Transforming Data into Meaningful

Information to Support Improved

Patient Care

Presented By:

Vickie Welch, Director, Informatics Centre of Excellence

Hakim Lakhani, Director, Reporting and Analytics, Informatics Centre of Excellence

Agenda

Ontario in Context: Basics & the Healthcare System

CCO in Context: Managing the Chronic Patient Journey and Access to Care

Breadth & Scope of Data and our Users

Informatics Centre of Excellence: Formation & Highlights

Ontario in Context: Ontario vs. New York

3

13.51 million Population 19.57 million

415,598 m² Area 54,556 m²

211 Hospitals 204

14 Cancer Centres 6 (NCI)

Ontario’s Healthcare System

4

14 Cancer Centres 211 Hospitals

Local Health Integration

Networks (LHINs)

Mixed Public - Private System

Funding:

Public – Ontario Health

Insurance Plan

Delivery:

Private not-for-profit

Private for- profit

• Healthcare is funded by the provinces which

are responsible for setting overall direction

and delivering care

Chronic Patient Journey & CCO

5

Oversees over 1 billion

in healthcare dollars

Implements

healthcare IM/IT

Transfers new research

into clinical practice

Focuses on quality

improvements and standards

Cancer Services Ontario Renal

Network Access to Care

As of 2009, an estimated 320,000 Ontarians were diagnosed with

cancer in the previous 10 years

65,000 new cases per year

1 million people screened for cancer yearly

115 hospitals performing cancer surgery

78 hospitals performing chemotherapy

15 hospitals performing radiation therapy

670+ Oncologists

Cancer in Ontario

8

26 programs Administering total 91 locations

Approximately 10,000 people in Ontario are receiving dialysis

Of these, 77% go to centres and 23% dialyze at home

In 2010, 537 kidney transplants were performed

1108 CKD patients on a waiting list to receive a kidney transplant

CKD costs the province $586 million/year

Chronic Kidney Disease in Ontario

OR

SETP

Diagnostic Imaging

Wait Time

MRI/CT

ER Wait Time

Leave ER Emergency

Room

Wait 3

ALC Wait Time

Wait 4

Acute Care Post-Acute

Care (Rehab, CCC, LTC, etc)

Post-Acute Care

(CCC, LTC, etc)

Home Care

Wait 1 Wait 2

Surgical Wait Time

Primary Care

Provider Specialist

SETP

OR

ER Wait Time

Leave ER Emergency

Room

Fo

cu

s

Are

a

ER/ALC Information Strategy

Surgery & DI

Wait Time Strategy

Surgical Efficiency

Diagnostic Imaging

Wait Time

MRI/CT

Wait 1 Wait 2

Surgical Wait Time

Primary Care

Provider Specialist

ER ALC

Wait 3

ALC Wait Time

Wait 4

Acute Care Post-Acute

Care (Rehab, CCC, LTC, etc)

Post-Acute Care

(CCC, LTC, etc)

Home Care OR

Access to Care in Ontario

10

Ontario Health System Challenges

Pervasiveness of

disease Value For

Money Accountability

11

11

11

Analytics can help…

Analytics - Managing the System

Health System

Information

Quality & Continuous

Improvement

Program Implementation

Standards & Best Practices

Service Planning &

Access to Care

Funding & Sustainability

Research & Innovation

Radiation,

Surgical and

Systemic

Treatment

Diagnostic

Assessment

Programs

ColonCancerCheck

&

Integrated Screening

Symptom

Management

Follow-up

Surveillance

Palliative

Care

Imaging,

Pathology &

Laboratory

Programs

Disease Pathway

Management

Chronic Patient Journey & CCO

Informatics Centre of Excellence

Our Objectives:

To build an Informatics Centre of

Excellence that will…

o Be closer to the customer

o Be more efficient

o Provide Value added services

o Have the right skills for the right

jobs

Through improved …

o Organizational Design

o Skills

o Processes

o Tools/technologies

Customer Intimacy

Operational Excellence

Product Leadership

Our Aim - Transformation

Aspirational (35%)

