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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (11): 13-28    DOI: 10.11925/infotech.2096-3467.2021.0260
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Exchanging Chinese Medical Information Based on HL7 FHIR
Sheng Shu1,Huang Qi1,2(),Yang Yang1,Xie Qiwen1,Qin Xinguo1,3
1School of Information Management, Nanjing University, Nanjing 210046, China
2Nanjing Research Based of National Information Management, Nanjing University, Nanjing 210093, China
3Information Office, Nanjing Audit University, Nanjing 211815, China
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[Objective] This paper explores the core framework of message exchange standard——Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR), aiming to standardize medical data formats and disease terms in Chinese. [Methods] We proposed a healthcare data interoperability method based on the FHIR framework. Then, we combined the ontology standardization conceptual model and the Disease Ontology to regulate the expression of disease terms, with ontology construction, mapping and migration techniques. [Results] We retrieved 176 pieces electronic medical records from the YiXiang platform with a Python crawler. After ontology mapping and migration, we fully standardized the medical records and disease term coding using the expression of FHIR data format. [Limitations] We did not standardize the semantics of heterogeneous medical data of multiple types. [Conclusions] This study provides a new perspective for constructing standard medical records system and related technology in China.

Key wordsHL7 FHIR      Information Exchange      Ontology Mapping      Migration     
Received: 01 April 2021      Published: 26 August 2021
ZTFLH:  G251  
Corresponding Authors: Huang Qi,ORCID:0000-0002-2806-3447     E-mail:

Cite this article:

Sheng Shu, Huang Qi, Yang Yang, Xie Qiwen, Qin Xinguo. Exchanging Chinese Medical Information Based on HL7 FHIR. Data Analysis and Knowledge Discovery, 2021, 5(11): 13-28.

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Display of HL7 FHIR Resource(Partial)
UML Diagram of Patient Resource
Overall Architecture of Information Exchange
Example Pipeline of Ontology Migration Rules
Standard Code Expression of “Heart Aneurysm” in Disease Ontology
Reference Relationship Between Resources
Entities Conceptual Diagram
FHIR Resource Representation as an Ontology Class(Partial)
Hierarchical Tree of the Medical Field Ontology
定义域 属性 值域
patient hasEMRs EMRs
EMRs hasProperty observation
disease has_record EMRs
patient hasCare careplan
patient hasInspection Imaging
diagnostic hasService patient
patient hasPay financial
diagnostic hasRefer Imaging
Object Properties Setting of Medical Field Ontology
类及字段名 所属类 含义 是否可空 字符串类型
PaId patient 患者ID String
PaName patient 患者名称 String
PaGender patient 患者性别 Boolean
PaAge patient 患者年龄 int
dateofConsul EMRs 就诊时间 dateTime
PaHistory patient 患者现病史 String
DiComplaints diagnostic 患者主诉 String
DiDepartCode diagnostic 患者就诊科室编码 String
DiDepartName diagnostic 就诊科室名称 String
DocId practitioner 患者诊疗医生编码 String
DocName practitioner 患者诊疗医生姓名 String
PaHosNum patient 患者住院编号 String
Data Properties Setting of Medical Field Ontology
FHIR概念名称 相似概念匹配 FHIR数据属性 相似属性匹配
本体O1概念 相似度 属性 相似度
Observation observation 0.98 PaName 0.63
Practitioners practitioner 0.92 Patient. identifier PaId 0.80
Patient patient 0.98 Patient. status PaStatus 0.84
Diagnostics diagnostic 0.89 Patient. telecom PaTel 0.51
Entity EMRs 0.18 Patient. gender PaGender 0.77
Medications medication 0.88 Patient. contact PaTel 0.31
