disease-specific event reduction “plausibility indicators” the reasons for widespread...

Disease-Specific Event Reduction

“Plausibility Indicators”The reasons for widespread

marketplace acceptance

© DMPC www.dismgmt.com 2006

Agenda

• Logic

• Dealing with Populations Subject to Trend

• Examples

• Acceptance


Reprise of Ariel’s Definition: Logic

• An asthma nurse is talking to asthma patients about asthma. Much if not most of the reduction in claims should be in asthma. It is not plausible to say, “We earned a 2:1 ROI in asthma” with a pre-post if there was no significant reduction in asthma admissions/ER visits (“plausibility”)


Pre-post Analysis vs. Event-Based “plausibility analysis”

• Example: Babies• Suppose you want to reduce your plan’s

birth rate (now 10,000 babies a year) by instituting free contraception and family planning

• For a pre-post analysis, to find eligibles, you take everyone with a claim for a birth during the last two years– That is the cohort with which you are working


Births in your 2-Year Baseline Cohort: Pre-post analysis

0100020003000400050006000700080009000

10000

Baseline

Intervention Year

Would you say:• “We achieved a 50%

reduction in births and costs of birth through our contraception and family planning programs” ?


Of course not. You would say:

• “This is absurd…you would never just measure births in a cohort. You’d measure in the entire plan.”– Measuring the entire plan is an event-based

plausibility analysis to check the pre-post, as in this example


Births in your entire Health Plan: The event-based plausibility analysis

0

2000

4000

6000

8000

10000

12000

Baseline

Intervention Year

Births taking placemore than two yearsago

Births in first-timemoms

# Births in 2-yearbaseline cohort


Births in your entire Health Plan: The event-based plausibility analysis

0

2000

4000

6000

8000

10000

12000

Baseline

Intervention Year

Births taking placemore than two yearsago

Births in first-timemoms

# births IN ORIGINALCOHORT

These people getMissed in a pre-post


Babies vs. chronic disease

• “This is absurd…you would never just measure births in a cohort. You’d measure in the entire plan.”

But this is precisely what you do when you measurepre-post for chronic disease and then track your performance vs. the baseline. Let’s use a hypotheticalfrom a chronic disease and include cost and show how Pre-post gives you a much different – and much less Valid – result than a plausibility-based event measurement


Example from AsthmaFirst asthmatic has a $1000 IP claim in 2004

2004 (baseline)

2005 (contract)

Asthmatic #1

1000

Asthmatic #2

Cost/asthmatic


Example from AsthmaSecond asthmatic has an IP claim in 2005 while

first asthmatic goes on drugs (common post-event)

2004 (baseline)

2005 (contract)

Asthmatic #1

1000 100

Asthmatic #2

0 1000

Cost/asthmatic


Cost/asthmatic in baseline?

2004 (baseline)

2005 (contract)

Asthmatic #1

1000 100

Asthmatic #2

0 1000

Cost/

asthmatic

$1000


Cost/asthmatic in contract period?

2004 (baseline)

2005 (contract)

Asthmatic #1

1000 100

Asthmatic #2

0 1000

Cost/asthmatic

$1000 $550


Using “event-based plausibility indicator” of

total primary asthma IP codes

2004 (baseline)

2005 (contract)

Asthmatic #1

1000 100

Asthmatic #2

0 1000

Number of IP codes

1 1


Conclusion

• The “plausibility analysis” showed no change in asthma events

• Invalid pre-post financial results caused by the unfound asthmatic in base year (just like unfound people getting pregnant in previous example)– This is very common—many people with a condition

don’t have disease-identifiable claims every year

• No money was saved despite pre-post “result” (costs went up)


Difference between babies and chronic disease

• Better-established trends in babies

• Unlikely to miscode a birth (though miscoding primary IP/ER events generally is a wash year over year)


What Can Happen to Move Chronic Disease Trend

• Change in demographics of plan (adding a large municipality, for example)

• Major change in usual care

• Major change in physician behavior

• Change in underlying rate of disease-specific events (due to prevalence or usual care)

Next slide will show that these tend to move the line in small percentages while DM should create large percentage declines

Recent Medicare Inpatient Admission Trends(note: similar in commercial – even major

Trends take place slowly)

