Health Data Management Research Articles

Data management practice of health extension workers and associated factors in Central Gondar Zone, northwest Ethiopia

IntroductionGenerating quality data for decision-making at all levels of a health system is a global imperative. The assessment of the Ethiopian National Health Information System revealed that health information system resources, data management, dissemination, and their use were rated as “not adequate” among the six major components of the health system. Health extension workers are the frontline health workforce where baseline health data are generated in the Ethiopian health system. However, the data collected, compiled, and reported by health extension workers are unreliable and of low quality. Despite huge problems in data management practices, there is a lack of sound evidence on how to overcome these health data management challenges, particularly among health extension workers. Thus, this study aimed to assess data management practices and their associated factors among health extension workers in the Central Gondar Zone.MethodAn institution-based cross-sectional study was conducted among 383 health extension workers. A simple random sampling method was used to select districts, all health extension workers were surveyed in the selected districts, and a structured self-administered questionnaire was used for data collection. The data was entered using EpiData version 4.6 and analyzed using STATA, version 16. Bivariable and multivariable binary logistic regression analyses were executed. An odds ratio with a 95% confidence interval and a p-value of <0.05 was calculated to determine the strength of the association and to evaluate statistical significance, respectively.ResultsOf the 383 health extension workers enrolled, all responded to the questionnaire with a response rate of 100%. Furthermore, 54.7% of the respondents had good data management practices. In the multivariable logistic regression analysis, being a married woman, having good data management knowledge, having a good attitude toward data management, having 1–5 years of working experience, and having a salary ranging from 5,358 to 8,013 Ethiopian Birr were the factors significantly associated with good data management practices among health extension workers. The overall data management practice was poor with only five health extension workers out of ten having good data management practices.

Knowledge and attitudes of the Italian population on sharing health data: a cross-sectional study

Abstract Background Healthcare digitization enhances data accessibility but faces data management and privacy challenges. The study sought to explore the Italian public’s understanding and views on storing and sharing health data for treatment and research purposes, as well as factors influencing their attitudes toward health data management and sharing. Methods A cross-sectional questionnaire assessed socio-demographic information, knowledge about health data sharing, and attitudes toward sharing data for treatment and scientific purposes. Descriptive analyses and multivariable logistic regressions were performed to examine the associations between sociodemographic factors and knowledge/attitudes about data storage and sharing. Results A total of 1,389 citizens participated. Most respondents (65.4%) thought that healthcare providers could access personal health data nationwide, while 94% expressed willingness to share personal health data nationwide. A high percentage of respondents (73%) fully agreed that storing and sharing personal health-related data could improve research and quality of care. Males and younger individuals (<41 years) were likelier to have higher data-sharing knowledge (adjusted Odds Ratio [adjOR] 1.50, 95% Confidence Interval (CI) 1.03 - 2.18, and adjOR 1.99, 95% CI 1.29 - 3.08 respectively). Lower educational-level respondents exhibited lower positive attitudes towards sharing health data for treatment and research purposes (adjOR 0.18, 95% CI 0.05-0.58 for high school, and adjOR 0.11, 95% CI 0.03-0.41 for middle school or lower, compared to those with a university degree). Conclusions The findings offer valuable perspectives for European policymakers, healthcare practitioners, and researchers who aim to enhance data handling, foster cooperation, and maximize the benefits of health data for tailored care and scientific progress. Key messages • Italian citizens show strong willingness to share health data nationwide for treatment and research, suggesting a favorable environment for collaborative healthcare initiatives. • Sociodemographic factors such as age and education level influence attitudes towards health data sharing, stressing interventions to ensure equitable access and participation in data-driven healthcare.

The empowering role of health literacy in combatting fake news, misinformation and infodemics