NutritionOrder careplan 0.32 Patient. birthdate PaAge 0.38
Specimen BloodSample 0.41 Patient. photo PaStatus 0.57
FHIR Financial financial 0.77 Observation. category ObCategory 0.81
Documents document 0.82 Observation. identifier ObId 0.22
Procedure procedure 0.92 Observation. note ObNote 0.65
Category disease 0.32 Observation. method ObMeasured 0.59
ImagingStudy Imaging 0.74 Observation. subject ObSubject 0.70
Workflow - 0 Procedure. report PrRec 0.66
RiskAssessment - 0 Procedure. note PrNote 0.75
Payment financial 0.39 Medication. batch MedBa 0.45
RelatedPerson patient 0.54 Medication. amount MedAmount 0.70
Similarity Results Between O 1 and O 2
患者ID 患者年龄 患者性别 诊断疾病 症状 治疗方案
1 61岁 高血压 头昏、活动后胸闷 Aspirin,一天0.1g
2 41岁 胃癌 腹痛、腹泻 波利特,一天20mg
3 45岁 2型糖尿病 口干、多饮、消瘦、四肢乏力 盐酸二甲双胍片(片),1天1.5mg
4 61岁 冠心病 全身浮肿、胸闷 拜阿司匹林(肠溶片),1天100mg
5 37岁 胃腺癌 吞咽困难、发烧、身目黄染 易善复(注射剂),一天20ml
6 45岁 2型糖尿病 血糖升高、手指麻痹、尿频、尿痛 盐酸二甲双胍片(片),1天0.75g,联合瑞易宁10mgqd降血糖治疗
7 54岁 肝癌 黄疸 \
8 47岁 乳腺浸润性导管癌 乳房肿块 枸橼酸他莫昔芬(片),1天20mg
9 51岁 脑梗塞 行为异常、神志不清、言语异常 丙种球蛋白(注射剂),1天25g
10 62岁 肺癌 气促、胸闷 灵芝孢子粉(胶囊),1天12粒
Examples of Patient Medical Records Dataset (Partial)
Data Properties Alignment on AML
Example of Synchronizing Patients Instance to FHIR
FHIR类 下位类 字段名 含义 实例
Patient - Patient. name 患者姓名 ZXX
Patient. age 患者年龄 46
Patient. gender 患者性别
Patient. identifier 患者ID 430XXXXXXXXXXXXXXX
Patient. other 患者备注 喜热茶,无抽烟不良嗜好
Condition - Condition. bodySite 身体部位 腹部
Condition. severity 症状严重程度
Condition. category 症状类别 疼痛
FamilyMemberHistory - FamilyMemberHistory.condition 与患者相关成员的存活状态 父母已故
MedicationRequest - MedicationRequest. dosageInstruction 药物剂量 0.1g
MedicationRequest. initialFill.duration 服药间隔时间 24小时 药物名称 舒敏(释缓片)
Encounter - - 患者过去经历、遭遇 -
Procedure Procedure.perform 患者过去诊疗时间 2010.6.30
Procedure.category 患者过去诊疗方式 化疗
Procedure.outcome 患者过去诊疗结果 术后效果良好
DiagnosticReport - DiagnosticReport.result 诊断报告中诊疗结果 淋巴癌‘127232002’
DiagnosticReport.category 诊疗报告采用方式 剖腹探查+腹腔淋巴结清扫术 comment 诊疗报告中影像评述 检测腹腔多发淋巴结肿大,纵隔淋巴结肿大
Patient Instance of HL7 FHIR Format
Patient Sample in Database with FHIR Format
[1] Topol E J. The Big Medical Data Miss: Challenges in Establishing an Open Medical Resource[J]. Nature Reviews Genetics, 2015, 16(5):253-254.
pmid: 26065035
[2] Pinto V B, de Oliveira R C R, Girão A I P T. SNOMED-CT as Standard Language for Organization and Representation of the Information in Patient Records[J]. Knowledge Organization, 2014, 41(4):311-318.
doi: 10.5771/0943-7444-2014-4
[3] Woods J W, Sneiderman C A, Hameed K, et al. Using UMLS Metathesaurus Concepts to Describe Medical Images: Dermatology Vocabulary[J]. Computers in Biology and Medicine, 2006, 36(1):89-100.
doi: 10.1016/j.compbiomed.2004.08.003
[4] Oemig F. HL7 Version 2.x Goes FHIR[J]. Studies in Health Technology and Informatics, 2019, 267:93-98.
[5] Catley C, Frize M. Design of a Health Care Architecture for Medical Data Interoperability and Application Integration[C]// Proceedings of the 2nd Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society IEEE, 2002: 1952-1953.
[6] Hussain M A, Langer S G, Kohli M. Learning HL7 FHIR Using the HAPI FHIR Server and Its Use in Medical Imaging with the SIIM Dataset[J]. Journal of Digital Imaging, 2018, 31(3):334-340.
doi: 10.1007/s10278-018-0090-y
[7] Maxhelaku S, Kika A. Improving Interoperability in Healthcare Using HL7 FHIR[C]// Proceedings of the 47th International Academic Conference. 2019: 9211566.
[8] Semenov I, Osenev R, Gerasimov S, et al. Experience in Developing an FHIR Medical Data Management Platform to Provide Clinical Decision Support[J]. International Journal of Environmental Research and Public Health, 2019, 17(1):E73.
