Data Masking for Chinese Electronic Medical Records with Named Entity Recognition

With the rapid development of information technology,the electronifi-cation of medical records has gradually become a trend.In China,the population base is huge and the supporting medical institutions are numerous,so this reality drives the conversion of paper medical records to electronic medical records.Electronic medical records are the basis for establishing a smart hospital and an important guarantee for achieving medical intelligence,and the massive amount of electronic medical record data is also an important data set for conducting research in the medical field.However,electronic medical records contain a large amount of private patient information,which must be desensitized before they are used as open resources.Therefore,to solve the above problems,data masking for Chinese electronic medical records with named entity recognition is proposed in this paper.Firstly,the text is vectorized to satisfy the required format of the model input.Secondly,since the input sentences may have a long or short length and the relationship between sentences in context is not negligible.To this end,a neural network model for named entity recognition based on bidirectional long short-term memory(BiLSTM)with conditional random fields(CRF)is constructed.Finally,the data masking operation is performed based on the named entity recog-nition results,mainly using regular expression filtering encryption and principal component analysis(PCA)word vector compression and replacement.In addi-tion,comparison experiments with the hidden markov model(HMM)model,LSTM-CRF model,and BiLSTM model are conducted in this paper.The experi-mental results show that the method used in this paper achieves 92.72%Accuracy,92.30%Recall,and 92.51%F1_score,which has higher accuracy compared with other models.

Deletion and Recovery Scheme of Electronic Health Records Based onMedical Certificate Blockchain

The trusted sharing of Electronic Health Records(EHRs)can realize the efficient use of medical data resources.Generally speaking,EHRs are widely used in blockchain-based medical data platforms.EHRs are valuable private assets of patients,and the ownership belongs to patients.While recent research has shown that patients can freely and effectively delete the EHRs stored in hospitals,it does not address the challenge of record sharing when patients revisit doctors.In order to solve this problem,this paper proposes a deletion and recovery scheme of EHRs based on Medical Certificate Blockchain.This paper uses cross-chain technology to connect the Medical Certificate Blockchain and the Hospital Blockchain to real-ize the recovery of deleted EHRs.At the same time,this paper uses the Medical Certificate Blockchain and the InterPlanetary File System(IPFS)to store Personal Health Records,which are generated by patients visiting different medical institutions.In addition,this paper also combines digital watermarking technology to ensure the authenticity of the restored electronic medical records.Under the combined effect of blockchain technology and digital watermarking,our proposal will not be affected by any other rights throughout the process.System analysis and security analysis illustrate the completeness and feasibility of the scheme.

Maturity Assessment of Hospital Information Systems Based on Electronic Medical Record Adoption Model (EMRAM)— Private Hospital Cases in Iran

Introduction: Today, information technology is considered as an important national development principle in each country which is applied in different fields. Health care as a whole and the hospitals could be regarded as a field and organizations with most remarkable IT applications respectively. Although different benchmarks and frameworks have been developed to assess different aspects of Hospital Information Systems (HISs) by various researchers, there is not any suitable reference model yet to benchmark HIS in the world. Electronic Medical Record Adoption Model (EMRAM) has been currently presented and is globally well-known to benchmark the rate of HIS utilization in the hospitals. Notwithstanding, this model has not been introduced in Iran so far. Methods: This research was carried out based on an applied descriptive method in three private hospitals of Isfahan—one of the most important provinces of Iran—in the year 2015. The purpose of this study was to investigate IT utilization stage in three selected private hospitals. Conclusion: The findings revealed that HIS is not at the center of concern in studied hospitals and is in the first maturity stage in accordance with EMRAM. However, hospital managers are enforced and under the pressure of different beneficiaries including insurance companies to improve their HIS. Therefore, it could be concluded that these types of hospitals are still far away from desirable conditions and need to enhance their IT utilization stage significantly.

Cautionary note:Electronic medical records,a potential disaster in the making?

Concern is expressed that electronic medical records may actually compromise care.Reports are electronically collated with patient charts, but when are they examined? Current electronic transmission of results to patients' electronic medical records do not seem to notify of new information.The unknown time from prescription to patient action and the variable time required for individual test performance seem to mandate that a physician attempting to be conscientious would have to examine all sections of every patient medical record in their practice, every day.That is quite inefficient and error-prone.Electronic medical record still contains what appear to be dangerous "bugs" which compromise our ability to provide the care we believe our patients deserve? I remain unsure that outpatient electronic medical records are "ready for prime time."

