Case Records as Medical Stories: A Song-dynasty Doctor’s Narration of His Own Medicine-Xu Shuwei (1080-1154)

The key point in studying or teaching the history of Chinese medicine is on the doctrines underlying it and on its perception of the body,physiology,pathology,and its treatment.Namely,there is often a tendency to focus on reading and analysing the classical canons and therapy-related texts including formularies and materia medica collections.However,focusing on these sources provides us with a one-sided presentation of Chinese medicine.These primary sources lack the clinical down-to-earth know-how that encompasses medical treatment,which are represented,for instance,in the clinical rounds of modern medical schools.Our traditional focus on the medical canons and formularies provides almost no clinical knowledge,leaving us with a one-sided narrative that ignores how medicine and healing are actually practiced in the field.This paper focuses on the latter aspect of medicine from a historical perspective.Using written and visual sources dating to the Song dynasty,clinical encounters between doctors and patients including their families are depicted based on case records recorded by a physician,members of the patient’s family,and bystanders.This array of case records or case stories will enable us to narrate the interaction between physicians and patients both from the clinical perspective and from the social interaction.This paper will also discuss visual depictions of the medical encounter to provide another perspective for narrating medicine during the Song dynasty.Medical case records and paintings depicting medical encounters are exemplary of the potential of Chinese primary sources for narrative medicine.

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.

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.

Analysis on the mechanism of core drugs in treating exogenous cough in Clinical Guidelines and Medical Records

Objective:To explore the core drug of Tian-shi Ye(1666 C.E.–1745 C.E.,a famous medical experts in Qing Dynasty)for treating exogenous cough and its mechanism of action.Methods:The database of prescriptions for treating exogenous cough was established in Clinical Guide to Medical Records,and the complex network was constructed with frequency analysis and Gephi 0.9.2 software to obtain the core drug for exogenous cough,and the network of traditional Chinese medicine-component-disease-target regulation was constructed through network pharmacology to reveal the potential mechanism.Results:Xingren(Amygdalus Communis Vas)is the core drug for the treatment of exogenous cough in the“Clinical Guidelines and Medical Records”.It contains 19 active ingredients and forms 27 traditional Chinese medicine-disease targets for the treatment of acute bronchitis.Kyoto Encyclopedia of Genes and Genomes analysis involving 114 pathways,including arachidonic acid metabolism pathways,peroxidase pathways,estrogen metabolism pathways,and tryptophan metabolism pathways are the main signal pathways involved.Conclusion:Xingren(Amygdalus Communis Vas)-acute bronchitis has a multi-molecule,multi-target,and multi-pathway,and may be regulating the arachidonic acid metabolism pathway,peroxidase pathway,estrogen metabolism pathway,and tryptophan metabolism pathway.Such interventions as various biological processes in the body play a role in the treatment of acute bronchitis.

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.

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.

Under the narrative medicine theory to establish the evidence-based medical record written by doctors and patients through integrated therapy of traditional Chinese and western on digestive system diseases

Objective:The aim of this article was to discuss the theory of doctor-patient co-operated evidence-based medical record and set up the preliminary frame of the doctor-patient co-operated evidence-based medical record following the concept of narrative evidence-based medicine.Methods:The information was searched from Pubmed,Embase,CBMdisc,CNKI.A preliminary agreement was reached by referring to the principles of narrative medicine and advises given by experts of digestive system and evidence-based medicine in both Traditional Chinese Medicine and Western Medicine.Result:This research is a useful attempt to discuss the establishment of doctor-patient co-operated evidence-based medical record guided by the direction of narrative evidence-based medicine.Reflection and outlook:Doctor-patient co-operated medical record can be a key factor of the curative effect evaluation methodology system of integrated therapy of Tradition Chinese Medicine and Western Medicine on spleen and stomach diseases.

Impact of an educational intervention on medical records documentation

BACKGROUND: Inaccurate and incomplete documentation can lead to poor treatment and medicolegal consequences. Studies indicate that teaching programs in this field can improve the documentation of medical records. The study aimed to evaluate the effect of an educational workshop on medical record documentation by emergency medicine residents in the emergency department.METHODS: An interventional study was performed on 30 residents in their first year of training emergency medicine(PGY1), in three tertiary referral hospitals of Tehran University of Medical Sciences. The essential information that should be documented in a medical record was taught in a 3-day-workshop. The medical records completed by these residents before the training workshop were randomly selected and scored(300 records), as was a random selection of the records they completed one(300 records) and six months(300 records) after the workshop.RESULTS: Documentation of the majority of the essential items of information was improved significantly after the workshop. In particular documentation of the patients' date and time of admission, past medical and social history. Documentation of patient identity, requests for consultations by other specialties, first and final diagnoses were 100% complete and accurate up to 6 months of the workshop.CONCLUSION: This study confirms that an educational workshop improves medical record documentation by physicians in training.

