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Journal = Healthcare
Section = Health Informatics and Big Data

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25 pages, 2417 KiB  
Article
Analytical Techniques for Supporting Hospital Case Mix Planning Encompassing Forced Adjustments, Comparisons, and Scoring
by Robert L. Burdett, Paul Corry, David Cook and Prasad Yarlagadda
Healthcare 2025, 13(1), 47; https://doi.org/10.3390/healthcare13010047 - 30 Dec 2024
Viewed by 406
Abstract
Background/Objectives: This article presents analytical techniques and a decision support tool to aid in hospital capacity assessment and case mix planning (CMP). To date, no similar techniques have been provided in the literature. Methods: Initially, an optimization model is proposed to [...] Read more.
Background/Objectives: This article presents analytical techniques and a decision support tool to aid in hospital capacity assessment and case mix planning (CMP). To date, no similar techniques have been provided in the literature. Methods: Initially, an optimization model is proposed to analyze the impact of making a specific change to an existing case mix, identifying how patient types should be adjusted proportionately to varying levels of hospital resource availability. Subsequently, multi-objective decision-making techniques are introduced to compare and critique competing case mix solutions. Results: The proposed techniques are embedded seamlessly within an Excel Visual Basic for Applications (VBA) personal decision support tool (PDST), for performing informative quantitative assessments of hospital capacity. The PDST reports informative metrics of difference and reports the impact of case mix modifications on the other types of patients present. Conclusions: The techniques developed in this article provide a bridge between theory and practice that is currently missing and provides further situational awareness around hospital capacity. Full article
(This article belongs to the Section Health Informatics and Big Data)
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11 pages, 235 KiB  
Article
Hepatitis B Virus Knowledge and HBV-Related Surveillance Status Among HBsAg-Positive Patients in Qidong City: A Rural-Based Cross-Sectional Survey
by Hailiang Liu, Jing Hong, Zhaoxian Yan, Mei Li, Xiaofeng Zhai, Bo Pan and Changquan Ling
Healthcare 2025, 13(1), 17; https://doi.org/10.3390/healthcare13010017 - 25 Dec 2024
Viewed by 708
Abstract
Objective: This study aimed to investigate hepatitis B knowledge and hepatitis B virus (HBV)-related surveillance status among HBsAg-positive patients, as well as to further explore the relevant influencing factors. Methods: A cross-sectional study was conducted on the HBsAg-positive patients from 8 October 2023 [...] Read more.
Objective: This study aimed to investigate hepatitis B knowledge and hepatitis B virus (HBV)-related surveillance status among HBsAg-positive patients, as well as to further explore the relevant influencing factors. Methods: A cross-sectional study was conducted on the HBsAg-positive patients from 8 October 2023 to 10 November 2023 in Qidong City. A self-report questionnaire was developed based on a literature review of similar studies. Univariate analysis of variance, multivariate logistic regression, and t-test analysis were conducted to analyze the collected data. Results: Of the 982 respondents who completed the on-site questionnaire, all participants were HBsAg-positive patients. Moreover, 51.32% had “good” knowledge of HBV. Multivariate logistic regression analysis showed that participants with a doctor in the family, those with an average monthly income above CNY 3000, and those with an average monthly income of CNY 1500–3000 were more likely to obtain a “good” cognitive evaluation (p < 0.001). The scores of the populations using HBV-related surveillance methods were low (2.02 ± 0.87); 64.87% (637/982) of the populations monitored had a score of no more than 2. Conclusions: This study suggests that the awareness of HBV prevention and treatment among participants, especially those of low-income classes and individuals lacking physician clinical management, should be promoted to increase the dissemination of HBV knowledge. Full article
(This article belongs to the Section Health Informatics and Big Data)
21 pages, 336 KiB  
Article
Optimizing Nurse Rostering: A Case Study Using Integer Programming to Enhance Operational Efficiency and Care Quality
by Aristeidis Mystakidis, Christos Koukaras, Paraskevas Koukaras, Konstantinos Kaparis, Stavros G. Stavrinides and Christos Tjortjis
Healthcare 2024, 12(24), 2545; https://doi.org/10.3390/healthcare12242545 - 17 Dec 2024
Viewed by 800
Abstract
Background/Objectives: This study addresses the complex challenge of Nurse Rostering (NR) in oncology departments, a critical component of healthcare management affecting operational efficiency and patient care quality. Given the intricate dynamics of healthcare settings, particularly in oncology clinics, where patient needs are acute [...] Read more.
