Healthcare Management Research Articles

Decentralized worker-centred occupational management in health care: nationwide survey and alpha testing.

Occupational stress among healthcare workers negatively impacts job satisfaction and patient care quality, jeopardizing healthcare system sustainability. Traditional employer-driven approaches often fail to address these challenges comprehensively, leading to persistent gaps in work condition transparency and well-being. To elucidate the working conditions of health workers and introduce a worker-centred, technology-based strategy moving beyond traditional practices and entrenched medical culture. A nationwide survey of Belgian medical residents evaluated occupational conditions and perceptions of management practices. Additionally, the alpha version of a decentralized mobile application was tested to gather user satisfaction and feedback on its usability. The data were surveyed using Pearson's chi-squared and Kruskal-Wallis rank sum tests to assess associations between categorical and ordinal variables, respectively. Alpha-testing results were evaluated using descriptive statistics. The nationwide survey, involving 257 participants, revealed significant associations between medical specialty, work choices and compensation. Notably, 91% of participants expressed strong interest in our proposed open, decentralized solution. In the alpha testing phase, 12 testers reported high satisfaction regarding time-tracking accuracy and payroll verification, though challenges related to administrative burden were also identified. The findings underscore the need for innovative, worker-centred occupational management solutions. The proposed solution shows promise in improving autonomy and transparency, potentially enhancing healthcare delivery and sustainability.

Public–private partnership in pipelining science of acute care ecosystem: Insights from Taiwan's Presidential Hackathon

AbstractIntroductionThe acute care system faced significant challenges in managing healthcare emergencies due to a lack of coordination between emergency services and logistical support. This disorganization undermined collaboration and response efficiency.MethodsTaiwan's Presidential Hackathon introduced an innovative approach to improving the trauma system by integrating digital pipeline science through public–private partnerships (PPPs). This initiative specifically addressed inefficiencies and complexities in the acute care ecosystem, brought to light by the catastrophic 2014 gas explosion in Kaohsiung City.ResultsThe hackathon led to the development of a unified digital platform for emergency data management. This platform significantly enhanced communication, data sharing, and coordination across healthcare sectors, culminating in the implementation of a digital pre‐hospital emergency care system across multiple administrative regions.ConclusionOur experience demonstrated the effectiveness of leveraging digital technologies, PPPs, and the hackathon model to revolutionize emergency healthcare management and response systems through cross‐sector collaboration.

Predictive Optimization of Patient No-Show Management in Primary Healthcare Using Machine Learning.

The "no-show" problem in healthcare refers to the prevalent phenomenon where patients schedule appointments with healthcare providers but fail to attend them without prior cancellation or rescheduling. In addressing this issue, our study delves into a multivariate analysis over a five-year period involving 21,969 patients. Our study introduces a predictive model framework that offers a holistic approach to managing the no-show problem in healthcare, incorporating elements into the objective function that address not only the accurate prediction of no-shows but also the management of service capacity, overbooking, and idle resource allocation resulting from mispredictions. Our approach simplifies preprocessing and eliminates the need for expert judgment in variable selection, thereby enhancing the model's usability in routine healthcare operations. Our research revealed that key predictors of no-shows are consistent across various studies. We employed semi-automatic feature selection techniques, achieving results comparable to state-of-the-art approaches but with significantly reduced complexity in their selection. This method not only streamlines the feature selection process but also enhances the overall efficiency and scalability of our predictive models, making them more adaptable to diverse healthcare settings. This comprehensive strategy enables healthcare providers to optimize resource allocation and improve service delivery, making our findings relevant for healthcare systems globally facing similar challenges. Future work aims to expand the analysis by incorporating additional third-party data sources, such as weather and commuting activities, to explore the broader impacts of external factors on patient no-show behavior. To the best of our knowledge, this innovative approach is expected to provide deeper insights and further enhance the predictability and effectiveness of no-show mitigation strategies in healthcare systems.

Trends in ready-to-use portable electrochemical sensing devices for healthcare diagnosis.