Medical Knowledge Extraction and Analysis from Electronic Medical Records Using Deep Learning

Objectives Medical knowledge extraction (MKE) plays a key role in natural language processing (NLP) research in electronic medical records (EMR),which are the important digital carriers for recording medical activities of patients.Named entity recognition (NER) and medical relation extraction (MRE) are two basic tasks of MKE.This study aims to improve the recognition accuracy of these two tasks by exploring deep learning methods.Methods This study discussed and built two application scenes of bidirectional long short-term memory combined conditional random field (BiLSTM-CRF) model for NER and MRE tasks.In the data preprocessing of both tasks,a GloVe word embedding model was used to vectorize words.In the NER task,a sequence labeling strategy was used to classify each word tag by the joint probability distribution through the CRF layer.In the MRE task,the medical entity relation category was predicted by transforming the classification problem of a single entity into a sequence classification problem and linking the feature combinations between entities also through the CRF layer.Results Through the validation on the I2B2 2010 public dataset,the BiLSTM-CRF models built in this study got much better results than the baseline methods in the two tasks,where the F1-measure was up to 0.88 in NER task and 0.78 in MRE task.Moreover,the model converged faster and avoided problems such as overfitting.Conclusion This study proved the good performance of deep learning on medical knowledge extraction.It also verified the feasibility of the BiLSTM-CRF model in different application scenarios,laying the foundation for the subsequent work in the EMR field.

Endoscopic electronic record:A new approach for improving management of colorectal cancer prevention

Digestive endoscopy is currently the main diagnostic procedure for investigation of the digestive tract when a digestive disease is suspected.The use of computers and electronic medical records for the management of endoscopic data are an important key to improving endoscopy unit efficiency and productivity.This technology supports optimal program operation,monitoring and evaluation colorectal cancer screening.This article is a comprehensive survey of endoscopic electronic medical records and information systems.Computerized clinical records have the capability of identifying patients due for screening and to calculate baseline rates of colorectal cancer screening by patient characteristics and by primary care physician and practice group.This paper describes data flow in the endoscopy unit,the minimum data set of colorectal cancer and key features of endoscopic electronic medical record.In addition,the researchers state standards in different aspects,especially terminology standards and interoperability standards for image and text.

Effect of Electronic Medical Record Utilization on Depression, Anxiety and Stress among Doctors and Nurses in Johor, Malaysia

Background： The usage of modem technology in healthcare record system is now a must throughout the world. However, many doctors and nurses has been reporting facing numerous challenges and obstacles in the implementation. The aim of the present study is to determine the prevalence of depression, anxiety and stress among doctors and nurses who utilize EMR （electronic medical record） and its associated factor. Methods： A comparative cross-sectional study was conducted ~om January till April 2012 among doctors and nurses in two public tertiary hospitals in Johor in which one of them uses EMR and the other one still using the MMR （manual medical record） system. Data was collected using self-administered validated Malay version of DASS-21 （Depression, Anxiety, and Stress Scales-21） items questionnaire. It comprises of socio-demographic and occupational characteristics. Findings： There were 130 respondents with a response rate of 91% for EMR and 123 respondents with a response rate of 86% for MMR. The mean （SD） age of respondents in EMR and MMR groups were 34.7 （9.42） and 29.7 （6.15） respectively. The mean （SD） duration of respondents using EMR was 46.1 （35.83） months. The prevalence of depression, anxiety and stress among respondents using EMR were 6.9%, 25.4% and 12.3%. There were no significant difference between the study groups related to the depression, anxiety and stress scores. In multivariable analysis, the significant factors associated with depression among respondents using EMR was age （OR 1.10, 95% CI 1.02, 1.19）. The significant factors associated with stress among respondents using EMR was marital status （OR 3.33, 95% CI 1.10, 10.09） and borderline significant was computer skill course （OR 2.94, 95% CI 0.98, 8.78）. Conclusion： The prevalence of depression, anxiety and stress of those who uses EMR were within acceptable range. Age, marital status and computer skill are the identified factor associated with the depression and stress level which need to be considered in its implementation.

