploratory sequential mixed methods research. A qualitative study was first conducted in order to gain an in-depth understanding about user's perception of MitraTB application through in-depth interviews. Data were analyzed through coding and categorizing. Based on qualitative finding, a questionnaire was developed and used in the following quantitative study. A cross sectional study was then conducted in quantitative phase. Data were analyzed using Rasch modeling. RESULT: The design of the MitraTB application looks simple and attractive to users. This application is useful to make it easier for private practitioners to report TB cases and it is easy to use. Respondents can accept the MitraTB application well. Most respondents have good perception about MitraTB application in dimensions; design (56.25%), usefulness (69.79%), ease of use (55.20%), and acceptance (73.96%). CONCLUSIONS: MitraTB application has a good design feature, useful, easy to use, and acceptable. This application facilitates the private sector to be involved in the TB program by reporting TB cases. Follow-up and local regulations are required for the continued use of this application.

A Mobile Application for Enhancing Caregiver Support and Resource Management for Long-Term Dependent Individuals in Rural Areas.

The "SmartCaregivers" 1.0 mobile application is a beacon of hope for caregivers (CG) in rural areas, often facing limited access to facilities and support. This study, conducted from February to August 2021, aimed to comprehensively analyze the need for developing a database system and a mobile application tailored to enhance caregiver support and resource management for long-term dependent individuals in the rural areas of Maha Sarakham province, Thailand. The research followed a rigorous research and development (R & D) approach, specifically the ADDIE model (analysis, design, development, implementation, and evaluation). Data were collected from 402 caregivers and 10 key informants through surveys and interviews, as well as from 402 caregivers during the implementation and evaluation phases. The application's impact was assessed using a quasi-experimental design with a one-group pre-post-test, and its acceptance was evaluated through the technology acceptance model (TAM). The application significantly improved caregivers' knowledge scores, with a mean increase from 10.49 ± 2.53 to 12.18 ± 2.76 post-intervention. High scores for perceived usefulness (4.36 ± 0.62) and ease of use (4.31 ± 0.59) reassure the audience about the application's effectiveness in providing rapid access to health information, aiding decision-making, and improving care coordination. The system quality was also highly rated, with users appreciating the variety of functions and structural design. This potential for transformation and improvement instills hope and optimism for the future of caregiving in rural areas.

Mapping the wildland-urban interface at municipal level for wildfire exposure analysis in mainland Portugal.

The Wildland-Urban Interface (WUI), where vegetation and built-up structures intermingle, encompasses a variety of territorial elements that interact spatially, being variable both in space and time. Mapping the WUI at finer scales is paramount to assess wildfire exposure and define tailored mitigation strategies. Our aim was to develop a semi-automated method to map the WUI at municipal level, leveraging recent advances in data and technology. We tested the procedure in four municipalities of mainland Portugal with different fire history, biophysical conditions, and sociodemographic contexts. We considered WUI as either intermix or interface. Our approach integrates both building location data and high-resolution vegetation maps, to calculate the density of buildings and forest cover proportion within different circular moving window sizes. Within each radius, we evaluated the total area and spatial distribution of the WUI types, as well as the number of buildings within WUI and within the fire perimeters recorded between the years 2000 and 2022 and analysed the differences between municipalities. We then compared the mapped WUI with previous WUI mappings for mainland Portugal, to identify common spots and potential spatial divergences. We found that the area mapped as WUI within all four municipalities ranged from about 400 km2 to 1135 km2 depending on the radius size. A distinct distribution for each type of WUI was observed as the radius size increased: the intermix WUI showed a tendency to increase, and the interface WUI increased only between the radius of 100 and 200 m, decreasing gradually in subsequent radii. Between 39.4% and 45.5% of the nearly 200,000 buildings in the study areas were within WUI, depending on radius size and a total of 5436 buildings were within the historic fire perimeter. Although the comparison with other maps showed fair agreement, due to differences in data and methodology, common areas mapped as WUI were found, which suggests that these areas should receive greater attention from decision-makers regarding fire management strategies, since their classification as WUI remains consistent across different methodologies.

Impact of preoperative back pain severity on PROMIS outcomes following minimally invasive lumbar decompression.

