Establishing and implementing an information management framework Informatics … and Hodge.pdf · 2015-09-07 · Establishing and implementing an information management framework

Establishing and implementing an information management framework

Informatics leading practices

Presented to: National Healthcare Leadership Conference

Prepared by: Julian Martalog, Director – ATC Informatics

Matthew Hodge, Chief Medical Information Officer

Date: June 7, 2011

Overview

� Informatics at CCO

� Informatics leading practices

� Real world examples at CCO

� Benefits of using a framework approach

3

CCO’s performance improvement approach

1ESTABLISH TARGETS

Set performance goals and outcomes for areas of focus (e.g. cancer, surgical wait times, chronic kidney disease, etc.)

2MEASURE PERFORMANCE

Define, agree upon and implement standard, provincial measures of performance

3MONITOR PERFORMANCE

Review current state performance on a regular basis

4ANALYZE AND INTERPRET INFORMATION

Analyze and interpret data to identify areas for improvement

5INTERVENE TO IMPROVE PERFORMANCE

Implement targeted interventions (e.g. process improvements, reorganization, redistribution, policy changes, resource allocation) to improve performance

6ASSESS IMPACT ON PERFORMANCE

Measure and evaluate interventions to determine impact, and course correct as necessary.

1. Establish

Targets

2. M

easure

Performance

3. Monitor

Perfo

rmance

4. Analyze and

Interpret

Information

5. Intervene to

Improve

Performance

6. Assess

Impact on

Perfo

rmance

PERFORMANCE

IMPROVEMENT

1. Establish

Targets

2. M

easure

Performance

3. Monitor

Perfo

rmance

4. Analyze and

Interpret

Information

5. Intervene to

Improve

Performance

6. Assess

Impact on

Perfo

rmance

PERFORMANCE

IMPROVEMENT

Our Clients & Stakeholders

Clinical Programs

Internal CCO External

Pop. Studies & Surveillance

Provincial Planning

Performance Management

Regional Cancer Programs

Cancer Quality Council of Ontario

Hospitals & Providers

Public Health Units

MoHLTC

Researchers

LHINs

Screening & Prevention

Finance National (CPAC, STC, PHAC)


Data Standards & Acquisition

Develop minimum data set (MDS) linked to KPIs and other key business processes

Align with and leverage existing data standards

Create data sharing agreements to acquire data from other agencies

Develop interim data collection system to meet urgent information needs

Develop stakeholder engagement strategy and documentation

Identify opportunities to collect once, use many times

Data Mgmt & Governance

Data access and request policies and procedures linked to privacy principles

Provincial operational team supporting data collection

Focus on front end business validation rules

Independent data certification committee for public reporting

Define and assign data steward role

Facility-specific lead for data management

Data Quality

Develop data quality improvement plan and integrate into overall R&A plan

Use data quality framework to guide activities

Data compliance matrix and escalation linked to data quality framework

Conduct initial assessment of reportable data

Multi-layered data quality improvement approach

Cultivate data supply chain to share DQ responsibilities through education sessions

Reporting &Analytics

Develop informatics vision and reporting plan (including stakeholder needs assessment)

Delivery model includes “push” (std reports) and pull (self-serve)

Project performance and track actuals against targets

Develop business intelligence strategy and action plan

Regular review and feedback on performance from clinical lead

Develop investigative analytics to support pgm mgmt & research

Simple, user-friendly public reporting

PerformanceMeasurement

KPIs meet defined criteria (e.g. linked to objectives, data of high quality, etc.)

Plot KPIs on quality measurement framework

Performance improvement program (case studies, data reviews, etc.)

Data modeling linked to performance management

Return on investment analysis

Balanced scorecard developed for leadership team and executive sponsor

Business intelligence dashboard









Wait Times Upload Tool

Interim data collection system developed in ~2weeks

Data used to meet urgent needs for contract allocation and public reporting purposes

Data used to model additional incremental funding ($108M in year funding)

System replaced by Wait Time Information System (WTIS)









CHIEFData Steward

Data Steward

Business Lead

Support Role

• Strategic oversight of data stewardship function• Ensures regulatory, privacy and information sharing policies are followed

• Monitor data access• Ensure data quality• Subject matter expert

• Identify data quality priorities• Business ownership of Data Sharing Agreements• Business requirements & endorsement of standards changes

