Management Of Health Care Resources Research Articles

Artificial intelligence: basic terms and concepts, the application in healthcare and clinical medicine

Objective: to explore the potential and challenges of artificial intelligence (AI) in clinical medicine and healthcare, and to determine the prospects for its implementation to improve diagnosis, treatment, and medical data management.Material and methods. A literature review on the main terms and concepts of AI, its classification by application area, technologies, and methodologies was carried out. The learning methods such as supervised, unsupervised, and reinforcement learning were considered, as well as examples of AI application in various areas of medicine, including disease diagnosis and personalized medicine.Results. AI shows significant potential in improving diagnosis, optimizing treatment processes, and managing healthcare resources. Main application areas are related to medical image analysis, developing individualized treatment plans, and healthcare management. However, using AI faces challenges such as data availability and bias, fragmentation of systems, and complexity of algorithm interpretation.Conclusion. Despite the existing challenges, the implementation of AI in medicine has great prospects, including improved diagnostic accuracy, reduced task completion time, and development of personalized medicine. It is important to consider the ethical aspects and the demand for further study of AI application in medicine to achieve the best results.

Peri-injury symptomatology as predictors of brain computed tomography (CT) scan abnormalities in mild traumatic brain injury (mTBI)

ObjectiveThis study aimed to identify predictors of brain CT abnormalities in patients who sustained a mild traumatic brain injury (mTBI).MethodsRetrospective observational cohort of adult patients with mTBI (Glasgow Coma Score 13–15) that occurred within the preceding 24 h.Results2548 (91%) of the cohort had a brain CT and 698 (27%) demonstrated abnormal findings. The most frequently observed CT abnormalities were bleeding (638, 25%) and fractures (190, 7.4%). Multivariate logistic regression analysis revealed several significant predictors associated with the presence of brain CT abnormalities including older age [P < 0.0001], male sex [P < 0.0001], loss of consciousness [P = 0.0041], associated vomiting [P = 0.0011], alteration of consciousness (AOC) [P = 0102], and GCS score [P < 0.0001]. This was a robust model with an R² of 14.2%.ConclusionIn this retrospective analysis, older age, male sex, the presence of loss of consciousness or alteration in consciousness, lower GCS score, and associated vomiting were found to be significant predictors of having an abnormal brain CT. These findings highlight the importance of considering these factors when determining the necessity of brain CT scans in patients with mTBI and suggest that existing clinical decision rules may be limited. These findings may also help to inform clinical decision rules. Early identification of individuals at a higher risk of CT abnormalities may assist in appropriate management and allocation of healthcare resources.

Clinically Guided Adaptive Machine Learning Update Strategies for Predicting Severe COVID-19 Outcomes

Background: Machine learning algorithms are essential for predicting severe outcomes during public health crises like COVID-19. However, the dynamic nature of diseases requires continual evaluation and updating of these algorithms. This study aims to compare three update strategies for predicting severe COVID-19 outcomes post-diagnosis: 'naive' (a single initial model), 'frequent' (periodic retraining), and 'context-driven' (retraining informed by clinical insights). The goal is to determine the most effective timing and approach for adapting algorithms to evolving disease dynamics and emerging data. Methods: A dataset of 1.11 million COVID-19 patients from diverse U.S. regions was used to develop and validate an XGBoost algorithm for predicting severe outcomes upon diagnosis. Data included patient demographics, vital signs, comorbidities, and immunity-related factors (prior infection and vaccination status) from January 2007 to November 2021. The study analyzed the performance of the three update strategies from March 2020 to November 2021. Results: Predictive features changed over the pandemic, with comorbidities and vitals being significant initially, and geography, demographics, and immunity-related variables gaining importance later. The 'naive' strategy had an average AUC of 0.77, the 'frequent' strategy-maintained stability with an average AUC of 0.81, and the 'context-driven' strategy averaged an AUC of 0.80, outperforming the 'naive' strategy and aligning closely with the 'frequent' strategy. Conclusion: A context-driven approach, guided by clinical insights, can enhance predictive performance and offer cost-effective solutions for dynamic public health challenges. These findings have significant implications for efficiently managing healthcare resources during evolving disease outbreaks.

Efficiency of night shifts in Primary Healthcare Centers, Aseer Region, Saudi Arabia: Data envelopment analysis

