Efficiency Of Medical Services Research Articles

PATIENT DATA PROTECTION IN THE DIGITAL ERA (CHALLENGES AND LEGAL SOLUTIONS)

Artificial intelligence (AI) has emerged as a promising technology in the field of healthcare, enabling the development of innovative solutions that enhance the quality and efficiency of medical services. The application of AI in the management of patient data has facilitated the acceleration, optimization, and precision of this process, which can subsequently improve the quality of healthcare services. This study aims to analyse the legal challenges that arise with the use of digital technology and artificial intelligence in patient data management, and to identify relevant legal solutions to overcome these problems. The study employs library research techniques, analysing statutory provisions and drawing on both written and unwritten positive law to examine legal issues. The results show that the integration of AI and digital technology in healthcare has yielded significant advantages, including enhanced efficiency, precision in diagnosis, and the harnessing of big data for patient care. However, a number of significant challenges remain, particularly with regard to the safeguarding of personal data. The formulation of an ethical framework for the utilisation of AI, the establishment of rigorous safety standards and the continuation of research in this domain are vital to enhance the advantages and mitigate the risks associated with the deployment of AI. It is imperative that health institutions and AI developers implement robust cybersecurity standards and periodic assessments for potential system vulnerabilities.

Influence of Blood Sampling Service Process Reengineering on Medical Services Supply: Quasi-Experimental Study.

Tertiary hospitals in China are confronted with significant challenges due to limited spatial capacity and workforce constraints, leading to saturated allocation of medical resources and restricted growth in medical service provision. The incorporation of digital health into medical service process reengineering (MSPR) marks a pivotal transformation and restructuring of conventional health service delivery models. Specifically, the application of MSPR to blood sampling services processes reengineering (BSSPR) holds promise for substantially enhancing the efficiency and quality of medical services through streamlining and optimizing these procedures. However, the comprehensive impact of BSSPR has been infrequently quantified in existing research. This study aims to investigate the influence of BSSPR on the efficiency and quality of medical services and to elucidate the key informative technological support points underpinning BSSPR. Data were collected from both the new and old laboratory information systems from August 1, 2019, to December 31, 2021. A combination of statistical description, chi-square test, and t test was used to compare check-in time and waiting time of outpatients before and after the implementation of BSSPR. An interrupted time-series design was used to analyze the impact of BSSPR on medical service efficiency and quality, enabling the control of confounding variables, including changes in medical human resources and both long- and short-term temporal trends. BSSPR had an impact on the efficiency and quality of medical services. Notably, there was a significant increase in the number of patients receiving blood sampling services, with a daily service volume increase of ~150 individuals (P=.04). The average waiting time for patients decreased substantially from 29 (SD 36) to 11 (SD 11) minutes, indicating a marked improvement in patient experience. During the peak period, the number of patients receiving blood sampling services per working hour statistically increased from 9.56 to 16.77 (P<.001). The interrupted time-series model results demonstrated a reduction in patients' waiting time by an average of 26.1 (SD 3.8; 95% CI -33.64 to -18.57) minutes. Although there was an initial decline in the number of outpatients admitted following BSSPR implementation, an upward trend was observed over time (β=1.13, 95% CI 0.91-1.36). BSSPR implementation for outpatients not only reduced waiting time and improved patients' experience but also augmented the hospital's capacity to provide medical services. This study's findings offer valuable insights into the potential advantages of BSSPR and underscore the significance of harnessing digital technologies to optimize medical service processes. This research serves as a foundational basis and provides scientific support for the promotion and application of BSSPR in other health care contexts. By continuing to explore and refine the integration of digital technologies in health care, we can further enhance patient outcomes and elevate the overall quality of medical services.

Application of mathematical models on efficiency evaluation and intervention of medical institutions in China

