Veterans Health Administration

F i MCCF C ll i Forecasting MCCF

C

ollections 

HERC M thl E i S i HERC

Monthly 

Economics 

Seminar

Veterans Health Administration (VHA)

Chief Business Office (CBO)

August 18, 2010

Ernesto Castro, MS, PMP

Director, Business Information Office

Geor

ge Miller, PhD

Altarum Institute
Medical Care Collections Fund (MCCF) Backgro

 The Department of Veterans Affairs Veterans Heal

Administration is authorized to seek reimbursemenAdministration is authorized to seek reimbursemen

Party health insurers for the cost of medical care fuinsured Veterans and bill copayments for nonservic

connected care

 Funds collected from First and Third Party bills are 

th VA h lth f ilit th t id d th f the VA

health care 

facility th

a

t provid

e

d the care 

fo

healthcare

 VA has demonstrated significant improvements in 

VA has demonstrated significant improvements in 

processes through a total collections increase from FY 2004 to $2.7B in FY 2009
Expected Results Background

 Chief Business Office establishes VAMC statio

collection goals (Expected Results) each fiscal collection goals (Expected Results) each fiscal 

 General expectation is to sustain and improve 

year s’ collection performance collection performance

 CBO uses statistically driven models to develo

Care Collections Fund (MCCF) Expected ResuCare Collections Fund (MCCF) Expected Resu

support President’s Budget formulation proces
Vision of More Robust CBO Collections Mo

 Integrate independent models (e.g., Enrollee Health Car

Model)

 Automate modeling activities that require human interve

develop some of the information and to pass data from oapplication to anothe

r

 Produce consistent, reasonable, and achievable modele I l d id f i bl t th St ti l l 

Inclu

de a wide range o

f variables a

t the St

ation level, su

Group status, age, demographics, economic market coninsurance coverage, historical performance, and policy i

$1 copayment increase, P8 expansion)

 Generate more timely responses to VHA CFO and OMB A d f i li d billi d ll i 

Accommo

date 

for regionalize

d billings an

d collections e

Consolidated Patient Account Centers (CPAC)
ICFM Purpose

 ICFM was developed to forecast annual VHA c

for each of three components of the MCCF: for each of three components of the MCCF:

` First Party inpatient and outpatient copayments` First Party pharmacy copayments First Party pharmacy copayments` Third Party collections

 Annual station Annual station-level forecasts are level forecasts are

` Rolled up to national level by year to produce te

forecasts of future collections as input to PresideBudget determination

` Used to establish station-level Expected Results 

next year next year

` Compared with current year to date collections t

p p gp 

otential problems in meeting Expected Results 
Benefits of ICFM

 Produces station-specific forecasts by fund; bottom

approach

 Provides a single, transparent modeling system Incorporates impacts of policy changes (i e Priorit

Incorporates impacts of policy changes (i.e., Priorit

enrollment impact, change in Pharmacy copaymen

 Includes local economic market conditions at statio

Includes local economic market conditions at statio

 Provides flexibility to modify and adapt to changes 

p y, p , olicy, performance, etc.

 Enables alternative scenario development for legis

outside of VA that will impact Veterans, such as HePolicy Reform 

 Ties in all data components to arrive at a scientifica

coll ti ti t t id th b d t llections estimate to aid the budget process
ICFM Analytic Architecture

Objective System

Develop 10-year

President’s bud

get

n10 year collectionsual cycle

g

submission

Workload projections,billing and collections

10-year collections

Anforecastn

Support

billing and collections

history, economic

conditions, projected

collection improvements,

policy changes 

negotiation and

legislation 10-year collections budget

Allocate next year’s

budget to stations Collection goals

by station

Update projection

of current year’s

collections

Local year-to-dateworkload, billing,

and collections 

Projected

shortfalls from

collection goals

hly cycle

Estimate metrics

associated with

collection

shortfalls

Local billing and

collections

history Collection metrics

Informatio

Month

Processin
ICFM Analytic Architecture

Current Capabilities

Develop 10-year

President’s bud

get

n10 year collectionsual cycle

g

submission

Workload projections,billing and collections

10-year collections

Anforecastn

Support

billing and collections

history, economic

conditions, projected

collection improvements,

policy changes 

negotiation and

legislation 10-year collections budget

Allocate next year’s

budget to stations Collection goals

by station

Update projection

of current year’s

collections

Local year-to-dateworkload, billing,

and collections 

Projected

shortfalls from

collection goals

hly cycle

Estimate metrics

associated with

collection

shortfalls

Local billing and

collections

history Collection metrics

Informatio

Month

Processin
ICFM Forecasting Strategy

 Conduct bottom-up forecasting starting with 20

level collections by MCCF component level collections by MCCF component

 Adjust future collections by year for 

` Changes in workload magnitude Changes in workload magnitude` Changes in collectability (e.g., due to change in 

demo

gp ) ra

phics

)