Compared withprevious decades, healthcare has emerged as a key global concern in light of the recurrent outbreak of pandemics. The initial stage in the provision of healthcare involves the process of diagnosis. Countries worldwide advocate for healthcare research due to its efficacy and capacity to assist diverse populations. Enhanced levels of healthcare management can be attained by the implementation of rapid diagnostic procedures and cognitive data analysis. Therefore, there is a constant need for smart therapeutics, analytical tools, and diagnostic systems to improve health and well-being. The past decade witnessed enormous growth in the sensing detection systems integrated into smartphones with printed electrodes and wearable patches for the screening of various healthcare diagnostics biomarkers and therapeutic drugs. This review focuses on the expansion of point-of-care technologies and their incorporation into a broader array of portable devices, a critical aspect in the context of decentralized societies and their healthcare systems. Discussions are broadly focused on the different sensing platforms such as solid electrodes, screen-printed electrodes, and paper-based sensing strategies for the detection of various biomarkers and therapeutic drugs. We also discuss the next-generation healthcare wearable sensing device importance and future research possibilities. Finally, theportable electrochemical sensing devices and their future perspective developments towards healthcare diagnosisare critically summarized.

Machine Learning in Healthcare System

Machine learning (ML) is transforming healthcare by improving diagnosis accuracy, customizing treatment regimens, and reducing administrative procedures. Its capacity to analyze large datasets and detect complex patterns has resulted in major improvements in patient care and operational efficiency. In diagnostics, ML algorithms have shown extraordinary skill in analyzing medical pictures such as X-rays and MRIs, frequently outperforming humans. For example, ML models in radiology may detect abnormalities with amazing precision, aiding early illness identification and increasing patient outcomes. Beyond diagnostics, ML helps to customize medicine by assessing patient data and tailoring treatment strategies. By taking into account individual features and reactions, ML-driven models may prescribe medicines that improve efficacy while reducing negative effects. This tailored approach is especially useful for controlling chronic illnesses and complicated situations. Operationally, machine learning optimizes healthcare management by anticipating patient admissions, controlling hospital resources, and enhancing supply chain logistics. Predictive analytics enables healthcare institutions to predict patient demands, assuring proper staffing and resource allocation, so improving patient care while lowering operating expenses. Despite these advances, incorporating machine learning into healthcare offers a number of hurdles, including data privacy issues, the requirement for big, high-quality datasets, and the need for openness in algorithmic decisions. Addressing these concerns is critical to the ethical and successful use of ML technology in healthcare contexts. In conclusion, machine learning has enormous potential to improve healthcare by improving diagnoses, customizing treatments, and increasing operational efficiency. Continuous research and development are required to overcome current hurdles and fully exploit the benefits of ML in healthcare systems. Keywords: Machine Learning, Artificial Intelligence, SMEs, healthcare- sector, Ecommerce, E- commerce.

A Review of the Application and Development of Ultra-thin Silicon Chips

The widespread applications of ultra-thin silicon chips across industries such as healthcare, telecommunications, automotive, artificial intelligence, and energy management underscore their pivotal role in driving technological advancements and societal progress by enhancing efficiency, sustainability, and overall performance in various electronic systems. This paper provides an in-depth review of the application and development of ultra-thin silicon chips in modern electronic technology. Ultra-thin silicon chips, characterized by small size, low power consumption, and efficient heat dissipation, offer significant advantages over traditional chips in various electronic products such as smartphones, tablets, and smart wearable devices. The paper discusses the basic principles, fabrication methods, and advantages of ultra-thin silicon chips, highlighting their potential applications in emerging fields like artificial intelligence and the Internet of Things. Furthermore, the paper explores the evolving trends in ultra-thin silicon chip manufacturing technology, emphasizing advancements in nano-technology applications and intelligent manufacturing practices. The diverse applications of ultra-thin silicon chips across industries such as healthcare, telecommunications, automotive, artificial intelligence, and energy management underscore their pivotal role in driving technological advancements and societal progress

Collaborative Healthcare Data Management Framework using Parallel Computing and the Internet of Things