• New or limited users of

analytics

• Focused on analytics at

point-of-need

• Turn to analytics for ways

to cut costs

Experienced (48%)

• Established users of analytics

• Seeking to grow revenue with focus on

cost efficiencies

• Seeking to expand ability to share

information and insights

Transformed (16%) • Analytic use is cultural norm

• Highest levels of analytics prowess and experience

• Seeking targeted revenue growth

• Feel the most pressure to do more with analytics

Source: Analytics: The New Path to Value, a joint MIT Sloan

Management Review and IBM Institute of Business Value study.

Copyright © Massachusetts Institute of Technology 2010. Sample

size Healthcare n= 116

Transformation Priorities

16

• Customer Intimacy Priority #1

• Data Management Priority #2

• Talent Management Priority #3

• Process Improvement Priority #4

• Performance Management Priority #5

17

Ontario Renal Network Cancer Access to Care

Strategic Analytics & Funding and Financial Analytics Teams

Informatics Centre of Excellence

Organizational Model

REPORTING AND ANALYTICS

Data Acquisition Data Architecture Data Governance

ENTERPRISE DATA MANAGEMENT

BUSINESS OFFICE

18

Analytic Spaces

Enterprise Data

Management

Data Acquisition

Presentation

CCO

Governance

Privacy and Security

Data Stewardship

Informatics Centre of Excellence

Functional Model

Transforming Health Data Into

Meaningful Information

19

19

Organizations

OCR

140+

Data Sets

180+

Terabytes of data

WTIS

NACRS

DAD

20

20

DE-IDENTIFICATION

DATA QUALITY

STANDARDIZED INBOUND AND OUTBOUND FLOWS

OPERATIONSAPPLICATION INTERFACES

DATA GOVERNANCE

FUTURE STATE ARCHITECTURE

DATA ARCHITECTURE

DATA WHAREHOUSE ANALYTICAL VIEWS

INCREMENTAL GROWTH

Data available in an optimized structure for reporting

Data Quality is understood and documented

A single version of truth exists

Data stewards know their data domain

Consistent data definition is in place

End users are able to access information products in a self serve manner based on their level of need

EDM Capabilities Enabled

21

21

Measuring Performance – The Spectrum

Provincial Level Outcome Indicators

Provincial Level Driver Indicators

Regional Indicators

Health Professional Level Indicators

Big

Dots

Little

Dots

CQCO Adapted from Heenan, M. Khan, & Binkley, D. (2010). “From boardroom to bedside: How to define and measure hospital quality.” Healthcare Quarterly,

13(1): 55-60.

Cancer

System

Quality Index

(CSQI)

Quarterly

Regional

Performance

Scorecard

CCO Special

Reports/

Program

Reports

Screening

Activity Reports

by Primary Care

Provider

Surgeon

Scorecard

Analytics to Improve System Performance

24

25

Data Sources : *Y2005-2006 - CCO Pathology Audits; Y2008-2010 PIMS, ePATH

Prepared by: Cancer Care Ontario, Informatics

Sample

2005

*

2006

*

2007

2008

2009

2010

Po

sit

ive M

arg

in (

%)

0

10

20

30

40

50

60

70

80

90

100

Radical Prostatectomies

% Positive surgical margin (PSM) rate for Radical Prostatectomies for pT2 patients in Ontario

CCO Program Target 2008/09: 25%

A Quality Improvement Example: CCO’s Performance Improvement Cycle in Action

Developed best practice guidelines

Analytics to Improve Regional Performance Regional Cancer Centre Performance Scorecard

