Healthcare Industry Research Articles

Evaluating User Acceptance of Mobile Payment Systems in Uganda

Purpose: This research evaluates user acceptance of mobile payment systems. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Combining machine learning with DW systems provides significant advantages in different fields. This synergy boosts analytical aptitudes, allowing the organization to go a notch higher than descriptive analytics in predictive and prescriptive analytics. However, such a decision is not simple as it has implementation matters such as data quality problems, scalability, and real-time processing problems. Integration best practices include in-database machine learning processing, a feature store, and proper MLOps practices. Real-life examples from the healthcare industry, banking and financial services, retail, and manufacturing industries show that this integration brings operational enhancements for the business and positive effects on customers and overall organizational performance. Recommendations: Mobile payment providers should prioritize user-centric design principles, focusing on simplicity, usability, and intuitive interfaces. Policymakers should develop regulatory frameworks that establish clear standards for mobile payment systems, focusing on data protection, privacy, and security. Keywords: User Acceptance, Mobile Payment Systems

The Impact and Acceptance of Large Language Models in Healthcare: A Perspective from China

This research explores the impact of Large Language Models like GPT on the healthcare industry, focusing on operational efficiency, patient experience, and cost structures to promote greater inclusive innovation. Employing a mixed-methods approach, this study integrates quantitative data from structured surveys with qualitative insights from interviews. Grounded in empirical evidence from 66 valid surveys conducted among healthcare professionals and patients in China, this study employs SmartPLS for robust statistical analysis. The findings suggest a significant potential of LLMs in enhancing healthcare delivery, marked by improvements in operational efficiency and patient experience. While LLMs are perceived to potentially lower costs, the study reveals that cost reduction alone does not significantly influence the acceptance of LLM-integrated healthcare solutions in the Chinese context. The high level of trust and acceptance in using LLMs for diagnosis and treatment planning among respondents underscores a shift towards prioritizing quality and effectiveness in healthcare over mere cost savings. This study contributes to the discourse on AI adoption in healthcare, challenging existing assumptions and indicating a future where quality and outcome improvements may be more significant factors for technology acceptance. The research advocates for a balanced approach to LLM integration, emphasizing the importance of both economic and qualitative benefits in enhancing healthcare practices.

Strengthening Healthcare Data Security with Ai-Powered Threat Detection

As the healthcare industry undergoes rapid digital transformation, the need for robust cybersecurity measures has never been more critical. AI-driven solutions, such as Machine Learning (ML) and anomaly detection, are proving to be pivotal in securing healthcare data. These technologies enable healthcare organizations to identify cyber threats proactively, automate incident response, and enhance data security. Furthermore, AI's ability to provide continuous network monitoring, predictive analytics, and real-time anomaly detection offers healthcare providers the tools needed to mitigate risks before they escalate. This research highlights the key applications of AI in healthcare data security, including its effectiveness in vulnerability management and regulatory compliance. It also delves into the ethical and operational challenges of integrating AI-driven threat detection systems into healthcare settings, including concerns related to bias in AI models, regulatory hurdles, and the complexity of AI system integration into existing healthcare infrastructures. In a 2023 study, Accenture reported that AI-based cybersecurity systems reduced detection and response time by up to 60%, illustrating how AI accelerates response to potential data breaches​. Similarly, HIMSS noted that the risk of data breaches in healthcare could be halved by AI technologies that continuously monitor and analyze data​. These findings emphasize AI's role in minimizing the delays and errors typically associated with human-driven responses​. AI techniques such as supervised, unsupervised, and semi-supervised learning play a crucial role in anomaly detection. These models identify irregular patterns in healthcare systems, flagging potential threats even when subtle​. For example, deep learning approaches, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), can uncover outliers in healthcare data, enhancing system security​. The ability of AI to continuously learn from previous attacks ensures that systems evolve with emerging threats, further solidifying its place in healthcare cybersecurity​. Additionally, AI aids in predictive analytics, which can forecast future cyber threats based on historical data, allowing healthcare providers to address vulnerabilities proactively​. By leveraging such capabilities, AI not only safeguards sensitive patient data but also enables healthcare institutions to stay compliant with stringent regulatory standards like HIPAA and GDPR​. However, AI implementation in healthcare security comes with its own challenges. Integrating AI into legacy systems requires substantial infrastructure upgrades, and the complexity of dynamic encryption can strain system resources​. Furthermore, the ethical concerns surrounding AI, such as the transparency of decision-making processes and potential bias in AI algorithms, must be carefully managed to ensure equitable and effective security solutions​

