Ying Su ISITC, Beijing, CHN suy.rspc@istic.ac

1. Assuring Data and Information Quality in Sharing Process of Population and Health Data (eHealth Systems) Ying SuISITC, Beijing, CHNsuy.rspc@istic.ac.cn Ling YinHospital 301, Chinayinling301@126.com 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.

2. Problems • Information Quality in Chinese Hospital • Data Quality in Chinese Information Systems Key Themes • Solution • Framework for assuring IQ in an eHealth context • to specify their IQ requirements by Semiotics • introduced Coupling and Explanation models • Methodology: • Describe information within a process • Calculate IQ and process performance • Validate the impact relationships by simulation • Results • Reputation, Believability and Trace-ability, • IQ is critical to patient care; • Quantifiable IQ and PP indicators. • Further work • What’s next?

3. 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

4. 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

5. CPACS：参加医院 北京 4/3, 4/2 75 医院 50 三级医院 25 二级医院 黑龙江 2/3 辽宁 4/3, 1/2 内蒙古 3/3, 1/2 山西 2/3, 3/2 新疆 3/3, 1/2 河北 4/3 山东 3/3,1/2 江苏 3/3 陕西 3/3, 3/2 河南 2/3,2/2 四川 2/3 上海 3/3, 4/2 湖北 1/3, 4/2 浙江 2/3, 2/2 湖南 4/3 广东 4/3

6. 项目在医院的实施-进度安排：

7. 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?

8. 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

9. Definition Assessment Analysis Assurance Assuring Principles Physician Agent IQ Expert Custodian External Info Quality Physical Level Secure Reputation Maintainable Speed Pragmatic Level Believable Clarity value Interactive Semantic Level Conformability Currency Traceable Concise Inherent Info Quality Syntactic Level Accuracy Integrity Timeliness Complete Specific Resources Data Schema Quality Indicators Service Types Data Items An IQ Assurance Framework

10. 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.

11. 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.

12. 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 Quality-Evidence; • 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-test-function; • Data-analysis-tool is based on Data-test-function;

13. Relation hasSubject Coupling hasObject SubClass Resource :THING locatedBy DataResource ResourceLocator ServiceResource locatedBy locatedBy DataLocator ServiceLocator WebService DataEntity Resource DataElement Resource isContainedIn FileLocator DBLocator DataCollection Resource XML Element Web Service Registry XML Schema Entity URLLocator XML Data Overview of the IQA coupling model

14. c: Resource Relation hasExplanation ExplanationResult hasExplanationElement ExplanationElement referenceTo hasResourceRef hasQtyEvidence s: QtyEvidence c: DataResource Structure of Explanation Model

15. eQualityHealth Program: NSFC-MOSTGoal and Service Oriented Approach to Assure Data and Information Quality in eHealth Systems

16. 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

17. eQualityHealth Architecture binding personalization references Information Systems Meta-Model Personalized Quality Model (PQM) General Quality Meta-Model Quality Service 1 Service Description … QFoundation PQM Service Description QManagement … Get Store Search Quality Requirements Service Registry (UDDI) QMediator Quality Service n Delegate

18. eQualityHealth Catalog • 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

19. Quality Catalog Quality Metrics Quality Dimensions Quality Factors

20. Adding Quality Dimension Consistency

21. Adding Quality Dimension Consistency

22. Adding Quality Factor Consistency

23. Adding Quality Factor Consistency

24. Adding Quality Metric Consistency Ratio

25. Adding Quality Metric Consistency Ratio

26. Web Services in eQualityHealth • Any quality service can be used in eQualityHealth • Relevant quality methods not published as web services can be • Methods embedded in quality tools • Code libraries containing quality methods Web Service Web Service Web Service Adapter Quality Tool Library public class { … } API Core

27. Services Catalog

28. 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

29. 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:

30. 人口计生委208会议室 Changzhou Case EHR Information portal Health Call center Wireless, Medical Devices, Database, Internet Health Service Organization 30

31. Rural doctors with Mobile Medical Workstation (MMW) Wireless connection Wireless connection Broadband wireless access (BWA) Rural doctors with MMW and Portable Biomedical Devices Rural doctors with Mobile Phone – Holter inside Bluetooth connection M-health Server Built-in Digital Holter Recorder Smart device Wired connection Wired connection County hospitals Township Healthcare Centers (THCs) Village Clinical Points (VCPs) Next StepsBlueprint of Human-centered eHealth FurtherWork

32. The 6th International Conference onCooperation and Promotion of Information Resources in Science and Technology (COINFO’11)International Workshop on Information & Data Qualityhttp://coinfo.istic.ac.cn/coinfo11/November 11-13, 2011, Hang zhou, Paradise in ChinaThanks

33. Thanks for your Listening Dr. Ying Su Institute of Scientific and Technical Information of China Associate Professor (suy.rspc@istic.ac.cn ) Director-in-Charge, IQL (Information Quality Lab) Post-Doctor, SEM (School of Economics and Management) Tsinghua University suy4@sem.tsinghua.edu.cn 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: jrtalburt@ualr.edu ; Phone: (501)-371-7616