Development of Medical Informatization in the Era of Big Data

The purpose of this paper is to discuss the development of medical informatization in the era of big data.Through literature review and theoretical analysis,the development of medical informatization in the era of big data is deeply discussed.The results show that medical informatization has developed rapidly in the era of big data,and its role in clinical decision-making,scientific research,teaching,and management has become increasingly prominent.The development of medical informatization in the era of big data has important purposes and methods,which can produce important results and conclusions and provide strong support for the development of the medical field.

Impact of Bar-Code Medication Administration and Electronic Medication Administration Record System in Clinical Practice for an Effective Medication Administration Process

Clinical innovations are ideations resulting from collective experiences that enhance the “norm” and embrace an avenue for change with an invention. As such, collective data that were suggestive of increased medication errors that compromised patient safety initiated the exploration of methods that could reduce multifactorial human errors. The pursuit for an appropriate system followed with the discovery of barcode medication administration system (BCMA) and electronic medication administration system (e-MAR). Prior to the adoption of BCMA and e-MAR, it was crucial to assess the impact of the new medication administration system and the rate of medication administration errors recorded, specifically those that resulted in harm. The purpose of the study was to evaluate BCMA and e-MAR usage outcomes, clinical practices, policies, and processes impacting nurses administering medications in the clinical environment using BCMA and e-MAR system. Thus, an annotated literature review was conducted prior to the implementation of the innovation which analyzed various studies that elaborated on their study methods, data collection and analysis that deliberated on the advantages and disadvantages of barcode medication administration system. It is evident in the researched journals that increased compliance was observed with appropriate guidance, processes and policies in place. There was also a significant reduction in reported errors. The incorporation of barcode technology with electronic medication administration record (e-MAR) had greatly improved the efficiency of the BCMA system. BCMAs method was one of the proposed solutions to medication administration errors and to enhance patient safety measures. As such, the innovation could significantly reduce medication error resulting from intrinsic and extrinsic factors. This paper will further elaborate on the advantages and disadvantages of BCMA and e-MAR, the strategies assumed in the development of BCMA and e-MAR system and its implementation process by identifying and overcoming potential challenges that may arise.

Integrating Electronic Systems for Requesting Clinical Laboratory Test into Digital Clinical Records: Design and Implementation

Clinical laboratory tests are basic elements that support healthcare tasks such as disease detection, diagnosis and monitoring of response to treatments. Current laboratory information systems focus on the patient database, tests and results, with multiple modules available, connecting with the various analytical systems or work areas. However laboratory information systems functioned as “islands of information”, because their design was fundamentally inward-looking and disconnected from other healthcare computer applications. Actually, the Electronic Health Register (EHR) is considered by clinicians as a tool with great potential healthcare benefits. The EHR, in the sense of a unique and complete record of a patient’s healthcare and state of health, regardless of the healthcare level used, is a real attempt to eliminate these “islands of information” and need modules to act as “bridges” with the laboratory information systems. This type of module, which in generic terms may be referred to as a laboratory test request module, has become an essential feature of the EHR. These modules need to use a laboratory coding system as a common language for exchanging information, ensuring that tests and results are unequivocally identified. The development of the laboratory test request module requires the commitment of professionals and political authorities, being necessary time for their design and an adequate pilot phase. The laboratory professionals have to assume a leadership role in the whole process of design, development and implementation of these modules, integrating in the equipment of information technologies of healthcare providers. In our manuscript we review the elements that may prove electronic systems for requesting clinical laboratory test into digital clinical records and the key elements to move from theory to practice.

A Proposed Layered Architecture to Maintain Privacy Issues in Electronic Medical Records

Securing large amounts of electronic medical records stored in different forms and in many locations, while making availability to authorized users is considered as a great challenge. Maintaining protection and privacy of personal information is a strong motivation in the development of security policies. It is critical for health care organizations to access, analyze, and ensure security policies to meet the challenge and to develop the necessary policies to ensure the security of medical information. The problem, then, is how we can maintain the availability of the electronic medical records and at the same time maintain the privacy of patients’ information. This paper will propose a novel architecture model for the Electronic Medical Record (EMR), in which useful statistical medical records will be available to the interested parties while maintaining the privacy of patients’ information.

