Management Of Health Care Resources Research Articles

Challenges in application of artificial intelligence in healthcare field and response strategies

The rapid development of artificial intelligence in the field of healthcare has greatly improved diagnosis accuracy, disease prediction, personalized treatment and healthcare resource management. However, with the widespread application of medical artificial intelligence, challenges has emerged in the aspects of medical data, model development and evaluation, and societal considerations. Therefore, this study aims to explore challenges in the application of artificial intelligence in healthcare and suggest a series of feasible solutions to improve medical professional and researchers' understanding of medical artificial intelligence and enhance the quality of healthcare in clinical practice.

Green Human Resources Management in Healthcare: Challenges and Barriers

Green Human Resources Management in Healthcare: Challenges and Barriers

Increasing the performance of a hospital department with budget allocation model and machine learning assisted by simulation

ABSTRACT The COVID-19 pandemic highlighted the critical need for efficient resource management in healthcare. In this study, the internal medicine outpatient clinic in a hospital is modelled by simulation method. Appropriate statistical distributions of the parameters are derived from past data. The results of a limited number of simulation runs are used as training data for machine learning techniques and an estimation model is selected among them. The estimation results are considered as input to a mathematical model which determines the optimal budget allocation for improving the system performance. Analysis considers patient waiting times and system throughput under varied parameters. A significant amount of time is saved by using machine learning to predict the simulation model outcomes, which had previously taken a total of around 7 hours reduced to 30–40 minutes. Time savings through machine learning are projected to be notably greater for more complex simulations comparing to current case.

Violence-Related Injuries: The Most Common Cases in Hyderabad, Pakistan

The law influences every aspect of human activity, including medical practice. The nature of casualty is affected by geographical regions, cultures, and social values. Determining characteristics of casualty can guide health policy and can assist in managing healthcare resources. Objective: To profile the cases at casualty department of a tertiary teaching hospital. Methods: The study was conducted at the casualty department of Liaquat University Hospital Hyderabad, Pakistan and Department of Forensic Medicine & Toxicology LUMHS, Jamshoro, over a period of one year from January 2021 to December 2021. A total of 3,487 cases were recorded and categorized into violence-related, transportation-related, substance-related, and sexual assault-related injuries. Results: The majority of cases were violence-related incidents (84.65%), followed by transportation-related accidents (14.65%), substance-related injuries (3.61%), and sexual assault-related injuries (0.69%). Violence-related injuries were divided into assault cases, firearm incidents, and police torture. Transportation-related injuries were categorized as road traffic accidents and train accidents. Assault and road traffic accidents were the leading cause of violence-related and transportation-related injuries, respectively. The highest numbers of cases were observed in April, June, and May. Conclusions: A substantial portion of cases at casualty department of Liaquat University Hospital, Hyderabad is contributed by violence-related injuries

A Model Proposal for Developing a Solution and Determining the Administrative Problems of Hospitals in Turkey: The BUY Model.

Background and Purpose: This study was conducted in line with the request of the senior management of a hospital. The objective of the current research is to identify managerial problems through the relationship between health employees' perceptions of organizational justice, trust in the manager and the organization, and job satisfaction; develop a solution proposal over the relationships between these variables and motivation variables; and present a model proposal (BUY Model) as a result. Methods: The study sample is comprised of 673 employees. The study data were collected online using a questionnaire consisting of five scales. The data were analyzed by descriptive statistical methods, correlation analysis, and regression analysis. Results: The findings obtained from the study showed that the participants' perceptions of organizational justice, trust in the manager and the organization, job satisfaction and motivation were generally at a moderate level. The study also determined that trust in the manager and the organization had a partial mediation effect on the effect of organizational justice on job satisfaction and the variables of trust in the organization, organizational justice, and trust in the manager, respectively, and especially, the job satisfaction variable also affected motivation. Conclusion: The BUY model was developed to identify problems related to the management of healthcare human resources and solve these problems. Considering the importance of the concepts of job satisfaction and motivation in terms of employee performance, health service quality, and patient satisfaction, it is thought that the developed model will benefit managers of health institutions in increasing the job satisfaction and motivation levels of health employees.

