Electronic Medical Record as Literature Study

Background: Medical records can be created manually or electronically. In the world of health, the development of information and communication technology is currently affecting health care services as a whole, including the implementation of electronic medical records. The application of electronic medical records must go through a careful planning stage, this is because electronic medical records involve many parties in health facilities and and require a lot of costs. For this reason, a comprehensive study of electronic medical records is needed. One way is to conduct a literature study of several articles related to the electronic medical record.Methods: In conducting this research, the literature review method was used, where the search for articles was not carried out systematically, but the scientific journal articles reviewed were selected by the researcher on one research topic, and selected based on the knowledge and experience possessed by the researcher (traditional review).Results: In this study, 7 articles were reviewed related to electronic medical records. There are some similarities in terms of benefits or obstacles in the application of electronic medical records in health facilities. Among the benefits of electronic medical records are the efficiency of using paper/medical record files, efficiency in the use of space/storage media, time efficiency in searching data and distributing medical record data, efficiency of human resources in finding medical record files and being able to detect errors in data entry. While some of the common obstacles to implementing electronic medical records in health facilities are the unpreparedness of officers at health facilities, so it takes time for socialization and training of human resources, problems with the network, lack of IT resources at health facilities that specifically handle electronic medical records, high implementation costs. expensive (hardware software) and there is no legal umbrella.Conclusions: There is a need for comprehensive research using the semantic review method of articles related to electronic medical records, so that the results can be used as a reference for health facilities in implementing electronic medical records. Thus, it is hoped that the migration and implementation process from manual medical records to electronic medical records can be carried out as expected.

Readiness of Application of Electronic Medical Records in Health Services (Literature Study)

Background: Information and Communication Technology is advancing rapidly and has a major impact on all life, especially in the health sector, especially medical records. This is manifested in the Electronic Medical Record (EMR), which has now been further developed into an Electronic Health Record (EHR). This technology is used to replace or complement paper medical records. The purpose of this literature study is to determine the readiness to apply electronic medical records in health services.Methods: This study uses a literature study obtained from searching scientific research articles from the 2010–2020 range. Keywords used in this study is readiness and DOQ-IT. The database used comes from Google Sholar, Garuda, Neliti, and One Search. The search found 130 articles, then a critical appraisal process was carried out to produce 10 suitable manuscripts.Results: Various literatures found that the readiness to apply electronic medical records using the DOQ-IT method was influencedby 4 factors including the readiness of human resources, orgnizational culture, insfrastructure, and leadership governance. It can be concluded that the readiness for the application of electronic medical recors in health services with the very ready category is 30%, the moderately ready category is 50%, then the unready category is 20%.Conclusions: From the discussion above, it can be concluded that EMR readiness in health services is categorized as quite ready (50%), very ready (30%), and not ready (20%).

SVBE: searchable and verifiable blockchain-based electronic medical records system

Central management of electronic medical systems faces a major challenge because it requires trust in a single entity that cannot effectively protect files from unauthorized access or attacks. This challenge makes it difficult to provide some services in central electronic medical systems, such as file search and verification, although they are needed. This gap motivated us to develop a system based on blockchain that has several characteristics: decentralization, security, anonymity, immutability, and tamper-proof. The proposed system provides several services: storage, verification, and search. The system consists of a smart contract that connects to a decentralized user application through which users can transact with the system. In addition, the system uses an interplanetary file system (IPFS) and cloud computing to store patients’ data and files. Experimental results and system security analysis show that the system performs search and verification tasks securely and quickly through the network.

Information Sharing and Privacy Protection of Electronic Nursing Record Management System

The traditional centralized storage of traditional electronic medical records (EMRs) faces problems like data leakage, data loss, and EMR misplacement. The current protection measures for patients’ privacy in EMRs cannot withstand the fast-developing password cracking technologies and frequency cyberattacks. This paper intends to innovate the information sharing and privacy protection of electronic nursing records (ENRs) management system. Specifically, the signature interception technology was introduced to EMRs, the different phases of certificateless signature interception scheme were depicted, and the validation procedures of the scheme were designed. Then, the six phases of ENR information sharing protocol based on alliance blockchain were described in detail. Finally, an end-to-end memory neural network was constructed for ENR classification. The proposed management scheme was proved effective through experiments.

Comparing the Clinical Characteristics and Mortality of Residential and Non-Residential Older People with COVID-19: Retrospective Observational Study

Nursing homes have accounted for a significant part of SARS-CoV-2 mortality, causing great social alarm. Using data collected from electronic medical records of 1,319,839 institutionalised and non-institutionalised persons ≥ 65 years, the present study investigated the epidemiology and differential characteristics between these two population groups. Our results showed that the form of presentation of the epidemic outbreak, as well as some risk factors, are different among the elderly institutionalised population with respect to those who are not. In addition to a twenty-fold increase in the rate of adjusted mortality among institutionalised individuals, the peak incidence was delayed by approximately three weeks. Having dementia was shown to be a risk factor for death, and, unlike the non-institutionalised group, neither obesity nor age were shown to be significantly associated with the risk of death among the institutionalised. These differential characteristics should be able to guide the actions to be taken by the health administration in the event of a similar infectious situation among institutionalised elderly people.

