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Assuring Data and Information Quality in Sharing Process of Population and Health Data (eHealth Systems). Ying Su ISITC, Beijing, CHN [email protected]. Ling Yin Hospital 301, China [email protected]. Institute of Scientific and Technical Information of China (ISTIC) - PowerPoint PPT Presentation
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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 SuISITC, Beijing, CHN
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.
Ling YinHospital 301, China
The Fifth China - U.S. Roundtable on Scientific Data Cooperation
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?
Further work
1. What’s next?
Key Themes
Problems
1. Information Quality in Chinese Hospital
2. Data Quality in Chinese Information Systems
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
50 三级医院
25 二级医院
黑龙江
2/3 辽宁
4/3, 1/2
河北
4/3山东
3/3,1/2江苏
3/3
上海
3/3, 4/2
河南
2/3,2/2
广东
4/3
湖北
1/3, 4/2
四川
2/3
陕西
3/3, 3/2
内蒙古
3/3, 1/2
北京
4/3, 4/2
浙江
2/3, 2/2
湖南
4/3
新疆
3/3, 1/2
山西
2/3, 3/2
项目在医院的实施 - 进度安排:
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
PhysicianPhysician
Definition
AgentAgentAssessment
IQ ExpertIQ ExpertAnalysis
CustodianCustodian
AssuranceAssuring Principles
Assuring Principles
Syntactic Level
Semantic Level
Pragmatic Level
Complete
CurrencyTraceable Concise Conformability
Believable InteractiveClarity value
Inherent Info Q
ualityE
xternal Info Q
uality
TimelinessIntegrity
Specific Resources
DataSchema
ServiceTypes
QualityIndicators
Data Items
Physical Level
Reputation Speed SecureMaintainable
Accuracy
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 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;
The Fifth China - U.S. Roundtable on Scientific Data Cooperation
Overview of the IQA coupling model
Coupling
Resource :THING
DataResourceServiceResource
DataEntityResource DataElement
Resource
DataCollectionResource
XMLSchema Entity
XML Element
XML Data
ResourceLocator
DataLocator ServiceLocator WebService
FileLocator
DBLocator
Web Service Registry
URLLocator
Relation
SubClass
hasObjecthasSubject
locatedBy
locatedBy locatedBy
isContainedIn
The Fifth China - U.S. Roundtable on Scientific Data Cooperation
Structure of Explanation Model
c: Resource
ExplanationResult
ExplanationElement
c: DataResource s: QtyEvidence
hasExplanation
hasExplanationElement
referenceTo
hasResourceRef hasQtyEvidence
Relation
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
17
QualityService 1
QualityService n
…
Service Registry(UDDI)
ServiceDescription
references
ServiceDescription
…
DelegateDelegate
QManagement
QMediator
Information Systems
Meta-Model
Information Systems
Meta-Model
General Quality Meta-Model
General Quality Meta-Model
Personalized Quality
Model (PQM)
Personalized Quality
Model (PQM)
personalizationbinding
QFoundationPQMPQM
Quality Requireme
nts
Quality Requireme
nts
Sto
re
Searc
hSearc
h
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
19
Quality DimensionsQuality DimensionsQuality FactorsQuality Factors Quality MetricsQuality Metrics
20
21
22
23
24
25
Library
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
26
Quality Tool
API
Core
public class
{ …
}
public class
{ …
}
Web Service Web ServiceWeb Service
Adapter
27
The Fifth China - U.S. Roundtable on Scientific Data Cooperation
Hospital operating room simulation modelResults
LocationsEntities(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 NetworksProcessingArrivalsShifts & BreaksCost
The Fifth China - U.S. Roundtable on Scientific Data Cooperation
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:
Results
30
Changzhou Case
15 September 2011 人口计生委 208会议室30
Health Service Organization
EHR
Health Call center
Wireless, Medical Devices, Database, Internet
Information portal
The Fifth China - U.S. Roundtable on Scientific Data Cooperation
Next StepsBlueprint of Human-centered eHealth
FurtherWork
Township Healthcare Centers (THCs)
Rural doctors withMMW and Portable Biomedical DevicesBluetooth connection
Broadband wirelessaccess (BWA)
County hospitals
Wireless connection
Wired connection
Rural doctors with Mobile Medical Workstation (MMW)
Village Clinical Points (VCPs)
Wired connection
Rural doctors with Mobile Phone –
Holter insideM-health Server
Digital Holter Recorder
Wireless connection
Built-in
Smart device
The Fifth China - U.S. Roundtable on Scientific Data Cooperation
32
The 6th International Conference on Cooperation 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
7.00-19.00 - 19 May 2011
The Fifth China - U.S. Roundtable on Scientific Data Cooperation
Thanks for your Listening
Dr. Ying SuInstitute of Scientific and Technical Information of ChinaAssociate 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), 2010Visiting Professor, UNIVERSITY OF ARKANSAS AT LITTLE ROCK (UALR)Invited by Professor John TalburtAdvisor for the Master of Science in Information Quality programDirector, UALR Laboratory for Advanced Research in Entity Resolution
and Information Quality (ERIQ)Smart eHealth Program between Provinces, CHINA and ARKANSAS, USEmail: [email protected] ; Phone: (501)-371-7616