Background/Objectives: This study addresses the complex challenge of Nurse Rostering (NR) in oncology departments, a critical component of healthcare management affecting operational efficiency and patient care quality. Given the intricate dynamics of healthcare settings, particularly in oncology clinics, where patient needs are acute and unpredictable, optimizing nurse schedules is paramount for enhancing care delivery and staff satisfaction. Methods: Employing advanced Integer Programming (IP) techniques, this research develops a comprehensive model to optimise NR. The methodology integrates a variety of constraints, including legal work hours, staff qualifications, and personal preferences, to generate equitable and efficient schedules. Through a case study approach, the model’s implementation is explored within a clinical setting, demonstrating its practical application and adaptability to real-world challenges. Results: The implementation of the IP model in a clinical setting revealed significant improvements in scheduling efficiency and staff satisfaction. The model successfully balanced workload distribution among nurses, accommodated individual preferences to a high degree, and ensured compliance with work-hour regulations, leading to optimised shift schedules that support both staff well-being and patient care standards. Conclusions: The findings underscore the effectiveness of IP in addressing the complexities of NR in oncology clinics. By facilitating a strategic allocation of nursing resources, the proposed model contributes to operational excellence in healthcare settings, underscoring the potential of Operations Research in enhancing healthcare delivery and management practices. Full article
(This article belongs to the Special Issue Data Driven Insights in Healthcare)
10 pages, 211 KiB  
Perspective
Sharing Data and Transferring Samples Within Pediatric Clinical Studies: How to Overcome Challenges and Make Them a Science Opportunity
by Annalisa Landi, Federica D’Ambrosio, Silvia Faggion, Francesca Rocchi, Carla Paganin, Maria Grazia Lain, Adriana Ceci and Viviana Giannuzzi
Healthcare 2024, 12(23), 2473; https://doi.org/10.3390/healthcare12232473 - 6 Dec 2024
Viewed by 764
Abstract
EPIICAL (Early treated Perinatally HIV-Infected individuals: Improving Children’s Actual Life) is a consortium of European and non-European research-driven organizations inter-connected with the aim of establishing a clinical and experimental platform for the early identification of novel therapeutic strategies for the pediatric Human Immunodeficiency [...] Read more.
EPIICAL (Early treated Perinatally HIV-Infected individuals: Improving Children’s Actual Life) is a consortium of European and non-European research-driven organizations inter-connected with the aim of establishing a clinical and experimental platform for the early identification of novel therapeutic strategies for the pediatric Human Immunodeficiency Virus (HIV). Within the EPIICAL project, several pediatric clinical studies were conducted, requiring the collection and transfer of biological samples and associated data across boundaries within and outside Europe. To ensure compliance with the applicable rules on pediatric data and sample transfer and to support the efforts of academic partners, which may not always have the necessary expertise and resources in place for designing, managing and conducting multi-national studies, the consortium established a dedicated expert Working Group. This group has guided the consortium since the start of the project through the complexities of the ethical and regulatory aspects of international clinical studies. The group provided support in the design and preparation of the prospective and retrospective multi-center and multi-national pediatric studies with a focus on the clinical study protocols, informed consent and assent forms. In particular, well-structured informed consent and assent templates were developed, and data sharing and material transfer agreements were set up to regulate the transfer of samples among partners and sites. We considered that such support and the implementation of ad hoc agreements could provide effective practical solutions for addressing ethical and regulatory hurdles related to sharing data and transferring samples in international pediatric clinical research. Full article
(This article belongs to the Special Issue Data Driven Insights in Healthcare)
14 pages, 4390 KiB  
Article
Spatial Disparities in Access to Dialysis Facilities in Texas: An Analysis of End-Stage Renal Data in 1974–2020
by Dongeun Kim, Yongwan Chun and Daniel A. Griffith
Healthcare 2024, 12(22), 2284; https://doi.org/10.3390/healthcare12222284 - 15 Nov 2024
Viewed by 727
Abstract
Background/Objectives: This study investigates the spatial disparities in access to dialysis facilities across Texas. The objective is to analyze how urbanization and socio-economic/demographic factors influence these disparities, with a focus on differences between urban and rural areas. Methods: The enhanced two-step floating catchment [...] Read more.