` Systemic changes (e.g., policies, co-pays)` Improvements in collections performance

 Use sum of station-level forecasts by year (for 

for President’s Budget submission

 Use next year’s (2011) station-level forecasts t

Expected Results
ICFM Forecasting Process

For each MCCF component, station, and future year: Estimate impacts of workload magnitude and collecta E ti t t it f i t i ll ti E

stima

te oppor

tunity 

for improvemen

ts in collections 

performance

 Estimate effects of future systemic changes Estimate effects of future systemic changes Estimate improvements in collections performance  Combine all effects to forecast future collections
ICFM Forecasting Process

For each MCCF component, station, and future year: Estimate impacts of workload magnitude and collecta E ti t t it f i t i ll ti E

stima

te oppor

tunity 

for improvemen

ts in collections 

performance

 Estimate effects of future systemic changes Estimate effects of future systemic changes Estimate improvements in collections performance Combine all effects to forecast future collections
Estimating Impacts of Workload & Collectab

Basic Equation

For a given fiscal year station and MCCF component let For

a given fiscal year, station, and MCCF component, let

C = projected collections under average historical perform

= W·B·R

whereW = 

p j ro ected annual workload

B = projected dollars billed per unit of utilization given ave

historical performance

R = projected fraction of dollars billed that would be collec

the year given average historical performance
Estimating Impacts of Workload & Collectab

Components of Basic Equation

C

=

W∙B∙R

EHCPM station‐level workload data by:

i it (1

2

7 8)

Non‐linear statio

• priorit

y group (1, 2,....,

7‐8) f ti

f

• age group (<45, 45‐64, 65+)

• service type (inpatient, outpatient, pharmacy)

as

a

function

o

f:

• priority group• age group

• service

typ

e

Non‐linear station‐level regression as

a function of:

• priority group

• age group

• service type• % of veterans with private, non‐HMO insurance

• local unemployment rate
Estimating Impacts of Workload & Collectab

Projecting Annual Workload (W)

 Begin with baseline and projected workload for year i fro

Begin with baseline and projected workload for year i fro

Health Care Projection Model (EHCPM) by Health Servi(HSC) and sector, expressed as:

` M di ll bl h f i ti t d t ti t 

M

edicare allowable c

harges 

for inpatien

t an

d ou

tpatien

t wo

` 30-day scripts for pharmacy (Rx) workload

 Aggregate to inpatient outpatient and Rx totals (w Aggregate to inpatient, outpatient, and Rx totals (wi) and 

) and 

3-digit station level using historical treatment patterns froAllocation Resource Center (ARC) data

 Estimate station-level workload associated with collectio

as

f·wi-1+(1

-f)wi

where f is an estimate of fraction of workload from year associated with collections in 

year i
Estimating Impacts of Workload & Collectab

Billings/Workload (B) – Priority Group Impa

50%

$206M

40%

Billed

30%

Workload

$512M

$335M

10%20%

Percent of W

$79M

$623M

$790M

0%

1 2 3 4 5 6

P

$79M

PriorityGroup Third Party, 2008
Estimating Impacts of Workload & Collectab

Billings/Workload (B) – Age Group Impac

20%

Billed 15%

$257M

$2,249M

10%

Workload

$1,066

5%

Percent of W

0%

<45 45‐64 65+

P

Age Group Third Party, 2008
Estimating Impacts of Workload & Collectab

Billings/Workload (B) – Service Type Impa

25%20%

Billed

$2,128M

10%15%

Workload

$1,122M

5%

10%

Percent of

$321

0%

Inpatient Outpatient Pharm

Service Type

Third Party, 2008
Estimating Impacts of Workload & Collectab

Projecting Annual Billings/Workload (B)