SYMP-

TOM

MGMT

DAPApr 2012-

Mar 2013

RCC Non-

RCCRCC

Non-

RCCRCC

Non-

RCCRCC

Non-

RCC

Province ▲ ▲ ▲ 100% ▲ ▼ ▲ 100% ▲ ▲ ▲ 100% ▲ ▲ ▼ 100% ▲ ▲ ▲

C Central ▲ ▲ ▲ 3% ▲ NA NA 5% ▲ ▲ 11% ▼ 9% ▼ NA ▲ ▼ 1 1 0

C Waterloo Wellington ▼ 4% ▼ NA ▲ ▲ 5% 4% ▲ ▼ 6% ▼ ▲ NA ▲ ▲ 3 2 1

C Central East ▲ 5% ▼ ▼ ▲ 11% ▼ NA ▲ 3% ▲ ▼ 15% ▲ NA 12 3 -1

C Erie St. Clair ▲ ▲ 3% ▲ NA NA 3% ▼ 3% ▲ ▼ 5% ▼ NA ▲ 1 4 0

CCentral West &

Mississauga Halton▼ ▼ ▼ 6% NA NA 5% ▲ ▲ 12% ▼ ▼ 3% ▲ NA 9 5 0

C North West 2% ▲ NA ▲ ▼ 3% NA 1% ▲ ▼ 3% ▲ NA ▲ NA NA 8 6 0

A Toronto Central South ▲ ▲ 21% ▲ NA NA ▲ 15% ▼ ▲ ▲ 21% 3% ▲ NA ▲ 6 7 3

C North East ▼ ▲ ▼ ▼ 5% ▲ ▲ 4% NA 3% ▲ ▲ ▼ 5% ▲ ▼ 5 8 0

A South West ▲ ▲ ▼ 9% ▲ NA ▼ 9% ▲ ▲ 10% ▲ ▼ 8% ▼ NA ▼ 10 9 0

A Toronto Central North 14% ▲ NA NA ▲ 11% ▲ 8% ▲ 1% ▲ NA 13 10 -3

A Champlain ▲ ▲ ▲ ▲ 10% ▲ 10% ▲ 10% ▼ 14% ▼ ▲ NA NA 7 11 2

C North Simcoe Muskoka ▼ ▼ ▲ 3% ▼ NA ▲ 4% NA 2% ▲ ▲ ▲ 2% ▼ NA 10 12 2

AHamilton Niagara

Haldimand Brant▲ ▼ ▼ 11% ▼ 9% ▲ ▲ 11% ▼ ▲ ▼ 17% ▼ ▲ ▲ 14 13 -2

A South East ▼ ▲ ▼ 5% NA NA 5% NA 3% ▲ ▲ ▲ 8% ▲ NA 4 14 -2

Change

from

Previous

QuarterCON(C)

RSTP

Level 1

& 2

Overall

Provincial

Rank

WT

Family

History

WT - Ref

to Diag

(Lung)

Data

QualityESAS

Apr

2012-

Mar

2013

SYSTEMIC

WT = Apr 2012-Mar 2013

Vol = Apr 2012-Mar 2013

RADIATION

WT = Apr 2012-Mar 2013

Vol = Apr 2012-Mar 2013

IMRT

SURGERY

WT = Apr 2012-Mar 2013

Vol = Apr 2012-Mar 2013

Vol

(cases)

COLONOSCOPY

WT = Apr 2012-Mar 2013

Vol = Apr 2012-Mar 2013

WT

+FOBT

WT Ref-Con

(% w/in 14 days)

WT Con-Tr

(% w/in 28 days)

WT

(% w/in target)

WT Ref-

Con

(% w/in

14

days)

Vol

(C1R)

% of

Budget

Vol

Vol

(C1S)

Region

WT RTT-

Tr

(% w/in

target)

CHPCA

*NURSING PROGRAM

As of September 30, 2012

*MCC

Q3

RCCNon-

RCC

Patient

Experience

(AOPSS)

Apr 2012 - Sept

2012

CON(C)

RSTP

Level 3

Emtional Support

Vol

% of

Budget

Vol

% of

Budget

Vol

% of

Budget

Vol

27

Analytics to Improve Local Performance Emergency Room Length of Stay Segment Dashboard

