Healthcare Industry Research Articles

Advancements in healthcare through 3D- printed micro and nanosensors: Innovation, application, and prospects

Integrating 3D printing technology with micro and nanoscale sensors has emerged as a transformative approach in healthcare, offering unparalleled opportunities for innovation and application. This review paper comprehensively explores the recent advancements in this interdisciplinary field, focusing on the synthesis, fabrication techniques, applications, and prospects of 3D- Printed micro and nanosensors in healthcare. The ideal manufacturing of sensors with intricate geometries and unique capabilities is made possible by combining 3D printing technology with micro and nanoscale sensors, completely changing the healthcare industry. This transformational strategy has resulted in the creation of highly sensitive, selective, and biocompatible sensors that enable real-time monitoring, early illness detection, and tailored drug delivery. These developments improve results, alter patient care, and optimize treatment approaches. Applications include medication administration, point-of-care testing, and diagnostics. Innovations such as portable lab-on-a-chip devices are improving these areas. Future work will concentrate on improving sensor flexibility and incorporating AI to forecast and treat new medical diseases.

Transfusion-Related Acute Lung Injury

Transfusion-Related Acute Lung Injury is one of the most vital forthcoming risks that need to be addressed, assessed, diagnosed and treated. Blood transfusion save lives, but the side effects of transfusion can be really adverse and concerning. Blood transfusion helps the patient recover from severe blood loss that might lead to severe complications like cardiogenic or hypovolemic shock. Over the years, the health care industry is working on reducing the possible risk and complications of blood transfusion. Worldwide trainings are provided on safe blood transfusion practices. Blood banks are verified with legal authority and inspections are done every now and then. Transfusion-Related Acute Lung Injury is the upcoming concerning issue that needs to be familiar with all health care workers especially nurses since they are the one who keep a close monitoring of patient during the blood transfusion process. Furthermore intervention can be planned accordingly to help manage Transfusion-Related Acute Lung Injury better and treat the symptoms as soon as possible.

Cyber-Threat Landscape in Healthcare Industry and Legal Framework Governing Personal Health Information in India

Cyber-Threat Landscape in Healthcare Industry and Legal Framework Governing Personal Health Information in India

MetaBlock: A Revolutionary System for Healthcare Industry Fusing Metaverse and Blockchain

The Metaverse is like another world functioning parallel to the actual physical world. It merges physical reality with digital virtuality by the convergence of different technologies that enable interactions with virtual objects and environment. The complete immersive experience with metaverse generates huge data which comes with its own set of challenges, a major one being the security and storage of user centric data. Considerably, blockchain offers a significant solution due to its exceptional features of decentralization, immutability, and transparency. For a detailed insight into the role of blockchain in the metaverse, the paper explores its usage in the healthcare industry. The paper then proposes a healthcare system named MetaBlock that is a fusion of both the metaverse and blockchain techniques. The paper discusses the components of the system from a detailed technical interpretation, such as data acquisition, data storage, virtual object and space simulation, data sharing and data privacy protection. Finally, an important research direction in terms of distributed consensus mechanism is discussed.

Web Scrapping Tools used in Healthcare Sector

Web scraping has become essential in the healthcare sector and provides vast opportunities for data access and analysis. This abstract outlines several key web scraping tools commonly used in healthcare:: Scrapy: A Python-based framework for structured data extraction from various medical sources, offering adaptability and scalability. Beautiful Soup: Facilitates seamless data extraction from medical websites and research articles with its user-friendly interface. Selenium: Particularly useful for extracting data from dynamic healthcare websites driven by JavaScript. Octoparse: Empowers healthcare professionals to extract data without programming expertise through an intuitive interface. ParseHub: Simplifies data extraction from complex healthcare websites with scheduling capabilities. Import.io: A visual API-based scraping solution for integrating and analyzing data from web pages and APIs. WebHarvy: Streamlines data retrieval from medical websites, journals, and forums with automatic configuration and diverse export options. Web scraping tools are invaluable for accessing and analyzing health information, enhancing assessment, decision-making, and patient outcomes. However, adherence to legal and ethical guidelines, including data privacy and consent considerations, is crucial for responsible use in the healthcare industry.

