Transforming Data into Meaningful
Information to Support Improved
Patient Care
Presented By:
Vickie Welch, Director, Infor...
Agenda
Ontario in Context: Basics & the Healthcare System
CCO in Context: Managing the Chronic Patient Journey and Access ...
Ontario in Context: Ontario vs. New York
3
13.51 million Population 19.57 million
415,598 m² Area 54,556 m²
211 Hospitals ...
Ontario’s Healthcare System
4
14 Cancer Centres211 Hospitals
Local Health Integration
Networks (LHINs)
Mixed Public - Priv...
Chronic Patient Journey & CCO
5
Oversees over 1 billion
in healthcare dollars
Implements
healthcare IM/IT
Transfers new research
into clinical practice
Fo...
As of 2009, an estimated 320,000 Ontarians were diagnosed with
cancer in the previous 10 years
65,000 new cases per year
1...
8
26 programs Administering total 91 locations
Approximately 10,000 people in Ontario are receiving dialysis
Of these, 77%...
OR
SETP
Diagnostic Imaging
Wait Time
MRI/CT
ER Wait Time
Leave ER
Emergency
Room
Wait 3
ALC Wait Time
Wait 4
Acute Care
Po...
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
Standard...
Radiation,
Surgical and
Systemic
Treatment
Diagnostic
Assessment
Programs
ColonCancerCheck
&
Integrated Screening
Symptom
...
Informatics Centre of Excellence
Our Objectives:
To build an Informatics Centre of
Excellence that will…
o Be closer to th...
Our Aim - Transformation
Aspirational (35%)
• New or limited users of
analytics
• Focused on analytics at
point-of-need
• ...
Transformation Priorities
16
• Customer IntimacyPriority #1
• Data ManagementPriority #2
• Talent ManagementPriority #3
• ...
17
Ontario Renal Network Cancer Access to Care
Strategic Analytics & Funding and Financial Analytics Teams
Informatics Cen...
18
Analytic SpacesEnterprise Data
Management
Data Acquisition Presentation
CCO
Governance
Privacy and Security
Data Stewar...
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
OPERATIONS
APPLICATION
INTERFACES
DATA GOVERN...
21
21
Measuring Performance – The Spectrum
Provincial Level
Outcome Indicators
Provincial Level
Driver Indicators
Regional Indic...
Analytics to Improve System Performance
24
25
Data Sources : *Y2005-2006 - CCO Pathology Audits; Y2008-2010 PIMS, ePATH
Prepared by: Cancer Care Ontario, Informatics...
Analytics to Improve Regional Performance
Regional Cancer Centre Performance Scorecard
SYMP-
TOM
MGMT
DAP
Apr 2012-
Mar 20...
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
47...
Analytics to Improve Provider Performance
Screening Activity Report
29
0
50
100
150
200
250
300
350
400
450
KneeReplacementWaitTime-90thPercentile/days
Dec '12 90th Percentile Wait Time
LHIN Ta...
Advanced Analytics in Action:
Hip & Knee Surgical Capacity Planning - Model
31
Real Time Surgical
Wait List Data
Surgical ...
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
Adv...
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 ...
Dialysis
crash start
Inpatient admissions
in pre-dialysis year
Serum albumin
at dialysis start
Emergency visits in
pre-dia...
Aspirational (35%)
• New of limited users of analytics
• Focused on analytics at point-of-need
• Turn to analytics for way...
38
On the Horizon
System-Wide
Analytics
• Funding
Reform
• HealthLinks
Opportunities
• Networking
across the
health system...
Questions?
Upcoming SlideShare
Loading in...5
×

iHT² Health IT Summit New York - Cancer Care Ontario Presentation "Transforming Data into Meaningful Information to Support Improved Patient Care"

1,015

Published on

iHT² Health IT Summit New York - Cancer Care Ontario Presentation "Transforming Data into Meaningful Information to Support Improved Patient Care"

Presentation "Transforming Data into Meaningful Information to Support Improved Patient Care"

Learning Objectives:

∙ Understand the information imperative for Cancer Care Ontario (CCO), one of the largest provincial health organizations in Canada, as it supports
population-based care co-ordination and administration for 3 clinical domains in the province of Ontario: cancer care, renal care, and access to
care
∙ Learn how the organization built the Informatics Centre of Excellence to better enable the acquisition, management, reporting, and analysis of one
of the broadest and richest data sets in the country
∙ Discuss concrete examples of how CCO has used leading-edge analytic techniques to drive health system performance.