Wearable health technology: integrating devices and health care systems for better monitoring

Wearable health technology has the potential to revolutionize the healthcare industry by allowing continuous monitoring, timely identification of health problems, and customized treatment. This paper presents a possible integration of wearable technology in a healthcare system, introducing a smartwatch, a fitness tracker, and biosensors in improving patient observation and care. The integrated wearable devices with electronic health records, artificial intelligence, and big data analytics lead toward better decision-making processes, improved patient outcomes, and cost efficiency in health care delivery. While discussing the wireless health technologies, the paper addresses the problems regarding data privacy, interoperability, and patient engagement in the implementation of wearable health technologies.

Exploring the Challenges and Needs of Arab Patients for Arabic Communication in Malaysian Healthcare Public Relations

Arabic communication is fundamental particularly for the Malaysian healthcare industry that handles the raising number of Arab patients coming to Malaysia to seek healthcare treatment. However, studies showed that most Arab patients are unable to speak other than their mother tongue language which is Arabic language, and they are very attached to their unique culture, which makes them prefer specialized services at healthcare centers. Thus, this research aims at acquiring the challenges faced by Arab patients and their utmost needs toward Arabic communication aspects to be addressed by healthcare personnel while attending them. This qualitative research employed semi-structured interview using needs analysis theory by Hutchinson and Waters (1987) toward 6 Arab patients at 5 different selected healthcare centers in Klang valley. The results demonstrated that the informants faced communication problems with limited understanding of formal Arabic including management and promotional issues. In addressing these problems, informants indicated their needs toward a competent healthcare public relations personnel who are able to assist them in their communication issues especially related to translation and interpretation, as well as knowledgeable in Arabic dialects and culture to boost effective communication. Good handling of Arabic communication with Arab patients has the potential to expedite the overall processes and procedures at all levels of healthcare management and hinder unprecedented problems throughout the entire process.

The Path of Integrated Development of Digital Technology and Health Care Industry under the Background of Aging Population

The Path of Integrated Development of Digital Technology and Health Care Industry under the Background of Aging Population

Sophorolipid: An Effective Biomolecule for Targeting Microbial Biofilms.

Biofilms are microbial aggregates encased in a matrix that is attached to biological or nonbiological surfaces and constitute serious problems in food, medical, and marine industries and can have major negative effects on both health and the economy. Biofilm's complex microbial community provides a resistant environment that is difficult to eradicate and is extremely resilient to antibiotics and sanitizers. There are various conventional techniques for combating biofilms, including, chemical removal, physical or mechanical removal, use of antibiotics and disinfectants to destroy biofilm producing organisms. In contrast to free living planktonic cells, biofilms are very resistant to these methods. Hence, new strategies that differ from traditional approaches are urgently required. Microbial world offers a wide range of effective "green" compounds such as biosurfactants. They outperform synthetic surfactants in terms of biodegradability, superior stabilization, and reduced toxicity concerns. They also have better antiadhesive and anti-biofilm capabilities which can be used to treat biofilm-related problems. Sophorolipids (SLs) are a major type of biosurfactants that have gained immense interest in the healthcare industries because of their antiadhesive and anti-biofilm properties. Sophorolipids may therefore prove to be attractive substances that can be used in biomedical applications as adjuvant to other antibiotics against some infections through growth inhibition and/or biofilm disruption.