Background The efficiency of medical services directly impacts the economic burden of healthcare, making it crucial to analyze the input-output efficiency of various types of medical institutions. However, while hospitals had been extensively analyzed for their efficiency, other types of medical institutions had received limited attention in this regard.Methods In this study, we employed data envelopment analysis (DEA) methods based on time series and internal benchmarks to autonomously assess the efficiency of 18 distinct categories of healthcare facilities in China over the past decade. The verification was conducted through the utilization of the critical incident technique (CIT). Additionally, we utilized the Delphi process (AHP) method to evaluate suppliers of medical consumables, implemented a multi-population genetic algorithm for managing these consumethod and analytic hierarchymables efficiently, and applied stakeholder theory to manage medical personnel efficiency. Results Our findings indicated that medical institutions capable of providing clinical services exhibited higher levels of efficiency compared to those unable to do so. Multiple indicators suggested redundancy within these institutions. Notably, comprehensive benefit evaluation revealed that clinical laboratory had performed poorly over the past decade. We selected an inefficient medical institution for intervention in reagent management and the work efficiency of medical staff. After implementing the Delphi method and multi-population genetic algorithm for consumable replenishment, the reagent cost was reduced by 40%, 39% and 31% respectively in each of the three experimental groups, compared to the control group. By applying stakeholder theory and process reengineering methods, we were able to shorten quality control management time for medical staff in the experimental group by 41 min per day, reduce clinical service time by 25 min per day, and extend rest time by 70 min per day, while the quality indicators were all meeting the targets.ConclusionBy employing various mathematical models as described above, we were able to reduce costs associated with medical consumables and enhance medical personnel work efficiency without compromising quality objectives.

Outpatient scheduling problem in smart hospital with two-agent deep reinforcement learning algorithm

As the internet and digital technologies revolutionize the healthcare sector, the development of smart hospitals is pivotal for improving medical service efficiency. This research presents a mathematical model for the outpatient scheduling problem and introduces the Graph-based Two-Agent Deep Reinforcement Learning algorithm (GTARS-DRL) to address the escalating demand for medical services and the scarcity of resources. The GTARS-DRL leverages graph neural networks for feature extraction of patient medical activities and applies the PDQPN algorithm to train agents for optimizing patient sequencing and service desk allocation, effectively minimizing wait times. Our experiments indicate that GTARS-DRL outperforms traditional scheduling rules in solution quality and matches the performance of Genetic Algorithms (GA), with a notable advantage in computational efficiency, boasting an ACPU of 0.762 s and an ARPT of 0.327 s. The algorithm’s efficacy is further supported by its successful application to larger-scale instances, showcasing its potential for real-world use. This study contributes a novel approach to outpatient scheduling in smart hospitals, providing theoretical backing and practical direction for the optimization of medical resource allocation, and is poised to significantly enhance service efficiency and patient satisfaction.

A multi-period capacitated facility location problem with maximum travel time and backup service for locating and sizing EMS stations

Emergency medical services (EMS) is a system that provides emergency medical care for incidents involving serious illness or injury. The location of EMS stations plays an essential role in delivering effective and efficient medical services. Numerous location models have been developed for locating and sizing EMS stations. However, it remains challenging to satisfy all EMS planning criteria within a single location model. In this study, a multi-period capacitated facility location problem with maximum travel time and backup service (EMSLSP) is proposed for locating and sizing EMS stations. The most important criteria for EMS planning are taken into account in EMSLSP: the demand changes due to population mobility, the maximum service capacity of an ambulance, the maximum number of ambulances at each EMS station, the maximum travel time from each EMS station to the locations it serves, the full coverage of dynamic demand, the minimum percent of population covered by EMS service in a specific travel time, and a backup station for each demand location in case of need. A case study in Zhengzhou, a large city in China, demonstrates that effective and efficient locations and sizes of EMS stations can be determined by solving the EMSLSP with various planning parameters. Compared with the existing EMS systems, the average ambulance travel time and the percentage of the population served are significantly improved. Simulations of ambulance scheduling confirm that the relocated and resized EMS stations perform better than those in the existing system. The evaluation-optimization-simulation method outlined in this paper provides a comprehensive and effective approach for EMS station planning.

A Biometric Identification for Multi-Modal Biomedical Signals in Geriatric Care

With the acceleration of global population aging, the elderly have an increasing demand for home care and nursing institutions, and the significance of health prevention and management of the elderly has become increasingly prominent. In this context, we propose a biometric recognition method for multi-modal biomedical signals. This article focuses on three key signals that can be picked up by wearable devices: ECG, PPG, and breath (RESP). The RESP signal is introduced into the existing two-mode signal identification for multi-mode identification. Firstly, the features of the signal in the time–frequency domain are extracted. To represent deep features in a low-dimensional feature space and expedite authentication tasks, PCA and LDA are employed for dimensionality reduction. MCCA is used for feature fusion, and SVM is used for identification. The accuracy and performance of the system were evaluated using both public data sets and self-collected data sets, with an accuracy of more than 99.5%. The experimental data fully show that this method significantly improves the accuracy of identity recognition. In the future, combined with the signal monitoring function of wearable devices, it can quickly identify individual elderly people with abnormal conditions, provide safer and more efficient medical services for the elderly, and relieve the pressure on medical resources.