Background/Objectives: This study investigates the spatial disparities in access to dialysis facilities across Texas. The objective is to analyze how urbanization and socio-economic/demographic factors influence these disparities, with a focus on differences between urban and rural areas. Methods: The enhanced two-step floating catchment area method is employed to calculate accessibility scores to dialysis facilities across the state. Additionally, Moran eigenvector spatial filtering is utilized to analyze the influence of urbanization and socio-economic/demographic factors on accessibility disparities. Results: The Moran eigenvector spatial filtering analysis revealed a significant level of spatial autocorrelation in accessibility scores, particularly highlighting disparities between urban and rural areas. Urban regions, especially major metropolitan areas, achieved higher accessibility scores due to the dense concentration of dialysis facilities. In contrast, rural areas, notably in western and northern Texas, exhibited lower accessibility, underscoring the challenges faced by residents in these regions. The model further identified urbanization and the percentage of the elderly population as critical covariates affecting accessibility, with urban counties showing higher accessibility and elderly populations in rural areas facing significant challenges. Conclusions: These findings emphasize the importance of considering spatial dependencies in healthcare accessibility studies. They suggest the need for targeted policy interventions to address the identified disparities, particularly in underserved rural regions, to improve access to dialysis facilities for vulnerable populations. Full article
(This article belongs to the Special Issue Implementation of GIS (Geographic Information Systems) in Health Care)
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12 pages, 243 KiB  
Article
The Paradox of Digital Health: Why Middle-Aged Adults Outperform Young Adults in Health Management Utilization via Technology
by Seo-Ha Jeong and Yeon Gyo Nam
Healthcare 2024, 12(22), 2261; https://doi.org/10.3390/healthcare12222261 - 13 Nov 2024
Viewed by 923
Abstract
Background/Objectives: Globally, life expectancy has been increasing with South Korea focusing on improving health to enhance quality of life. The COVID-19 pandemic further emphasized the need for digital transformation in healthcare, accelerating digital health adoption. This study explores the digital divide between ‘Digital [...] Read more.
Background/Objectives: Globally, life expectancy has been increasing with South Korea focusing on improving health to enhance quality of life. The COVID-19 pandemic further emphasized the need for digital transformation in healthcare, accelerating digital health adoption. This study explores the digital divide between ‘Digital Natives (20–39 Y)’ and ‘Digital Immigrants (40–69 Y)’, focusing on digital device usage and confidence. Methods: This study utilized national survey data from the Digital Health Literacy Survey Results and Policy Implications, focusing on differences in digital device use and confidence between young adults (20–39 Y) and middle-aged adults (40–69 Y). The participants comprised 1000 adults aged 20 to 69 in the Republic of Korea. Respondents were queried about their use of digital health tools, such as wearable devices and mobile apps. Confidence in using digital systems and managing health via digital tools was assessed using a five-point Likert scale. Results: The findings indicated that while young adults have lower rates of using digital devices for healthcare, they exhibit higher confidence in using such devices. In contrast, middle-aged adults, despite having lower confidence, report higher usage of digital devices for healthcare purposes. Conclusions: This study explored differences in digital confidence and healthcare usage between age groups and aimed to propose effective health management strategies based on digital accessibility. Full article
(This article belongs to the Section Health Informatics and Big Data)
22 pages, 3610 KiB  
Article
A Comparative Study of Hospitalization Mortality Rates between General and Emergency Hospitalized Patients Using Survival Analysis
by Haegak Chang, Seiyoung Ryu, Ilyoung Choi, Angela Eunyoung Kwon and Jaekyeong Kim
Healthcare 2024, 12(19), 1982; https://doi.org/10.3390/healthcare12191982 - 4 Oct 2024
Viewed by 750
Abstract
Background/Objectives: In Korea’s emergency medical system, when an emergency patient arises, patients receive on-site treatment and care during transport at the pre-hospital stage, followed by inpatient treatment upon hospitalization. From the perspective of emergency patient management, it is critical to identify the high [...] Read more.