Abstract Background In times of the rapid digital transformation, people need to acquire specific knowledge, skills and attitudes to deal with data, digital information and technology. Such skills are particularly important in times of crisis, as the COVID-19 pandemic has shown. Alongside the pandemic, a so called “infodemic” has emerged, i.e., an overabundance of information, accompanied by misinformation, which impedes sound decision-making processes, affects health literacy (HL) and impacts public health. However, while misinformation poses a well-known threat to our health and well-being, we still lack viable concepts and approaches to satisfactorily solving this issue. Thus, we aimed at reviewing the concept of HL in view of the infodemic and health-related challenges. Methods In a 12-month participatory process in Switzerland in 2023, we investigated the empowering role of HL in light of the polycrisis. On behalf of the Swiss Federal Office of Public Health, we conducted a literature review on existing HL definitions. Then, we conducted 6 expert interviews and 2 focus group interviews with other 10 experts in HL and associated domains, which guided and informed the review process. Results As a result, HL can be understood as a bundle of competencies to proactively deal with health-related information, services, and challenges and thereby empowers people to manage their and other’s health and well-being. Thus, HL empowers people to better manage digital information and services and promotes critical thinking. This in turn is necessary to assess information quality, uncover misinformation and to adequately manage health data and information. Conclusions HL represents a crucial prerequisite for individuals, professionals, and decision-makers to find trustworthy health information, to reflect upon the quality and credibility of sources and content, and to understand the complex interrelations of the determinants of health and is therefore crucial for public health. Key messages • Health literacy empowers people to adequately deal with fake news, misinformation and infodemics. • Strengthening health literacy offers great potential for public health to promote critical thinking and to rebuild trust.

A comprehensive survey of artificial intelligence adoption in European laboratory medicine: current utilization and prospects.

As the healthcare sector evolves, Artificial Intelligence's (AI's) potential to enhance laboratory medicine is increasingly recognized. However, the adoption rates and attitudes towards AI across European laboratories have not been comprehensively analyzed. This study aims to fill this gap by surveying European laboratory professionals to assess their current use of AI, the digital infrastructure available, and their attitudes towards future implementations. We conducted a methodical survey during October 2023, distributed via EFLM mailing lists. The survey explored six key areas: general characteristics, digital equipment, access to health data, data management, AI advancements, and personal perspectives. We analyzed responses to quantify AI integration and identify barriers to its adoption. From 426 initial responses, 195 were considered after excluding incomplete and non-European entries. The findings revealed limited AI engagement, with significant gaps in necessary digital infrastructure and training. Only 25.6 % of laboratories reported ongoing AI projects. Major barriers included inadequate digital tools, restricted access to comprehensive data, and a lack of AI-related skills among personnel. Notably, a substantial interest in AI training was expressed, indicating a demand for educational initiatives. Despite the recognized potential of AI to revolutionize laboratory medicine by enhancing diagnostic accuracy and efficiency, European laboratories face substantial challenges. This survey highlights a critical need for strategic investments in educational programs and infrastructure improvements to support AI integration in laboratory medicine across Europe. Future efforts should focus on enhancing data accessibility, upgrading technological tools, and expanding AI training and literacy among professionals. In response, our working group plans to develop and make available online training materials to meet this growing educational demand.

A systematic review on artificial intelligence approaches for smart health devices

In the context of smart health, the use of wearable Internet of Things (IoT) devices is becoming increasingly popular to monitor and manage patients’ health conditions in a more efficient and personalized way. However, choosing the most suitable artificial intelligence (AI) methodology to analyze the data collected by these devices is crucial to ensure the reliability and effectiveness of smart healthcare applications. Additionally, protecting the privacy and security of health data is an area of growing concern, given the sensitivity and personal nature of such information. In this context, machine learning (ML) and deep learning (DL) are emerging as successful technologies because they are suitable for application to advanced analysis and prediction of healthcare scenarios. Therefore, the objective of this work is to contribute to the current state of the literature by identifying challenges, best practices, and future opportunities in the field of smart health. We aim to provide a comprehensive overview of the AI methodologies used, the neural network architectures adopted, and the algorithms employed, as well as examine the privacy and security issues related to the management of health data collected by wearable IoT devices. Through this systematic review, we aim to offer practical guidelines for the design, development, and implementation of AI solutions in smart health, to improve the quality of care provided and promote patient well-being. To pursue our goal, several articles focusing on ML or DL network architectures were selected and reviewed. The final discussion highlights research gaps yet to be investigated, as well as the drawbacks and vulnerabilities of existing IoT applications in smart healthcare.