The Impact of Job Crafting Dimensions on Work Engagement Among Nurses: The mediating role of psychological capital

Work significantly influences individual well-being and human growth. Jobs are not solely formulated by companies based on their needs but are also actively modified by the individuals who hold those positions. High-stress professions, such as nursing, highlight how stress levels are influenced by factors like resource availability, patient demands, colleague interactions, and workplace cultural dynamics. Hence, this study investigates the effect of job crafting dimensions on work engagement, and examines psychological capital's mediating role in this relationship. It aims to reveal how job crafting can improve both personal and organizational well-being. This study is a descriptive cross-sectional study. The study was conducted with 348 nurses at Al Dhafra Hospitals in Abu Dhabi, UAE. The analysis utilized in this study was conducted using Structural Equation Modeling (SEM) through SmartPLS 4 and SPSS version 27 for descriptive and correlation analysis. The findings demonstrate that the impacts of job crafting dimensions—task crafting, cognitive crafting, and relational crafting—on work engagement and psychological capital are statistically significant. Additionally, the influence of psychological capital on work engagement is statistically significant. Path analysis revealed that psychological capital mediates the interaction between job crafting dimensions and work engagement. These relationships were shown to be statistically significant. This study reinforces the significance of psychological capital and job crafting dimensions as crucial attributes in the workplace. Hence, it advocates for the healthcare industry to consider the implementation of targeted programs aimed at fostering these attributes within nursing teams. Such strategic initiatives are poised to improve individual well-being among nurses, which is expected to result in enhanced organizational efficiency and a notable decrease in turnover rates over time.

Examining the impact of personalization and carefulness in AI-generated health advice: Trust, adoption, and insights in online healthcare consultations experiments

Artificial intelligence (AI) technologies, exemplified by health chatbots, are transforming the healthcare industry. Their widespread application has the potential to enhance decision-making efficiency, improve the quality of healthcare services, and reduce medical costs. While there is ongoing discussion about the opportunities and challenges brought by AI, more needs to be known about the public's attitude towards its use in the healthcare domain. Understanding public attitudes can help policymakers better grasp their needs and involve them in making decisions that benefit both technological development and social welfare. Therefore, this study presents evidence from two between-subjects experiments. This study aims to compare the public's adoption and trust levels in health advice provided by human vs. AI doctors and explore the potential effects of personalization and carefulness on the public's attitudes. Experimental designs adopt a trust-centered, cognitively and emotionally balanced perspective to study the public's intention to adopt AI. In Experiment 1, the experimental conditions involve the types of decision-makers providing online consultation advice, either AI or human doctors. In Experiment 2, the experimental conditions involve varying levels of perceived personalization and carefulness (high vs. low). A total of 734 participants took part in the study. They were randomly assigned to one of the intervention conditions and responded to manipulation checks after reading the materials. Using a seven-point Likert-type scale, participants rated their cognitive and emotional trust levels and intention to adopt the advice. Partial Least Squares Structural Equation Modeling (PLS-SEM) is conducted to estimate the proposed theoretical perspective. Qualitative interviews on both real-world and AI-generated treatment recommendations further enriched the understanding of public perceptions.The results show that AI-generated advice is generally slightly less trusted and adopted by the public. However, a noticeable inclination towards AI-generated advice emerges when AI demonstrates proficiency in understanding individuals' health conditions and providing empathetic consultations. Further analyses confirm the mediating influence of emotional trust between cognitive trust and adoption intention. These findings provide deeper insights into the process of adoption and trust formation. Moreover, they offer guidance to digital healthcare providers, empowering them with the knowledge to co-design AI implementation strategies that cater to the public's expectations.