Background/Objectives: In Korea’s emergency medical system, when an emergency patient arises, patients receive on-site treatment and care during transport at the pre-hospital stage, followed by inpatient treatment upon hospitalization. From the perspective of emergency patient management, it is critical to identify the high death rate of patients with certain conditions in the emergency room. Therefore, it is necessary to compare and analyze the determinants of the death rate of patients admitted via the emergency room and generally hospitalized patients. In fact, previous studies investigating determinants of survival periods or length of stay (LOS) primarily used multiple or logistic regression analyses as their main research methodology. Although medical data often exhibit censored characteristics, which are crucial for analyzing survival periods, the aforementioned methods of analysis fail to accommodate these characteristics, presenting a significant limitation. Methods:Therefore, in this study, survival analyses were performed to investigate factors affecting the dying risk of general inpatients as well as patients admitted through the emergency room. For this purpose, this study collected and analyzed the sample cohort DB for a total of four years from 2016 to 2019 provided by the Korean National Health Insurance Services (NHIS). After data preprocessing, the survival probability was estimated according to sociodemographic, patient, health checkup records, and institutional features through the Kaplan–Meier survival estimation. Then, the Cox proportional hazards models were additionally utilized for further econometric validation. Results: As a result of the analysis, in terms of the ‘city’ feature among the sociodemographic characteristics, the small and medium-sized cities exert the most influence on the death rate of general inpatients, whereas the metropolitan cities exert the most influence on the death rate of inpatients admitted through the emergency room. In terms of institution characteristics, it was found that there is a difference in determinants affecting the death rate of the two groups of study, such as the number of doctors per 100 hospital beds, the number of nurses per 100 hospital beds, the number of hospital beds, the number of surgical beds, and the number of emergency beds. Conclusions: Based on the study results, it is expected that an efficient plan for distributing limited medical resources can be established based on inpatients’ LOS. Full article
(This article belongs to the Special Issue Data Driven Insights in Healthcare)
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14 pages, 2843 KiB  
Technical Note
Visualizing Hospital Management Data in R Shiny—A Case Study
by Benjamin Voellger, Milica Malesevic-Lepir, Mohamed A. Hafez Abdelrehim and Dalibor Bockelmann
Healthcare 2024, 12(18), 1846; https://doi.org/10.3390/healthcare12181846 - 14 Sep 2024
Viewed by 1018
Abstract
Objective: There is a demand to make hospital management information beyond basic key performance indicators (KPIs) accessible for clinicians. Methods: We developed an interactive application (IAPP) in R Shiny to visualize such information. We provided the IAPP source code online. As a use [...] Read more.