A recent review of ransomware attacks on healthcare industries

A recent review of ransomware attacks on healthcare industries

Comparisons of some new and existing EWMA schemes for binomial count processes with applications to monitoring hospital No-Shows

ABSTRACT Applications of several exponentially weighted moving average (EWMA) schemes for monitoring binomial count data have been widely used in the manufacturing and healthcare industries. However, practitioners may be confused about selecting the appropriate scheme for their needs. In this paper, we compare the performance of some existing EWMA schemes and a new EWMA scheme utilizing the weighted likelihood ratio test (WLRT), referred to as the EWLRT scheme. Firstly, some properties of the EWLRT scheme for binomial proportion are outlined. Next, a method for choosing the optimal smoothing parameter based on the relative mean index (RMI) is described, and various competing schemes are compared at the optimal level, which was not done in most previous studies. Additionally, current comparisons consider the zero-state (ZS) run-length performance and the conditional expected delay (CED) for a delayed shift. The results based on the Monte Carlo method indicate that the EWLRT scheme’s efficacy is somewhat more encouraging than that of its competitors in detecting small to moderate changes in terms of CED. Finally, a real example of monitoring hospital No-Shows, a necessary healthcare management process, describes the implementation.

Fostering sustainability in the healthcare organization through green HRM practices: the mediation analysis of perceived organizational support

PurposeThis study aims to investigate the influence of green human resource management (GHRM) practices on healthcare organization sustainability performance in Pakistan. It explores how perceived organizational support (POS) mediates the relationship between GHRM practices and healthcare organizational sustainability.Design/methodology/approachIn the quantitative method, a questionnaire was used to acquire the perception of individuals via a simple random method. A total of 320 questionnaires were collected from the employees in the healthcare organizations of Pakistan, with a 47.70% response rate. Hypotheses were tested using SmartPLS (PLS-SEM).FindingsThe results reveal a positive relationship between GHRM practices and healthcare organization sustainability performance. POS partially mediated the relationship, strengthening the effectiveness of GHRM practices in boosting organizational sustainability.Originality/valueThe present study contributes to the understanding of GHRM practices in the healthcare industry, particularly in the context of emerging nations like Pakistan. It demonstrates a novel mediation role of POS to bolster the effectiveness of GHRM practices for gaining sustainability performance. The research proposes significant insight for both scholars and practitioners on how supportive corporate cultures affect the GHRM initiatives that foster economic, environmental and social sustainability.

Impact of plasmonic silver on the perovskite Bi9Ti6FeO27: Removal of tetracycline and antimicrobial activity

The synthesis, characterization, photocatalytic, and antibacterial analysis of plasmonic Ag- loaded Bi9Ti6FeO27 at various concentrations of Ag are presented in this study. The synthesis of Ag/Bi9Ti6FeO27 is based on the impregnation of different weight percentages of Ag (20, 50, and 80) on Bi9Ti6FeO27. The XRD and HRTEM result shows that the perovskite Bi9Ti6FeO27 maintained orthorhombic crystallinity having space group Pnm (No.62). Upon addition of Ag, do not disturb the crystallinity of Bi9Ti6FeO27. XPS analysis displays the valence states and surface chemical composition of the as-prepared Ag/Bi9Ti6FeO27. All the composites exhibit good UV–visible absorbance in the range of 200–800 nm, and the band-gap energy gradually decreases from 3.30 eV to 2.73 eV with an increase in silver content. The photocatalytic degradation of 40 ppm tetracycline is examined at 120 min at neutral pH (7). Loading of Ag on Bi9Ti6FeO27 significantly promoted the photocatalytic degradation efficiency of tetracycline (84 %). Furthermore, the antibacterial activity of Ag/Bi9Ti6FeO27 was investigated against “Escherichia coli”, Klebsiella pneumoniae”, and “Acinetobacter baumannii” bacteria. Higher loading of Ag on Bi9Ti6FeO27 exhibited the most potent antibacterial activity by effectively inhibiting the growth of both bacterial strains through reactive oxidative species and the presence of silver nanoparticles. The composite has proved its stability through the five repeated cycles. This work provides an emerging strategy to finely design the nanocomposites and highlight their potential for environmental remediation and healthcare industries.

