Ying Su ISITC, Beijing, CHN [email protected]

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

[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.

Ling YinHospital 301, China

[email protected]


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


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


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：


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?


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


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


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.


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.


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;


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


Structure of Explanation Model

c: Resource

ExplanationResult

ExplanationElement

c: DataResource s: QtyEvidence

hasExplanation

hasExplanationElement

referenceTo

hasResourceRef hasQtyEvidence

Relation


eQualityHealth Program: NSFC-MOST

Goal and Service Oriented Approach to Assure Data and Information Quality in eHealth Systems


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


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


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


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


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


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