Objective: There is a demand to make hospital management information beyond basic key performance indicators (KPIs) accessible for clinicians. Methods: We developed an interactive application (IAPP) in R Shiny to visualize such information. We provided the IAPP source code online. As a use case, we recorded basic KPIs (numbers of patients (NPs), reimbursed valuation ratios (RVRs), mean length of stay (LOS)), main diagnoses (MDGNs), main procedures (MPRCs), and catchment area (CA) by district from April 2022 to March 2024 at the index department in central Germany, where a neurotrauma and spinal surgery service was resumed on 1 April 2022. Case mix indexes (CMIs) were calculated. We retrieved information about online-reported patient satisfaction (ORPS) from an online physician rating platform between January 2022 and March 2024. Information on longitudes and latitudes of the index department and neighbouring hospitals was collected. We calculated car travelling isochrones (CTIs) of the hospitals as a proxy variable for accessibility. Chi-square and Fisher’s exact served as statistical tests. Results: During the observation period, the monthly NPs increased from 26 to 43, the RVR showed a 3.96-fold increase, the CMI showed a 2.41-fold increase, and the LOS reached a steady state in the 2nd year after service resumption. CA (p = 0.03), MDGNs, and MPRCs diversified. ORPS trended towards better overall evaluation after service resumption (p = 0.09). CTI mapping identified a unique market position of the index department. Conclusions: The IAPP makes extended hospital management data accessible to clinicians, can inform other stakeholders in healthcare, and can be tailored to local conditions. Full article
(This article belongs to the Special Issue Data Management for a Better Understanding of Health Fields)
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19 pages, 9720 KiB  
Article
Enhancing Emergency Department Management: A Data-Driven Approach to Detect and Predict Surge Persistence
by Kang Heng Lim, Francis Ngoc Hoang Long Nguyen, Ronald Wen Li Cheong, Xaver Ghim Yong Tan, Yogeswary Pasupathy, Ser Chye Toh, Marcus Eng Hock Ong and Sean Shao Wei Lam
Healthcare 2024, 12(17), 1751; https://doi.org/10.3390/healthcare12171751 - 2 Sep 2024
Viewed by 1610
Abstract
The prediction of patient attendance in emergency departments (ED) is crucial for effective healthcare planning and resource allocation. This paper proposes an early warning system that can detect emerging trends in ED attendance, offering timely alerts for proactive operational planning. Over 13 years [...] Read more.
The prediction of patient attendance in emergency departments (ED) is crucial for effective healthcare planning and resource allocation. This paper proposes an early warning system that can detect emerging trends in ED attendance, offering timely alerts for proactive operational planning. Over 13 years of historical ED attendance data (from January 2010 till December 2022) with 1,700,887 data points were used to develop and validate: (1) a Seasonal Autoregressive Integrated Moving Average with eXogenous factors (SARIMAX) forecasting model; (2) an Exponentially Weighted Moving Average (EWMA) surge prediction model, and (3) a trend persistence prediction model. Drift detection was achieved with the EWMA control chart, and the slopes of a kernel-regressed ED attendance curve were used to train various machine learning (ML) models to predict trend persistence. The EWMA control chart effectively detected significant COVID-19 events in Singapore. The surge prediction model generated preemptive signals on changes in the trends of ED attendance over the COVID-19 pandemic period from January 2020 until December 2022. The persistence of novel trends was further estimated using the trend persistence model, with a mean absolute error of 7.54 (95% CI: 6.77–8.79) days. This study advanced emergency healthcare management by introducing a proactive surge detection framework, which is vital for bolstering the preparedness and agility of emergency departments amid unforeseen health crises. Full article
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23 pages, 1129 KiB  
Article
Machine Learning and Wearable Technology: Monitoring Changes in Biomedical Signal Patterns during Pre-Migraine Nights
by Viroslava Kapustynska, Vytautas Abromavičius, Artūras Serackis, Šarūnas Paulikas, Kristina Ryliškienė and Saulius Andruškevičius
Healthcare 2024, 12(17), 1701; https://doi.org/10.3390/healthcare12171701 - 26 Aug 2024
Cited by 2 | Viewed by 1857
Abstract
Migraine is one of the most common neurological disorders, characterized by moderate-to-severe headache episodes. Autonomic nervous system (ANS) alterations can occur at phases of migraine attack. This study investigates patterns of ANS changes during the pre-ictal night of migraine, utilizing wearable biosensor technology [...] Read more.