Implications of Personal Data Protection Law in Consumer Health Data Management to Improve Secure and Confidential Handling in Indonesia

The purpose of this study is to understand the impact of the implementation of the Personal Data Protection Law (PDP Law) on the management of consumer health data in Indonesia, with a focus on enhancing secure and confidential handling. In the midst of the rapidly evolving digitalisation era, the management of personal data, especially those related to health information, requires strict and effective legal regulation. This is becoming increasingly urgent given the increasing incidents of data leaks and privacy violations, one of which occurred in the leak of the Indonesian Ministry of Health's server which collected 6 million patient medical record data. The research method used is normative juridical with a conceptual approach and a statutory approach, where secondary data is broken down into three different types of legal materials, namely primary, secondary, and tertiary legal materials. The PDP Law and Consumer Protection Law are the primary legal sources, accompanied by the use of relevant literature as secondary legal sources as well as dictionaries and encyclopedias as tertiary legal materials to further explain. The novelty of this research lies in the comprehensive analysis of the existing regulations and their applicability in the health context, as well as considering the increasing public awareness of their rights to personal data. The results show that although challenges such as uneven technological infrastructure and lack of awareness among health staff still exist, the measures taken through the PDP Law have created a strong foundation for better health data protection in the future, improving security and confidentiality in health data management.

Sharing for Caring? A Patients' and Clinicians' View on Handling Personal Medical Data in the Context of Digitization: An Exploratory Study.

The healthcare sector is currently undergoing a significant transformation, driven by an increased utilization of data. In this evolving landscape, surveys are of pivotal importance to the comprehension of patient needs and preferences. Moreover, the digital affinity of patients and physicians within the healthcare system is reforming the manner in which healthcare services are accessed and delivered. The utilization and donation of data are influencing the future of medical research and treatment, while artificial intelligence (AI) is empowering patients and physicians with knowledge and improving healthcare delivery. In order to evaluate the opinions of patients and physicians regarding the management of personal health data and the functionality of upcoming data management devices in the context of healthcare digitization, we conducted an exploratory study and designed a survey. The survey focused on a number of key areas, including demographics, experience with digitization, data handling, the identification of needs for upcoming digitization, and AI in healthcare. A total of 40 patients and 15 physicians participated in the survey. The results indicate that data security, timesaving/administrative support, and digital communication are aspects that patients associate with patient-friendly digitization. Based on the responses provided by physicians, it might be concluded that future digital platforms should prioritize usability, time efficacy, data security, and interoperability. In terms of expectations for future digital platforms, there is a notable overlap between the needs expressed by patients and those identified by physicians, particularly in relation to usability, time management, data security, and digital communication. This suggests that the requirements of different stakeholders can be combined in a future system, although individual issues may still require attention.

Design and Development of a Smart Fidget Toy Using Blockchain Technology to Improve Health Data Control.

This study explores the integration of blockchain technology in wearable health devices through the design and development of a Smart Fidget Toy. We aimed to investigate design challenges and opportunities of blockchain-based health devices, examine the impact of blockchain integration user experience, and assess its potential to improve data control and user trust. Using an iterative user-centered design approach, we developed a mid-fidelity prototype of a physical fidget device with a blockchain-based web application. Our key contributions include the design of a fidget toy using blockchain for secure health data management, an iterative development process balancing user needs with blockchain integration challenges, and insights into user perceptions of blockchain wearables for health. We conducted user studies, including a survey (n = 28), focus group (n = 6), interactive wireframe testing (n = 7), and prototype testing (n = 10). Our study revealed high user interest (70%) in blockchain-based data control and sharing features and improved perceived security of data (90% of users) with blockchain integration. However, we also identified challenges in user understanding of blockchain concepts, necessitating additional support. Our smart contract, deployed on the Polygon zkEVM testnet, efficiently manages data storage and retrieval while maintaining user privacy. This research advances the understanding of blockchain applications in health wearables, offering valuable insights for the future development of this field.

Audit of Geriatric Emergency Medicine-Unit staff compliance in providing care in line with the Age Friendly Health Systems Conceptual Framework

Abstract Background The Geriatric Emergency Medicine Unit (GEMU) in University Hospital Limerick (UHL) is in the Emergency Department for the assessment of those over the age of 75. The unit is staffed by a multi-disciplinary team with specialist skills in older person’s care. The aim of this audit was to determine assessment and documentation compliance with the Age Friendly Health Systems conceptual framework for older persons who present to GEMU. Methods A retrospective documentation review took place from patients who attended GEMU between January-March 2023. Medical record numbers were selected at random from the Health and Social Care Professionals (HSCP) team data management system with individual patient records subsequently retrieved from the Therefore Emergency Medicine (EM) patient management system. Patient records were reviewed by SW, which included Nursing, EM Doctors documentation and HSCP documentation. Results 15 charts were reviewed. Compliance varied across all areas from 0-100%. Compliance was greatest in the domains of the EWS score, mobility assessments, falls history and 4AT. Hydration and the documentation of ‘What Matters’/patient care preference were both 0%. Otherwise, compliance broadly was >50%. The first re-audit of 15 charts took place from December 2023-January 2024. Improvements were noted across all assessments, with 100% compliance additionally achieved in CFS, cognitive status, co-morbidities and nutritional intake. A second re-audit of 15 charts took place from March-April 2024, with further improvements seen in terms of lying and standing blood pressures and polypharmacy assessments. Conclusion Results demonstrate the continued improvements made amongst the team in adhering to the Age Friendly Health Systems Framework. Efforts are ongoing to focus specifically on the care preference component, with structured QIPs in place and a view to re-auditing in Q4 2024.