Ethnobotanical study of medicinal plants by indigenous community of Aek Guo Village, Mandailing Natal District, Indonesia

Abstract. Rambey R, Nelasufa F, Athoriez APM, Solihin, Rahmawaty, Susilowati A, Afifuddin Y. 2024. Ethnobotanical study of medicinal plants by indigenous community of Aek Guo Village, Mandailing Natal District, Indonesia. Biodiversitas 25: 1046-1056. Aek Guo Village, Mandailing Natal District, Indonesia, falling under the jurisdiction of Forest Management Unit or Kesatuan Pengelolaan Hutan (KPH) IX Panyabungan, serves as a home for the Mandailing indigenous tribe, primarily engaged in agriculture with a significant dependence on the forest for sustenance. Within the cultural fabric of the Aek Guo community, there exists a profound tradition of employing medicinal plants as an alternative form of treatment, a practice deeply rooted in ethnobotanical knowledge passed down through generations. The study aimed to identify the diverse array of medicinal plants, their utilization practices, and key parameters such as Use Value (UV), Family Use Value (FUV), Plant Part Value (PPV), and Fidelity Level (FL) associated with their applications. A comprehensive approach that integrates qualitative and quantitative methods, the study involves semi-structured interviews and meticulous documentation. The findings reveal a rich repertoire of 66 plant species distributed across 36 families, constituting a vital component of the Aek Guo community. Noteworthy is the pre-eminence of ginger (Zingiber officinale) with the highest UV value (0.68), while paku gajah (Angiopteris evecta) being the lowest (0.01). Family Use Value (FUV) demonstrated variations, with Fabaceae leading the chart (0.39), and Simaroubaceae recording the lowest value (0.01). A closer examination of Plant Part Value (PPV) indicated a preference for leaves, comprising 56.06% of the utilized plant parts, while flowers contribute minimally at 1.51%. Fidelity Level (FL) values, crucial for assessing the significance of particular plants within the community, peak for kumis kucing (Orthosiphon aristatus) and ginger (Z. officinale) at 22.54%. In contrast, jerangau (Acorus calamus), pasak bumi (Eurycoma longifolia), salam (Syzygium polyanthum), and paku gajah (A. evecta) exhibit the lowest FL values at 1.41%. These findings contribute significantly to the broader understanding of ethnobotanical practices across different Mandailing communities, with potential implications for conservation, healthcare, and sustainable resource management.

El rol enfermero en el comité de tumores de la unidad funcional de mama en un hospital de segundo nivel del área metropolitana de Barcelona

IntroductionThe Rapid Cancer Diagnosis Program was established in Catalonia in 2005. This work aims to understand the role of the nurse case manager (EGC) in the preparation, decision-making and execution of the tumor committee collected in the bibliography and compare it with the role in the breast cancer tumor committee of a regional hospital in the metropolitan area of Barcelona. MethodA descriptive and comparative study was carried out through a bibliographic review and the identification of the functions performed by the breast cancer EGCs in the study center. ResultsThe articles highlight the theoretical importance of the nurse's participation in the decision-making committee, due to her knowledge of the patient and management of healthcare resources. Despite this statement, all of them make it explicit that the EGC has little participation in the team discussion or their contributions receive little consideration from the other team members. In the Breast Tumor Committee of the study center, nurses develop all the functions of coordination, anticipation and execution that we find in the literature. ConclusionsThe figure of the EGC has the need to clarify and unify its functions within the tumor committees, ensuring its contribution, which will allow personalization of treatment decisions, improving the patient's experience and the optimal evolution of the process. In this way, the EGC in our center is a basic figure in the tumor committee due to its leadership; it needs to systematize its role and obtain explicit recognition from the organization.