[9] Kiourtis A, Mavrogiorgou A, Menychtas A, et al. Structurally Mapping Healthcare Data to HL7 FHIR Through Ontology Alignment[J]. Journal of Medical Systems, 2019, 43(3):43-62.
doi: 10.1007/s10916-019-1164-1
[10] Saripalle R, Runyan C, Russell M. Using HL7 FHIR to Achieve Interoperability in Patient Health Record[J]. Journal of Biomedical Informatics, 2019, 94:103188.
doi: S1532-0464(19)30106-6 pmid: 31063828
[11] Hong N, Wen A, Shen F C, et al. Developing a Scalable FHIR-Based Clinical Data Normalization Pipeline for Standardizing and Integrating Unstructured and Structured Electronic Health Record Data[J]. JAMIA Open, 2019, 2(4):570-579.
doi: 10.1093/jamiaopen/ooz056 pmid: 32025655
[12] Yan H C, Xiao L, Tian J B. Clinical Decision Support Based on FHIR Data Exchange Standard[C]// Proceedings of the 2nd International Conference on Mechatronics Engineering and Information Technology. 2017: 424-430.
[13] Mukhiya S K, Rabbi F, Pun V K I, et al. A GraphQL Approach to Healthcare Information Exchange with HL7 FHIR[J]. Procedia Computer Science, 2019, 160:338-345.
doi: 10.1016/j.procs.2019.11.082
[14] Ullah F, Habib M A, Farhan M, et al. Semantic Interoperability for Big-Data in Heterogeneous IoT Infrastructure for Healthcare[J]. Sustainable Cities and Society, 2017, 34:90-96.
doi: 10.1016/j.scs.2017.06.010
[15] Katehakis D G, Kondylakis H, Koumakis L, et al. Integrated Care Solutions for the Citizen: Personal Health Record Functional Models to Support Interoperability[J]. European Journal for Biomedical Informatics, 2017, 13(1):48-56.
[16] Rivera S Y K, Demurjian S A, Baihan M S, et al. A Service-Based RBAC & MAC Approach Incorporated into the FHIR Standard[J]. Digital Communications and Networks, 2019, 5(4):214-225.
doi: 10.1016/j.dcan.2019.10.004
[17] Rajkomar A, Oren E, Chen K, et al. Scalable and Accurate Deep Learning with Electronic Health Records[J]. NPJ Digital Medicine, 2018, 1: Article No.18.
[18] 何雨生, 王力华, 闫华. 国内首例HL7在医院信息系统集成中的应用[J]. 当代医学, 2002, 8(10):42-44.
[18] (He Yusheng, Wang Lihua, Yan Hua. The First Domestic Case of HL7 in the Integration of Hospital Information System[J]. China Contemporary Medicine, 2002, 8(10):42-44.)
[19] 吴志禄, 李小坚. HL7标准的医保信息交换探究[C]// 全国冶金自动化信息网2011年年会论文集. 2011.
[19] (Wu Zhilu, Li Xiaojian. Research on HL7 Standard Medical Insurance Information Exchange[C]// Proceedings of the 2011 Annual Conference of National Metallurgical Automation Information Network. 2011.)
[20] 唐春波, 郭文明, 严静东, 等. FHIR数据集成平台研究及其在连续医疗中的应用[J]. 生物医学工程研究, 2017, 36(2):178-182.
[20] (Tang Chunbo, Guo Wenming, Yan Jingdong, et al. The Research of Fast Healthcare Interoperability Resources Data Integration Platform and Its Application in Continuous Healthcare[J]. Journal of Biomedical Engineering Research, 2017, 36(2):178-182.)
[21] Al-Aswadi F N, Chan H Y, Gan K H. Automatic Ontology Construction from Text: A Review from Shallow to Deep Learning Trend[J]. Artificial Intelligence Review, 2020, 53(6):3901-3928.
doi: 10.1007/s10462-019-09782-9
[22] Alani H. Kim S, Millard D E, et al. Automatic Ontology-Based Knowledge Extraction from Web Documents[J]. IEEE Intelligent Systems, 2003, 18(1):14-21.
doi: 10.1109/MIS.2003.1234764
[23] Liu G, Zhang H W. An Ontology Constructing Technology Oriented on Massive Social Security Policy Documents[J]. Cognitive Systems Research, 2020, 60(5):97-105.
doi: 10.1016/j.cogsys.2019.09.005
[24] Fawei B, Pan J Z, Kollingbaum M, et al. A Semi-Automated Ontology Construction for Legal Question Answering[J]. New Generation Computing, 2019, 37(4):453-478.
doi: 10.1007/s00354-019-00070-2
[25] Zhuang L S, Schouten K, Frasincar F. SOBA: Semi-Automated Ontology Builder for Aspect-Based Sentiment Analysis[J]. Journal of Web Semantics, 2020, 60:100544.
doi: 10.1016/j.websem.2019.100544
[26] 王思丽, 祝忠明, 刘巍, 等. 基于深度学习的领域本体概念自动获取方法研究[J]. 情报理论与实践, 2020, 43(3):145-152, 144.