BACKGROUND: Efficient allocation of healthcare resources is essential for meeting the increasing demand for healthcare services. This study aimed at exploring the performance of both one- and two-shift Primary Healthcare Centers (PHCCs) and examining the efficiency of healthcare centers to discover how efficiency is affected by the levels of resource allocation and staffing. MATERIALS AND METHODS: The study was conducted at 31 PHCCs assigned to cover extended hours, from 4:00 PM to 8:00 AM during May-October 2023. Study employed a quantitative approach and the data envelopment analysis technique to assess the efficiency of PHCCs, specifically focusing on night shift performance. Data were collected through interviews and surveys with primary healthcare staff including 99 doctors, 119 nurses, and 139 support staff using structured questionnaire. The DEA technique was utilized to measure the relative efficiency of the PHCCs by comparing the inputs and outputs. RESULTS: Out of 31 PHCCs, 45.2% run two shifts. Over the reported period, a total of 126,619 patient visits were recorded; 81.7% came to the two-shift PHCCs and 18.3% to the one-shift PHCCs. On average, each two-shift PHCC received approximately 161 visits/ shift, and the one-shift received 123 visits. Upper respiratory tract infections accounted for 30% of morbidities and chronic diseases for more than 20%. The performance of the two-shifts PHCCs revealed varying levels of efficiency with a score of 1.00 for the most efficient PHCC and 0.31 for the least efficient PHCC. CONCLUSION: This study underscores the importance of data-driven decision-making for healthcare administrators. Ongoing assessments and benchmarking are essential for continuously improving the quality of healthcare service and optimizing utilization of resources. Future research in this field can further explore the factors that contribute to healthcare efficiency and provide deeper insights into healthcare resource management and optimization of service delivery.

Development and validation of a machine learning model integrated with the clinical workflow for inpatient discharge date prediction.

Discharge date prediction plays a crucial role in healthcare management, enabling efficient resource allocation and patient care planning. Accurate estimation of the discharge date can optimize hospital operations and facilitate better patient outcomes. In this study, we employed a systematic approach to develop a discharge date prediction model. We collaborated closely with clinical experts to identify relevant data elements that contribute to the prediction accuracy. Feature engineering was used to extract predictive features from both structured and unstructured data sources. XGBoost, a powerful machine learning algorithm, was employed for the prediction task. Furthermore, the developed model was seamlessly integrated into a widely used Electronic Medical Record (EMR) system, ensuring practical usability. The model achieved a performance surpassing baseline estimates by up to 35.68% in the F1-score. Post-deployment, the model demonstrated operational value by aligning with MS GMLOS and contributing to an 18.96% reduction in excess hospital days. Our findings highlight the effectiveness and potential value of the developed discharge date prediction model in clinical practice. By improving the accuracy of discharge date estimations, the model has the potential to enhance healthcare resource management and patient care planning. Additional research endeavors should prioritize the evaluation of the model's long-term applicability across diverse scenarios and the comprehensive analysis of its influence on patient outcomes.

GIS-technologies and algorithm for developing regional systems for handling medical waste on the example of three regions

Introduction. GIS is a modern computer technology designed for collecting, storing, analyzing and visualizing spatial data – mapping. GIS is also actively used in healthcare to solve many regional problems, including optimization and management of healthcare resources. Over last decade, many researchers have used mathematical software models for healthcare waste management. A review of the literature shows facility location, risk to the public, and economic feasibility to be among the issues that have been extensively covered in the literature. Designing a reverse logistics network using mathematical software tools is an efficient and effective way to manage healthcare waste. The objective of the study. To determine the optimal approach to creating a functional model of a system for managing medical waste of class B and C using GIS-technologies for each of the three regions, based on the characteristics of each. Materials and methods. Medical institutions in three regions (St. Petersburg, Leningrad Region and Krasnoyarsk Territory) as sources of medical waste generation were considered taking into account their capacity and specialization. The composition and volumes of waste by class were determined using a calculation method based on indicative waste generation standards. The identification of local technologies for the neutralization of medical waste existing in medical institutions was carried out. The obtained data was processed statistically and displayed on electronic maps for subsequent use in GIS technologies, as well as for the development and justification of a waste management system for medical institutions, taking into account the prospects for the development of healthcare. Results. Based on the analysis of data on medical waste of classes B and C in individual medical institutions, as well as an analysis of the power of existing technologies for neutralizing this waste using GIS technologies, proposals were developed to improve the medical waste management system in three regions. The proposals are aimed at improving the sanitary and epidemiological situation in the regions over a five-year period and take into account the features of the existing transport infrastructure and the requirements of sanitary legislation. Limitations. The model of a medical waste management system using GIS technologies was used only for managing waste of classes B and C. Conclusion. To ensure the safe and environmentally sustainable functioning of the waste management system of classes B and C in the regions, it is necessary to apply an integrated approach based on GIS technologies. This approach should take into account the regional characteristics of the management of the medical waste disposal system and its economic feasibility and take into account the regional characteristics of transport logistics and the prospects for the development of healthcare.