Healthcare data management has become a critical research area, primarily driven by the widespread adoption of personal health monitoring systems and applications. These systems generate an immense volume of data, necessitating efficient and reliable management solutions for lossless sharing. This article introduces a Collaborative Data Management Framework (CDMF) that leverages the combined strengths of parallel computing and federated learning. The proposed CDMF is designed to achieve two primary objectives: reducing computational complexity in data handling and ensuring high sharing accuracy, regardless of the data generation rate. The framework employs parallel computing to streamline the scheduling and processing of data acquired at various intervals. This approach minimizes processing delays by operating on a less complex scheduling algorithm, making it suitable for handling high-frequency data generation. Federated learning, on the other hand, plays a pivotal role in verifying data distribution and maintaining sharing accuracy. By enabling decentralized learning, federated learning ensures that data remains on local devices while sharing only the necessary model updates. This approach enhances privacy and security, a critical consideration in healthcare data management. It ensures that data distribution and sharing are verified based on appropriate requests while avoiding latency issues. By decentralizing the learning process, federated learning enhances privacy and security, as raw data does not leave the local systems. This cooperative interaction between parallel computing and federated learning operates in a cyclic manner, allowing the framework to adapt dynamically to increasing monitoring intervals and varying data rates. The performance of the CDMF is validated through improvements in two key metrics. First, the framework achieves a 15.08% enhancement in sharing accuracy, which is vital for maintaining data integrity and reliability during transfers. Second, it reduces computation complexity by 9.48%, even when handling maximum data rates. These results highlight the framework’s potential to revolutionize healthcare data management by addressing the dual challenges of scalability and accuracy.

Development of a pandemic-related core set of quality indicators for quality and patient safety in University Hospitals in Germany

BackgroundThe COVID-19 pandemic entailed a global health crisis, significantly affecting medical service delivery in Germany as well as elsewhere. While intensive care capacities were overloaded by COVID cases, not only elective cases but also non-COVID cases requiring urgent treatment unexpectedly decreased, potentially leading to a deterioration in health outcomes. However, these developments were only uncovered retrospectively. Especially university hospitals, which were meant to take on a central coordinating role, did not have detailed information on expected healthcare utilization, available resources and capacities, and the quality of medical care.The experience of compromised healthcare and a lack of monitoring during the COVID-19 pandemic made it clear that healthcare systems should be better prepared. Therefore, the aim of this work was to develop a set of indicators suited to detect undesirable developments concerning the provision of inpatient healthcare.Material & methodsThe study employed a literature review, online surveys, expert interviews, and a multistep evaluation process to develop a core set of quality indicators (QIs) suitable for assessing the resilience of university hospitals during pandemics. This initial set of indicators was refined through consultations with a) quality and risk management officials from German university hospitals via an online survey and b) a diverse panel of experts.ResultsThe comprehensive evaluation identified two primary strands: organizational/management indicators (Strand A, 60 indicators) and disease-specific clinical quality and patient safety indicators (Strand B, 20 indicators for critical conditions like stroke, myocardial infarction, and cancer.) Three additional indicators were added after a final expert panel meeting, resulting in a final set of 83 indicators.Discussion and conclusionThe developed QIs mark a significant advancement in the operational preparedness of university hospitals for pandemics. The study contributes to quality management in healthcare during pandemics by creating the basis for a structured approach to pandemic preparedness and response. This unique set of QIs within the German context presents an opportunity for establishing quality improvement, underscoring the importance of a robust, adaptable quality management framework as a basis for safeguarding against future health crises.

The love-hate state of mobile device management in healthcare: An international survey

The love-hate state of mobile device management in healthcare: An international survey

Building Narratives of Effectiveness and Customer Orientation at the Interface of Different Actors in the Lifestyle Guidance Service Process.