Medicare Discharges/1000

0.00

5.00

10.00

15.00

20.00

25.00

1998 1999 2000 2001 2002 2003

Year

Dis

ch

arg

es

/10

00

Diabetes

Bronchitis/Asthma

COPD

Heart

Source: CMS reports, 2000-2006 and thanks to Ian Duncan, who supplied it

Enlarging the picture…

Medicare Discharges/1000

1.00

1.20

1.40

1.60

1.80

2.00

2.20

2.40

2.60

1998 1999 2000 2001 2002 2003

Year

Dis

ch

arg

es

/10

00

Diabetes

Bronchitis/Asthma

Annualized trends: Diabetes: 2.1%; Asthma: -2.8%;COPD: 0.2%; Heart: 0.8%


Agenda

• Logic• Dealing with Populations Subject to Trend• Examples

– Note that the vendor report example would require major inflections of trend

– Note that it is events only, not procedures (which are discontinuous)

– Note that it is consistent with Ariel’s research showing that significant event rate changes needed to get ROI

• Acceptance


Example of just looking at Diagnosed people in pre-post: Vendor Report of

Asthma Cost/patient Reductions

-25%

-20%

-15%

-10%

-5%

0%

1st year 2nd year

ER ER

IP

IP


What we did to verify…

• We looked at the actual asthma ER/IP primary codes across the entire plan (planwide event-based plausibility analysis)

• Two years of codes pre-program to establish trend– Note that historic “trend” in plans could be used or

else Ian’s Medicare slide – about the same trend

• Then compared the two program years


Two years’ Baseline health plan trend for asthma ER and IP Utilization

493.xx Primary-coded ER visits and IP stays/1000 planwide

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

1999(baseline)

2000(baseline)

ER ER

IP IP


If pre-post was accurate, plausibility expectation is something like…

(493.xx primary-coded ER visits and IP stays/1000 planwide)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

1999(baseline)

2000(baseline)

2001 (study)2002 (study)

ER ER ER ER

IP IP IP IP


Plausibility indicator Actual: Validation for Asthma savings from same plan

including ALL CLAIMS for asthma(493.xx ER visits and IP stays/1000 planwide)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

1999(baseline)

2000(baseline)

2001 (study)2002 (study)

ER ER ER ER

IP IP IP IP


Two more Plausibility Examples

• IBM (Matria) CAD

• Pacificare (Alere) CHF


IBM Angina “Plausibility Analysis”Hosp Admits ICD-9 '413.xx' per 1000 MM

0.1404

0.12830.1172

0.0913

0.0000

0.0200

0.0400

0.0600

0.0800

0.1000

0.1200

0.1400

0.1600

2001 2002 Expected2003

Actual 2003

2001

2002

Expected 2003

Actual 2003


Acute MI “Plausibility Analysis” Hosp Admits ICD-9 '410.xx' per 1000 MM

0.1469

0.13560.1251

0.1051

0.0000

0.0200

0.0400

0.0600

0.0800

0.1000

0.1200

0.1400

0.1600

2001 2002 Expected2003

Actual 2003

2001

2002

Expected 2003

Actual 2003


Alere Customer CHF UtilizationCommercial Membership

Enterprise Commercial Admits Combined

0.000.100.200.300.400.500.600.700.80

I-2 I-1 I I+1 I+2

Intervention Time Period

Ad

mit

s p

er

10

00

-15%-10%-5%0%5%10%15%20%25%

Pe

rce

nt

Ch

an

ge

Admits per 1000 Year over Year % change


Agenda

• Logic

• Dealing with Populations Subject to Trend

• Examples

• Acceptance of event-based plausibility


Number of Payors Using This Approach

0

5

10

15

20

25

30

35

2004 2005 2006 2007 Est

2004

2005

2006

2007 Est


Partial list of Payors using this methodology (DMPC certified only)

Blue Cross of Alabama

Blue Cross of Delaware

Blue Cross of Vermont

Bluegrass Family Health Plan

CareFirst

Capital District Physicians' Health Plan, Inc. (CDPHP)

CHA Health

Connecticare

Empire Blue Cross

Georgia Department of Community Health (Medicaid)

Great-West Health Care

Harvard Pilgrim Health Care

Health First Health Plans

Health Net

HealthPartners (MN)

IBM

Illinois Department of Family Health

Pacificare

PreferredOne

Premera Blue Cross

Procter & Gamble

State Teachers Retirement System of Ohio

SummaCare

Wyoming Medicaid


Conclusion

• In “normal” situations any financial result should be tested with an event-based plausibility analysis. If the financial result shows significant savings but the event rate trend didn’t change noticeably, there is likely a mistake in the financial result– Preferable to have both analyses show consistent

results

• Make sure to understand demographic trend changes before reaching that conclusion

disease-specific event reduction “plausibility indicators” the reasons for widespread...

Documents

prepost slide

contract asthmatic

prepost analysis

pre post

cost asthmatic

costasthm atic slide

asthma nurse

asthma patients