Exploring the decision-making experience of elderly diabetes patients regarding their health-seeking behaviour: a descriptive qualitative study

BackgroundDiabetes has emerged as a critical global public health issue. The burden of diabetes is escalating in developing countries, including China. For individuals with diabetes, making informed and rational decisions...

Technology And Innovation In HR

In India, the high cost of medical education, particularly for an MBBS degree, poses significant challenges for new doctors, who often find that their qualifications do not justify the Expenses incurred. This financial strain is exacerbated when these highly trained professionals are employed for routine tasks such as insurance treatment and billing verification, roles that are not commercially viable given their level of education and potential. As a cost-effective measure, many insurance companies and third-party administrators (TPAs) opt to employ doctors with BHMS or BMS degrees for these tasks. However, these professionals are often underqualified for the complexities of medical verification, leading to inefficiencies and errors in treatment validation and anomaly detection. The situation also adversely affects programs like the Ex-Servicemen Contributory Health Scheme (ECHS), where the need for accurate and efficient cashless medical services is critical. Delays and inaccuracies in medical verification can complicate the provision of timely healthcare to veterans and their families, underscoring the need for improvement in these processes. This paper proposes the implementation of an Artificial Intelligence (AI) ecosystem designed to function as highly qualified medical agents. This AI system will be capable of independently verifying diagnoses, treatment plans, and medical reports for accuracy while also identifying procedural anomalies. The primary objective is to augment the capabilities of the current workforce, enhancing overall efficiency and effectiveness in medical management. By deploying AI medical agents, the project aims to not only address the gap in qualification and task complexity but also improve the accuracy and reliability of medical administrative processes. This initiative promises to revolutionize the way medical verifications are handled, providing a sustainable, scalable solution to a pressing healthcare management issue in India.

Application of Artificial Intelligence Models in Nursing Research

In recent years, the rapid development of artificial intelligence has enhanced the efficiency of medical services, accuracy of disease prediction, and innovation in the healthcare industry. Among the many advances, machine learning has become a focal point of development in various fields. Although its use in nursing research and clinical care has been limited, technological progress promises broader applications of machine learning in these areas in the future. In this paper, the authors discuss the application of machine learning in nursing research and care. First, the types and classifications of machine learning are introduced. Next, common neural machine learning models, including recurrent neural networks, transformers, and natural language processing, are described and analyzed. Subsequently, the principles and steps of machine learning are explored and compared to traditional statistical methods, highlighting the quality-monitoring strategies used by machine learning models and the potential limitations and challenges of using machine learning. Finally, interdisciplinary collaboration is encouraged to share knowledge between information technology and nursing disciplines, analyze the advantages and disadvantages of various analytical models, continuously review the research process, and reflect on methodological limitations. Following this course, can help maximize the potential of artificial-intelligence-based technologies to drive innovation and progress in nursing research.

Цифровая трансформация здравоохранения: технологические и организационные аспекты

The article shows that the digital transformation of domestic healthcare leads to the emergence of new opportunities for its qualitative development and improvement of the efficiency of services provided, in this regard, such a transformation is one of the key directions of the national Healthcare project implemented in the Russian Federation. It shows how telemedicine, electronic medical records, artificial intelligence, big data analysis, etc. They provide an increase in the efficiency of medical services, allowing the formation of technological and organizational aspects for the effective development of the healthcare system within the framework of the so-called "4p medicine", which largely embodies the paradigm of its development in the digital age. The emergence of 4P medicine represents a transition from a reactive to a proactive model of the healthcare system, focusing on prevention, early detection and personalized treatment in order to improve the effectiveness of the healthcare system and reduce the cost of its operation.

Digital Transformation: A Challenge for the Romanian Health System

This study analyzes the current status of the digitalization of the Romanian Health System (RHS). Data were collected from 135 active public and private health professionals using an online questionnaire with 102 items. The results of the analysis show that, if the qualification level and the experience of managers are high, seniority in management positions is an essential factor in the adoption of digital technologies, the digitalization of health services increases the efficiency and quality of medical and management services, and the success of the implementation of digital technologies is conditioned by the harmonization of a variety of factors because there are differences between the public and private sectors in terms of the economic efficiency determined by the adoption of digital technologies. There are also differences in the implementation of digital technologies between the national and worldwide levels, there are specific technologies that positively influence managerial performance, and the innovation process is conditioned by the management level. Because Romanian health service managers are updated with new technologies, they can ensure the implementation of digital technologies, considering that economic efficiency and managerial performance are directly related to the level of adoption and the type of technologies implemented.

