The Fifth China - U.S. Roundtable on Scientific Data Cooperation Assuring Data and Information Quality in Sharing Process of Population and Health Data.
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The Fifth China - U.S. Roundtable on Scientific Data Cooperation Assuring Data and Information Quality in Sharing Process of Population and Health Data (eHealth Systems) Ying Su Ling Yin ISITC, Beijing, CHN [email protected] Hospital 301, China [email protected] Institute of Scientific and Technical Information of China (ISTIC) Led by the Ministry of Science and Technology; Funded in October, 1956 Information Quality Lab (IQL): delivering information quality services focused on facilitating decision-making processes and on improving customer satisfaction. The Fifth China - U.S. Roundtable on Scientific Data Cooperation Problems Key Themes 1. Information Quality in Chinese Hospital 2. Data Quality in Chinese Information Systems Solution 1. Framework for assuring IQ in an eHealth context 2. to specify their IQ requirements by Semiotics 3. introduced Coupling and Explanation models Methodology: 1. Describe information within a process 2. Calculate IQ and process performance 3. Validate the impact relationships by simulation Results 1. Reputation, Believability and Trace-ability, 2. IQ is critical to patient care; 3. Quantifiable IQ and PP indicators. Further work 1. What’s next? The Fifth China - U.S. Roundtable on Scientific Data Cooperation Information Quality Problems in Chinese Hospitals The phenomenon of "three-long, one-short” three-long: the time of registration, waiting to see the doctor and getting the medicine one-short :getting the treatment The Fifth China - U.S. Roundtable on Scientific Data Cooperation Data Quality Problems in Chinese Information Systems Clinical Pathways for Acute Coronary Syndromes in China (CAPCS) • 卫生部医政司项目 • 中国急性冠脉综合征临床路径研究 CPACS：参加医院 75 医院 内蒙古 山西 50 三级医院 25 二级医院 黑龙江 4/3, 4/2 2/3 辽宁 4/3, 1/2 3/3, 1/2 2/3, 3/2 新疆 北京 河北 3/3, 1/2 4/3山东 3/3,1/2 江苏 陕西 3/3 河南 3/3, 3/2 2/3,2/2 四川 上海 2/3 湖北 3/3, 4/2 浙江 1/3, 4/2 湖南 广东 4/3 4/3 2/3, 2/2 项目在医院的实施-进度安排： 准备阶段 2011.7-2011.8 • 负责人参加北 京项目启动会 • 组建医院QCI 小组 • 协调员参加北 京项目培训会 • 完善急诊、心 内科ACS患者 登记册，急诊 留观病历、住 院病历 • 培训资料收集 员、资料录入 员 预运行阶段 2011.9 • 资料收集人员 登录项目网站， 录入5例资料 • 项目管理中心 核查数据，检 查数据的合理 性 • 预运行满意后 通知医院进入 正式收集病例 阶段 基线阶段 干预阶段 2011.10-分组干 预时点 分组干预时点2015.3 • 收集ACS患者 资料并签订知 情同意 • 录入ACS患者 资料 • 接受项目管理 中心的监查 • 实施干预 • 收集ACS患者 资料并签订知 情同意 • 录入ACS患者 资料 • 接受项目管理 中心的监查 The Fifth China - U.S. Roundtable on Scientific Data Cooperation IDQ Problems Try to Solve: How to describe information and related data within a process, and how to describe the controllable factors among them? How to calculate information quality and process performance? How to build the impact relationship between the indicators above and then verify? The Fifth China - U.S. Roundtable on Scientific Data Cooperation Objectives of this presentation Propose an extensible IQ semiotics containing basic domain-independent IQ terms, upon which definitions of domain-specific concepts can be built. IQ descriptions for specific resources need to be computed and associated with those resources. This can be done by attaching origin information to the RDF explanation instances. Resources include data and services; both of these kinds of resource are modeled by concepts in the IQ semiotics, so that the semiotics can express which kinds of IQ descriptor make sense for which kinds of resource. We refer to these relationships as couplings, which can be captured using an RDF schema The Fifth China - U.S. Roundtable on Scientific Data Cooperation An IQ Assurance Framework Definition Physician Assessment Analysis Assurance Custodian Agent IQ Expert Physical Level Reputation Speed Maintainable Secure Pragmatic Level Believable Clarity value Interactive Semantic Level Traceable Concise Syntactic Level Accuracy Complete Specific Resources Data Schema Quality Indicators External Info Quality Assuring Principles Conformability Currency Data Items Inherent Info Quality Integrity Timeliness Service Types The Fifth China - U.S. Roundtable on Scientific Data Cooperation Basic Semiotics Structure • In the semiotics, we model IQ concepts by introducing Quality Assurances (QA); these are decision procedures that are based upon some Quality Evidence (QE), which consists either of measurable attributes called Quality Indicators, or recursively, of functions of those indicators, Quality Metrics. Three main sources of indicators are common in practice: Origin metadata, which provides a description of the processes that were involved in producing the data. Quality functions that explicitly measure some quality property, these functions are typically available from toolkits for data quality assessment with reference to specific issues. Metadata that is produced as part of the data processing. The Fifth China - U.S. Roundtable on Scientific Data Cooperation Methodology • We model the indicator-bearing environment as a collection of Data Analysis Tools that may incorporate multiple Data Calculation functions, and which are applied to some Data Entity. • Indicators are either parameters to or output of these analysis tools. A QA is applied to collections of data items, which are individuals of the Data Entity class, using the values for the indicators associated to those items. The practical quality metrics are part of the output of a calculation function called QMCalculator, used in the IQA Calculator Analysis Tool. • A quality metric called IQA