Setting up a Customized Electronic Health Record System Fit for Internal Medicine in Dakar, Senegal

Objective: Describe the design and implementation of an electronic medical record—E-INTMED—customized for Internal Medicine in Dakar, Senegal. Methodology: This study was carried out in a public teaching hospital in Dakar Senegal. It entailed collaboration between physicians specialized in various fields in Internal Medicine and Computer Scientists to carry out the compilation of data and their electronic transcription to produce a prototype which met users’ needs. Results: E-INTMED software is structured around several hierarchical tables allowing users to register and store all relevant patients’ information. E-INTMED structures patient’s data to provide a clear overview of their medical history and users’ activity performance. E-INTMED makes medical users’ life so much easier. Users can generate and send letters and prescriptions quickly and efficiently using the customized templates which they can modify or create new ones. In addition to these capabilities, all of the features expected in an Internal Medicine EHR are handled by E-INTMED, such as lab orders and results, mechanisms for continuity of care, embedding and access to images and documents, and so much more. E-INTMED provides medical students with a number of educational, practical and administrative advantages. Conclusion: Computerization of medical records has become a necessity today. Crossing the line to Electronic medical records could help to improve medical practice and medical training.

Implementing of Electronic Medical Record in Pelvic Floor Ward: A Pilot Study

The electronic medical record is an essential technology tool to improve the quality of care. In present study we reported on the design and feasibility of electronic medical records in Female Pelvic Floor Dysfunction Ward. Our main goal was documentation with the least possible missed data, evidence-based decision making, documented active patient follow up and increasing patient’s satisfaction. The Electronic Registry System of Female Pelvic Floor Dysfunction (Vali e Asr Hospital, Tehran, Iran) was designed in mid 2014 and tested till March 2015. The software description was designed based on previous paper questionnaire used in this ward. The electronic questionnaires were filled in upon hospitalization and thereafter including follow ups. The questionnaire included 10 demographic and 15 main questions. A digital analog scale (1 - 10) in each part quantified the effects of problem on patient’s quality of life and also the effects of interventions as well. Entered information in each step was available for those with defined access. Reporting design was dependent on the needed data. Our supervised data entry was a guarantee to the quality of information with the least errors. Access to all para clinical data made rapid and evidence-based decision making. Patient satisfaction was achieved because of unified approach. The most interesting part was access to evidence-based results and data to be used in research projects. This study showed that EMRs in Female Pelvic Floor Dysfunction Wards could provide valuable information, improve the quality of care and increase patient’s satisfaction.

Secure approach to sharing digitized medical data in a cloud environment

Without proper security mechanisms, medical records stored electronically can be accessed more easily than physical files. Patient health information is scattered throughout the hospital environment, including laboratories, pharmacies, and daily medical status reports. The electronic format of medical reports ensures that all information is available in a single place. However, it is difficult to store and manage large amounts of data. Dedicated servers and a data center are needed to store and manage patient data. However, self-managed data centers are expensive for hospitals. Storing data in a cloud is a cheaper alternative. The advantage of storing data in a cloud is that it can be retrieved anywhere and anytime using any device connected to the Internet. Therefore, doctors can easily access the medical history of a patient and diagnose diseases according to the context. It also helps prescribe the correct medicine to a patient in an appropriate way. The systematic storage of medical records could help reduce medical errors in hospitals. The challenge is to store medical records on a third-party cloud server while addressing privacy and security concerns. These servers are often semi-trusted. Thus, sensitive medical information must be protected. Open access to records and modifications performed on the information in those records may even cause patient fatalities. Patient-centric health-record security is a major concern. End-to-end file encryption before outsourcing data to a third-party cloud server ensures security. This paper presents a method that is a combination of the advanced encryption standard and the elliptical curve Diffie-Hellman method designed to increase the efficiency of medical record security for users. Comparisons of existing and proposed techniques are presented at the end of the article, with a focus on the analyzing the security approaches between the elliptic curve and secret-sharing methods. This study aims to provide a high level of security for patient health records.

