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.

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.

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.

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.

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.

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."

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.

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.

A Digital System for Managing HL7/CDA Electronic Medical Records Stored in iButtons^(█)

The goal of this research was to develop a digital system that could allow managing electronic medical records (EMRs) codified under specifications of the Health Level 7/Clinical Document Architecture (HL7/CDA) international standard, and saving them in a portable digital storage device called iButton?. To this end, an USB-based hardware interface for reading and storing EMRs in iButtons was designed and implemented. In addition, a software application for invoking read/write operations on stored EMRs and showing their content on a graphical user interface was also developed, following the Extreme Programming (XP) software development methodology and using Visual Basic .NET as programming language. Tests conducted on the hardware interface showed that it was able to recognize any iButton type, reading and writing data on them as well. Moreover, the system helped in creating empty EMRs in conformance with the HL7/CDA standard, adding and viewing information, and updating it in the iButton. This system offers an easy way for managing and visualizing medical records codified in HL7/CDA, and allows patients to take their updated medical history with them everywhere.

名中医杨毅从“以平为期”理论探讨中医治疗肺癌思路

总结杨毅教授治疗肺癌的临床思路,临证时注重外感祛邪宣肺为要,因势利导。内伤调理脏腑为主,以平为安,辨证施治,匠心独运,验效颇丰。现择取杨师验案进行分析,为治疗肺癌拓宽思路,提高临床疗效。

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.

基于关联规则的中医药知识发现——以肺病医案为例

[目的/意义]中医医案作为重要的医疗资源含有大量的数据与信息,深度挖掘中医医案,围绕医案内容进行知识发现,有利于隐性知识的发掘,提高中医临床诊疗的效率与医疗服务的精准性。[方法/过程]以实地肺病医案为数据源,基于统计分析与关联规则的方法对中医医案内容进行挖掘,以咳嗽为主证为出发点,挖掘相关中医用药规律与临床症状以及症—药规律,探究关联规则分析在中医药知识发现方面的创新应用。[结果/结论]本地治疗咳嗽的高频药物包括炙甘草、杏仁、半夏、川贝母、茯苓、陈皮等,舌诊症状以苔薄白、苔薄黄、舌红、舌暗红、有齿印等为常见。基于Apriori算法的关联规则分析显示常见的药物配伍为丹参,桔梗—炙甘草、柴胡,桔梗—炙甘草、前胡,枳壳—桔梗等;舌诊症状以薄淡黄—舌暗红、有齿印—舌暗红、苔薄腻淡黄—有齿印等组合为强关联组合,苔薄淡黄—舌暗红症状组合的强关联药物有桔梗、炙甘草、丹参、白前等。咳嗽患者的类型有外感咳嗽与内伤咳嗽,涉及风热证、痰湿证、阴虚证、肺气虚证、痰热壅肺证等证型。

病案为载体的心电图教学与传统心电图教学效果的比较

目的探讨适合护理实习生的心电图教学法,提高学生对临床心电图的识别和护理实践能力。方法选取2021年9月至2022年12月江西省胸科医院心内科的160名护理实习生作为研究对象,按照随机数字表法分为研究组(n=80)与对照组(n=80),研究组采用临床心电图案例教学方法,对照组采用传统心电图教学方法,比较两组护理实习生的心电图基础理论评分、心电图测试评分,心电监护考核评分以及心电图护理教学满意度。结果研究组的心电图基础理论评分、心电图测试评分均高于对照组,差异有统计学意义(P<0.05)。研究组的心电监护考核评分、心电图护理教学效果满意度高于对照组,差异有统计学意义(P<0.05)。结论相较于传统心电图教学,病案为载体的心电图教学法更具优势,能切实有效增强护理实习生心电图理论及实践能力,提高护理实习生对心电图护理教学的满意度,从而提升教学质量。