Co-production is expected to lead to more efficient and effective services. In response, this study aims to describe and understand the construction of effectiveness and the manifestation of customer orientation among the actors involved in the service process of lifestyle guidance. A qualitative narrative study was designed, and data were collected through the thematic interviews (n = 9) with the management, employees and customers of a Finnish public healthcare and social welfare organisation. The main narratives were as follows: (1) Shared value creation narrative, (2) Organisational strategy and customer meeting narrative, (3) Service co-production narrative and (4) Effective service process narrative. Based on the context, input, process and product (CIPP) model, they construct a plot narrative about the customer's need for individual guidance and the goal of transferring responsibility to the organisation. They create cross-pressure on the service process of lifestyle guidance. Achieving the effectiveness of lifestyle guidance requires the service provider to ensure the service is implemented more flexibly between general, self-directed and individually supportive guidance.

Evidence-Based Care Practices for Children Exposed to a Diabetes-Complicated Pregnancy

This study investigated the frequency with which nurse practitioners (NPs) incorporate evidence-based primary care practices for children exposed to diabetes-complicated pregnancies (DCPs). Using a cross-sectional design, a sample of 21 NPs participated in a web-based survey. Findings indicate considerable variability in NP care practices, suggesting that primary care NPs inconsistently implement evidence-based health promotion and care management strategies with these children. Continuing education tailored to the specific care needs of children exposed to hyperglycemia in utero could bridge current practice gaps. Further research is needed to explore this issue in a larger, more geographically diverse sample to enhance care consistency.

Transformative AI Solutions for Trauma Management and Advanced Mental Health Care Approaches

Transformative AI Solutions for Trauma Management and Advanced Mental Health Care Approaches

Exploring medical error taxonomies and human factors in simulation-based healthcare education.

This study aims to provide an updated overview of medical error taxonomies by building on a robust review conducted in 2011. It seeks to identify the key characteristics of the most suitable taxonomy for use in high-fidelity simulation-based postgraduate courses in Critical Care. While many taxonomies are available, none seem to be explicitly designed for the unique context of healthcare simulation-based education, in which errors are regarded as essential learning opportunities. Rather than creating a new classification system, this study proposes integrating existing taxonomies to enhance their applicability in simulation training. Through data from surveys of participants and tutors in postgraduate simulation-based courses, this study provides an exploratory analysis of whether a generic or domain-specific taxonomy is more suitable for healthcare education. While a generic classification may cover a broad spectrum of errors, a domain-specific approach could be more relatable and practical for healthcare professionals in a given domain, potentially improving error-reporting rates. Seven strong links were identified in the reviewed classification systems. These correlations allowed the authors to propose various simulation training strategies to address the errors identified in both the classification systems. This approach focuses on error management and fostering a safety culture, aiming to reduce communication-related errors by introducing the principles of Crisis Resource Management, effective communication methods, and overall teamwork improvement. The gathered data contributes to a better understanding and training of the most prevalent medical errors, with significant correlations found between different medical error taxonomies, suggesting that addressing one can positively impact others. The study highlights the importance of simulation-based education in healthcare for error management and analysis.

Development and Validation of the Nursing Information Security Questionnaire.

Ensuring the security of nursing information holds substantial importance to nursing outcomes and healthcare system management. The awareness of information security among nurses in China is generally inadequate, and there is a lack of standardized evaluation tools for nurse information security in nursing practice. The nursing sector necessitates the establishment of a robust culture surrounding information security. The aim of this study was to construct a self-reporting instrument for evaluating nursing information security. The research team utilized literature analysis and group discussions to draft the item pool. After two rounds of Delphi consultation by 15 experts and pilot testing, the initial questionnaire was formed. Item analysis was carried out on the questionnaire, and the validity and reliability of the instrument were statistically tested by computing the Keiser-Meier-Olkin and Bartlett's tests, an exploratory factor analysis (EFA), a confirmatory factor analysis (CFA), convergent and discriminative validity, descriptive statistics, Cronbach's α, and test-retest reliability. A total of 501 nurses participated in the study, supplemented by the inclusion of five experts who were invited to contribute to the assessment of content validity. Four factors were formed using EFA (n = 250), and the cumulative variance contribution rate was found to be 60.10%. The CFA (n = 251) showed that the model fit was good. The overall Cronbach's α coefficient of the questionnaire was 0.948, and the test-retest reliability was 0.837. Finally, the nursing information security questionnaire (NIS-Q) with 38 items and three dimensions of knowledge, attitude, and practice were formed. A promising assessment instrument for gauging the degree of nursing information security was introduced. Further, a foundational platform was established for implementing specific enhancement strategies aimed at advancing nursing information security.