Migraine is one of the most common neurological disorders, characterized by moderate-to-severe headache episodes. Autonomic nervous system (ANS) alterations can occur at phases of migraine attack. This study investigates patterns of ANS changes during the pre-ictal night of migraine, utilizing wearable biosensor technology in ten individuals. Various physiological, activity-based, and signal processing metrics were examined to train predictive models and understand the relationship between specific features and migraine occurrences. Data were filtered based on specified criteria for nocturnal sleep, and analysis frames ranging from 5 to 120 min were used to improve the diversity of the training sample and investigate the impact of analysis frame duration on feature significance and migraine prediction. Several models, including XGBoost (Extreme Gradient Boosting), HistGradientBoosting (Histogram-Based Gradient Boosting), Random Forest, SVM, and KNN, were trained on unbalanced data and using cost-sensitive learning with a 5:1 ratio. To evaluate the changes in features during pre-migraine nights and nights before migraine-free days, an analysis of variance (ANOVA) was performed. The results showed that the features of electrodermal activity, skin temperature, and accelerometer exhibited the highest F-statistic values and the most significant p-values in the 5 and 10 min frames, which makes them particularly useful for the early detection of migraines. The generalized prediction model using XGBoost and a 5 min analysis frame achieved 0.806 for accuracy, 0.638 for precision, 0.595 for recall, and 0.607 for F1-score. Despite identifying distinguishing features between pre-migraine and migraine-free nights, the performance of the current model suggests the need for further improvements for clinical application. Full article
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14 pages, 867 KiB  
Article
Prediction of COVID-19 Hospitalization and Mortality Using Artificial Intelligence
by Marwah Ahmed Halwani and Manal Ahmed Halwani
Healthcare 2024, 12(17), 1694; https://doi.org/10.3390/healthcare12171694 - 26 Aug 2024
Cited by 1 | Viewed by 1177
Abstract
Background: COVID-19 has had a substantial influence on healthcare systems, requiring early prognosis for innovative therapies and optimal results, especially in individuals with comorbidities. AI systems have been used by healthcare practitioners for investigating, anticipating, and predicting diseases, through means including medication development, [...] Read more.
Background: COVID-19 has had a substantial influence on healthcare systems, requiring early prognosis for innovative therapies and optimal results, especially in individuals with comorbidities. AI systems have been used by healthcare practitioners for investigating, anticipating, and predicting diseases, through means including medication development, clinical trial analysis, and pandemic forecasting. This study proposes the use of AI to predict disease severity in terms of hospital mortality among COVID-19 patients. Methods: A cross-sectional study was conducted at King Abdulaziz University, Saudi Arabia. Data were cleaned by encoding categorical variables and replacing missing quantitative values with their mean. The outcome variable, hospital mortality, was labeled as death = 0 or survival = 1, with all baseline investigations, clinical symptoms, and laboratory findings used as predictors. Decision trees, SVM, and random forest algorithms were employed. The training process included splitting the data set into training and testing sets, performing 5-fold cross-validation to tune hyperparameters, and evaluating performance on the test set using accuracy. Results: The study assessed the predictive accuracy of outcomes and mortality for COVID-19 patients based on factors such as CRP, LDH, Ferritin, ALP, Bilirubin, D-Dimers, and hospital stay (p-value ≤ 0.05). The analysis revealed that hospital stay, D-Dimers, ALP, Bilirubin, LDH, CRP, and Ferritin significantly influenced hospital mortality (p ≤ 0.0001). The results demonstrated high predictive accuracy, with decision trees achieving 76%, random forest 80%, and support vector machines (SVMs) 82%. Conclusions: Artificial intelligence is a tool crucial for identifying early coronavirus infections and monitoring patient conditions. It improves treatment consistency and decision-making via the development of algorithms. Full article
(This article belongs to the Special Issue Data Driven Insights in Healthcare)
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19 pages, 1825 KiB  
Systematic Review
A Systematic Review of Online Medical Consultation Research
by Tian Shen, Yu Li and Xi Chen
Healthcare 2024, 12(17), 1687; https://doi.org/10.3390/healthcare12171687 - 23 Aug 2024
Cited by 1 | Viewed by 2597
Abstract
Online medical consultation is a form of medical service that facilitates interactions between patients and doctors online, offering significant utility and value. This review aims to retrieve, screen, and analyze articles related to online medical consultations, formulating a theoretical framework and proposing future [...] Read more.
