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Health informatics,also known as healthcare informatics or biomedical informatics, is a multidisciplinary field that focuses on the scientific study and practice of managing health data. In this paper authors have conceptualized health information literacyand health informatics. Discourses include: the role of medical libraries and medical librarians, Electronic Health Systems including Health Information Systems, Electronic Medical Record (EMR). Also, Medical Data Management, its benefits and challenges were also highlighted. Health information literature is also reviewed. The authors concluded by reiterating the importance of health information literacy, health informatics and its technological applications and the need for librarians and medical practitioners in the healthcare environments to acquire the requisite knowledge, skills and technologies that could enhance health information services delivery on the part of the librarians and healthcare services delivery on the part of the health practitioners
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Medical InformaticsABSTRACT
With the rapid development of healthcare big data and artificial intelligence technology, how to utilize the massive medical data generated based on clinical diagnosis and treatment has become an important issue to be solved in the field of clinical research. Clinical diagnosis and treatment data is an essential part of healthcare big data, and also the main field of healthcare big data research. With the continuous deepening and extensive development of informatization, hospitals have accumulated a large number of patient-centered clinical diagnosis and treatment data. Deeply mining and analyzing these data through big data technology can provide reference for precise diagnosis and treatment, and standardized prevention and control of diseases. However, conducting relevant research still faces many difficulties and blockages, such as the increased risk of data leakage or abuse, and the difficulty in implementing informed consent. To safely, legally and efficiently utilize clinical diagnosis and treatment data to conduct clinical research and fully tap into the value of these precious medical resources, a tertiary hospital in Beijing has built a research big data platform and developed relevant systems to effectively solve the problems of blockages and difficulties in the application of rich clinical resources to clinical research, and improve the service quality of medical institutions and the conversion rate of scientific research achievements. By introducing the key points and management methods in the implementation of clinical research based on the scientific research big data platform, analyzing and exploring the existing problems and improvement measures, this paper aimed to provide theoretical basis and system reference for high-quality and efficient health and medical big data clinical research, inspire and promote the continuous improvement of medical research management, and promote the development of medical and health science and technology innovation.
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The medical data processed and analyzed in clinical research often contain a large number of personal information. Therefore, researchers should pay attention to the safety management of medical data during clinical research. The Data Security Law of People's Republic of China and the Personal Information Protection Law of People's Republic of China implemented on 1 st September and 1 st November 2021 respectively establish legal basis for data security and personal information protection and point out the direction for medical data security, which indicate that data governance has entered the 'strong regulatory era'. In the process of medical data collection and application of clinical research, respecting and protecting the privacy and safety of patients, ensuring the quality of medical data, safely managing medical data and carrying out high-quality clinical research will be an important test for the collection and application of clinical scientific research data under the new legal background.
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The beginning of EHR (Electronic Health Record) can be traced back to the development of the Medical Markup Language (MML) from 1995 to 2000. In 2001, EHR with MML as a database structure was developed and expanded to Kumamoto, Miyazaki, Tokyo, and Kyoto (Dolphin Project). After that, the need for medical information management at the national level was recognized, and the need for secondary use of medical information was also recognized, and in 2015, the national level version of the EHR, the “Millennial Medical Record Project” began. The number of connected medical institutions reached 106 in the four years up to FY2018. In December 2019, the Life Data Initiative, a general incorporated association, became the first certified company under the Next Generation Medical Infrastructure Law, and is operating with the aim of achieving independent profitability, including the EHR department, which does not depend on subsidies.
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Objective:To strengthen the integration and sharing of medical data resources, provide high-quality and usable data for clinical researchers, and promote the medical data use in clinical research.Methods:According to the development and application goals of the medical big data platform, data from different major clinical information systems in our hospital are integrated and then cleaned, processed and analyzed, and finally aggregated into a unified platform and turned to valuable and usable data resources.Results:A medical big data platform for clinical research in our hospital has been developed. It has stored over 13.42 million patients′ clinical data of more than 50 million visits since 2004 in our hospital; an analysis-oriented common data model (CDM) for clinical research has been designed; clinical researchers can query and extract clinical data according to CDM; the standard clinical research data service mechanisms have been established.Conclusions:The medical big data platform in our hospital helps to provide usable data of good quality for high-level scientific research based on medical big data, and improve the efficiency and quality of clinical research; at the same time, it also provides a efficient way to manage and control clinical research data use while ensuring data security and regulatory compliance.