 Regression modeling establishes a billings/workload basel

station using VistA Direct Extract (VDE) historical billings to 

 Projections of billings/workload ratios are a function of

` Priority group (1,2,...,7-8)` A ( 45 45 Age group 

(

<45, 45

-64 65 ) 64, 65

+

)

` Service type (inpatient, outpatient, Rx)` Forecasts of station-level unem

py y 

lo

yment rates from Econom

y

Party Rx billings)

` Fraction of users with private, non-HMO insurance (for Third P

 Fraction of users with private non Fraction of users with private, non

-HMO insurance is estim

HMO insurance is estim

` Regressions based on results from 2008 Survey of Veteran E

and Reliance upon VA to estimate VISN-level fraction of users HMO insurance HMO insurance

` Economy.com forecasts of station-level unemployment` Relation between change in unemployment and change in ins

t j t f t i t b t ti to projec

t

f

u

ture insurance coverage ra

tes 

by s

t

ation
Estimating Impacts of Workload & Collectab

Projecting Annual Billings/Workload (B) – Exa

 For First Party Inpatient and Outpatient billings: 

B

(

) ij = pi

(1+aj

)

where

B f ti f t ti ’ kl d th t i bill d 

ij = 

fraction o

f s

t

ation’s workloa

d th

a

t is bill

e

d as 

copayments for priority group i and age grou

pi = unadjusted average billing rate for priority gr

unadjusted average billing rate for priority gr

(regression coefficient)

aj = age group adjustment (regression coefficien

 Forms for Third Party billings and First Party Rx billing

complicated, and include effects of

` Projected fraction of users with private non Projected fraction of users with private, non

-HMO insuran

HMO insuran

Party billings

` Projected local unemployment rates for First Party Rx bill
Estimating Impacts of Workload & Collectab

Collections/Billings (R) – Priority Group Imp

40%

$128M

$78M

30%40%

llected

$128M

$190M

$211M

$78M

$290M

20%

Billings Col

$20M

10%

Percent of

0%

1 2 3 4 5 6

P

PriorityGroup

Third Party, 2008
Estimating Impacts of Workload & Collectab

Collections/Billings (R) – Age Group Impac

40%

$868M

30%

llected

$93M

$868M

$331M

20%

Billings Col

10%

Percent of

0%

<45 45‐64 65+

P

Age Group

Third Party, 2008
Estimating Impacts of Workload & Collectab

Collections/Billings (R) – Service Type Impa

40%

$814M

30%

llected

$369M

$814M

$108

20%

Billings Col

10%

Percent of

0%

Inpatient Outpatient Pharm

P

Service Type Third Party, 2008
Estimating Impacts of Workload & Collectab

Projecting Annual Collections/Billings (R

 Regression modeling establishes a collections/billings 

Regression modeling establishes

a collections/billings 

for each station using VDE historical collections to billfor First Party and Third Party

 Projections of collections/billings ratios are a function 

` Priority group (1,2,...,7-8)` A ( 45 45 Age group 

(

<45, 45

-64 65 ) 64, 65

+

)

` Service type (inpatient, outpatient, pharmacy)

 Functional forms are similar to those used to project Functional forms are similar to those used to project

billings/workload
ICFM Forecasting Process

For each MCCF, station, and future year: Estimate impacts of workload magnitude and collecta E ti t t it f i t i ll ti E

stima

te oppor

tunity 

for improvemen

ts in collections 

performance

 Estimate effects of future systemic changes Estimate effects of future systemic changes Estimate improvements in collections performance Combine all effects to forecast future collections
Estimating Opportunity to Improve Collect

Approach

 Basic equation describes average collections by station, ta

account differences that affect abilit

y ( p 

 to collect 

(such as 

pr

mix, age mix, service category mix, and percent with insur

 When summed by VISN, average collections are used to e

potential for collections for each VISN by assuming that thpotential for collections for each VISN by assuming that th

whose actual collections exceeded its average in 2009 by factor (“best practice multiplier”) represents best practice (

practice potential practice potential ). ”

 Best practice potential for each station equals collections t

have been realized if station performed at this same level correcting for differences that affect ability to collect It eqcorrecting for differences that affect ability to collect. It eq

average collections multiplied by it’s VISN’s best practice 

 Each station’s opportunity for improved performance (“gap

h h b tt t ti ld h f d if it t it how muc

h

b

etter s

t

ation would

have performe

d if it me

t its 

practice potential. It equals the difference between stationpractice potential and actual collections, expressed as a p

actual.
Estimating Opportunity to Improve Collect

Example (VISN Level)