Calculator Ranking associates a score to each data in the set, using a function of indicators. This score can be used either to classify data as acceptable/non acceptable according to a user-defined threshold, or to rank the data set. Here we will assume that our decision procedure is an grade function called QA-Func, that provides a simple binary grade of the data set according to the credibility score and to a user-defined threshold. The Fifth China - U.S. Roundtable on Scientific Data Cooperation Classes and Relationships Introduced • Summary of the classes and relationships introduced above, using informal notation for the sake of readability; user-defined axioms. – Quality-Assurance is based on QualityEvidence; – Quality-Indicator is-a Quality-Evidence; – Quality-Metric is-a Quality-Evidence; – Quality-Metric is based on Quality-Indicator; – Quality-Evidence is output of Data-testfunction; – Data-analysis-tool is based on Data-testfunction; The Fifth China - U.S. Roundtable on Scientific Data Cooperation Overview of the IQA coupling model Relation hasSubject SubClass Coupling hasObject Resource :THING locatedBy DataResource ResourceLocator locatedBy ServiceResource locatedBy DataLocator ServiceLocator DataElement Resource isContainedIn FileLocator DataEntity Resource DataCollection Resource XML Schema Entity XML Data WebService DBLocator XML Element Web Service Registry URLLocator The Fifth China - U.S. Roundtable on Scientific Data Cooperation Structure of Explanation Model c: Resource Relation hasExplanation ExplanationResult hasExplanationElement ExplanationElement referenceTo hasResourceRef hasQtyEvidence c: DataResource s: QtyEvidence The Fifth China - U.S. Roundtable on Scientific Data Cooperation eQualityHealth Program: NSFC-MOST Goal and Service Oriented Approach to Assure Data and Information Quality in eHealth Systems The Fifth China - U.S. Roundtable on Scientific Data Cooperation eQualityHealth • eQualityHealth is a metadata platform for quality assessment • eQualityHealth allows the definition of high-level quality goals and the specialization of typical measurement services according to quality goals 16 binding Information Systems Meta-Model personalization Personalized Quality Model (PQM) General Quality Meta-Model PQM Get Store Quality Requirements QMediator Service Description … QManagement Service Description Quality Service 1 … QFoundation references Service Registry (UDDI) Delegate Quality Service n 17 eQualityHealth provides an extensible catalog of quality metrics, which presents general quality concepts and behaviors It also provides a catalog for the services that implement the quality metrics 18 Quality Factors Dimensions Quality Metrics 19 20 21 22 23 24 25 Any quality service can be used in eQualityHealth Relevant quality methods not published as web services can be Web Service Web Service Web Service Adapter Methods embedded in quality tools Code libraries containing quality methods API Core Quality Tool public class { … } Library 26 27 The Fifth China - U.S. Roundtable on Scientific Data Cooperation Results Hospital operating room simulation model Locations Entities (Documents, people, or phone calls should be modeled as entities.) Resources (a person, equipment, device used for transporting entities, performing operations, performing maintenance on locations) Path Networks Processing Arrivals Shifts & Breaks Cost The Fifth China - U.S. Roundtable on Scientific Data Cooperation Results Assumption of impact relationship of IQ to PP The hypotheses of the effect relationship of information quality to process performance Takes Reputation as an example: Changzhou Case Information portal EHR Wireless, Medical Devices, Database, Internet Health Call center Health Service Organization 15 September 2011 人口计生委208会议室 30 30 The Fifth China - U.S. Roundtable on Scientific Data Cooperation Further Work Built-in Next Steps Blueprint of Human-centered eHealth Rural doctors with Mobile Wireless connection Medical Workstation (MMW) Wireless Broadband wireless Rural doctors with connection access (BWA) MMW and Portable Rural doctors with Biomedical Devices Mobile Phone – Bluetooth connection Holter inside M-health Server Digital Holter Smart device Recorder Wired connection Wired connection County hospitals Village Clinical Points (VCPs) Township Healthcare Centers (THCs) The Fifth China - U.S. Roundtable on Scientific Data Cooperation The 6th International Conference on Cooperation and Promotion of Information Resources in Science and Technology (COINFO’11) International Workshop on Information & Data Quality http://coinfo.istic.ac.cn/coinfo11/ November 11-13, 2011, Hang zhou, Paradise in China Thanks 7.00-19.00 - 19 May 2011 32 The Fifth China - U.S. Roundtable on Scientific Data Cooperation Thanks for your Listening Dr. Ying Su Institute of Scientific and Technical Information of China Associate Professor ([email protected] ) Director-in-Charge, IQL (Information Quality Lab) Post-Doctor, SEM (School of Economics and Management) Tsinghua University [email protected] Co-Chair of International Conference on Information Quality(ICIQ), 2010 Visiting Professor, UNIVERSITY OF ARKANSAS AT LITTLE ROCK (UALR) Invited by Professor John Talburt Advisor for the Master of Science in Information Quality program Director, UALR Laboratory for Advanced Research in Entity Resolution and Information Quality (ERIQ) Smart eHealth Program between Provinces, CHINA and ARKANSAS, US Email: [email protected] ; Phone: (501)-371-7616