Design of an Efficient QOS Aware Trust-Based Security Model with Bioinspired Sidechains for Healthcare Deployments

Increased adoption of blockchain technology in healthcare systems has paved the way for transparent and secure data management. However, the inherent difficulties of scalability, performance, and security in blockchain networks necessitate the development of efficient models to ensure Quality of Service (QoS) and reliability. In this paper, we propose a novel QoS-aware trust-based security model for healthcare blockchain deployments that addresses these challenges by taking temporal energy consumption, temporal delay, temporal throughput, and temporal Packet Delivery Ratio (PDR) levels into account when selecting miner nodes. For efficient miner node selection, our model employs trust-based analysis. While for the formation of sidechains an efficient Grey Wolf Optimizer (GWO) Model is used, which is a metaheuristic technique inspired by hunting behaviour of Wolves in real-time scenarios. Effectively balancing the trade-off between exploration and exploitation, the GWO algorithm enables the selection of optimal miner nodes that meet the desired QoS requirements. By incorporating temporal metrics, our model adapts dynamically to changing network conditions, ensuring optimal resource utilization and enhanced network performance levels. To assess the efficacy of our proposed model, we ran extensive simulations and compared its performance to that of existing sidechaining models. The outcomes demonstrate significant enhancements in multiple aspects. In comparison to state-of-the-art sidechaining models, our model achieves an impressive 8.5% reduction in delay, 3.9% reduction in energy consumption, 4.5% increase in throughput, and 2.5% improvement in PDR levels. These enhancements demonstrate the efficacy and efficiency of our healthcare blockchain deployment models. The proposed model has applications in a variety of real-time healthcare scenarios. It can be used in electronic health record (EHR) systems where data integrity, confidentiality, and accessibility are crucial. By ensuring QoS-aware miner node selection, our model contributes to dependable and efficient data management, allowing for streamlined access to patient records while maintaining the necessary security standards. Moreover, the trust-based approach of our model improves the overall security of healthcare blockchain deployments. Our model reduces the risks associated with malicious or compromised miner nodes by incorporating trustworthiness metrics such as reputation and behavior analysis. This is especially important in the healthcare industry, where the sensitive nature of patient data necessitates stringent security measures.

Urbanization factors and the vagaries of the rural health care industry in Nigeria: an analysis of the accessibility of healthcare services by older adults in the Nsukka Local Government Area

This study sought to determine the effects of urbanization on access to healthcare services by older adults in Nsukka Local Government Area. The Nigerian healthcare industry is organized under the three tiers of government: Federal, State and Local governments. As such, most of the healthcare facilities are located in urban areas while majority of older adults reside in different local governments in rural communities where there are a few of the facilities. This has led to poor utilization of medical services by older adults in rural areas. This study adopted a mixed method approach. While questionnaire was used to collect quantitative data, interviews were used to collect qualitative data for the study. Questionnaire survey was used to collect data from 1180 older adults between the ages of 60 years and above from six communities in the study area. Descriptive statistics and Chi square were used to analyze the data while frequency distribution tables and percentages were used to present the results. The research showed that there are inadequate health facilities for the older adults in rural communities in Nsukka LGA. It also found that the older adults suffered challenges arising from lack of medical doctors, nurses, equipments, and drugs for effective healthcare delivery. It is recommended that more efforts should be geared towards providing adequate health facilities and health workers by government at public hospitals in rural communities to increase accessibility and adequate utilization by the older adults.