PatCen: A blockchain-based patient-centric mechanism for the granular access control of infectious disease-related test records.

The recent global outbreaks of infectious diseases such as COVID-19, yellow fever, and Ebola have highlighted the critical need for robust health data management systems that can rapidly adapt to and mitigate public health emergencies. In contrast to traditional systems, this study introduces an innovative blockchain-based Electronic Health Record (EHR) access control mechanism that effectively safeguards patient data integrity and privacy. The proposed approach uniquely integrates granular data access control mechanism within a blockchain framework, ensuring that patient data is only accessible to explicitly authorized users and thereby enhancing patient consent and privacy. This system addresses key challenges in healthcare data management, including preventing unauthorized access and overcoming the inefficiencies inherent in traditional access mechanisms. Since the latency is a sensitive factor in healthcare data management, the simulations of the proposed model reveal substantial improvements over existing benchmarks in terms of reduced computing overhead, increased throughput, minimized latency, and strengthened overall security. By demonstrating these advantages, the study contributes significantly to the evolution of health data management, offering a scalable, secure solution that prioritizes patient autonomy and privacy in an increasingly digital healthcare landscape.

System interoperability and data linkage in the era of health information management: A bibliometric analysis.

Background: Across the world, health data generation is growing exponentially. The continuous rise of new and diversified technology to obtain and handle health data places health information management and governance under pressure. Lack of data linkage and interoperability between systems undermines best efforts to optimise integrated health information technology solutions. Objective: This research aimed to provide a bibliometric overview of the role of interoperability and linkage in health data management and governance. Method: Data were acquired by entering selected search queries into Google Scholar, PubMed, and Web of Science databases and bibliometric data obtained were then imported to Endnote and checked for duplicates. The refined data were exported to Excel, where several levels of filtration were applied to obtain the final sample. These sample data were analysed using Microsoft Excel (Microsoft Corporation, Washington, USA), WORDSTAT (Provalis Research, Montreal, Canada) and VOSviewer software (Leiden University, Leiden, Netherlands). Results: The literature sample was retrieved from 3799 unique results and consisted of 63 articles, present in 45 different publications, both evaluated by two specific in-house global impact rankings. Through VOSviewer, three main clusters were identified: (i) e-health information stakeholder needs; (ii) e-health information quality assessment; and (iii) e-health information technological governance trends. A residual correlation between interoperability and linkage studies in the sample was also found. Conclusion: Assessing stakeholders' needs is crucial for establishing an efficient and effective health information system. Further and diversified research is needed to assess the integrated placement of interoperability and linkage in health information management and governance. Implications: This research has provided valuable managerial and theoretical contributions to optimise system interoperability and data linkage within health information research and information technology solutions.

Blockchains in health information systems: A literature review on use cases and status of implementation of blockchains for electronic health records

BACKGROUND: The careful management of health data is crucial due to its immense value and high sensitivity. Blockchain technologies can manage data in a trustworthy way. OBJECTIVE: The central aim is to identify the current state of blockchain technologies applied to Electronic Health Records (EHR) to identify common structures and functionalities. This common ground could be the starting point for defining clear global standards. METHODS: A systematic literature review is used. RESULTS: The benefits of enhancing the digitalization and cross-institutional accessibility of health data are undoubted. Four main application areas of blockchain for the EHR can be identified: storing, sharing, audit logging, and managing the identity of data accessors. Since on-chain transactions are slow and inefficient, most research promotes a hybrid approach for handling transactions as a combination of off-chain and on-chain approaches. CONCLUSIONS: Several approaches, frameworks, and models exist for applying blockchain technologies in the context of EHR. The research revealed that a) only a few concepts are already implemented, b) the existing system implementations are based on different backgrounds and technology stacks, and c) a lack of comprehensive and global standards and norms. All these factors are barriers to a broader usage of blockchain-based EHRs.