Leveraging AI Algorithms to Combat Financial Fraud in the United States Healthcare Sector

Financial fraud is a major problem in the healthcare industry because it causes large financial losses and compromises the integrity and trust of healthcare systems. The intricacy and sophistication of contemporary fraudulent operations make conventional fraud detection techniques which rely on manual audits and rule-based systems increasingly inadequate. AI algorithms have become a viable way to improve financial fraud detection and prevention. Hence, this paper examines how AI algorithms can be used to detect and stop fraud in the healthcare industry, emphasizing how these algorithms could revolutionize fraud control procedures. This study suggests that AI algorithms greatly improve the identification of financial fraud in the healthcare industry by spotting intricate patterns and abnormalities frequently overlooked by already existing techniques. Machine learning models have proven to be highly accurate in predicting fraudulent claims and transactions. However, while AI provides numerous opportunities to improve fraud detection skills, its effective application necessitates resolving important issues, including ethical considerations, data governance, and model interpretability.

A-206 Exploring the Surge in Clinical Laboratory Inflation: A Comprehensive Review

Abstract Background The healthcare industry has been experiencing significant inflationary trends, particularly in clinical laboratory services. This surge in inflation is attributed to various factors, including the increased demand for laboratory services, changes in the practice of clinical laboratory science due to technological advances, and vacancy rates that exceed the number of Medical Laboratory Science (MLS) graduates. Additionally, supply chain shortages, increased operational costs, and staffing budget increases, further exacerbating the inflationary pressures on clinical laboratory services. Methods The review of the factors behind the surge in clinical laboratory inflation involved a comprehensive review of industry data and insights from supply chain leaders, and recent surveys conducted by the author sent to 50 laboratory leaders with a response rate of 50%, Data from the Bureau of Labor Statistics (BLS) and industry interviews were integrated to provide an understanding of the inflationary trends affecting the healthcare industry, with a specific focus on clinical laboratory services. Results The review revealed that domestically produced healthcare inputs, labor, diagnostics, and lab equipment have seen dramatic price increases. For instance, prices manufacturers charge for diagnostic reagents for testing have increased by 9.2%, while lab equipment prices have risen by 7.1%. Some product lines have experienced increases of 15-20%. Moreover, the post pandemic has led to attrition rates climbing from 10%-37% to the current rate of 35%-61%, resulting in 30%-50% staffing budget increases. 36% of lab leaders feel Group Purchasing Organizations (GPO’s) are incentivized to allow increases, it’s estimated that as much as 80% of GPO revenue is generated by fees paid by vendors. 28% feel the vendors are passing along but not sharing in the inflationary pressures as reimbursements decline. 20% feel it’s an excuse for vendors to pad margins. 16% feel that it’s due to lack resources or lab specific procurement expertise in supply chain departments. Conclusions These factors have contributed to the significant surge in clinical laboratory inflation. The surge in is a complex phenomenon influenced by multiple interconnected factors. Addressing this inflationary trend will require a collaborative approach that considers the impact of supply chain shortages, increased operational costs, and staffing. Proactive measures to mitigate the adverse effects of inflation on clinical laboratory services, such as ensuring reliable data in negotiations, addressing the workforce shortage and strong negotiations with vendors will be essential to sustain the cost, quality, and accessibility of these critical healthcare services.

A-204 Commitment to Sustainability From Laboratory Medicine

Abstract Background According to McGill University, sustainability means “meeting our needs without compromising the ability of future generations to meet their own needs”. Sustainable environmental practices improve water and air quality, reduce landfills, and increase renewable energy sources in the long term. In response to the growing urgency of global sustainability challenges, the healthcare industry is taking a step forward to reduce its environmental impact. University of Malaya Medical Centre (UMMC) is a government-funded teaching hospital and medical institution, which processes about 3.95 million chemistry and 371,000 immunoassay tests per year. This study aims to evaluate the sustainability aspects of Atellica Solution, focusing on water usage, power consumption, carbon dioxide emissions and plastic waste generations. Methods Power and water consumption for the analysers Advia Chemistry 2400 & XPT and Advia Centaur XPT were obtained from Atellica Solution using the year 2022 test volumes and compared with those of Siemens legacy system for the year 2019 test volumes. In addition, the reduction of plastic usage was also assessed for the year 2019 and 2022. Results With innovative Atellica Solution replacing Siemens legacy system, the data showed reduction in water and energy consumption by 36.44% and 47.74% respectively, whereas the annual throughput increased by 13.7%. By using Atellica INTELIQ QC management, fewer aliquot tubes were used and thus plastic waste was reduced by 168 kg. These reductions combined to lower CO2 emissions by about 16 tons. Conclusions Atellica Solution is designed for high throughput and sustainability, reducing water and energy consumption while also significantly reducing CO2 emissions. Furthermore, it enables faster, streamlined quality control management with Atellica INTELIQ QC management system. UMMC is strongly committed to minimizing laboratories’ environmental footprint and provide better access to care eventually contributing to the well-being of patients.