Research Trends and Hot Spots in Telemedicine for the Elderly: A Scientometric Analysis.

As the elderly population rapidly grows, age-related health issues are increasing. Telemedicine helps older adults adapt by providing efficient and accessible health management and medical services. This study employs bibliometric analysis to examine research focus areas, emerging trends, and collaboration networks in telemedicine for older adults over the past three decades. The Web of Science Core Collection served as the primary data source for the publications on telemedicine and the elderly since the database's inception through June 2024. Using CiteSpace.6.2.R4 software, keyword and collaboration network visualizations were generated, including clusters, co-authors, and co-citations. This study analyzed 586 papers from 252 countries or regions, which were published across 246 journals and written by 2750 authors. The analysis revealed three primary research directions encompassing 42 clusters: (1) health literacy and technology adaptation; (2) telemedicine technology and health management; and (3) social interaction and economic impact. Research hotspots include elderly fitness, mobile health, technology acceptance, telemedicine, elderly care, and health literacy. Despite the potential benefit of telemedicine, challenges persist in areas such as technology acceptance, usability, effectiveness, service quality, and privacy concerns. This review provides a comprehensive overview of current research on telemedicine for the elderly and highlights emerging trends in the field.

A certificateless designated verifier sanitizable signature in e-health intelligent mobile communication system

With the widespread use of mobile communication and smart devices in the medical field, mobile healthcare has gained significant attention due to its ability to overcome geographical limitations and provide more efficient and high-quality medical services. In mobile healthcare, various instruments and wearable device data are collected, encrypted, and uploaded to the cloud, accessible to medical professionals, researchers, and insurance companies, among others. However, ensuring the security and privacy of healthcare data in the context of mobile networks has been a highly challenging issue. Certificateless signature schemes allow patients to conceal their respective privacy information for different sharing needs. Nevertheless, existing mobile healthcare data protection solutions suffer from costly certificate management and the inability to restrict signature verifiers. This paper proposes a certificateless designated verifier sanitizable signature for mobile healthcare scenarios, aiming to enhance the security and privacy of mobile healthcare data. This scheme enables the sanitization of sensitive data without the need for certificate management and allows for the specification of signature verifiers. This ensures the confidentiality of medical data, protects patient privacy, and prevents unauthorized access to healthcare data. Through security analysis and experimental comparisons, it is demonstrated that the proposed scheme is both efficient and effectively ensures data security and user privacy. Therefore, it is well-suited for privacy protection in mobile healthcare data.

Research on Big Data Capability and Medical Service Performance

With the rapid development of information technology, Big data technology has been widely applied in various fields.In the field of medical services, Big data capabilities not only help improve the efficiency and quality of medical services, but also promote the innovative development of the medical industry.This article deeply analyzes the connotation of Big data capabilities and their application in improving medical service performance, and combines the practical work of large-scale general hospitals and community medical service institutions to analyze the specific value and application effect of Big data in medical service practice. At the same time, based on medical reform, it summarizes the challenges, coping strategies, and development trends of Big data in medical service performance practice, with the ultimate goal of providing reference and guidance for practical work.

Medical information management system based on multi-source heterogeneous big data

ABSTRACT This study aims to provide a medical information management system using multi-source heterogeneous big data to improve medical service quality and efficiency, with a motivation on its potential in medical insurance DRG payment. The system framework uses Back Propagation Neural Network (BPNN) technology to efficiently process and analyze multi-source medical data. Comparative experiments and parameter adjustments evaluated the system’s performance. Results show that the BPNN model achieved excellent accuracy (92.5%), recall (93%), and F1 value (92.8%) on the test data set, outperforming other models such as PSO(88%), CNN(89%), and RNN(90%). The system’s response speed was also significantly improved, with an average response time of 0.38 seconds, compared to 0.89 seconds for traditional systems. A 72-hour stability test confirmed the system’s reliability and ability to meet user needs. The proposed system demonstrates improved performance and user experience, making it a promising solution for medical information management and DRG payment applications.