Online medical consultation is a form of medical service that facilitates interactions between patients and doctors online, offering significant utility and value. This review aims to retrieve, screen, and analyze articles related to online medical consultations, formulating a theoretical framework and proposing future research directions. According to PRISMA guidelines, a systematic search was conducted in Web of Science, EBSCO, ScienceDirect, PubMed, and Scopus, retrieving a total of 4072 English records on 16 December 2023. After rigorous screening, 75 articles were included in this review. Among these, 8 articles focused on patients utilizing online medical consultation platforms, 5 on doctors participating in online medical platforms, 18 on patients’ choice of doctors, 12 on doctors providing services, 7 on online reviews of patients, 14 on service quality for patients, 8 on rewards to doctors, and 11 on the spillover effect between online and offline services. These themes comprise the theoretical framework of the starting point, process, and outcomes of the online medical consultation system, providing a comprehensive understanding of the field and a foundation for future research. Full article
(This article belongs to the Section Health Informatics and Big Data)
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24 pages, 2472 KiB  
Article
Spatial Allocation Rationality Analysis of Medical Resources Based on Multi-Source Data: Case Study of Taiyuan, China
by Lujin Hu and Shengqi Cai
Healthcare 2024, 12(16), 1669; https://doi.org/10.3390/healthcare12161669 - 21 Aug 2024
Viewed by 1271
Abstract
Reasonably allocating medical resources can effsectively optimize the utilization efficiency of such resources. This paper took Taiyuan City as an example and established a model to evaluate the rationality of medical resource spatial allocation, incorporating two key dimensions: the spatial layout and the [...] Read more.
Reasonably allocating medical resources can effsectively optimize the utilization efficiency of such resources. This paper took Taiyuan City as an example and established a model to evaluate the rationality of medical resource spatial allocation, incorporating two key dimensions: the spatial layout and the supply and demand of medical resources. In terms of the spatial layout, three indexes were included: Firstly, the service coverage rates of different levels of medical institutions, based on residents’ medical orientations, were calculated using network analysis methods. Secondly, the Huff-2SFCA method was improved to calculate the accessibility of medical resources for four different modes of transportation. Then, the Health Resource Agglomeration Degree (HRAD) and Population Agglomeration Degree (PAD) were used to quantify the equity of medical resources. In terms of the supply and demand of medical resources, one index was included: the supply–demand ratio of medical resources during sudden public health events, which was calculated using the number of beds per thousand people as an indicator. These four indexes were weighted using the entropy weight method to obtain the rationality grade of medical resource spatial allocation in Taiyuan City. The study found that the rationality evaluation level of medical resource allocation in the central urban area of Taiyuan City followed a “concentrically decreasing” pattern. The rating ranged from “very reasonable” to “less reasonable”, with the area of each level expanding gradually. The areas rated within the top two categories only accounted for 19.92% of the study area, while the area rated as “less reasonable” occupied 38.73% of the total area. These results indicate that the model accounted for residents’ travel for various medical orientations and the availability of resources during public health emergencies. It considered both the spatial layout and supply and demand of medical resources, offering recommendations for the precise allocation of urban medical resources. Full article
(This article belongs to the Special Issue Implementation of GIS (Geographic Information Systems) in Health Care)
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12 pages, 286 KiB  
Article
From Validation to Assessment of e-Health Literacy: A Study among Higher Education Students in Portugal
by Leandro Oliveira, Renata Puppin Zandonadi, Eduardo Yoshio Nakano, Sulaiman Almutairi, Haitham Alzghaibi, Maria João Lima, Edite Teixeira-Lemos, Ariana Saraiva and António Raposo
Healthcare 2024, 12(16), 1626; https://doi.org/10.3390/healthcare12161626 - 15 Aug 2024
Cited by 1 | Viewed by 1150
Abstract
Despite their familiarity with technology, higher education students often lack the critical skills needed to assess the credibility of online health information, potentially impacting their health decisions and well-being. This study aims to validate and assess the e-Health Literacy Scale among those in [...] Read more.