Abstract 3463: Beyond staging systems in cutaneous squamous cell carcinoma: Validation of two thresholds for a model-estimated metastatic risk

Abstract Background: Cutaneous squamous cell carcinoma (cSCC) is the second most common skin cancer, therefore causing a death toll comparable to that of melanoma, despite its low propensity to metastasize (2-5%). We recently developed a clinicopathological (CP) model that predicts absolute metastatic risk in cSCC patients. We have recently shown that, in a nationwide Dutch cohort, the model enhances the risk stratification provided by the American Joint Committee on Cancer, eighth edition (AJCC) and the Brigham and Women's Hospital (BWH) staging systems. The current study aims to evaluate the refined risk stratification and designed risk thresholds in an independent, nationwide UK cohort. Methods: In the Dutch cohort, we defined patients at increased risk within the low-risk group (AJCC: T1-T2, BWH: T1-T2a) as those with an estimated risk above 1%; and in the high-risk group (AJCC: T3-T4, BWH: T2b-T3), as those with a risk above 3.5%. Both risk thresholds were evaluated in the nested case-control (NCC) cohort (n=696), sampled from the nationwide UK cohort (n=31981). The CP model was applied to the NCC cohort and results were binarized as CP Low-Risk or CP High-Risk. Weighted metrics were computed to adjust for the difference in proportion of cases and controls between NCC and nationwide cohorts. Results: In the low-risk group as per AJCC (T1-T2) and BWH (T1-T2a), the metastatic risk is respectively 0.93% and 1.07%. Within this group, CP High-Risk patients represent respectively 1.69% and 6.11% of the AJCC and BWH groups and have increased metastatic risk of 7.54% in AJCC and 5.36% in BWH. In the high-risk group as per AJCC (T3-T4) and BWH (T2b-T3), the metastatic risk is respectively 7.79% and 13.80%. Within this group, CP High-Risk patients represent respectively 7.42% and 24.40% of the AJCC and BWH groups. They have an increased metastatic risk of 27.92% in AJCC and 24.07% in BWH. Conclusions: This study validates the designed risk thresholds in an independent nationwide cohort. Differences in performance compared to the Dutch cohort can be explained by the exclusion of patients with multiple missing variables and worse annotation of pathological features in the UK cohort. Additionally, this study confirms that our risk model can enhance cSCC staging systems by a refined risk stratification in both low-risk and high-risk groups. Specifically, the CP High-Risk subgroup within the staging systems' low-risk group might benefit from heightened surveillance and diagnostic screening, such as lymph node ultrasonography. Similarly, the CP High-Risk subgroup within the staging systems’ high-risk group could be considered for intensified surveillance and (adjuvant) treatment. In conclusion, adding the CP model to current cSCC staging systems would enable more personalized decisions about follow-up and treatment, while also facilitating the management of healthcare resources. Citation Format: Olivia FM Steijlen, Lara Pozza, Barbara Rentroia-Pacheco, Celeste. J. Eggermont, Domenico Bellomo, Sheril Alex, Jvalini Dwarkasing, Antien L. Mooyaart, Loes M. Hollestein, Marlies Wakkee. Beyond staging systems in cutaneous squamous cell carcinoma: Validation of two thresholds for a model-estimated metastatic risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3463.

Human resources management in healthcare: recruitment, retention, and workforce development: A review

This research paper explores Human Resources Management (HRM) in healthcare, focusing on the critical areas of recruitment, retention, and workforce development. Through a comprehensive literature review, the study highlights the challenges faced in attracting and retaining skilled healthcare professionals, alongside the importance of ongoing staff development. The paper examines evidence-based HR strategies, the role of technology and innovation, and future directions for HRM in the healthcare sector. Key findings emphasize the need for comprehensive recruitment and retention programs, investment in professional development, and technology adoption to improve HR practices. Implications for healthcare HR policy and practice are discussed, with recommendations for future research to enhance HRM effectiveness in healthcare settings. This study contributes valuable insights into improving HRM practices to ensure high-quality patient care and organizational effectiveness in the healthcare industry.

Reviewing business analytics in healthcare management: USA and African perspectives