Adversarial robust image processing in medical digital twin

Recent advancements in state-of-the-art technologies, including Artificial Intelligence (AI), Internet of Things (IoT), and cloud computing, have led to the emergence of an innovative technology known as digital twins (DTs). A digital twin is a virtual replica of the physical entity, with data connections in between. This technology has proven highly effective in several industries by improving decision-making and operational efficiency. In critical areas like healthcare, digital twins are increasingly being used to address the limitations of conventional approaches by creating virtual simulations of hospitals, medical equipment, patients, or even individual organs. These medical digital twins (MDT) revolutionize the healthcare industry by offering advanced solutions to enhance treatment outcomes and overall patient care. However, these systems are challenging because of the security and critical issues involved. Therefore, despite their achievements, the numerous security threats make it crucial to address the security challenges of digital twin technology. Given the lack of research on attacks targeting MDT functionalities, we concentrated on a specific cyber threat called adversarial attacks. Adversarial attacks exploit the model’s performance by introducing small, carefully crafted perturbations to manipulate the input data. To assess the vulnerability of medical digital twins to such attacks, we carried out a proof-of-concept study. Using image processing techniques and an artificial neural network model, we created a digital twin to diagnose breast cancer through thermography images. Then, we employed this digital twin to initiate an adversarial attack. For this purpose, we inserted adversarial perturbation as input to the trained model. Our results demonstrated the vulnerability of the digital twin model to adversarial attacks. To tackle this problem, we implemented an innovative modification to the digital twin’s architecture to enhance its robustness against various attacks. We proposed a novel defense method that fuses wavelet denoising and adversarial training, substantially strengthening the model’s resilience to adversarial attacks. Furthermore, the proposed digital twin is evaluated using a dataset of diabetic foot ulcers. To the best of our knowledge, it is the first defense method that makes the medical digital twin significantly robust against adversarial attacks.

Enhancing healthcare data integrity: fraud detection using unsupervised learning techniques

Data in healthcare forms the backbone of any treatment and decision-making for patients. However, the data from a healthcare institution can sometimes be prone to abnormalities, thereby putting treatment and patient safety in jeopardy. This paper points to the dire need for reliable anomaly detection systems in the healthcare industry. It employs various unsupervised learning methods, including Isolation Forest, Local Outlier Factor (LOF), K-Nearest Neighbours (KNN), and autoencoder models for detecting abnormalities in healthcare data with better accuracy. Anomaly detection capabilities also allow health providers to reduce risks and provide some assurance of the integrity of the data, as these capabilities are more likely to indicate unusual patient profiles or incorrect test results. Isolation Forest, LOF, and KNN are the preliminary methods for performing anomaly detection in this work, with Isolation Forest yielding the best results. Then, autoencoder models that learn subtle variations and complex patterns in data are employed. This paper seeks to enhance anomaly detection in terms of accuracy and reliability to ensure better healthcare data quality and patient safety.

Does total quality management mediate the relationship between Industry 4.0 and business sustainability in private hospitals?

PurposeThe paper aims to study the influence of Industry 4.0 technologies on the business sustainability of private hospitals by focusing on the mediating role of total quality management practices in private hospitals in the West Bank.Design/methodology/approachData from the top and middle managers of private hospitals in the West Bank regions of Palestine were collected through a questionnaire assessed for validity and reliability. Furthermore, we used the structural equation modelling technique of partial least squares for the data analysis.FindingsThe findings confirm that Industry 4.0 technologies do not affect business sustainability. The findings also emphasise the association between Industry 4.0 technologies and total quality management philosophy, and total quality management completely mediates the relationship between Industry 4.0 and business sustainability.Practical implicationsThis study provides practical implications for achieving the hospital sector's sustainability by merging Industry 4.0 technologies with total quality management practices, which provides valuable insights into the hospital's policies and practices and individuals vital to data exchange and policy enforcement within the sustainability of organisations.Originality/valueThis study is one of the first to investigate the combined effects of Industry 4.0 technologies, total quality management and business sustainability in the healthcare industry following the COVID-19 outbreak. This research is one of few empirical works exploring the interface between Industry 4.0 technologies and total quality management in developing countries, specifically Palestine.