• IT and EDW operations• Security & privacy• Data quality• Data architecture
















Data Quality







Key

Determinants

Elements

Components

Timeliness ReliabilityValidity Usability

Freshness

Currency

ConsistencyAccuracy

Comprehensiveness

Completeness

Relevance

Accessibility

Interpretability

Standardization

Historical Comparability

Linkability

Precision










Cancer Surgery

90th Percentile Wait Time

March YTD 2007/08 vs 2008/09

0 10 20 30 40 50 60 70 80 90 100

South West

Toronto Central PMH

Champlain

South East

Central West and Miss. Halton

Waterloo Wellington

Central East

North East

Hamilton NHB

North Simcoe Muskoka

Toronto Central Odette

Central

North West

Erie St. Clair

PROVINCE

Days

Target for 2008/09

Apr - Mar 2007/08

Apr - Mar 2008/0955Provincial Target

Cancer Surgery Wait Times in the Province

Decision-to-Operate to Operation

for All Priority Categories and All Specialties

40%

45%

50%

55%

60%

65%

70%

75%

80%

85%

Apr

May

June

July

Aug

Sept

Oct

Nov

Dec

Jan

Feb

Mar

Apr

May

June

July

Aug

Sept

Oct

Nov

Dec

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sept

Oct

Nov

Dec

Jan

Feb

Mar

2006-07 2007-08 2008-09

Perc

en

t o

f P

atien

ts

40

50

60

70

80

90

100

Days

% of Patients Treated Within Target

90th Percentile

Target for % of Patients w/in Target

Target for 90th Percentile

















Quality

Dimensions ��

Patient Journey

��

Safe Effective Accessible Responsive Efficient Equitable Integrated

Prevention

Screening

Diagnosis

Treatment

Recovery

Palliative & end-

of-life care

Surveillance Cancer incidence, mortality, survival, prevalence

Population Health Risk factors & socio-demographic factors

Structure/Process/Outcome

Macro/Meso/Micro
















Data Quality























Informatics leading practices in action: real world examples at CCO

Chronic Kidney Disease (ORN)

CKD leading practices and associated year of implementation

Develop minimum

data set (MDS) linked

to KPIs and other key

business processes

Align with and

leverage existing

data standards

Create data sharing

agreements to

acquire data from

other agencies

Develop interim data

collection system to

meet urgent information

needs

Develop

stakeholder

engagement

strategy and

documentation

Identify

opportunities to

collect once, use

many times

Y1 Y1 Y1 Y1 Y1 Y1

Develop data quality

improvement plan

and integrate into

overall R&A plan

Use data quality

framework to

guide activities

Data compliance

matrix and

escalation linked to

data quality

framework

Conduct initial

assessment of

reportable data

Multi-layered data

quality

improvement

approach

Cultivate data

supply chain to

share DQ

responsibilities via

education sessions

Y2 Y1 Y1 Y1 Y1 Y2Develop informatics

vision and reporting

plan (including

stakeholder needs

assessment)

Delivery model

includes “push”

(std reports) and

pull (self-serve)

Project

performance

and track

actuals against

targets

Develop

business

intelligence

strategy and

action plan

Regular review

and feedback on

performance

from clinical

lead

Develop

investigative

analytics to

support program

mgmt & research

Simple, user-

friendly

public

reporting

Y2 Y3 Y2 Y2 Y2 Y3 Y2KPIs meet defined

criteria (e.g. linked

to objectives, data of

high quality, etc.)

Plot KPIs on

quality

measurement

framework

Performance

improvement

program (case

studies, data

reviews, etc.)

Data

modeling

linked to

performance

management

Return on

investment

analysis

Balanced

scorecard

developed for

leadership team

and executive

sponsor

Business

intelligence

dashboard

Y1 Y1 Y3 Y2 Y2 Y2 Y3

Data Standards &Acquisitions

Y1: fiscal year Oct’09 - Mar’10; Y2: fiscal year Apr ‘10 – Mar ’11; Y3: fiscal year Apr ‘11 – Mar ‘12

Data Quality

Reporting & Analytics (R&A)

Performance Measurement

Data access and

request policies and

procedures linked to

privacy principles

Provincial

operational team

supporting data

collection

Focus on front end

business validation

rules

Independent data

certification committee

for public reporting

Facility-specific

lead for data

management

Define and assign

data steward role

Y1 Y3 Y1 Y2 Y2 Y1


CKD leading practices and associated year of implementation

Develop minimum

data set (MDS) linked

to KPIs and other key

business processes

Align with and

leverage existing

data standards

Create data sharing

agreements to

acquire data from

other agencies

Develop interim data

collection system to

meet urgent information

needs

Develop

stakeholder

engagement

strategy and

documentation

Identify

opportunities to

collect once, use

many times

Y1 Y1 Y1 Y1 Y1 Y1

Use data quality

framework to

guide activities

Data compliance

matrix and

escalation linked to

data quality

framework

Conduct initial

assessment of

reportable data

Multi-layered data

quality

improvement

approach

Y1 Y1 Y1 Y1

KPIs meet defined

criteria (e.g. linked

to objectives, data of

high quality, etc.)

Plot KPIs on

quality

measurement

framework

Y1 Y1

Data Standards &Acquisitions

Y1: fiscal year Oct’09 - Mar’10; Y2: fiscal year Apr ‘10 – Mar ’11; Y3: fiscal year Apr ‘11 – Mar ‘12

Reporting & Analytics (R&A)

Performance Measurement

Data Quality

Data access and

request policies and

procedures linked to

privacy principles

Y1

Define and assign

data steward role

Y1

Focus on front end

business validation

rules

Y1


Benefits of using a framework

� Approach is business-driven, non-technical and

readily understood by a variety of stakeholders

� Clear linkage of business objectives, information

requirements, and data acquisition approach

� Ensures data quality is not an afterthought

� Repeatable process with tangible deliverables

� Facilitates alignment with corporate standards,

processes and policies

Questions?

Contact Information

Julian MartalogDirector, Access to Care Informatics

[email protected]

(416) 217-1230

Matthew Hodge

Chief Medical Information Officer

[email protected]

Establishing and implementing an information management framework Informatics … and Hodge.pdf · 2015-09-07 · Establishing and implementing an information management framework

Documents