The effects of an abundance of natural resources on the healthcare industry: The global evidence

AbstractThe current study investigates the effects of an abundance of natural resources on the healthcare industry worldwide. Various natural resources proxies such as rents for coal, mineral, natural gas, and oil are used and analyzed by using a quarterly datasets for the period 2000:Q1–2020:Q4. Results from regional and income group datasets show that various forms of natural resources abundance around the globe exert statistically significant, but a mix of positive and adverse effects on the healthcare sector. Results also reveal that changes in and shocks given to natural resources precede significant changes in the healthcare sector in general. This study advises that countries need to achieve careful resource management, open governance, and economic diversification top priority in order to lessen reliance on a single resource and mitigate any negative effects. Further implications are provided at the end of this study.

Performance Evaluation of Refractive Index Biosensor in THz Regime for Clinical Applications: A Simulation Approach

In this manuscript, a novel innovative HC-PCF sensor model in THz regime is introduced integrated with an optimization approach. The suggested sensor architecture provides crucial advantages precise identification of healthy and ill tissues in healthcare industry. The HC-PCF, meticulously constructed with specific dimensions, significantly increases the sensor sensitivity and specificity to 99.37% and 99.75% respectively. Healthcare industries are at the core of investigations and are undoubtedly crucial to modernize the prognosis procedures. It is a discipline that is continually expanding and searching for new approaches to raise the standard for efficacy, sensitivity, and accuracy. Recently, THz PCF has emerged with incredible potential in all the areas of biomedical applications. The importance in using THz sensors in this research is to detect the ill tissues, an important component in the categorizing diabetes. The integrated sensor architecture provides higher level of sensitivity, with a confinement loss of 0.05 in 0.23 s processing time for a RI range of 1.28–1.39. This research highlights the capability of combining PCF with optimization to improvise the healthcare industry, offering an economical and efficient diagnostic solution across the fields.

Embracing the Changing Season: Quantifying Pharmacy's Role in Improving Medication Quality and Safety.

As a profession, pharmacy is experiencing its own changing season. The health care industry's capacity for addressing pharmacy's role in medication quality and safety is becoming more favorable. State pharmacy associations continue to expand pharmacists' scope of practice, and ASCP and other national pharmacy associations are united in their push for the passage of the Equitable Community Access to Pharmacist Services (ECAPS) Act (H.R. 1770/S. 2477) legislation. These forward-looking policy priorities are creating a climate that is ripe for change in pharmacy. Further, the need for team-based approaches to address the soaring costs and increasing complexity of medication therapies could not be more apparent. Undoubtedly, pharmacists and pharmacy team members are essential in improving medication quality and safety, especially in the older population, but accurately and consistently quantifying the impact of pharmacists remains complex and challenging.