Medical Image Segmentation : A Comparative Study and Survey

Image segmentation involves dividing an image into distinct segments. It is important to distinguish this from image enhancement, which focuses on improving the image’s visual attributes, such as brightness, contrast, and texture. In segmentation, specific parts of the image are emphasized based on the problem being addressed. Clinical imaging techniques are continuously evolving, aiming to improve the quality of services in the healthcare industry. Methods such as interpolation, image registration, compression, and diagnosis need to be updated to meet the increasing demands of the field, especially with advancements in mobile and cloud computing technologies. The integration of medical devices with wearable technologies presents a promising area for further exploration. This paper provides valuable insights into the domain of medical imaging systems and aims to outline the future potential of work in this field.

Role of online health communities in patient compliance: a social support perspective

PurposeOnline health communities (OHC) can transform the healthcare industry, particularly in developing economies. Technology advancement and increased health literacy pave the way for these communities to become powerful tools for empowering patients. The purpose of this study was to empirically validate the linkages between social support and how it overarchingly influences patient compliance. Following social support theory, this study delineates how support from the community affects the patient–physician relationship (PERP) and consequently patient compliance regarding the treatment plan. This study also invents the role of patient trust in an OHC in moderating the relationship between PERP and engagement.Design/methodology/approachThis paper is based on social support and empowerment theories to investigate the importance of social support in improving patients’ health behaviours and health outcomes via patient empowerment, patient engagement and patient compliance. The authors surveyed users from three Facebook cancer communities in India to collect data. The authors used partial least squares structured equation modelling and necessary condition analysis (NCA) with 265 participants to support the proposed model.FindingsThe result demonstrates that PERP is a crucial factor for patient engagement in OHC, and patient engagement has a significant effect on patient compliance. The results also showed that trust was a significant moderator between PERP and engagement. The NCA analysis shows all the relationships are significant; however, emotional support is not a necessary condition for PERP.Research limitations/implicationsBy empowering cancer patients and enabling them to meet their emotional and informational needs through OHCs, the study model can aid in the development of solutions that will improve compliance with their treatment in an emerging economic context. The findings indicate the potential chain reaction of social support and PERP in online cancer health communities. This study also contributes to quantifying the social impacts of online healthcare services and how to enhance the healthcare compliance framework.Originality/valueThis study combines social support and empowerment theory with patient, physician, and technology to provide a fine-grained picture of PERP in OHC. It explains how social support in OHC promotes self-care behaviour. This linkage validation enables readers and the community at large to gain a more nuanced understanding of how social support – through PERP, engagement and trust – enables patient compliance using primary data.

Does Hospital–Physician Integration Improve Hospital Performance? Results from a USA Longitudinal Study

In a dynamic healthcare industry, aligning the goals and objectives of hospitals and physicians through integration has been suggested to influence performance. Physicians’ leadership and active involvement in governance can direct resource usage, Electronic Health Record (EHR) implementation, price negotiation, better coordination, and continuity of services for patients, thus affecting performance. This study aimed to examine the relationship between physician integration and hospital performance, investigating both financial and quality outcomes. We used a longitudinal study design. Our sample was hospital-level data from 2014 to 2019, which contained 6000 U.S. hospital-year observations. The dependent variables were quality outcomes (readmission rates) and financial outcomes (total and operating margins). The independent variable explored three dimensions of integration: high, low, and overall integration. Findings showed no impact of hospital–physician integration on quality outcomes and financial performance. High-integration hospitals did not show any significant relationships with quality outcomes and financial performance compared to hospitals that did not have high integration. Hospital–physician integration may have little potential to bring clinical integration even though vertical integration is present. A commitment to improving quality as a strategic priority may be vital in impacting quality outcomes, followed by financial performance.