Management of Public Health: A Study on Novel Strategy for Securing Health Data

The effective management and security of sensitive health data is becoming exponentially important to public health administration. This research examines and evaluates innovative ways to improve health data security in public health management systems to provide accessibility for authorized stakeholders. The study introduces the War Strategy Optimized Elliptic Curve Cryptography (WSO-ECC) encryption method for improving the security of primary healthcare medical records. The WSO-ECC technique, based on a war strategy framework, optimizes encryption and decryption processes by balancing exploration and exploitation phases, enhancing security while maintaining efficiency. It significantly reduces encryption and decryption times compared to traditional methods.The proposed method offers a robust solution for safeguarding medical information in public health management systems, demonstrating superior performance of 24.6 in encryption time, 23 decryption times, execution efficiency, and delay.

Secured Public Health data Management Framework using the Internet of Things in a Smart City Environment

The Internet of Things (IoT) is being embraced and impacts various aspects of lives. Integrating IoT technology in the healthcare sector paves the way for continuous remote monitoring of individuals' health status. The healthcare (HC) sector has traditionally produced substantial data. Due to the implementation of the IoT, this quantity has significantly increased. Ensuring proper HC storage is imperative to derive meaningful insights from the data. This study presents a public health (PH) framework that utilizes cloudlets and IoT technology. The primary objective of this PH architecture is to facilitate the retrieval of real-time data by using cloudlets. The research provides and executes an HC data management system to store vast HC data and handle consumer queries to retrieve HC information. The efficacy of the proposed model has been evaluated in terms of data transmission time, energy usage, query response time, and data packet loss. The proposed model outperforms existing PH systems by comparing the assessment findings of the proposed approach with the performance of typical PH systems.

Transforming Public Health Data Management in IoMT Networks Based on Innovative Offloading Scheme

Healthcare platform monitoring IoT-oriented technologies constitute the idea of Internet of Medical Things (IoMT) in public health and medical services. The amount and quality of created data have a substantial influence on data management and privacy compute offloading solutions are unable to keep up with the growing needs of the health industry, especially when fast and dependable communication was needed. The study suggests a unique approach called Mutated Barnacles Mating Optimization (MBMO) for assisting the data management issues in IoMT systems. The suggested MBMO framework successfully addresses problems that are common in the medical industry by using a data offloading technique. To overcome the issues of discrete tasks and resource allocation, guarantees the needs of dependable and efficient communication. We implemented Java software. The evaluation of the performance step encompasses several measures, such as energy consumption (J/ms) and Time delay (ms)model to assess the efficiency of the suggested forecasting algorithm. We performed an assessment of comparison with other established approaches results indicate that the suggested model produces superior results for assisting the data management issues in IoMT systems.

LEGAL ANALYSIS OF EU ARTIFICIAL INTELLIGENCE ACT (2024): INSIGHTS FROM PERSONAL DATA GOVERNANCE AND HEALTH POLICY

Background: This study correlates the up-to-date ethical, functional and legal evaluations related to the management and governance of artificial intelligence (AI) under European Union (EU) law, particularly impacting the health data sector and medical standards as provided by the Artificial Intelligence Act within the Regulation adopted by the European Council in May 2024. The initial proposal for the management and governance of the AI sector was submitted in April 2021. Three years later, on 13 March 2024, the European Union Artificial Intelligence Act (EU AIA) was adopted by the European Parliament. Subsequently, on 21 May 2024, the Council adopted an innovative legislative framework that harmonises the standards and rules for AI regulation. This framework is set to take effect in May 2026, with the central objective of stimulating and motivating a fair, safe, legal single market that respects the principles of ethics and the fundamental rights of the human person. Methods: The current legal analysis focuses on the European Union’s new institutional governance involving a multistage approach to managing health data, ethical artificial intelligence, generative artificial intelligence and classification of types of AI by considering the degree of risk (e.g. artificial intelligence systems with limited risk and systems with high risk) and medical devices. It outlines the legal framework for AI regulation and governance in the EU by focusing on compliance with the previously adopted legislation in the Medical Devices Regulation (2017) and the In-Vitro Diagnostic Regulation (2017). The paper also examines the application of the newly adopted EU Artificial Intelligence Act in relation to national justice systems, previous EU regulations on medical devices and personal data protection regulation, and its correlation with the European Court of Human Rights jurisprudence. This opens up complex discussions related to judicial reform and access to justice. For this purpose, as a research objective, the legal analysis includes an innovative perspective following an integrative discussion on the latest legal reforms and regulations of the AI sector in Eastern Europe launched in 2024 with a special focus on the latest developments in the EU Candidate Countries namely Ukraine and the Republic of Moldova. Results and conclusions: The present research facilitates the exploration of the real benefits of managing innovative AI systems for medical data, research, and development, as well as within the medical technology industry.

