louise edmonds

8/2/2019 Louise Edmonds

1/13

An Enterprise Approach to

Data Quality:

The ACT Health Experience

Data Quality Asia Pacific Awards

2011 Winner


2/13

Healthcare in Australia

Health System Challenges

Ageing population

Increase in prevalence of

chronic disease

Rising health care costs Shortage of skilled healthcare

workers


3/13

Healthcare in Australia

Healthcare Reform

Better individual & population

health outcomes

Improved safety, quality &

sustainability of thehealthcare system

Cost effectiveness of

healthcare spending

Greater transparency &

accountability


4/13

ACT Health DirectorateImportance of Data Quality

Focus Patient Care

patient details, clinical

pathways

Customer Service

Efficiency in a stressfulenvironment

Research & Planning

Accurate information

supporting policy reform &

early intervention

Funding

Activity Based Funding


5/13

Enterprise Approach


6/13

Data Quality Framework

Based on the ABS national standard to maintain

consistency in Data Quality with stakeholders;

Provide tools and templates that aid process analysis

and create an audit trail for elements of a given data

collection;

Provide qualitative ratings and statements for data

suitability assessment in business decision making;

Further develop an iterative monthly validation,

correction, notification cycle;


7/13


Provide the techniques for training people in theapplication of the DQ framework within their own

reporting and quality improvement projects;

Identify potential gaps in metadata and activity

capture; Bridge the enterprise and operational gaps in quality

reporting;

Shortened reporting timeframes; and

Demonstrated value in maintaining a dedicated dataquality resource


8/13

Innovations

2011 where we were then


development

Data Quality Policy revision

Validation Enginedevelopment

Data Governance

2012 and now

Data Quality Framework pilot and

evaluation results

Broader Policy and Standards

work plan aligned with BI

Strategy implementation Validation Engine aligned with

AIHW & external agencies

Organisational Restructure

dedicated Data Governance &

Standards unit

Data Governance structuresrevisited in response to Audit &

BI strategy recommendations


9/13

Data Quality Framework pilot

A framework with key principles (eg accuracy,

timeliness, interpretability, removal of the

Data Quality Statement

A self evaluation check list based on theprinciples and

Revised data quality policy and standards

framework.


10/13



11/13

Data Quality IndicatorKey Metadata & Modelling

0-1 Raw Data

No

Metadata,

Nomodelling

Some

Metadata,

notmodelled

Metadata,

Not

Modelled

Metadata

under

review by

BMWG, NotModelled

Metadata

Reviewed &

Verified by

BMWG, NotModelled

Metadata

Defined as

Standard

& DataModelled

2-3 Low4 Low-Medium

5 Medium

6 Medium-High

7-8 High

9-10 Governed

Data

QualityProcesses

Raw Data 0 1 2 3 4 5

Local Quality

Improvement 1 2 3 4 5 6

Locally Validated /

Issues Identified 2 3 4 5 6 7

Locally Validated &

Cleansed 3 4 5 6 7 8

Validated through

enterprise level

processing4 5 6 7 8 9

Validated,

Cleansed, and

supplied from an

enterprise levelData Warehouse

5 6 7 8 9 10

Any use of the Data Quality Indicator either in part or total must reference ACT Health as concept author Louise Edmonds ACT Health


12/13

Further Developments

Validation Engine

Supports transparency of

business rules

Supports one stop shop for

validation requirements and

deployment across data

streams

Supports both business

level and technical level

interactions


13/13

Data Quality FrameworkFurther developments

Data Standardisation expanding definitional

development

Addressing problems with data elements and

codes sets

Data Validation improving point of entry

Clinical Relevance enhancing ownership

Education and feedback programs Data Quality Metrics

louise edmonds

Documents