Vickie Welch
Director, Informatics Centre of Excellence
Cancer Care Ontario

Hakim Lakhani
Director, Reporting and Analytics, Informatics Centre of Excellence
Cancer Care Ontario

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
1,015
On Slideshare
0
From Embeds
0
Number of Embeds
4
Actions
Shares
0
Downloads
10
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

iHT² Health IT Summit New York - Cancer Care Ontario Presentation "Transforming Data into Meaningful Information to Support Improved Patient Care"

  1. 1. 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
  2. 2. 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
  3. 3. 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)
  4. 4. Ontario’s Healthcare System 4 14 Cancer Centres211 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
  5. 5. Chronic Patient Journey & CCO 5
  6. 6. 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
  7. 7. 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. 8. 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
  9. 9. 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 Focus Area 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 CareOR Access to Care in Ontario
  10. 10. 10 Ontario Health System Challenges Pervasiveness of disease Value For Money Accountability
  11. 11. 11 11 11 Analytics can help…
  12. 12. Analytics - Managing the System Health System Information Quality & Continuous Improvement Program Implementation Standards & Best Practices Service Planning & Access to Care Funding & Sustainability Research & Innovation
  13. 13. 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
  14. 14. 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
  15. 15. 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
  16. 16. Transformation Priorities 16 • Customer IntimacyPriority #1 • Data ManagementPriority #2 • Talent ManagementPriority #3 • Process ImprovementPriority #4 • Performance ManagementPriority #5
  17. 17. 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. 18. 18 Analytic SpacesEnterprise 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. 19 19 Organizations OCR 140+ Data Sets 180+ Terabytes of data WTIS NACRS DAD
  20. 20. 20 20 DE-IDENTIFICATION DATA QUALITY STANDARDIZED INBOUND AND OUTBOUND FLOWS OPERATIONS APPLICATION 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. 21 21
  22. 22. 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
  23. 23. Analytics to Improve System Performance
  24. 24. 24
  25. 25. 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 PositiveMargin(%) 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
  26. 26. Analytics to Improve Regional Performance Regional Cancer Centre Performance Scorecard SYMP- TOM MGMT DAP Apr 2012- Mar 2013 RCC Non- RCC RCC Non- RCC RCC Non- RCC RCC 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 C Central 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 A Hamilton 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 toDiag (Lung) Data Quality ESAS 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 WTRef-Con (% w/in14 days) WTCon-Tr (% w/in28 days) WT (% w/intarget) WTRef- Con (% w/in 14 days) Vol (C1R) % of Budget Vol Vol (C1S) Region WTRTT- Tr (% w/in target) CHPCA *NURSING PROGRAM As of September 30, 2012 *MCC Q3 RCC Non- 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. 27. 27 Analytics to Improve Local Performance Emergency Room Length of Stay Segment Dashboard
  28. 28. 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 Apr08 Jun08 Aug08 Oct08 Dec08 Feb09 Apr09 Jun09 Aug09 Oct09 Dec09 Feb10 Apr10 Jun10 Aug10 Oct10 Dec10 Feb11 Apr11 Jun11 Aug11 Oct11 Dec11 Feb12 Apr12 Jun12 Aug12 Oct12 Dec12 Feb13 Apr13 Jun13 ERVolume Volumes Wait Times Emergency Department Volumes Emergency Department Length of Stay
  29. 29. Analytics to Improve Provider Performance Screening Activity Report 29
  30. 30. 0 50 100 150 200 250 300 350 400 450 KneeReplacementWaitTime-90thPercentile/days Dec '12 90th Percentile Wait Time LHIN 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?
  31. 31. 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
  32. 32. Advanced Analytics in Action Capacity Allocation to Improve Access to Care 32
  33. 33. 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
  34. 34. Could we have predicted high cost patients when they started dialysis? 34
  35. 35. 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 YearDialysis Incident Day Fed 80 Input Variables
  36. 36. 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
  37. 37. 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. 38. 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
  39. 39. Questions?
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.

×