Moving From Hindsight to Foresight – Unlocking the 1% Challenge Young Lee – Deloitte National...

Moving From Hindsight to Foresight – Unlocking the 1% Challenge

Young Lee – Deloitte National Health Services

May 29, 2013

Copyright © 2012 Deloitte Development LLC. All rights reserved.2

Faculty/Presenter Disclosure

Presenter: Young Lee

No Conflicts to Disclose

3 Copyright © 2012 Deloitte Development LLC. All rights reserved.

Objectives of Today’s Session

• Setting the Context

• Hindsight – Insight – Foresight

• Enabling Improvement Through Advanced Analytics


A portrait of our healthcare system

• 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*

“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

* 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%

1

2

3

– 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

1

23

Leveraging advanced data analytics to better manage patient profiles

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


Examining the Top 1%

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

1

2

3

• 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

Group 3


Hindsight Insight Foresight

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?

0%

5%

10%

15%

20%

25%

30%

35%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Rea

dmis

sion

Rat

e

Week

Readmission Rate by Facility

Alpha

Bravo

Charlie

Delta

0%10%20%30%40%50%60%70%

Rea

dmis

sion

R

ate Rx History

0%

10%

20%

30%

40%

50%

60%

70%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

Rea

dmis

sion

Rat

e

Length of Stay

Length of Stay

0%

5%

10%

15%

20%

25%

30%

1 4 7 10131619222528313437404346495255586164

Rea

dmis

sion

Rat

e

Age

Age

0% 10% 20% 30% 40% 50%

Heart Failure & Shock

Psychoses

Esophagitis and Gastroentritis

PTCA

Joint Replacement

Chest Pain

Back & Neck Proc

Spinal Fusion

Readmission Rate

DRG

0%

20%

40%

60%

80%

100%

0 1 2 3 4 5 6 7 8 9 10 11 ≥12

Rea

dmis

sion

Rat

e

Number of Claims in Past 90 days

Transport Claims

0%

20%

40%

60%

80%

100%

0 1 2 3 4 5 6 ≥ 7

Rea

dmis

sion

Rat

e

Number of Claims in Past 90 days

CHF

Patient ID: X12345

Age: 29 Sex: Male

Primary DX: 996.12(Mechanical Complication of vascular device / implant)

ReadmissionPropensity

84%

History: Anemia, CHF, Hypertension

Rx History: G.I. Drugs, Beta blockers, Diuretics, Antihypertensives, Nitrates, Anticoagulants, Hypnotics

180 day horizon

DRG 144 – Other Circulatory System Diagnosis with CC

Readmission Rate48%

Advanced Data Analytics


• Inter-hospital Readmission Rates

• Intra-hospital Readmission Rates

Hindsight


HindsightInter-hospital Readmission Rates

All hospitals can be compared with all Disease Classes


HindsightInter-Hospital Readmission Rates

Hospital ID 5 and 12 can be compared across time


HindsightInter-Hospital Readmission Rates

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


Insight


InsightReadmission Rates Analysis

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


InsightReadmission Rates Analysis

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

Foresight


ForesightCase 1 – Heart Failure

Solution shows the reasons for readmission and their relative effect

Solution shows the history for this patient

83% propensity for readmission within 180 days

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.


ForesightCase 2 – Complications from prior treatment

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.


ForesightCase 3 – Psychosis

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

* The New England Journal of Medicine

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

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%

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

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

Examples


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

• 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

Nanaimo Regional General Hospital (British Columbia) Saguenay-Lac-St-Jean (Quebec)

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

• 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

Hindsight Insight Foresight

• Broad historical reporting


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

• Advanced analysis and modeling

• 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

There is an Opportunity to Do More


?Questions

Moving From Hindsight to Foresight – Unlocking the 1% Challenge Young Lee – Deloitte National...

Documents

Transcript of Moving From Hindsight to Foresight – Unlocking the 1% Challenge Young Lee – Deloitte National...