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."

Algorithms of mining data records from website automatically

In order to improve the accuracy and integrality of mining data records from the web, the concepts of isomorphic page and directory page and three algorithms are proposed. An isomorphic web page is a set of web pages that have uniform structure, only differing in main information. A web page which contains many links that link to isomorphic web pages is called a directory page. Algorithm 1 can find directory web pages in a web using adjacent links similar analysis method. It first sorts the link, and then counts the links in each directory. If the count is greater than a given valve then finds the similar sub-page links in the directory and gives the results. A function for an isomorphic web page judgment is also proposed. Algorithm 2 can mine data records from an isomorphic page using a noise information filter. It is based on the fact that the noise information is the same in two isomorphic pages, only the main information is different. Algorithm 3 can mine data records from an entire website using the technology of spider. The experiment shows that the proposed algorithms can mine data records more intactly than the existing algorithms. Mining data records from isomorphic pages is an efficient method.

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.

Sleep disturbances and predictors of nondeployability among active-duty army soldiers: an odds ratio analysis of medical healthcare data from fiscal year 2018

Background:The impact of sleep disorders on active-duty soldiers’medical readiness is not currently quantified.Patient data generated at military treatment facilities can be accessed to create research reports and thus can be used to estimate the prevalence of sleep disturbances and the role of sleep on overall health in service members.The current study aimed to quantify sleep-related health issues and their impact on health and nondeployability through the analysis of U.S.military healthcare records from fiscal year 2018(FY2018).Methods:Medical diagnosis information and deployability profiles(e-Profiles)were queried for all active-duty U.S.Army patients with a concurrent sleep disorder diagnosis receiving medical care within FY2018.Nondeployability was predicted from medical reasons for having an e-Profile(categorized as sleep,behavioral health,musculoskeletal,cardiometabolic,injury,or accident)using binomial logistic regression.Sleep e-Profiles were investigated as a moderator between other e-Profile categories and nondeployability.Results:Out of 582,031 soldiers,48.4%(n=281,738)had a sleep-related diagnosis in their healthcare records,9.7%(n=56,247)of soldiers had e-Profiles,and 1.9%(n=10,885)had a sleep e-Profile.Soldiers with sleep e-Profiles were more likely to have had a motor vehicle accident(p OR(prevalence odds ratio)=4.7,95%CI 2.63–8.39,P≤0.001)or work/duty-related injury(p OR=1.6,95%CI 1.32–1.94,P≤0.001).The likelihood of nondeployability was greater in soldiers with a sleep e-Profile and a musculoskeletal e-Profile(p OR=4.25,95%CI 3.75–4.81,P≤0.001)or work/dutyrelated injury(p OR=2.62,95%CI 1.63–4.21,P≤0.001).Conclusion:Nearly half of soldiers had a sleep disorder or sleep-related medical diagnosis in 2018,but their sleep problems are largely not profiled as limitations to medical readiness.Musculoskeletal issues and physical injury predict nondeployability,and nondeployability is more likely to occur in soldiers who have sleep e-Profiles in addition to these issues.Addressing sleep problems may prevent accidents and injuries that could render a soldier nondeployable.

Impact of mobile health and medical applications on clinical practice in gastroenterology

Mobile health apps (MHAs) and medical apps (MAs) are becoming increasinglypopular as digital interventions in a wide range of health-related applications inalmost all sectors of healthcare. The surge in demand for digital medical solutionshas been accelerated by the need for new diagnostic and therapeutic methods inthe current coronavirus disease 2019 pandemic. This also applies to clinicalpractice in gastroenterology, which has, in many respects, undergone a recentdigital transformation with numerous consequences that will impact patients andhealth care professionals in the near future. MHAs and MAs are considered tohave great potential, especially for chronic diseases, as they can support the selfmanagementof patients in many ways. Despite the great potential associated withthe application of MHAs and MAs in gastroenterology and health care in general,there are numerous challenges to be met in the future, including both the ethicaland legal aspects of applying this technology. The aim of this article is to providean overview of the current status of MHA and MA use in the field ofgastroenterology, describe the future perspectives in this field and point out someof the challenges that need to be addressed.