This comprehensive review explores the application and impact of business analytics in healthcare management, focusing on the distinctive perspectives of the United States and Africa. In an era where data-driven decision-making is paramount, healthcare organizations globally are increasingly turning to business analytics to enhance efficiency, improve patient outcomes, and optimize resource allocation. The review begins by examining the state of business analytics in healthcare management within the United States, a pioneer in adopting advanced analytics solutions. The analysis encompasses the utilization of predictive modeling, data mining, and artificial intelligence to streamline operations, enhance clinical decision-making, and improve patient care. Case studies and success stories from leading healthcare institutions in the USA illustrate the transformative power of business analytics in areas such as patient risk stratification, resource optimization, and population health management. Contrastingly, the review delves into the unique challenges and opportunities that characterize the adoption of business analytics in healthcare across the African continent. Despite facing resource constraints and varying levels of technological infrastructure, African nations are increasingly recognizing the potential of analytics to address pressing healthcare challenges. The discussion encompasses initiatives and collaborations aimed at leveraging data analytics to improve healthcare delivery, disease surveillance, and resource management in the African context. The comparative analysis between the USA and Africa highlights the diversity of approaches and contexts in implementing business analytics in healthcare management. Factors such as data accessibility, infrastructure development, and cultural considerations emerge as critical determinants shaping the successful integration of analytics tools in healthcare decision-making. The review underscores the pivotal role of business analytics in reshaping healthcare management practices, while acknowledging the need for tailored strategies that align with the specific challenges and opportunities inherent in the USA and African healthcare landscapes. This study provides valuable insights for policymakers, healthcare administrators, and researchers seeking to navigate the evolving landscape of healthcare analytics on both a global and regional scale.

Machine Learning Applications in Healthcare: Current Trends and Future Prospects

The integration of machine learning (ML) in healthcare has witnessed remarkable advancements, transforming the landscape of medical diagnosis, treatment, and overall patient care. This article provides a comprehensive review of the current trends and future prospects of machine learning applications in the healthcare domain.The current landscape is characterized by the utilization of ML algorithms for disease diagnosis and risk prediction, personalized treatment plans, and efficient healthcare resource management. Notable applications include image recognition for radiology and pathology, predictive analytics for disease prognosis, and the development of precision medicine tailored to individual patient profiles.This review explores the evolving role of ML in improving patient outcomes, enhancing clinical decision-making, and optimizing healthcare workflows. It delves into the challenges faced in integrating ML into existing healthcare systems, such as data privacy concerns, interpretability of complex models, and the need for robust validation processes.Additionally, the article discusses future prospects and emerging trends in ML healthcare applications, including the potential for predictive analytics to preemptively identify health issues, the integration of wearable devices and remote monitoring for continuous patient care, and the intersection of ML with genomics for personalized medicine.The overarching goal of this article is to provide healthcare professionals, researchers, and policymakers with insights into the current state of ML applications in healthcare, along with an outlook on the transformative potential that machine learning holds for the future of healthcare delivery and patient outcomes.

Management and use of healthcare resources in patients with chronic lymphocytic leukemia initiating venetoclax in routine clinical practice

Venetoclax is a first-in-class B-cell lymphoma-2 (BCL-2) inhibitor approved as continuous monotherapy and in combination with rituximab as fixed-treatment duration for relapsed and refractory chronic lymphocytic leukemia (R/R CLL). DEVOTE was a 24-week, multicenter observational study (NCT03310190) evaluating the safety, healthcare resource utilization (HCRU) and health-related quality of life (HRQoL) of patients initiating venetoclax for R/R CLL in Canada. Overall, 89 patients received 1 dose of venetoclax; 80% had prior exposure (42% resistant) to ibrutinib. Biochemical tumor lysis syndrome (TLS) occurred in five patients. We observed differences in hospitalization across Canadian provinces including in patients at low risk for TLS with no clear impact on TLS incidence. Additionally, a rapid and sustained improvement in several domains of HRQoL was observed during venetoclax initiation. Early adoption of venetoclax was mainly for R/R CLL patients with few treatment options; nonetheless, acceptable toxicity and a positive impact on HRQoL were observed.

A retrospective observational study on characteristics, treatment patterns, and healthcare resource use of patients with myasthenia gravis in England.