28

Analytics to Improve Local Performance Emergency Room Length of Stay

350,000

370,000

390,000

410,000

430,000

450,000

470,000

490,000

Ap

r 0

8

Jun

08

Au

g 0

8

Oct

08

De

c 0

8

Feb

09

Ap

r 0

9

Jun

09

Au

g 0

9

Oct

09

De

c 0

9

Feb

10

Ap

r 1

0

Jun

10

Au

g 1

0

Oct

10

De

c 1

0

Feb

11

Ap

r 1

1

Jun

11

Au

g 1

1

Oct

11

De

c 1

1

Feb

12

Ap

r 1

2

Jun

12

Au

g 1

2

Oct

12

De

c 1

2

Feb

13

Ap

r 1

3

Jun

13

ER V

olu

me

Volumes

Wait Times

Emergency Department Volumes

Emergency Department Length of Stay

Analytics to Improve Provider Performance

Screening Activity Report

29

0

50

100

150

200

250

300

350

400

450

Kn

ee

Re

pla

cem

en

t W

ait

Tim

e -

90

th P

erc

en

tile

/ d

ays

Dec '12 90th Percentile Wait TimeLHIN Target

LHINS #1 #2 #3 #4 #5 #6 #7 #8 #9 #10 #11 #12 #13 #14

Advanced Analytics in Action:

Hip and Knee Surgical Capacity Planning

LHINs need an Integrated Orthopedic Capacity Plan (IOCP) for next two fiscal years to

meet their 90th percentile wait time targets for joint replacement surgery.

30

Regions Ministry

IOCP

Targets

Demand? Supply?

Performance?

Advanced Analytics in Action:

Hip & Knee Surgical Capacity Planning - Model

31

Real Time Surgical Wait List Data

Surgical Demand Forecast

Surgical Arrival Dynamics

Surgery Activity Data

Surgery Dynamics

Regional Hip and Knee Surgery

Queuing Model

Regional Surgical Waitlist

Performance Model

Surgical Volume Forecast

What-If Analysis model given to the LHINs

Advanced Analytics in Action Capacity Allocation to Improve Access to Care

32

Can we improve patient care and reduce

health system costs?

55% of Cost 45% of Cost

10% of Patients 90% of Patients

33

Advanced Analytics in Action:

High Intensity Inpatient Users

Could we have predicted high cost

patients when they started dialysis?

34

into a Machine Learning algorithms to compute joint probabilities

to identify predictor variables of high intensity acute hospital users within the

first year of starting dialysis

Ontario Renal Reporting System

Inpatient Records (DAD)

Ambulatory Records (NACRS)

Pre-Dialysis Year Dialysis Incident Day

Fed 80 Input Variables

Dialysis crash start

Inpatient admissions in pre-dialysis year

Serum albumin at dialysis start

Emergency visits in pre-dialysis year

Inpatient admissions in pre-dialysis quarter

Followed by Nephrologist before dialysis

Creatinine at dialysis start

Clinical Screening

Policy Analysis

Aspirational (35%) • New of limited users of analytics • Focused on analytics at point-of-need • Turn to analytics for ways to cut costs

Experienced (48%)

• Established users of analytics • Seeking to grow revenue with focus on cost

efficiencies • Seeking to expand ability share information

and insights

Transformed (16%) • Analytic use is cultural norm • Highest levels of analytics prowess and

experienced • Seeking targeted revenue growth • Feel the most pressure to do more with

analytics

Our Aim - Transformation

Cancer Care Ontario

Source: Analytics: The New Path to Value, a joint MIT Sloan Management Review and IBM Institute of Business Value study. Copyright © Massachusetts Institute of Technology 2010. Sample size Healthcare n= 116

38

On the Horizon

System-Wide

Analytics

• Funding Reform

• HealthLinks

Opportunities

• Networking across the health system

• Strategic Analytics Advisory Panel

Continuous Improvement

• Improved analytics process

• Increased partner involvement

• Talent management

Questions?