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.

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.

Misdiagnosis Features of Ancient Clinical Records Based on Apriori Algorithm

Objective:To analyze misdiagnosis features in clinical cases of“Classified Medical Cases of Famous Physicians”and“Supplement to Classified Case Records of Celebrated Physicians.”Materials and Methods:Two hundred and five ancient misdiagnosed cases were analyzed in aspects of locations(exterior-interior type,qi-blood type and Zang‑Fu organs type)and patterns(heat-cold type and deficiency-excess type)by Apriori Algorithm Method.Results:The main types of misdiagnosis in those medical casesare as follows::Zang‑Fu location misjudgment,misjudging the interior as the exterior,misjudging deficiency pattern as excess pattern,and misjudging cold pattern as heat pattern.Among them,the most outstanding type is the misjudgment of deficiency–cold pattern as excess–heat pattern.Conclusions:(1)Accurate judgment of location and differentiation of deficiency and excess patterns are the key points in diagnosing the diseases correctly.The confusion of true deficiency–cold and pseudo‑excess–heat pattern should be taken seriously.(2)Data mining on ancient clinical cases offers a new methodology for assisting clinical diagnosis of traditional Chinese medicine.

Oxaliplatin Induced Neurotoxicity among Patients with Colorectal Cancer: Documentation in Medical Records—A Pilot Study

Patients with colorectal cancer (CRC) can have chemotherapy with oxaliplatin postoperatively. Oxaliplatin can cause acute and chronic neurotoxicity. It is important to be aware of neurotoxic side effects so they can be documented and action taken at an early stage. The study aimed to identify and explore neurotoxic side effects documented in the medical records of patients with colorectal cancer treated with oxaliplatin-based adjuvant chemotherapy. Data in this study were medical records;presenting documentation about patients treated at the University Hospital in the south of Sweden between 2009 and 2010. A summative content analysis approach was used to explore the neurotoxic side effects. Identification and quantification of the content of medical records were carried out by using a study-specific protocol. “Cold sensitivity” and “tingling in the hands” were the most frequently documented neurotoxicity-related terms in the medical records. This identification was followed by interpretation. Three categories were identified in the interpretive part of the study: acute, chronic, and degree of neurotoxicity. The results show the importance of awareness of neurotoxic side effects so that they can be documented and action taken at an early stage. The documentation could be more reliable if patient-reported structured measurements were used, combined with free descriptions in the medical records. Being able to follow the progression of the symptoms during and after treatment would improve patient’s safety and also quality of life. The protocol that we developed and used in this review of medical records may be helpful to structure the documentation in the electronic system for documentation of neurotoxicity side effects.

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.

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.

Optimization of the Classic Transfer-Stacking Model Migration Algorithm: A Way to Solve Time-Varying Performance Degradation of Acute Kidney Injury Clinical Prediction Model

Acute Kidney Injury (AKI) is one of the most common acute and critical illnesses in general wards and intensive care units. Its high morbidity and high fatality rate have become a major global public health problem. There are often serious lags in clinical diagnosis of AKI. Early diagnosis and timely intervention and effective care become critical. The use of electronic medical record data to build an AKI risk prediction model has been proven to help prevent the occurrence of AKI. However, in actual clinical applications, the distribution of historical data and new data will continue to vary over time, resulting in a significant decrease in the performance of the model. How to solve the problem of model performance degradation over time will be a core challenge for the long-term use of predictive models in clinical applications. Aiming at the above problems, this paper studies the classic Transfer-Stacking model migration algorithm. Aiming at the lack of this algorithm, such as the loss of a large amount of feature information of the target domain and poor fit when integrating the model of the target domain, the Accumulate-Transfer-Stacking algorithm is proposed to improve it. Improvements include: 1) Optimize the input vector and model integration algorithm of Transfer-Stacking’s target domain model. 2) Optimize Transfer-Stacking from a single-source domain model to a multi-source domain model. The experimental results show that for the improved algorithm proposed in this paper when the data is sufficient and insufficient, the average AUC value of the model on the data of subsequent years is 0.89 and 0.87, and the average F1 Score value is 0.45 and 0.36. Moreover, this method is significantly better than the unimproved Transfer-Stacking algorithm and baseline method, and can effectively overcome the problem of data distribution heterogeneity caused by time factors.