The Medicine Revolution Through Artificial Intelligence: Ethical Challenges of Machine Learning Algorithms in Decision-Making.

The integration of artificial intelligence (AI) and its autonomous learning processes (or machine learning) in medicine has revolutionized the global health landscape, providing faster and more accurate diagnoses, personalization of medical treatment, and efficient management of clinical information. However, this transformation is not without ethical challenges, which require a comprehensive and responsible approach. There are many fields where AI and medicine intersect, such as health education, patient-doctor interface, data management, diagnosis, intervention, and decision-making processes. For some of these fields, there are some guidelines to regulate them. AI has numerous applications in medicine, including medical imaging analysis, diagnosis, predictive analytics for patient outcomes, drug discovery and development, virtual health assistants, and remote patient monitoring. It is also used in robotic surgery, clinical decision support systems, AI-powered chatbots for triage, administrative workflow automation, and treatment recommendations. Despite numerous applications, there are several problems related to the use of AI identified in the literature in general and in medicine in particular. These problems are data privacy and security, bias and discrimination, lack of transparency (Black Box Problem), integration with existing systems, cost and accessibility disparities, risk of overconfidence in AI, technical limitations, accountability for AI errors, algorithmic interpretability, data standardization issues, unemployment, and challenges in clinical validation. Of the various problems already identified, the most worrying are data bias, the black box phenomenon, questions about data privacy, responsibility for decision-making, security issues for the human species, and technological unemployment. There are still several ethical problems associated with the use of AI autonomous learning algorithms, namely epistemic, normative, and comprehensive ethical problems (overarching). Addressing all these issues is crucial to ensure that the use of AI in healthcare is implemented ethically and responsibly, providing benefits to populations without compromising fundamental values. Ongoing dialogue between healthcare providers and the industry, the establishment of ethical guidelines and regulations, and considering not only current ethical dilemmas but also future perspectives are fundamental points for the application of AI to medical practice. The purpose of this review is to discuss the ethical issues of AI algorithms used mainly in data management, diagnosis, intervention, and decision-making processes.

Advancing Refugee Health Data Management: The Implementation of ICD-11 in UNRWA's Primary Care System.

The United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA) was one of the earliest healthcare systems globally to implement the International Classification of Diseases, Eleventh Revision (ICD-11) across its 140 clinics serving 5.9 million Palestine refugees. This paper discusses the integration of ICD-11 into UNRWA's cloud-based electronic medical record (EMR) system, identifying both the barriers and facilitators involved and analyzing trends in clinical documentation and healthcare utilization. The key challenges included data privacy provisions, integration into a coordinated care model, complex classification schema for primary care settings, frequent staff turnover, and limited data analysis capabilities. Conversely, facilitators included physician-tailored training and on-site support, system compatibility, a multidisciplinary team approach, policy support from UNRWA and the World Health Organization (WHO), and leadership commitment and effective change management. Medical officers (MOs) using ICD-11 reported greater satisfaction with the system's capabilities in managing and visualizing health information. This article contributes to the discourse on health data management in complex humanitarian settings, offering insights into the benefits and challenges of implementing advanced classification systems like ICD-11. Future research should explore longitudinal impacts and further integration with global health systems, ensuring that the advancements in classification continue to support the overarching goal of health equity and access in vulnerable and hard-to-reach populations.

Analysis of the Impact of Digital Transformation on the Management of Elderly Care Institutions

With the rapid advancement of technology, digital transformation has become a crucial trend in the management and transformation of various industries. This article aims to analyze the application of digital technologies such as intelligent surveillance systems, health data management systems, and interactive platforms in elderly care institutions. It discusses the impact of digital transformation on the management of these institutions and addresses the challenges faced during this transformation. The article proposes corresponding implementation strategies, offering new perspectives for the research and development of digital transformation in elderly care institutions.

ShinyAnonymizer Enhanced Version and Beyond: A Further Exploration of Privacy-Preserving Solutions in Health Data Management

ShinyAnonymizer Enhanced Version and Beyond: A Further Exploration of Privacy-Preserving Solutions in Health Data Management