PURPOSE: To evaluate the effect of baseline back pain severity on PROMIS mental health outcomes following minimally invasive lumbar decompression (LD). METHODS: Patients undergoing elective, primary, single-level LD were retrospectively reviewed from a prospective single spine surgeon registry. Perioperative characteristics, demographics, and the following patient-reported outcomes (PROs) were extracted: Oswestry Disability Index (ODI)/Patient-Health Questionnaire-9 /PROMIS-Physical Function/Anxiety/Pain Interference/Sleep Disturbance (PROMIS-PF/A/PI/SD). Two cohorts were created: preoperative VAS-B < 7 and VAS-B ≥ 7. Change in PROs (ΔPROs) from baseline to six weeks/final follow-up were determined. Average patient follow-up was 13.4 ± 8.8 months. Minimal clinically important difference (MCID) achievement rates were calculated and compared through multivariable logistic regression. Postoperative scores and ΔPROs, were compared with multivariable linear regression while all other data was compared between groups with inferential statistics. RESULTS: Altogether, 347 patients were included, with 190 in the VAS-B < 7 group. VAS-B ≥ 7 reported worse outcomes preoperatively (p ≤ 0.013, all). At six weeks, VAS-B ≥ 7 reported worse VAS-B (p = 0.017), with no other significant differences. At final follow-up, patients with worse VAS-B reported worse ODI (p = 0.040) and VAS-B while all other PROs were similar (p ≥ 0.078, all). VAS-B ≥ 7 experienced greater 6-week improvements in VAS-B/ODI/PROMIS-PI/PROMIS-SD (p ≤ 0.009, all), greater VAS-B/ODI/PROMIS-SD improvement by final follow-up (p ≤ 0.009, all) and greater MCID achievement in ODI/VAS-B (p ≤ 0.027). CONCLUSION: Patients with worse baseline back pain report inferior baseline scores that converge with those with milder preoperative back pain by 6 weeks after LD and reported greater 6-week improvements in disability, pain interference, and sleep disturbance by 6 weeks, and greater improvements in disability and sleep disturbance by final follow-up.

Use of TAM Questionnaire for Telemedicine and IT Healthcare Systems.

A quality systematic literature review was conducted about the use of TAM questionnaire for Telemedicine and IT healthcare systems worldwide by health professionals to investigate most valuable predictors for IT systems acceptance and possible parameters that influence them. The results highlight that perceived usefulness and perceived ease of use are the most important predictors and the development of the tool should also involve individual, social and organizational factors.

Improving Health Information Systems Data Quality in Sub-Saharan Africa.

Data quality in health information systems (HIS) is essential for informed decision-making in the health sector, particularly in sub-Saharan Africa (SSA) where these systems face many challenges like resource limitations and weak infrastructure. This systematic review assessed the quality of HIS data in the region, focusing on the dimensions, and factors influencing this quality. It highlights the importance of systematic evaluation, ongoing training for data collectors in the analysis and use of data for decision-making, and the adoption of information and communication technologies in the healthcare system to improve data quality. These findings point the way to better use of health data and the need for a more integrated approach to digital health in SSA.

3D dynamic landscape simulation of artificial intelligence in environmental landscape design.

Three-dimensional (3D) simulations and precise landscape visualizations are crucial for various applications, like landscape management and planning, computer and connection of the landscape, evaluation, and tracking of land use. The consequences of several plans and a large scene cannot be communicated using older methods of comprehensive environmental planning and development in a timely, rational, and coordinated manner. Architects have trouble incorporating ideas into other comprehensive planning implementation processes. Architects did not thoroughly investigate the neighbourhood's demographics and matching behavioural needs and lacked critical thinking. The 3D dynamic landscape simulation is a detailed computerized three-dimensional simulation of the environment that can be dynamically presented. With the aid of Artificial Intelligence (AI) technology, the system possesses a strong sense of reality, a user-friendly interface, and interactive features that can be tailored to the requirements of the contemporary urban environmental landscape. Regarding exterior publicity, domestic assistance, environmental land use planning, and information systems. The novelty of the proposed Interactive Design System based on AI (IDS-AI) is to create a 3D dynamic landscape model based on a real-life environmental scene, utilizing a Geographic Information System (GIS) to optimize landscape vision. Secondly, 3D environmental landscape design simulation was implemented using GIS spatial analysis in conjunction with the Fuzzy Analytical Hierarchical Process (FAHP) to reduce the data overlap rate and help make an accurate decision. Finally, the design incorporates the development of the interactive interface system application of landscape design and environmental resources for viewing the landscape, the factors that affect them, and the area coverage ratio of various land cover types. The experimental outcomes show that the suggested IDS model increases the gradient sensitivity level of 98.3 % and area coverage ratio of 93.4 % compared to other existing models.

Geographic information systems adoption model: A partial least square-structural equation modeling analysis approach.

The ability of Geographic Information System (GIS) to organize, analyze, visualize and integrate spatial data has been at the top of its primary uses among professional industries. However, considering the extensive adoption of Information System (IS) throughout history for government organizations' or citizens' disaster response, the implementation of geographical elements is still minimal. Previous GIS models and framework studies, particularly in developing countries, were affected by pandemic pressure, competitiveness pressure, change management, and security factors. Thus, this study aims to develop a model for the successful adoption of GIS using the Technology Acceptance Model (TAM), and De Lone and Mc Lean Information Success Model and analyze the applicability of the existing factors to enhance the performance of Public Sector Organizations (PSOs). From the study, a new conceptual framework was proposed to examine the effects of factors on GIS adoption that impact performance among PSOs from the perspective of Saudi Arabia. Quantitative methods were used to collect data through a questionnaire distributed to 350 respondents from PSO, and only 272 were found to be valid. Partial Least Square Structural Equation Modeling (PLS-SEM) validated the GIS model. The finding revealed that system quality, service quality, change management, competitiveness pressure, perceived ease of use, perceived usefulness, and security factors significantly and positively affected GIS adoption. The study also showed that GIS adoption substantially affected PSO performance. The proposed model provides insight into how GIS adoption can eventually enhance performance among PSOs. In essence, the study contributes to the running of PSO and the decisions taken by policymakers.