Smart Dynamic Resource Allocation Model for Patient-Driven Mobile Medical Information System Using C4.5 Algorithm

A mobile medical information system (MMIS) is an integrated application (app) of traditional hospital information systems (HIS) which comprise a picture archiving and communications system (PACS), laboratory information system (LIS), pharmaceutical management information system (PMIS), radiology information system (RIS), and nursing information system (NIS). A dynamic resource allocation table is critical for optimizing the performance to the mobile system, including the doctors, nurses, or other relevant health workers. We have designed a smart dynamic resource allocation model by using the C4.5 algorithm and cumulative distribution for optimizing the weight of resource allocated for the five major attributes in a cooperation communications system. Weka is used in this study. The class of concept is the performance of the app, optimal or suboptimal. Three generations of optimization of the weight in accordance with the optimizing rate are shown.

Serum Sodium Fluctuation Prediction among ICU Patients Using Neural Network Algorithm:Analysis of the MIMIC-IV Database

Sodium homeostasis disorder is one of the most common abnormal symptoms of elderly patients in intensive care unit(ICU),which may lead to physiological disorders of many organs.The current prediction of serum sodium in ICU is mainly based on the subjective judgment of doctors’experience.This study aims at this problem by studying the clinical retrospective electronic medical record data of ICU to establish a machine learning model to predict the short-term serum sodium value of ICU patients.The data set used in this study is the open-source intensive care medical information set Medical Information Mart for Intensive Care(MIMIC)-IV.The time point of serum sodium detection was selected from the ICU clinical records,and the ICU records of 25risk factors related to serum sodium were extracted from the patients within the first 12 h for statistical analysis.A prediction model of serum sodium value within 48 h was established using a feedforward neural network,and compared with previous methods.Our research results show that the neural network learning model can predict the development of serum sodium in patients using physiological indicators recorded in clinical electronic medical records within 12 h,and has better prediction effect than the serum sodium formula and other machine learning models.

基于后结构化技术的临床病种库系统设计与应用

目的:为解决传统临床病种库系统存在的依赖大量人工判断、缺乏辅助标注、电子病历数据可用性差等问题,设计一种基于后结构化技术的临床病种库系统。方法:先通过I2B2标准以及双向长短期记忆网络(bi-directional long short-term memory,BiLSTM)模型构建实体识别模型,形成病历模板库,然后组合病历模板库形成关系模板,抽取复杂的医学实体,实现电子病历的后结构化。之后,基于电子病历后结构化技术构建包括病历结构化、结构化评估、数据标注、常规功能和系统管理5个模块的临床病种库系统。结果:该系统可以将电子病历文本转化为结构化语言,提供更精细化的数据要素提取、更智能的结构化服务,提高了临床和科研工作的效率。结论:该系统提高了临床病种的数据可用性,减轻了用户数据加工的工作强度,保证了数据应用的高质量,为医学研究、临床辅助决策打下了坚实的基础。

基于电子病历的电子化衰弱指数构建思路

目的:全面梳理国内外电子化衰弱指数的研究进展,为国内构建电子化衰弱指数提供思路。方法:检索了四川大学数字图书馆中相关文献,并从衰弱风险因子的选择及应用效果评价等方面进行总结。结果:建议进一步扩增衰弱风险指标池,并利用医学术语系统及特征选择等方法构建适用于国内的电子化衰弱指数。结论:电子化衰弱指数对于量化和分级老年人的衰弱状况具有重要作用,有利于早期发现和管理衰弱症状,甚至能对高风险因素进行预防干预,从而降低疾病带来的负担。

图注意力网络与句法融合的医疗实体识别

电子病历数据中包含大量的医疗实体词,对这些实体词的自动识别有益于提升计算机对电子病历数据的理解。待识别的医疗实体词通常由医疗专业术语和非规范医疗词汇构成,大量生僻词汇、长难词汇和病历行文中的省略现象给医疗实体识别任务带来了挑战。针对以上问题,该文提出一种图注意力网络与句法融合的医疗实体识别方法,该方法结合字词共现关系和句法依存关系,基于电子病历数据构建了交互式字词关系图和依存关系图,并利用图注意力网络完成多种图信息的融合。实验结果表明,在电子病历的命名实体识别中,该文方法得到88.91%的F_(1)值,较基线模型提高1.04%,验证了该方法的有效性。