Factors associated with the non-retention of health sector employees recruited in Iran: a survival analysis

BackgroundHealth staff are an essential component of the health system and a significant factor in improving health outcomes. As a result, without a suitable number of trained and supported workforces, health-related goals will not be achieved. As a result, one of the most significant tasks of the government and healthcare human resource management in healthcare organizations is focusing on development and maintenance of personnel.MethodThis study was a longitudinal and historical cohort study. Data was collected on the 40 medical universities and healthcare organizations under the jurisdiction of Iran Ministry of Health and Medical Education from 2008 to 2018 from the human resources database. In this study the COX regression and survival analysis was used to assess the factors of staff maintenance, retention rate, and the risk of turnover.Result46,939 health workforces were included in the study, of which 13,328 (28.4%) were men and 33,611 (71.6%) were women. Based on the finding of cox regression model, the retention of staff were statistically different between male and female. Employees whose current work place and birthplace were similar had a significantly higher probability of retention. Accordingly, the cox regression result showed, the risk of employee turnover for single personnel was higher than the married ones.Conclusionan applicable policy for increasing maintenance among workforce recruitment, could be considering the native born professionals instead of non-natives born which reduce the costs of employee turnover, including re-hiring, initial and on-the-job training, housing, and other extra living expenses away from home and family.

Strategic Human Resource Management in Healthcare: Elevating Patient Care and Organizational Excellence through Effective HRM Practices

Human Resources (HR) are a key factor in the operations and service quality of hospitals. This study aims to analyze the role of HR in enhancing the efficiency and effectiveness of healthcare services in hospitals. The research method used includes a literature review and field surveys of medical and non-medical staff at several hospitals. The results show that effective HR management significantly contributes to increased patient satisfaction, reduced medical errors, and operational efficiency. Additionally, regular training and career development are proven to be effective in improving employee competence and motivation. This study recommends HR management strategies focused on skill enhancement, the use of information technology, and the development of a positive work culture to achieve higher service standards.

Harnessing Artificial Neural Networks for Spinal Cord Injury Prognosis.

Background: Prediction of neurorehabilitation outcomes after a Spinal Cord Injury (SCI) is crucial for healthcare resource management and improving prognosis and rehabilitation strategies. Artificial neural networks (ANNs) have emerged as a promising alternative to conventional statistical approaches for identifying complex prognostic factors in SCI patients. Materials: a database of 1256 SCI patients admitted for rehabilitation was analyzed. Clinical and demographic data and SCI characteristics were used to predict functional outcomes using both ANN and linear regression models. The former was structured with input, hidden, and output layers, while the linear regression identified significant variables affecting outcomes. Both approaches aimed to evaluate and compare their accuracy for rehabilitation outcomes measured by the Spinal Cord Independence Measure (SCIM) score. Results: Both ANN and linear regression models identified key predictors of functional outcomes, such as age, injury level, and initial SCIM scores (correlation with actual outcome: R = 0.75 and 0.73, respectively). When also alimented with parameters recorded during hospitalization, the ANN highlighted the importance of these additional factors, like motor completeness and complications during hospitalization, showing an improvement in its accuracy (R = 0.87). Conclusions: ANN seemed to be not widely superior to classical statistics in general, but, taking into account complex and non-linear relationships among variables, emphasized the impact of complications during the hospitalization on recovery, particularly respiratory issues, deep vein thrombosis, and urological complications. These results suggested that the management of complications is crucial for improving functional recovery in SCI patients.

Coupling coordination degree of healthcare resource supply, demand and elderly population change in China

ObjectTo analyze the trend of the coupling and coordination of the supply and demand of healthcare resources between the elderly population and healthcare resources in China during the period of 2012–2022, to reveal the impact of the growth of the elderly population on the relationship between the supply and demand of healthcare resources, and to put forward suggestions to improve the coupling and coordination between the supply and demand of healthcare resources and the elderly population, in order to cope with the challenges of an aging society.MethodsBy obtaining relevant data from authoritative data sources such as China Statistical Yearbook, Health and Health Statistics Yearbook, and the Chinese government website from 2012 to 2022, we constructed a comprehensive measurement index for the three systems of elderly population, healthcare resource supply, and healthcare resource demand; Using the entropy value method to assign weights to the indicators, combined with the coupling coordination degree model, to reveal the changes of the elderly population change and the supply and demand of medical and health resources; using ArcGIS technology, to study the spatial characteristics of the elderly population change and the supply and demand of medical and health resources.ResultsFrom 2012 to 2022, the supply and demand of healthcare resources and the variation of the elderly population in China show a continuous growth trend, and the comprehensive development level of the system gradually climbs from a low level to a high level. The fluctuation of coupling degree and coordination degree rises, although the coordination degree has always been lower than the coupling degree, but the distance between the coordination degree and the coupling degree gradually narrows with the passage of time. The coordination degree between population aging and medical and health resources development shows spatial heterogeneity in China, with the eastern region significantly higher than the western region/.ConclusionsThe coupling degree between population aging and healthcare resource supply and demand in China from 2012 to 2022 shows a general upward trend from low coupling to medium-high coupling, but it is worth noting that even though the degree of coupling increases, the degree of coordination is still relatively lagging behind, suggesting that the government and relevant departments need to pay more attention to coordinated allocation and management of healthcare resources. At the same time, the spatial differences in the degree of coordination among provinces suggest that future policymakers should take regional differences into full consideration in policymaking and sustainable development.

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 &amp; 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.