IM4.0EF: Tele-Medical Realization via Integrating Vague T2NSs with OWCM-RAM Toward Intelligent Medical 4.0 Evaluator Framework

In the modern world, when nearly everyone considers the Internet to be indispensable, there is a constant desire to discover new applications for the technologies that are already in place. As well, recent research has found several synonyms for the adoption of information and communication technology. Industry 4.0, Industry 5.0, and modern technologies are some of these synonyms. Harnessing these technologies in important domains such as medical services and healthcare is vital. Wherein, the medical field has undergone changes, going from version 1.0 to version 4.0 currently. Medical 4.0 represents a significant advancement in medical practices and systems, leveraging cutting-edge digital technology, data analytics, automation, and artificial intelligence (AI) within the healthcare sector. Therefore, it is essential to give priority to important enabling aspects (EAs) that influence these cutting-edge technologies adoption into the healthcare industry in a methodical and successful manner. Inspired by these facts, the prioritizing of the determined EAs is robust motivator in this study. Hence, we constructed intelligent Medical 4.0 evaluator framework (IM4.0EF) which responsible for prioritizing EAs based in a set of benchmarks (Bs) through leveraging Opinion Weight Criteria Method (OWCM) as a novel multi-criteria decision-making (MCDM) technique to obtain benchmarks’ weights. Wherein these weights are utilized in Root Assessment Method (RAM) as a novel MCDM to rank EAs and recommending the most influence and optimal EA as well as the worst EA.

Identification of Potent CHK2 Inhibitors‐Modulators for Therapeutic Application in Cancer: A Machine Learning Integrated Fragment‐Based Drug Design Approach

AbstractThe CHK2 protein regulates the cell division cycle and responds to DNA damage. Additionally, it facilitates the repair of DNA damage and maintains the integrity of its biological processes. Dysregulation of the CHK2 protein is associated with a predisposition to harmful diseases. The current research protocol was designed to identify novel hit molecules as CHK2 inhibitors and disrupt the normal biological function of the CHK2 protein via a fragment‐based drug discovery approach. The protocol involved generating fragments using the MacFrag tool, followed by a chemical similarity search utilizing RDKit to identify fragment molecules analogous to previously established CHK2 inhibitor scaffolds. The bioactive molecules were constructed using the Fragmenstein tool, followed by molecular docking simulations to investigate their binding affinity. In addition, pharmacokinetic properties were analyzed, and a molecular dynamics simulation study was conducted to assess the stability of selected compounds with CHK2 protein. Finally, five novel compounds were identified as excellent CHK2 inhibitors through the FBDD and show good binding interactions at active sites of CHK2 with beneficial ADMET properties. This research work presents novel CHK2 inhibitor molecules that have the potential to be utilized in drug discovery, serving as key leads for future advancements in healthcare industries and sectors.

Hybrid modes in multilayer/antenna architecture set side-band-selective molecular Raman scattering

Abstract Technologies with tag-less capacity for analyzing molecules, are poised to make significant advancements in the healthcare industry with a substantial potential to improve sensitivity for individual molecules and binding events. Plasmonics has the potential for enhanced system response when emitters are included in the full architecture. Here, a hybrid multilayer including a sequence of metals and dielectrics has been examined. The surface of the multilayer is covered with 30-μm height features working as plasmonic antennae. Their contribution to the emission of the system has been analyzed. The multilayer has been coupled with a prism to excite the polaritons in an experimental optical setup to measure the reflected and transmitted signals. The measurements demonstrate the surface plasmon polariton/antenna mode hybridization. The optomechanics of the plasmonic resonant multilayer has been studied with reference to the refractive index of the surrounding medium as well as to the incident angle of the exciting beam. Then, the experimental shifts have been measured according to the optomechanical spectrum of the naïve plasmon resonant multilayer. The optomechanics of the plasmonic resonant multilayer, which has shown a natural resonance at ω = 96 MHz , has been coupled with a molecular emitter, e.g. a dye. As the concentration of the red dye increases, the intensity of the peaks at ω > 96 MHz raises, suggesting an important sensitivity in the anti-Stokes domain. On the other side, the decay is equal to κ = 1.1 × 10 − 2 MHz showcasing a high quality factor ( Q = 9.6 × 10 3 ). For validation of increased sensitivity in the anti-Stokes region, bovine serum albumin (BSA), a protein active in the Raman region, has been tested and, through an image-based analysis, the molecular pattern recorded. The demonstrated potential of utilizing optical resonance shifts to investigate molecular patterns is highly promising. Therefore, the proposed sensing method represents a significant advancement in the field, offering new opportunities for the sensitive detection of biomolecules.