Intelligent Techniques for Analysis of Big Data About Healthcare and Medical Records

To define huge datasets, the term of big data is used. The considered “4 V” datasets imply volume, variety, velocity and value for many areas especially in medical images, electronic medical records (EMR) and biometrics data. To process and manage such datasets at storage, analysis and visualization states are challenging processes. Recent improvements in communication and transmission technologies provide efficient solutions. Big data solutions should be multithreaded and data access approaches should be tailored to big amounts of semi-structured/unstructured data. Software programming frameworks with a distributed file system (DFS) that owns more units compared with the disk blocks in an operating system to multithread computing task are utilized to cope with these difficulties. Huge datasets in data storage and analysis of healthcare industry need new solutions because old fashioned and traditional analytic tools become useless.

Clinical features, treatment, and outcome of dogs with Coccidioides osteomyelitis

OBJECTIVE To describe signalment, clinical signs, serologic test results, treatment, and outcome of dogs with Coccidioides osteomyelitis (COM) and to compare those findings with findings for dogs with osteosarcoma (OSA). ANIMALS 14 dogs with COM and 16 dogs with OSA. PROCEDURES Data were retrospectively gathered from electronic medical records. RESULTS Dogs with COM were younger and weighed less than dogs with OSA. Six dogs with COM had appendicular lesions, 5 had axial lesions, and 3 had both appendicular and axial lesions; 9 had monostotic disease, and 5 had polyostotic disease. Axial lesions and nonadjacent polyostotic disease were more common in dogs with COM than in dogs with OSA, but radiographic appearance was not different between the 2 groups. Median IgG titer at diagnosis of COM was 1:48 and was significantly decreased after 6 and 12 months of treatment. Percentage of dogs with COM that had clinical signs was significantly decreased after 1, 3, 6, and 12 months of treatment. One year after initiation of treatment, 9 of 9 dogs were still receiving fluconazole and 8 of 9 dogs had positive results for serum IgG titer testing. CLINICAL RELEVANCE Dogs with COM typically had a rapid improvement in clinical signs after initiating treatment with fluconazole but required long-term antifungal treatment. Dogs with COM differed from dogs with OSA, but radiographic features had a great degree of overlap between groups, confounding the ability to make a diagnosis on the basis of diagnostic imaging alone.

Entity recognition of Chinese medical text based on multi-head self-attention combined with BILSTM-CRF

<abstract> <p>Named entities are the main carriers of relevant medical knowledge in Electronic Medical Records (EMR). Clinical electronic medical records lead to problems such as word segmentation ambiguity and polysemy due to the specificity of Chinese language structure, so a Clinical Named Entity Recognition (CNER) model based on multi-head self-attention combined with BILSTM neural network and Conditional Random Fields is proposed. Firstly, the pre-trained language model organically combines char vectors and word vectors for the text sequences of the original dataset. The sequences are then fed into the parallel structure of the multi-head self-attention module and the BILSTM neural network module, respectively. By splicing the output of the neural network module to obtain multi-level information such as contextual information and feature association weights. Finally, entity annotation is performed by CRF. The results of the multiple comparison experiments show that the structure of the proposed model is very reasonable and robust, and it can effectively improve the Chinese CNER model. The model can extract multi-level and more comprehensive text features, compensate for the defect of long-distance dependency loss, with better applicability and recognition performance.</p> </abstract>

An Unsupervised Approach to Structuring and Analyzing Repetitive Semantic Structures in Free Text of Electronic Medical Records

Electronic medical records (EMRs) include many valuable data about patients, which is, however, unstructured. Therefore, there is a lack of both labeled medical text data in Russian and tools for automatic annotation. As a result, today, it is hardly feasible for researchers to utilize text data of EMRs in training machine learning models in the biomedical domain. We present an unsupervised approach to medical data annotation. Syntactic trees are produced from initial sentences using morphological and syntactical analyses. In retrieved trees, similar subtrees are grouped using Node2Vec and Word2Vec and labeled using domain vocabularies and Wikidata categories. The usage of Wikidata categories increased the fraction of labeled sentences 5.5 times compared to labeling with domain vocabularies only. We show on a validation dataset that the proposed labeling method generates meaningful labels correctly for 92.7% of groups. Annotation with domain vocabularies and Wikidata categories covered more than 82% of sentences of the corpus, extended with timestamp and event labels 97% of sentences got covered. The obtained method can be used to label EMRs in Russian automatically. Additionally, the proposed methodology can be applied to other languages, which lack resources for automatic labeling and domain vocabulary.