A Survey to Assess Job Satisfaction of Doctors in Bangladesh

Job satisfaction can be defined as the positive and negative emotions an employee associates with their job or the degree of happiness derived from their job (Singh, J.K., and Jain, M., 2013). Consequently, job satisfaction stands as one of the most extensively subjects in the field of organizational psychology (Spector, P.E., 1997). According to Locke (Locke, E., 1976), job satisfaction is the positive and enjoyable emotional state that results from assessing one’s job or job-related experiences. Previous studies have revealed that when an employee is content with their job, they are more likely to perform at their best to fulfill the organization’s objectives (Jalagat, R., 2016). Highly satisfied employees tend to exhibit regular attendance and punctuality, increased productivity, stronger commitment, and an overall sense of satisfaction in their lives (Lease, S.H., 1998). In addition to a great deal of responsibility, doctors frequently deal with difficult and complex situations (World Medical Association, Inc., 2015; European Junior Doctors Association, 2016). Over the past thirty years, stress in the medical industry has been intensively examined (Cooper et al.). Occupational stress is particularly high in the dental, medical, aviation, law enforcement, mining, and social work fields (Cooper et al., 1988). The strain in medicine comes from having to care for people instead of things and from knowing how much their actions affect other people’s lives (Caplan et al., 2017). Given that mistakes can have major consequences for patients as well as doctors, doctors’ competency is routinely assessed (Payne & Firth-Cozens, 1987). Medical professionals frequently deal with emotionally draining circumstances including patient suffering, mishaps, and mortality. When dealing with complicated events like death, doctors may find these situations extremely difficult to handle if they are not well prepared. Notably, a large percentage of physicians suffer from stress and burnout, particularly in their early training years (European Junior Doctors Association, 2016; World Medical Association, Inc., 2015). Compared to other professionals, doctors are more likely to experience burnout, particularly those in frontline care specialties (Shanafelt et al., 2012). Doctors at hospitals are more likely to become burned out than those in private practices or research institutions, according to Olkinuora’s research (Olkinuora et al., 2018). While extensive research has explored job satisfaction across various sectors in Bangladesh, there exists a significant gap in the literature concerning physicians’ job satisfaction within the hospital setting (Chen et al., 2004). Job satisfaction among physicians is a critical aspect that directly impacts the quality of healthcare delivery and overall healthcare outcomes (Franco et al., 2002). Despite its importance, there is limited research focusing specifically on physicians’ job satisfaction within the context of hospitals in Bangladesh. Existing studies on job satisfaction among healthcare workers in Bangladesh have primarily focused on the public sector, with limited attention given to physicians working in hospital settings, especially within the private sector. Moreover, while different studies have highlighted factors such as working hours, administrative tasks, rewards, recognition, and remuneration influencing physicians’ job satisfaction, it remains unclear whether similar factors hold true for physicians in Bangladesh (Syed Ghazaili & Daud, 2016). Given the unique healthcare landscape in Bangladesh and the potential differences in healthcare systems compared to high-income countries, there is a need for context-specific research to understand the factors influencing physicians’ job satisfaction in Bangladesh hospitals. This research proposal aims to address this critical knowledge gap by conducting a rigorous examination of the different factors contributing to job satisfaction among physicians working in hospitals across Bangladesh. By focusing on the unique context of healthcare in Bangladesh, this study endeavors to shed light on the specific factors that drive job satisfaction among physicians. The findings of this research will not only contribute to the limited body of knowledge on physician job satisfaction in Bangladesh but also have practical implications for healthcare management and policy development aimed at enhancing the quality of healthcare delivery in the country.

A Blockchain based Solution for Efficient and Secure Healthcare Management

A Blockchain based Solution for Efficient and Secure Healthcare Management

Health Disparities in Rheumatology in the United States.