The spatial-temporal distribution of hepatitis B virus infection in China,2006-2018.

OBJECTIVES: Hepatitis B is a liver disease caused by Hepatitis B virus (HBV) infection and is highly prevalent in China. To better understand the epidemiological characteristics of hepatitis B in China and develop effective disease control strategies, we employed temporal and spatial statistical methods. METHODS: We obtained HBV incidence data from the Public Health Science Data Center of the Chinese Center for Disease Control and Prevention for the years 2006 to 2018. Using Geographic Information System (GIS) and SaTScan scanning technology, we conducted spatial autocorrelation analysis and spatiotemporal scan analysis to create a map and visualize the distribution of hepatitis B incidence. RESULTS: While hepatitis B incidence rebounded in 2011 and 2017, the overall incidence in China decreased.In the trend analysis by item, the incidence varies from high to low. The global spatial autocorrelation analysis revealed a clustered distribution, and the Moran index analysis of spatial autocorrelation within local regions identified five provinces as H-H clusters (hot spots), while one province was an L-L cluster (cold spot). Spatial scan analysis identified 11 significant spatial clusters. CONCLUSIONS: We found significant clustering in the spatial distribution of hepatitis B incidence and positive spatial correlation of hepatitis B incidence in China. We also identified high-risk times and regional clusters of hepatitis B incidence.

Spectral enhancement of PlanetScope using Sentinal-2 images to estimate soybean yield and seed composition.

Soybean is an essential crop to fight global food insecurity and is of great economic importance around the world. Along with genetic improvements aimed at boosting yield, soybean seed composition also changed. Since conditions during crop growth and development influences nutrient accumulation in soybean seeds, remote sensing offers a unique opportunity to estimate seed traits from the standing crops. Capturing phenological developments that influence seed composition requires frequent satellite observations at higher spatial and spectral resolutions. This study introduces a novel spectral fusion technique called multiheaded kernel-based spectral fusion (MKSF) that combines the higher spatial resolution of PlanetScope (PS) and spectral bands from Sentinel 2 (S2) satellites. The study also focuses on using the additional spectral bands and different statistical machine learning models to estimate seed traits, e.g., protein, oil, sucrose, starch, ash, fiber, and yield. The MKSF was trained using PS and S2 image pairs from different growth stages and predicted the potential VNIR1 (705 nm), VNIR2 (740 nm), VNIR3 (783 nm), SWIR1 (1610 nm), and SWIR2 (2190 nm) bands from the PS images. Our results indicate that VNIR3 prediction performance was the highest followed by VNIR2, VNIR1, SWIR1, and SWIR2. Among the seed traits, sucrose yielded the highest predictive performance with RFR model. Finally, the feature importance analysis revealed the importance of MKSF-generated vegetation indices from fused images.

GIS mapping of agricultural plastic waste in southern Europe.

The escalating use of plastics in agriculture, driven by global population growth and increasing food demand, has concurrently led to a rise in Agricultural Plastic Waste (APW) production. Effective waste management is imperative, prompting this study to address the initial step of management, that is the quantification and localization of waste generated from different production systems in diverse regions. Focused on four Southern European countries (Italy, Spain, Greece, and Portugal) at the regional level, the study uses Geographic Information System (GIS), land use maps, indices tailored to each specific agricultural application and each crop type for plastic waste mapping. Furthermore, after the data was employed, it was validated by relevant stakeholders of the mentioned countries. The study revealed Spain, particularly the Andalusia region, as the highest contributor to APW equal to 324,000 tons per year, while Portugal's Azores region had the lowest estimate equal to 428 tons per year. Significantly, this research stands out as one of the first to comprehensively consider various plastic applications and detailed crop cultivations within the production systems, representing a pioneering effort in addressing plastic waste management in Southern Europe. This can lead further on to the management of waste in this area and the transfer of the scientific proposition to other countries.

Design and Development of a Fully Intelligent Nursing Information System Based on International Standard Nursing Terminology.

OBJECTIVE: Design and develop a Clinical Care Classification (CCC) nursing information system aligned with nursing terminology CCC, emphasizing standard procedures and a responsibility-based nursing model to enhance efficiency and quality of care. METHODS: Conduct thorough investigation into clinical nursing informatics needs, analyze existing system shortcomings, utilize Microsoft.net for development, integrate standard nursing procedures and clinical operating protocols into system functions. Structure database based on bed characteristics, implant CCC Nursing Terminology and clinical nursing knowledge base. RESULTS: Successfully design and develop CCC Nursing Information System featuring patient list, nurse assignment, nursing evaluation, diagnosis, goals, plan, interventions, special care, shift handover, record query, workload statistics, and intelligent guidance based on patient assessment and nursing elements. CONCLUSION: The CCC Nursing Information System advances standard nursing procedures in clinical practice, promoting standardization and responsibility-based holistic care. It harnesses big data to enhance system intelligence.