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Abstract The ambitious task in the domain of medical informatics is medical data classification. From medical datasets, intention to ameliorate human burden with the medical data classification entails to taking in classification designs. The medical data classification is the major focus of this paper, where a Decision Tree based Salp Swarm Optimization (DT-SWO) algorithm is proposed. After pre-processingthe hybrid feature selection method selects the medical data features. The high dimensional features are reduced by Discriminant Independent Component Analysis (DICA) and DT-SWO is to classify the most relevant class of medical data. The details of four datasets namely Leukemia, Diffuse Larger B-cell Lymphomas (DLBCL), Lung cancer and Colon relating to four diseases for heart, liver, cancer and lungs are collected from the UCI machine learning repository. Ultimately, the experimental outcomes demonstrated that the proposed DT-SWO algorithm is suitable for medical data classification than other algorithms.
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With the rapid development of " Internet+ healthcare" , medical data has gained an ever important role. How to determine the reasonable use boundary of medical data and leverage its supporting role, has grown an urgent problem to be solved. The authors analyzed the characteristics of medical data, and held that medical data is not equivalent to information, as it does not have the characteristics of legal objects in nature. In addition, it lacks originality. Therefore it is difficult to protect through the existing rights system. However, due to its property interest and personality, the authors tried to establish a new right for protection. In the construction of such medical data right, the authors claimed that patients should be the subject of the right, and medical institutions can appropriately restrict the freedom of patients′ through reasonable use rules and legal licensing system. In this way, we can not only promote medical data sharing and the development of social health, but also fully protect the legitimate rights and interests of patients.
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Block chain, as a decentralized, trustless database technology program with an intact information trans-parency and a privacy protection function, can be use to construct a highly effective and reliable transmission sys-tem and promote the Internet to become an infrastructure in building the society-trusted network. Block chain is of significant advantages in optimizing the business processes, reducing the operation cost and improving the synergis-tic efficiency in financial industry, and is thus rapidly applied in other industries. The construction of health and medical big data is faced with the challenge of both information security and privacy protection in health field. Block chain is characterized by high fault tolerance, unaltered infrastructure and privacy protection function, and has thus a large room for its application in medical treatment, pharmaceutical industry, medical insurance and ge-nomics.
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The current sharing of medical data and its restrictive factors were surveyed by literature and question-naire investigation and analyzed with statistical analysis tools such as SPSS and Excel, which showed the influence of different restrictive factors on different populations. Certain measures were put forward for speeding up the sha-ring of scientific data, improving the medical treatment and academic level, strengthening the competitive power of hospitals and higher education institutions in Hunan Province.
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Objective To explore hospital medical data so as to decrease the cost for medical information and construction while increase the efficiency of information system. Methods Hospital medical data were classified, and kinds of data layering technologies were applied to constructing the data center,and overall considerations and hierarchical arrangement were executed for types of medical data.The high efficiencies and high feasibility of business systems were realized to ensure data security. Results Classified and hierarchical storage technology enhanced business response and reliability with the same hardware infrastructure.Conclusion Types of technologies have to be combined according to the characteristics of the business system and its data so as to implement high-efficiency and-reliability storage of hospital medical data.
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The paper introduces the study status of domestic and foreign geographic mapping,analyzes the new demands of medical geographic mapping system,studies the medical geographic mapping engine with the function of converting medical data to medical geographic data,and specifically designs the system architecture of the engine,including working mechanism,structure and content,and interlayer function call strategies,etc.
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Objective To investigate the usage of intravenous infusion and the antibiotic intravenous infusion in different hospitals nationwide ,and to evaluate the influence of the hospital bed number ,hospital area and hospital grade on the clinical application of intravenous infusion .Methods Intravenous infusion volume ,rate and other related indexes were analyzed based on the inpatient information obtained from the regional medical big data net for 156 hospitals .Results 1323852 inpatients were included in this study .93 .13％ of those patients received intravenous infusion therapy .The average daily infusion volume was 782 .67 ml per bed .The average infusion time was 7 .39 days per patient .44 .78％ of inpatients received intravenous antibi-otic treatment .The average daily antibiotic infusion volume was 92 .48 ml per bed .Conclusion Generally ,the inpatient per-centage of intravenous infusion was getting higher in China .The greater infusion volume in larger hospitals suggested that the patient′s condition is relatively more serious in the larger hospital .The higher grade hospitals used smaller antibiotic infusion rate and volume ,indicating the antibiotic use in high grade hospitals is relatively more standardized .
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<p>The utilization of medical information is a pressing issue for pharmaceutical companies, particularly from the perspective of pharmacovigilance applications. There are currently some types of adverse reaction risks that cannot be detected in Japan but can be detected in Europe and the U.S.A. because secondary use of medical information is possible there. To remedy this lag, it is essential that we update the framework of Japan's system. We must create medical information standards that allow public use and we must define a level of quality control.</p>
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ABSTRACT Digital watermarking has emerged as major technique for ensuring security for various types of data like medical data, digital copyright protection, transaction tracing and so on. With the advancement in digital data distribution over the network there has been increase in the need for protection of such data from unauthorized copying or usages. Watermarking helps in providing the security to some extent. Robustness against any sort of unauthenticated attack is the major requirement of watermarking. In this paper we proposed an efficient watermarking technique for medical data security with the aid of neural network. Usage of neural network is generally used to create and control watermarking strength automatically. This method provides better watermarked data which can be highly secured to unauthorized usage. It is observed that the proposed method provides better security for the multimedia data when compared with other data security methods.