LEGAL REGULATION OF DIGITISING AND OUTSOURCING MEDICAL RECORDS DEPARTMENT IN INDIA

The whole deal with digitizing and outsourcing medical records in India is complicated. You've got a bunch of laws, guidelines, and ethical considerations all playing a role in ensuring data privacy, security, and patient confidentiality are on point. As the healthcare sector embraces digital solutions more and more, moving from old-fashioned paper records to electronic health records (EHRs) has become a big part of managing healthcare. The Information Technology (IT) Act of 2000, along with its updates, lays the groundwork for all the legal stuff surrounding electronic data and how it's kept secure. Then there's the Personal Data Protection Bill of 2019, which hasn't been passed yet but has some pretty comprehensive measures in place to protect personal data, including health info. On top of all that, the Ministry of Health and Family Welfare (MoHFW) has introduced the National Digital Health Mission (NDHM). This aims to create a national digital health system and puts a lot of emphasis on making sure data privacy and security are top-notch. When it comes to outsourcing the management of medical records to outside parties, there's a need to stick to legal standards and international best practices, like the ones laid out by the Health Insurance Portability and Accountability Act (HIPAA) in the United States. Healthcare providers need to make sure that the folks they outsource to are following confidentiality agreements and data protection protocols. It's all about reducing the risks of data breaches and unauthorized access. The legal framework also requires regular audits, safe data storage solutions, and using fancy encryption technology. To wrap it up, digitizing and outsourcing medical records in India is all about blending existing IT laws, upcoming data protection rules, and specific guidelines for the healthcare industry. The goal is to keep patient data safe while making healthcare more efficient. As the world of digital health data keeps changing, ongoing legal reforms and clear regulatory standards will be important to keep everything in check.

Ethical Leadership as a Driver of Supervisor Technical and Social Effectiveness: A Triple Helix for Cultivating Employees' Sense of Purpose

ABSTRACTA sense of purpose is generated when individuals perceive an authentic connection between their work and a broader transcendent life purpose beyond the self. Academics have shown significant positive effects of this driving force in life for employees and organizations, and thus the literature demands studies that analyze its antecedents, i.e., the potential factors that shape an individual's sense of purpose in life. Following an Aristotelian approach to virtue ethics in business, we analyze (1) whether ethical leadership enhances the technical and social effectiveness of supervisors, and (2) whether this moral asset of leaders enhances employee sense of purpose, either directly or by interacting with their technical and social effectiveness‐related dimensions. Using data from 395 employees in the Iraqi insurance and health care industry, structural equation modeling analysis revealed that, as expected, the ethical dimension of supervisors can influence employees' sense of purpose, both directly and by improving their technical and social effectiveness as leaders. Our findings thus encourage managers to practice ethical leadership to become more effective in leadership and in encouraging employees to have a sense of purpose in what they do.

Block Medcare: Advancing Healthcare Through Blockchain Integration

In an era driven by information exchange, transparency and security hold crucial importance, particularly within the healthcare industry, where data integrity and confidentiality are paramount. This paper investigates the integration of blockchain technology in healthcare, focusing on its potential to revolutionize Electronic Health Records (EHR) management and data sharing. By leveraging Ethereum-based blockchain implementations and smart contracts, we propose a novel system that empowers patients to securely store and manage their medical data. Our research addresses critical challenges in implementing blockchain in healthcare, including scalability, user privacy, and regulatory compliance. We propose a solution that combines digital signatures, Role-Based Access Control, and a multi-layered architecture to enhance security and ensure controlled access. The system's key functions, including user registration, data append, and data retrieval, are facilitated through smart contracts, providing a secure and efficient mechanism for managing health information. To validate our approach, we developed a decentralized application (dApp) that demonstrates the practical implementation of our blockchain-based healthcare solution. The dApp incorporates user-friendly interfaces for patients, doctors, and administrators, showcasing the system's potential to streamline healthcare processes while maintaining data security and integrity. Additionally, we conducted a survey to gain insights into the perceived benefits and challenges of blockchain adoption in healthcare. The results indicate strong interest among healthcare professionals and IT experts, while also highlighting concerns about integration costs and technological complexity. Our findings underscore the transformative potential of blockchain technology in healthcare, pointing towards a new era of patient-centric and secure healthcare services. By addressing current limitations and exploring future enhancements, such as integration with IoT devices and AI-driven analytics, this research contributes to the ongoing evolution of secure, efficient, and interoperable healthcare systems.

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.