The impact of the scale and hierarchical structure of health human resources on the level of medical services-based on China’s four major economic regions

BackgroundEnsuring that the scale and hierarchical structure of health human resources are rational, and that medical services are efficient and fair, is an important task of practical significance. On this basis, examining the impact of health human resources on the level of medical services presents a new and formidable challenge. This study aims to delve into how the scale and hierarchical structure of health human resources in China’s four major economic regions affect the fairness and efficiency of medical services, and to identify optimization strategies.MethodsThis study utilizes provincial panel data from China’s four major economic regions spanning the years 2009 to 2021. Initially, it provides a statistical description of the current state of health human resources and the level of medical services. Subsequently, it employs a fixed-effects model to analyze the impact of the scale and hierarchical structure of health human resources, as well as their interactive effects, on the fairness and efficiency of medical services, and discusses the interactive mechanisms between medical service fairness and medical service efficiency. Furthermore, after conducting a comprehensive evaluation of the level of medical services using the entropy weight method, it explores the regional heterogeneity and temporal dynamics in the influence of the scale and hierarchical structure of health human resources on the level of medical services. Finally, the study examines the scientific validity and rationality of the research findings through various robustness checks, including the substitution of research variables and models.ResultsThe study found that the scale of health human resources has a promoting effect on the equity of medical services (β ≤ 0.643, p ≤ 0.01), but exhibits an inhibitory effect on the efficiency of medical services (β ≥ -0.079, p ≤ 0.1); the hierarchical structure of health human resources shows a positive impact on both the equity and efficiency of medical services (βequity ≤ 0.160, p ≤ 0.01; βefficiency ≤ 0.341, p ≤ 0.05); at the same time, the results indicate that the interactive effect of the scale and hierarchical structure of health human resources promotes equity in medical services (β = 0.067, p ≤ 0.01), but restricts the efficiency of medical services (β ≥ -0.039, p ≤ 0.01); the mechanism by which health human resources affect the level of medical services in China’s western and northeastern regions is more pronounced than in the central and eastern regions; after the implementation of the “Healthy China 2030” Planning Outline, the role of health human resources in the level of medical services has been strengthened; in the robustness tests, the model remains robust after replacing the core explanatory variables, with R2 maintained between 0.869 and 0.972, and the dynamic GMM model test shows a significant second-order lag in the level of medical services (βequity ≤ 0.149, p ≤ 0.01; βefficiency ≤ 0.461, p ≤ 0.01); the channel test results prove that managerial personnel and other technical personnel are key pathways in regulating the impact of medical staff on the level of medical services.ConclusionThis study provides an in-depth analysis of the impact of health human resources on the level of medical services, revealing that both the scale and hierarchical structure of health human resources significantly affect the equity and efficiency of medical services. Furthermore, the influence of health human resources on the level of medical services exhibits regional heterogeneity and temporal characteristics. Robustness tests ensure the scientific validity and robustness of the research conclusions. This provides effective references for optimizing the allocation of health human resources and improving the level of medical services.

Correlation between psychological traits and the use of smart medical services in young and middle-aged adults: An observational study

BACKGROUND Psychological problems affect economic development. However, there is a huge gap between mental health service resources and mental health service needs. Existing mental health service technology and platforms cannot meet all the diverse mental health needs of people. Smart medicine is a new medical system based online that can effectively improve the quality and efficiency of medical services and make mental health services accessible. AIM To explore the level of intelligent medical use among young and middle-aged people and its correlation with psychological factors. METHODS Convenience sampling was used to select 200 young and middle-aged patients with medical experience at the Third People's Hospital of Chengdu between January 2022 and January 2023 as the research subjects. The general condition Questionnaire, Eysenck Personality Questionnaire, Symptom Checklist-90, General Health Questionnaire, and Smart Medical Service Use Intention Questionnaire were used to collect data. Pearson’s correlation was used to analyze the correlation between the participants’ willingness to use smart medical services and their personality characteristics, psychological symptoms, and mental health. RESULTS The results revealed that the mental health of young and middle-aged people was poor, and some had psychological problems such as anxiety, depression, and physical discomfort. Familiarity, acceptance, and usage of smart healthcare in this population are at a medium level, and these levels correlate with psychological characteristics. Acceptance was positively correlated with E, and negatively correlated with P, anxiety, fear, anxiety/insomnia, and social dysfunction. The degree of use was negatively correlated with P, obsessive-compulsive symptoms, depression, anxiety, hostility, paranoia, and somatic symptoms. CONCLUSION The familiarity, acceptance, and usage of smart medical services among the middle-aged and young groups are related to various psychological characteristics.