[26] (Wang Sili, Zhu Zhongming, Liu Wei, et al. Method of Domain Ontology Concept Automatic Extraction Based on Deep Learning[J]. Information Studies: Theory & Application, 2020, 43(3):145-152, 144.)
[27] 石湘, 刘萍. 基于知识元语义描述模型的领域知识抽取与表示研究——以信息检索领域为例[J]. 数据分析与知识发现, 2021, 5(4):123-133.
[27] (Shi Xiang, Liu Ping. Extraction and Representation of Domain Knowledge with Semantic Description Model and Knowledge Elements——Case Study of Information Retrieval[J]. Data Analysis and Knowledge Discovery, 2021, 5(4):123-133.)
[28] McMurray J, Zhu L, McKillop I, et al. Ontological Modeling of Electronic Health Information Exchange[J]. Journal of Biomedical Informatics, 2015, 56:169-178.
doi: 10.1016/j.jbi.2015.05.020 pmid: 26065983
[29] Plastiras P, O’Sullivan D M. Combining Ontologies and Open Standards to Derive a Middle Layer Information Model for Interoperability of Personal and Electronic Health Records[J]. Journal of Medical Systems, 2017, 41(12):1-15.
doi: 10.1007/s10916-016-0650-y
[30] Harrow I, Balakrishnan R, Jimenez-Ruiz E, et al. Ontology Mapping for Semantically Enabled Applications[J]. Drug Discovery Today, 2019, 24(10):2068-2075.
doi: S1359-6446(18)30421-5 pmid: 31158512
[31] Annane A, Bellahsene Z, Azouaou F, et al. Building an Effective and Efficient Background Knowledge Resource to Enhance Ontology Matching[J]. Journal of Web Semantics, 2018, 51(8):51-68.
doi: 10.1016/j.websem.2018.04.001
[32] Nakhla Z, Nouira K. Automatic Approach to Enrich Databases Using Ontology: Application in Medical Domain[J]. Procedia Computer Science, 2017, 112:387-396.
doi: 10.1016/j.procs.2017.08.221
[33] 楼雯, 王慧, 鞠源. 基于二值相似度计算的异构本体融合方法[J]. 情报学报, 2019, 38(6):622-631.
[33] (Lou Wen, Wang Hui, Ju Yuan. An Ontology Fusion Method Based on Binary Similarity Calculation[J]. Journal of the China Society for Scientific and Technical Information, 2019, 38(6):622-631.)
[34] 陈东华, 张润彤, 付磊, 等. SNOMED CT体系下医疗健康大数据映射和迁移方法研究[J]. 情报学报, 2018, 37(5):524-532.
[34] (Chen Donghua, Zhang Runtong, Fu Lei, et al. Mapping and Migration of Medical and Health Big Data with SNOMED CT[J]. Journal of the China Society for Scientific and Technical Information, 2018, 37(5):524-532.)
[35] Kiourtis A, Nifakos S, Mavrogiorgou A, et al. Aggregating the Syntactic and Semantic Similarity of Healthcare Data Towards Their Transformation to HL7 FHIR Through Ontology Matching[J]. International Journal of Medical Informatics, 2019, 132:104002.
doi: 10.1016/j.ijmedinf.2019.104002
[36] Kilintzis V, Chouvarda I, Beredimas N, et al. Supporting Integrated Care with a Flexible Data Management Framework Built Upon Linked Data, HL7 FHIR and Ontologies[J]. Journal of Biomedical Informatics, 2019, 94:103179.
doi: S1532-0464(19)30097-8 pmid: 31026596
[37] 王兰成. 知识集成方法与技术: 知识组织与知识检索[M]. 北京: 国防工业出版社, 2010.
[37] (Wang Lancheng. Knowledge Integration Methods and Technologies——Knowledge Organization and Knowledge Retrieval[M]. Beijing: National Defense Industry Press, 2010.)
[38] Faria D, Pesquita C, Santos E, et al. The AgreementMakerLight Ontology Matching System[C]// Proceedings of OTM Confederated International Conferences “On the Move to Meaningful Internet Systems”. 2013: 527-541.
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