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After a brief description of standards for HL7 and its application in foreign countries and China, the lo-calization and implementation principles of HL7-based application of integrated medical information system, and its advantages and disadvantages were analyzed with its development trend in China pointed out.
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OBJECTIVE: The development of a dictionary of clinical terminology based on medical concepts is essential for understanding the precise meanings of the clinical terminologies used in EMR systems. For an unambiguous presentation and retrieval of the terminologies in practical data entry, this study propose a clinical terminology dictionary, which integrates and manages the wide range of data in EMR Systems. METHODS: The structure of the system and attributes were defined. The structures should satisfy the following: all terminologies should be consistent with the medical concepts, all concepts have multiple relationships, all concepts have many synonyms, all concepts can be mapped to concepts in an external medical terminology system, and all concepts can be grouped as value sets by setting the "domain". RESULTS: With the derived entity objects and attributes, the physical clinical terminology database was constructed and an editor was developed using MySQL 5.0.45 and JAVA Swing. To verify the structure and contents of the developed clinical terminology dictionary, the terminology experts used the editor to search and register the medical concepts. CONCLUSION: Although the contents refinement and complements are an unsolved problem, it is anticipated that the proposed research will provide unambiguous meanings of the clinical terminology and be applicable to many services in EMR systems.
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Complement System Proteins , IndonesiaABSTRACT
The paper introduces the significance of medical data mining from traditional process of data mining, analyzes the key contents of privacy protection in data mining, emphasizes on discussing data mining based on secure multi-party compution, privacy-preserving oriented original data and privacy-preserving oriented the mining knowledge as well as its implementation technology.
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Objective To explore the application of association rules with Constraints in medical data analysis.Methods Constraints were applied in mining association rules of medical data by analyzing the characteristics of the data.An effective algorithm for mining association rules with constraints from medical data called CAMD was developed.The algorithm was applied to the analysis ofcoronary heart disease dataset.Results Assigning minimum support and minimum confidence as 0.20 and 0.60,respectively,with data from 303 cases of coronary heart dieses analyzed,8 association rules were tained.Crrelations between age,sex,electrocardiograph results,pathogeny and diagnosis of coronary heart disease were discovered.Conclusion Results from our experiments show that CAMD scans the database fewer times and has no need of pruning candidate itemsets.Thus,it greatly decrease the amount of useless rules produced and rapidly discover interesting association rules of medical data.
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The government had a plan to set up a new medical information law last October, which allows hospitals to share medical records of patients with given consent. The aim of this act is to manage and protect medical information effectively. However, civic groups and medical organizations strongly claim that there is a high possibility of leakage of individuals' private medical information that can raise conflicts of interest under the medical information act. Computerization of personal medical information is essential for the business efficiency, however, it may cause a serious damage to the society as well as to individuals should the information be used without the individuals' consent. In the age of knowledge and information, the improved medical service and its effectiveness via informatization is an unavoidable choice, however, it is also hoped that the issues of human rights be thoroughly considered in the process of medical informatization to prevent a potential aftermath to the people and the society.
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Humans , Commerce , Hope , Human Rights , Jurisprudence , Medical Records , PrivacyABSTRACT
With the emergence of networked computing and the Internet, this paper addresses the changed medical privacy environment, in which medical data protection now has to be reassessed. It is mainly based on the Korea situation as a country without an omnibus medical data protection law. It touches also on data security, as medical data privacy cannot be effectively protected without strict regimes for data security. The contemporary state of the medical records in Korea stood to gain from the growth of informatics and the spread of trans-institutions data flows. It is in this context that there appears to be a need to review the recent medical records protection acts which are already showing signs of this age both the public sector and private sector. Some recommendations will be adopted to establish baseline principles and goals and to provide guidance on the future work of the medical data privacy act. One of these, the protection of privacy on electronic medical records, recognizes the ubiquitous nature of digital computer and network technologies today. Medical information should be acquired, disclosed and used only in ways that respect an individual's privacy. It should not be improperly altered or destroyed. And medical information should be accurate, timely, complete and relevant to the purpose for which it is provided and used. And it upholds and protects medical data security. For example a right to encrypt personal information effectively, a right to fair treatment in key public infrastructures so that no person is unfairly excluded in a way that would prejudice that person's ability to protect their privacy, a right to human checking of adverse automated decisions and a right to understand such decisions, a right, going beyond the aspiration of the 'openness principle', of disclosure of the collections to which others will have access and which might affect the projection of the profile of the individual concerned.