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Moving From Hindsight to Foresight – Unlocking the 1% Challenge. Young Lee – Deloitte National Health Services May 29, 2013. Faculty/Presenter Disclosure. Presenter: Young Lee No Conflicts to Disclose. Objectives of Today’s Session. Setting the Context Hindsight – Insight – Foresight

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Moving From Hindsight to Foresight – Unlocking the 1% Challenge

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Moving From Hindsight to Foresight – Unlocking the 1% Challenge

Young Lee – Deloitte National Health Services

May 29, 2013

Faculty/Presenter Disclosure

Presenter: Young Lee

No Conflicts to Disclose

Objectives of Today’s Session

  • Setting the Context

  • Hindsight – Insight – Foresight

  • Enabling Improvement Through Advanced Analytics

A portrait of our healthcare system

“Ontarians regard health care as the single most important public policy issue; and they will not tolerate anything that causes deterioration in access and quality of care” – Drummond Report

  • There is no shortage of literature and studies that suggest our health system is not meeting the needs of those who need it most

  • Investments have been made to implement a variety of strategies and programs to improve both the quality and efficiency of service delivery

  • However, the solutions implemented have not addressed the overall health system pressures and dynamics at play in managing patient flow and care transitions

As a result, the top 1% consumes 33% of all health-care dollars and the top 5% consumes two-thirds*

* Deb Matthews – Ontario Minister of Health and Long-Term Care

Understanding who is using our health system

  • As a health system, a wealth of data exists to inform our improvement strategies

  • Analysis typically has focused on examining service utilization data points such as patient volumes, patient demographics, diagnostic segmentation, and process metrics (e.g., wait times), instead of solutions that meet the specific requirements of high-needs patients

  • The limitation of this traditional approach lies in the fact that those who use the health system the most frequently and have the greatest need are relatively small in number

  • Our traditional approaches and lessons learned inform us that we need to leverage our hindsight to better manage the top 1% and 5% users

Advanced data analytics can enable us to seek out these patient profiles to better understand how to manage these users of the health system

Segmenting patients and their healthcare consumption through the use of Advanced Data Analytics

  • An illustrative example of patient emergency department (ED) consumption:

  • There are 3 important segments of patient profiles that need to be properly managed:

    • Group 1 – Infrequent users

    • Group 2 – Frequent users (the Top 5%)

    • Group 3 – the Top 1%




  • Patient flux – patient profiles are not static, which means patients easily move from group-to-group, and thus need to be actively managed, to prevent conversion from Group 1 to either Groups 2 or 3

Leveraging advanced data analytics to better manage patient profiles

  • – Proactively manage this group to prevent patients from becoming a frequent user of the ED;

  • – Disrupt the cycle and transition these patients out of this group towards Group 1

  • – Manage the Top 1% by understanding who they are, what their needs are, and how to meet their needs




Examining the Top 1%

  • An illustrative example of patient emergency department (ED) consumption:






  • A small proportion of patients (6.5% or 4,280 patients) are seen 3 or more times within a 1-year period by a hospital’s ED, consuming 25% of all ED time

  • An even smaller proportion of patients (0.6% or 395 patients) visit the ED 6 times or more per year

  • The patient profile of the frequent users are not merely represented by older patients; as such, patient needs should be assessed and commitment should be made to better manage these patients to shift them out of Group 3 and into Group 1

How do we unlock the Top 1%?

Broad historical reporting on key performance indicators.

What happened?

Statistical analyses (e.g. profiling and segmentation) help organizations understand historical performance.

Why did it happen?

Advanced analysis, machine learning and modeling predict future performance.

What could happen?

Advanced Data Analytics


  • Inter-hospital Readmission Rates

  • Intra-hospital Readmission Rates

Inter-hospital Readmission Rates

  • Hindsight

All hospitals can be compared with all Disease Classes

Inter-Hospital Readmission Rates

  • Hindsight

Hospital ID 5 and 12 can be compared across time

Inter-Hospital Readmission Rates

  • Hindsight

Hospital ID 5 and 12 can be compared across time for Circulatory diseases


Readmission Rates Analysis

  • Insight

Insight can be gained by looking into factors for readmissionWe show Age, CMG/DRG, Length of Stay, and Prescription History

Light blue bars have too few cases

Readmission Rates Analysis

  • Insight

Filters can be applied to specific Hospitals. Showing Hospital IDs #5 and #12


  • Case 1 – Heart failure

  • Case 2 – Complications from prior treatment

  • Case 3 – Psychosis

Case 1 – Heart Failure

  • Foresight

83% propensity for readmission within 180 days

Solution shows the history for this patient

Solution shows the reasons for readmission and their relative effect

Suggested intervention – Patient should be coached about their condition and management of their disease. Their family members should also be coached on how to take care of the patient. Active care management may be considered.

Case 2 – Complications from prior treatment

  • Foresight

Suggested intervention – Patient is at high risk of readmission due to complexity of illness. We suggest enrolling the patient into a care management program before discharge.

Case 3 – Psychosis

  • Foresight

Suggested intervention – Patient has liver disease and electrolytic imbalance complicating psychosis. Given the young age of the patient, a care coordination program should be considered along with coaching the patient’s parents on specific care strategies.

Advanced Data Analytics combined with Care Management has improved the American health system

  • Similar to the Canadian health system, in the United States, 10% of patients account for 70% of total health care expenditures*

  • Medicare beneficiaries with 5 or more chronic conditions accounted for 76% of all Medicare expenditures

  • Care management is a healthcare innovation that can reduce costs while enhancing quality for patients with complex health care needs


Frequent Users of Health Services Initiatives (The California Endowment and California HealthCare Foundation)

Care Management Plus (Oregon Health & Science University)

Guided Care (Johns Hopkins University)

  • Reduced the number of hospital days by 24% and insurers’ net costs by 11%

  • Reduced patient odds of hospital admission by 24-40%

  • 61% reduction in ED visits and 62 % decrease in inpatient days over two years

* The New England Journal of Medicine

Advanced Data Analytics has enabled much improved patient transitions in BC and QC

Data analytics focused on identifying the frequent users of care has enabled more efficient patient transition, thereby reducing costs to the health system

Nanaimo Regional General Hospital (British Columbia)

Saguenay-Lac-St-Jean (Quebec)

  • Following analysis of high needs patients in the Ste-Agathe region of Quebec; care models that were targeted to support the needs of the top 200 healthcare users were implemented

  • To improve performance indicators and CTAS time at the emergency department

Advanced Data Analytics

  • Over a 3-year period, ED visits have been reduced from 760 to 212 visits; inpatient days by an equivalent of 9.4 beds; and hospital admissions from 514 to 88

  • Enabled the identification of various process bottlenecks, created fast track processes for CTAS 4 and 5, and improved overall patient flow

How to leverage Advance Data Analytics to improve your healthcare organization

There is an Opportunity to Do More




Advanced Data Analytics

  • Statistical analyses (e.g. profiling and segmentation)

  • Advanced analysisand modeling

  • Broad historical reporting

  • Understand patient needs and historical behaviour

  • Understand patient profiles and patient segments (e.g., top 1% and top 5%)

  • Predict the future to prevent unnecessary use

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