There are limited data on the real-world healthcare resource use (HCRU) and management costs of myasthenia gravis (MG) in England. This study aims to assess the burden of disease for patients with MG in England. A retrospective, observational cohort study of adult patients diagnosed with MG, using data from the Hospital Episode Statistics data warehouse. Patients with a first-ever recorded diagnosis of MG between 30 June 2015 and 30 June 2020 were followed up until 30 June 2021 or death, whichever occurred first. Post-diagnosis patient characteristics, treatment patterns, HCRU, and costs were described. Costs were evaluated using National Health Service reference costs. A total of 9087 patients with a median follow-up time of 2.9 years (range, 1.7-4.3 years) were included. The mean age at diagnosis was 66.5 years and 53% of the patients were male. A large proportion of patients (72.8%) were admitted as inpatients during follow-up with a mean number of 1.3 admissions. Patients hospitalized for MG-related complications spent a mean of 9.7 days per patient-year in the hospital. During follow-up, 599 (6.6% of the total cohort) and 163 (1.8%) patients had a record of rescue therapy with intravenous immunoglobulin (IVIg) and plasma exchange (PLEX), respectively. Rituximab was administered to 81 (0.9%) patients and 268 (2.9%) patients underwent thymectomy. In those patients receiving rescue therapy or rituximab, >10% received at least three cycles of the same treatment. The average annual cost of hospital admissions across all patients treated with IVIg, PLEX, and rituximab were £907,072, £689,979, and £146,726, respectively. A majority of MG patients required hospitalization or accident and emergency attendance, resulting in high HCRU and costs. A subset of patients required rescue therapy (including IVIg and PLEX), rituximab administration, ventilation, or thymectomy.

Strategic Improved K-Means Clustering in Mining Blood Donor Data Analysis and IoT-based Allocation

This manuscript proposes Strategic Improved K-Means Clustering to simplify blood donor data analysis and distribution. The technique optimizes blood donor system resources via K-Means++ initialization, hierarchical clustering, and smart data dissemination. The paper begins with a comprehensive overview of clustering techniques and their healthcare applications. It illustrates the need for contemporary blood donor data analysis methods for cluster quality and resource allocation. Cluster purity, silhouette coefficient, Davies-Bould in the index, and other performance indicators are used to rigorously compare the recommended technique to 10 established clustering methods. The approach routinely fulfils these conditions, proving that it creates accurate, well-fitting groupings. Ablation tests how much-enhanced initialization, hierarchical clustering, and strategic data placement improve the entire. The study found that these make the procedure dependable and successful for numerous sorts of data. The study shows that the approach may be applied to other data besides blood donor data. Hierarchical clustering provides important information about the dataset's hierarchical patterns, making clustering findings easier to grasp. Resources are better distributed with strategic data dissemination. The recommended strategy is effective in emergencies and areas with changing blood needs. To conclude, Strategic Improved K-Means Clustering evaluates and distributes blood donor data comprehensively. Its flexibility, adaptability, and speed make it excellent for managing healthcare resources and making collective choices …

The problem of strategic human resource management in healthcare in Poland

Objective — to study the basic principles of the strategic human resource management in healthcare, to analyze the features of human resource management in healthcare organizations, to analyze the long‑term trends of the human resources in the healthcare in Poland and to propose the recommendations for the implementation of measures for strategic human resources management in healthcare organizations.
 Results. The problems of the long‑term and short‑term tendencies of human resources in the healthcare in Poland are discussed in the article. The age, gender and regional distribution of the human resources in the healthcare are shown and the main factors influenced on the dynamics of these indicators are described. Taking into account different problems in the healthcare in Poland the importance of the strategic human resource management is presented and the general recommendations for health institutions are given.
 Conclusions. Strategic management and analysis of human resources development are crucial in Polish healthcare. Health institutions need strategic plans and performance assessments for medical facilities. The expanded strategic management of medical organizations involves assessing both the external and internal environment, considering macroeconomic changes, regional shifts, market dynamics, mobility, qualifications, motivation of medical personnel, and evaluating the organization’s image among patients and public entities. In strategic human resources analysis, key factors include strategic intentions, forecasting challenges, positioning, and prioritizing human resources management. Given the complex macro environment of medical organizations, it’s crucial to identify the most significant factors, evaluate their potential impact, and formulate response measures within the organization’s strategy.