Underserved populations are often at risk of experiencing systematic healthcare disparities. Existing disparities in care access, quality of care received, and treatment outcomes among patients with rheumatic disease are not well understood. We conducted a targeted literature review to understand disparities in health outcomes, treatment patterns, and healthcare management faced by rheumatology patients in the United States, with a focus on rheumatoid arthritis (RA), psoriatic arthritis (PsA), and ankylosing spondylitis (AS). The findings of this review indicate that disparities in RA, PsA, and AS affect several historically underserved populations, including underrepresented racial and ethnic groups, persons with lower socioeconomic status (SES), persons experiencing homelessness, and patients with Medicare or Medicaid insurance types. The disparities experienced by these populations include greater disease activity and severity, decreased or delayed access to specialist care, decreased likelihood of receiving advanced therapeutics, and worse clinical outcomes. To provide equitable healthcare for all patients with RA, PsA, and AS, multiple closely linked health disparities must be addressed. Possible solutions include partnerships between healthcare systems and community-based organizations, targeted outreach tailored to patients with low SES, interventions to improve patient adherence and knowledge, and interventions to improve access to care for rural-residing and unhoused patients. In all, the findings of this literature review underscore the need for mitigation of health disparities in rheumatology care and may serve as a foundation for developing strategies to reduce disparities.

Pharmaceutical Waste Management in Healthcare: A Comprehensive Study on Environmental Implications, Current Practices and Strategies for Sustainable Solutions

Pharmaceutical Waste Management in Healthcare: A Comprehensive Study on Environmental Implications, Current Practices and Strategies for Sustainable Solutions

Effectiveness of robot-assisted exercise regimen parameters on extremity function and quality of life among stroke population – a scoping review

Background. The use of advanced technology in healthcare management is widespread in current practice. Many innovative technologies assist healthcare professionals in providing accurate, high-quality intervention protocols to alleviate patient issues effectively. Similarly, many technology-assisted evaluation and treatment methods are also in practice in the field of physiotherapy. In this study, our objective was to identify advanced innovative technologies used in the physiotherapy management of neurological problems, especially in the stroke population. Methods. Recent literature from the period of 01/01/2018 to 31/12/2023 was searched in the PubMed, Cochrane, and Medline databases using specific keywords: assisted technology, robotic therapy, assisted device, stroke population, and recent trends in physiotherapy. More than 50 articles were selected from the databases and carefully evaluated, including device-assisted exercises in the stroke population, with systematic reviews and meta-analyses identifying relevant RCT studies. Approximately 9 articles were scrutinized based on criteria for bias reduction and evaluated for the advantages and disadvantages of advanced technology support in physiotherapy interventions. Results. Robotic therapy-assisted intervention was compared with conventional management in many studies, revealing that quality of life and learning outcomes were more beneficial in the technology-assisted exercise group, especially among ischemic hemiplegic subjects. Upper extremity and hand dexterity functions also improved more in the advanced technology-assisted intervention. Limitations of the study were identified in the frequency and intensity of the technology-assisted exercise protocol, with its benefits not clearly outlined in previous studies. Conclusion. Advancement in physiotherapy management in association with technology device-assisted intervention will play a major role in the future. We recommend further investigation into exercise parameters such as duration, frequency, and intensity to better understand their beneficial effects for stroke patients, which could aid future physiotherapy practice

Analysis of the impact of public-private partnership on the development of the domestic medical industry

The development of the social sphere in the country depends on the development of healthcare and medical industry, which serve as its basis, both at the municipal and regional levels, and show the level of its development. The standard of living and health of the population depend on this sphere. Its qualitative development directly conditioned by the effective model of the healthcare management system, which consists in optimising sources of financing. The study has assessed the most significant pressing problems related to the system and mechanism of financing the healthcare system and medical industry, new possible options for the sources of financing and directions for optimising the distribution of public financing have been explored as well. One of the most effective areas of financing medical institutions at the present stage is public private partnership (hereinafter referred to as PPP). With its help, issues related to modernisation of infrastructure, optimisation of the resource management process in the medical institutions as well as improvement of the level of services provided are addressed. The study also examines risks arising from the use of the PPP in terms of its application as financing for the medical industry and healthcare sector.