叙事医学视域下现代病历的人文反思

在对反思概念深入理解基础上,聚焦于病历书写实践,强调了叙事医学对现行病历功能缺失的审视。以临床实践的病历书写为路径,借由叙事医学的视角对病历的功能、价值和意义进行分层次的阐释,亦可能对叙事医学的临床落地作出重要贡献。通过焦点小组访谈和专家访谈等形式,围绕临床实践中的现行病历书写经验,考量医学人文属性在制度化空间流转的可能路径等主题。病历书写实践被裹挟于多重影响因素中,病历书写和流转过程存在一定的矛盾和张力。研究借鉴人类学的“整体性”概念,对病历书写实践进行不同层次的反思,旨在进一步确立起方法论意义,为引导医学界形成叙事医学临床路径的一般性框架和多种可能性作出贡献。

PDCA循环模式联合西医病案在中医学教学中的应用

目的探讨PDCA循环模式联合西医病案在中医学教学中的应用效果。方法选择2021年9月—2022年7月在武汉大学第一临床学院学习中医学课程的临床医学专业学生作为研究对象,2017级八年制和2018级五年制,共101人作为对照组;2018级八年制和2018级5+3年制,共109人作为实验组。对照组应用传统方法教学,实验组应用PDCA循环模式联合西医病案方法教学。比较两组学生的理论成绩和教学满意度。结果实验组学生的理论成绩和教学满意度均高于对照组,差异有统计学意义(P<0.05)。结论PDCA循环模式联合西医病案教学法能够激发学生的学习兴趣,提高理论成绩,提升教学满意度,改善教学质量,有一定的应用价值,值得推广。

基于古今医案云平台探究中医治疗失眠的辨证思路及用药规律研究

目的运用古今医案云平台整理并分析中医治疗失眠的辨证思路及用药规律。方法收集古今医案云平台(V2.3.8)名医医案库中名老中医及其书籍中的医案数据,以古今医案云平台(V2.3.8)对用药频次、药味药性归经、舌象脉象等进行统计分析、聚类分析、复杂网络分析。结果共纳入医案794个,涉及中药477味,主要有柴胡、白芍、甘草、酸枣仁、川芎、远志、赤芍、当归、合欢皮、茯苓、郁金、栀子。中药药性以平为主;药味多属甘、苦、辛;主要归肝经。高频中药系统聚类分析聚为4组,第一组:酸枣仁、茯苓;第二组:远志、合欢皮、郁金;第三组:白芍、栀子;第四组:柴胡、川芎、赤芍。药物关联分析得到:柴胡与白芍、柴胡与赤芍的关联性最强,其次是柴胡与川芎。复杂网络分析得到7组中医证候,从高到底依次为:肝阳上亢证,痰热扰心证,肝火扰心证,气滞血瘀证,心脾两虚证,肝肾阴虚证,肝郁气滞证。结论中医治疗失眠以清热助眠为法,平调阴阳为纲,多使用清热安神之品,值得临床参考运用。

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。

叙事病历可行性探讨:从概念到临床实践

基于病历反思功能的学理思考、缓和医疗领域病历书写实践的重要发现、对叙事医学工具的概念辨析等内容,结合实证调查材料与分析,聚焦于病历书写实践展开思考与研究。主要内容包括:对叙事病历概念进行界定,即叙事病历是应用于临床,融入叙事性内容的病历;明晰其不同层次的特征与功能;探索其运用于临床的实践路径。在对北京协和医学院叙事医学实践独特性深入发掘的基础上,强调以叙事思维来书写病历的必要性;具体落脚于以叙事的思维与形式助力推动现行病历书写的改善,进一步寻求叙事医学临床路径的一般性框架和多种可能性。

某专科医院DRGs结算模式下未入组病例分析及改进措施

目的分析某专科医院在疾病诊断相关组(diagnosis related groups,DRGs)结算模式下存在的未入组病例问题,并提出相应的改进措施。方法通过回顾性分析和专家讨论,选取并总结某专科医院2021年1月—2022年6月上传至市医保平台病例信息管理系统中病例33935例中未入组病例300例,并对所有的未入组病例进行深入分析。结果未入组病例原因主要为主要诊断编码或者手术编码为灰码、医保版本切换导致原本入组的有效主诊断变成无效主诊断、主要诊断编码国家临床版医保版没有做好对照和临床医师主要诊断选择错误等方面。结论通过对医院DRGs结算模式下未入组病例的全面分析,找到改进措施,不断加强对临床医师和编码员的培训、加强病案首页质控、加强信息系统建设,从而提高DRGs入组率。