Human resource management research in healthcare: a big data bibliometric study

Human resource management (HRM) in healthcare is an important component in relation to the quality and efficiency of healthcare delivery. However, a comprehensive overview is lacking to assess and track the current status and trends of HRM research in healthcare. This study aims to describe the current situation and global trends in HRM research in healthcare as well as to indicate the frontiers and future directions of research. The research methodology is based on bibliometric mapping using scientific visualization software (VOSviewer). The data were collected from the Web of Science(WoS) core citation database. After applying the search criteria, we retrieved 833 publications, which have steadily increased over the last 30 years. In addition, 93 countries and regions have published relevant research. The United States and Australia have made significant contributions in this area. Current research articles focus on topics clustered into performance, hospital/COVID-19, job satisfaction, human resource management, occupational/mental health, and quality of care. The most frequently co-occurring keywords are human resource management, job satisfaction, nurses, hospitals, health services, quality of care, COVID-19, and nursing. There is limited research on compensation management and employee relations management, so the current HRM research field still has not been able to present a complete and systematic roadmap. We propose that our colleagues should consider focusing on these research gaps in the future.

Digital personnel records as an element of human resources management in healthcare

Aim of the review to analyze existing software for personnel management, to introduce a design of a unified digital database of healthcare specialists integrated with the platform of the Unified State Information System in Healthcare (USISH). The creation of digital personnel records on the existing information platform, integration with other state IT-systems will not only systematize and accumulate data on healthcare specialists, but also serve as a tool for personnel policy implementation both at the level of a medical organization and the level of a region, federal district and whole country.

Serum High-Mobility Group Box 1 and Heme Oxygenase-1 as Biomarkers in COVID-19 Patients at Hospital Admission.

The careful monitoring of patients with mild/moderate COVID-19 is of particular importance because of the rapid progression of complications associated with COVID-19. For prognostic reasons and for the economic management of health care resources, additional biomarkers need to be identified, and their monitoring can conceivably be performed in the early stages of the disease. In this retrospective cross-sectional study, we found that serum concentrations of high-mobility group box 1 (HMGB1) and heme oxygenase-1 (HO-1), at the time of hospital admission, could be useful biomarkers for COVID-19 management. The study included 160 randomly selected recovered patients with mild to moderate COVID-19 on admission. Compared with healthy controls, serum HMGB1 and HO-1 levels increased by 487.6 pg/mL versus 43.1 pg/mL and 1497.7 pg/mL versus 756.1 pg/mL, respectively. Serum HO-1 correlated significantly with serum HMGB1, oxidative stress parameters (malondialdehyde (MDA), the phosphatidylcholine/lysophosphatidylcholine ratio (PC/LPC), the ratio of reduced and oxidative glutathione (GSH/GSSG)), and anti-inflammatory acute phase proteins (ferritin, haptoglobin). Increased heme catabolism/hemolysis were not detected. We hypothesize that the increase in HO-1 in the early phase of COVID-19 disease is likely to have a survival benefit by providing protection against oxidative stress and inflammation, whereas the level of HMGB1 increase reflects the activity of the innate immune system and represents levels within which the disease can be kept under control.

The meteorological contrast index in the context of climate change and public health

In the context of climate change, extreme weather events and sudden shifts in weather patterns are becoming increasingly frequent. The atmosphere is considered a source of meteorological and climatic risks for human beings and living organisms. Numerous studies have examined the correlation between meteorological variables and human morbidity and mortality. However, only a few authors have investigated the impact of environmental changes on human health and, to our knowledge, there are no meteorological indices proposing a methodology for assessing changes in atmospheric conditions. Under the hypothesis that meteorological disruptions have an impact on human health, this article proposes a method to calculate a new index, the Meteorological Contrast Index (MCI), based on weather changes. This index takes into account three variables: i) categorization based on the type of atmospheric process, ii) changes in these categories over a specific time period, and iii) the level of stress associated with these changes, considering the severity of the transition from one category to another. If the predictive value of this index is proven for a specific meteorological variable and disease, it could be valuable in defining biometeorological early warning systems for the prevention and management of healthcare resources.•The Meteorological Contrast Index is the first index that proposes a method to assess changes in atmospheric conditions.•Atmospheric changes are a significant source of biometeorological distress, which can be quantitatively defined using the Meteorological Contrast Index.•Certain diseases are sensitive to the weather, and their incidence may increase under specific sequences of weather types.

GDASC: Data-Driven Incentive Strategies for Effective Human Resource Management in Healthcare

GDASC: Data-Driven Incentive Strategies for Effective Human Resource Management in Healthcare