Development and Prospect of ChatGpt in the Medical Field

In the current era, there exists an uneven distribution of global medical resources, making it a crucial focal point for medical digital transformation. This paper aims to explore the application status, potential challenges, and future development prospects of ChatGPT, a large language model based on OpenAI, in the field of medicine. Given the current scarcity of global medical resources, this study analyzes existing medical use cases and examines the ethical and regulatory environment surrounding ChatGPT under the Transformer model within specialties like orthopedics and ophthalmology. The findings propose that its present advantages lie in its ability to support multiple languages while enhancing efficiency and quality of medical services. Furthermore, measures should be taken to reinforce privacy and security protocols for ChatGPT data usage while ensuring ethical considerations are met. Additionally, it is recommended that future advancements in ChatGPT focus on deepening its medical knowledge base and developing more specialized modules.

Importance of Hospital Performance Indicators in Contracting and Financing Medical Services in Surgical Wards.

In the Romanian healthcare system, public hospitals' contract for the provision of healthcare services with the National Health Insurance Services (NHIS) is the main source of revenue in a hospital's income and expenditure budget. In Romania, for acute diseases, payment is made on a per-case basis for hospitals financed under the diagnostic-related groups (DRG) system, which is calculated according to the indicators achieved. The main objective of the study aimed at the quantitative and comparative analysis of hospital performance indicators used in the calculation of the contracted amount with NHIS, in order to quantify the results and evaluate the effectiveness of the measures taken in the surgical wards of the Emergency Clinical County Hospital of Oradea (ECCHO) compared to the annual average values at the national level, in the period 2012-2022. The indicators, such as the number of beds, number of cases, average length of hospitalization (LOH), and case complexity index (CCI), were analyzed at the level of the ECCHO, a tertiary care hospital in the surgical wards. Rehabilitation and modernization of wards, laboratories, operating theatres, and high-performance equipment have been made possible through efficient management and monthly monitoring of medical and financial activity. The average LOH actually carried out on the surgical wards has been decreasing over the 11 years analyzed, with a lower number of hospital days than at the national level. The CCI achieved by the hospital's surgical wards has had an increasing evolution throughout the period analyzed, higher than the national value. The maximum amount contracted and the amount contracted by the hospital is higher when the LOH and CCI indicators achieved by each section are included in the formula than the indicators established at the national level. A lower LOH and a higher CCI than the national values facilitated the contracting of a higher amount. Optimization of the indicators by hospital performance is correlated with improved funding by the NHIS. Increasing the contracted amount facilitates the contracting of an exponential annual number of cases, resulting in more efficient medical services in the surgical wards.

Empowering Family Nurse Practitioners for Sustainable Community Health

&lt;p&gt;醫療自主權於現今醫療環境中日趨重要，以病人為中心照護的重要環節，就是鼓勵病患積極參與醫療決策，以提升照護品質與滿度度。台灣衛生福利部自2016年開始推行醫病共享決策（shared decision making, SDM），期望促使病人積極參與醫療決策，以提升醫病關係、醫療品質及病人安全。推動至今，共享決策已廣泛運用於臨床實務中。但是，偏鄉及高齡長者，其就醫習慣、喜好、價值觀和醫療新知取得與都市居民有極大的差異。傳統以「醫生」作決策的觀念深植，參與SDM之意願以及推廣策略，值得探討。因此，本文將從專科護理師之角色去探討共享決策於偏鄉醫院推動影響因素，推行過程所遇到困境與經驗，綜整考量偏鄉居民特質及觀念，因應出不同之SDM推行之道。&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;&lt;p&gt;Various challenges, such as a nursing workforce shortage, an aging society, and health inequality,seriously affect the sustainable development of Taiwan. Although the specialized nursing system represents a significant milestone in nursing development in Taiwan, it is mainly focused on hospital training, neglecting community health needs. Community health care can be effectively enhanced by promoting the family nurse practitioner system, especially in rural areas. Family nurse practi-tioners provide medical care services, boost local economies, increase employment opportunities, and strengthen community cohesion. The cultivation and investment in family nurse practitioners are crucial for improving the efficiency and quality of medical services. With the adjustment of the med-ical training system in Taiwan, nurse practitioners could redefine their roles and responsibilities in a new environment, and eventually contribute to the sustainability of Taiwan&amp;rsquo;s primary health care and improve community health. Through the empowerment of family nurse practitioners, Taiwan will be better equipped to address challenges and achieve sustainable development goals&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;