Despite their familiarity with technology, higher education students often lack the critical skills needed to assess the credibility of online health information, potentially impacting their health decisions and well-being. This study aims to validate and assess the e-Health Literacy Scale among those in Portuguese higher education. In addition, this study focused on measuring their e-health literacy levels and investigating how these skills relate to different sociodemographic variables. This cross-sectional study was conducted in two phases. Initially, the test–retest reliability and reproducibility of measured e-health literacy were assessed with a convenience sample of 20 participants. Subsequently, the e-health scale was applied to a group of 245 Portuguese higher education students. The research took place from January 2023 to April 2024. The scale exhibited robust internal consistency and reproducibility. Male gender consistently correlates with higher levels of e-health literacy. Students demonstrate good levels of e-health literacy (24/40), reflecting their ability to effectively navigate and utilize health information online. By integrating strategies to further enhance this literacy into university health programs, students can develop essential skills necessary for making informed decisions about their health. This proactive approach not only empowers students to access reliable health resources but also fosters a culture of health literacy that can positively impact their well-being both during their academic journey and beyond graduation. Full article
13 pages, 1260 KiB  
Article
Exploration of Therapeutic Strategies of Herbal Prescriptions for Carbuncle Treatment to Suggest Modern Approaches to Inflammatory Bowel Disease: Cluster and Network Analyses of the Book «Liu Juan Zi Gui Yi Fang»
by Dasol Park, Heonyoung Jeong and Jungtae Leem
Healthcare 2024, 12(15), 1499; https://doi.org/10.3390/healthcare12151499 - 28 Jul 2024
Viewed by 1102
Abstract
Inflammatory bowel disease (IBD) treatments in East Asian traditional medicine (EATM) originate from principles for treating abscesses and carbuncles. Understanding the therapeutic principles of Liu Juan Zi Gui Yi Fang (GYF) is essential for optimizing EATM treatment strategies for IBD, but [...] Read more.
Inflammatory bowel disease (IBD) treatments in East Asian traditional medicine (EATM) originate from principles for treating abscesses and carbuncles. Understanding the therapeutic principles of Liu Juan Zi Gui Yi Fang (GYF) is essential for optimizing EATM treatment strategies for IBD, but quantitative analysis is lacking. This study aims to extract quantitative information on therapeutic strategies from GYF and present the EATM conceptual framework for IBD treatment. Oral prescriptions for carbuncles were selected, and their constituent herbs and indications were standardized and tokenized for analysis. An EATM expert group classified prescriptions based on the similarity of herbs and indications. Hierarchical and k-means cluster analyses were performed based on herb similarity. The herb–indication (H-I) network for all prescriptions was constructed. Additionally, H-I subnetworks based on the expert group’s classifications and the k-means clustering results were constructed and compared to identify treatment goals and the herbs used for each goal. The results showed that the treatment focused on abscess status, wound healing, and patient’s recovery capacity, with ‘fever’ and ‘deficiency’ as the main indications addressed by tonifying and anti-inflammatory herbs. The therapeutic principles identified in this study can serve as a foundation for developing future herbal intervention units. Further preclinical and clinical research is needed to validate these findings. Full article
(This article belongs to the Section Health Informatics and Big Data)
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