25%20%

cted

Best Practice Potential Actual

Best Practice Multiplier = 188% of Average

Gap for This VISN 

15%

rkload CollecGap for This VISN

10%

ercent of Wo

0%

P5%e

VISN

Third Party Outpatient Collections, 2009
ICFM Forecasting Process

For each MCCF, station, and future year: Estimate impacts of workload magnitude and collecta E ti t t it f i t i ll ti E

stima

te oppor

tunity 

for improvemen

ts in collections 

performance

 Estimate effects of future systemic changes Estimate effects of future systemic changes Estimate improvements in collections performance Combine all effects to forecast future collections
Estimating Future Changes

Approach

 Systemic changes are represented as shifts in best prac

Systemic changes are represented as shifts in best prac

(e.g., increase in First Party Rx co-pay)

 Performance im

p p rovements are re

presented as reductio

size of the gap

` Performance improvements from 2008 to 2009 serve as st

estimating changes in subsequent years estimating changes in subsequent years

` These can be modified to reflect effects of anticipated prac

that will improve performance (e.g., CPAC implementation 

` Performance improvements tend to be greater if gap is larg

with greater room for improvement are projected to increasmore than those that are close to their best practice potent
Estimating Future Changes

Example

y

= 0.054x

+ 0.135

60%

y 0.054x

+ 0.135

R2

= 0.0773

40%50%

08‐2009)*

30%40%

ollections (20

Average Performance 

Improvement Was 5.4% of Gap

10%20%

ncrease in Co

0%

10%

0% 50% 100% 150% 200% 

In

Best Practice Gap (2008) Systemic Change Caused Overall

Increase of 13.5%

* Adjusted for changes in workload and insurance coverage rates

Third Party Outpa
Estimating Future Changes

Incorporating CPAC Effects into Gap Clos

Gap Closure Period

Histo

Rate

Attributable to CPAC

(current value= 12 months)

Gap

Closure

(%) G

A

Break-in P id A

(

Perio

d

(current

value = 3 

months)

Historical

Rate Rate = 0

CPAC Becomes

Operational for a VISN

(based on current schedule)

Break-in

Period Ends

CPAC Effects

Fully Realized
ICFM Forecasting Process

For each MCCF, station, and future year: Estimate impacts of workload magnitude and collecta E ti t t it f i t i ll ti E

stima

te oppor

tunity 

for improvemen

ts in collections 

performance

 Estimate effects of future systemic changes Estimate effects of future systemic changes Estimate improvements in collections performance  Combine all effects to forecast future collections
Forecasting Future Collections

Approach

Step 1: Step 1: For each station fund and service type compute 

For each station, fund, and service type, compute 

practice potential in year i as:Pi = Ci

Mi

Ai

where

Ci = impact of workload magnitude and collectability – a

coll ti f kl d i i i hi t i l llections 

for workloa

d in year i using hi

s

torical p

levels (computed with the basic equation)

Mi = best practice multiplier (1.88 in our example) best practice multiplier (1.88 in our example)Ai = impact of systemic changes – adjustment for chang

practice from year to year (1.135 in our example)
Forecasting Future Collections

Approach (continued)

Step 2: Step 2: For each station fund and service type compute 

For each station, fund, and service type, compute 

gap in year i as:Gi = Gi-1(1-

gi

)

/

(1+

gi

Gi-1) i i 1(

gi)( gi i 1)

where

gi = annual impact of performance improvement – gap c

rate (.054 in our example)

G

0 = observed value of the gap for the station, fund, and 

service type in the base year (2009) ( 95 in our exaservice type in the base year (2009)

(.95 in our exa
Forecasting Future Collections

Approach (continued)

Step 3: Step 3: Forecast future collections by station fund and se

Forecast future collections by station, fund, and se

as:

Fi = Pi

/

(1+Gi

) i i (

where

Pi = best practice potential in year i for that station

and service type

Gi = current value of the gap in year i for the statio

and service type and service type
Forecasting Future Collections

Approach (concluded)

Step 4: Make final adjustments For President’s Budget submission, sum results over stat

year

 For Expected Results determination: For Expected Results determination:

` Convert each station’s 2011 collections forecast to station’s s

fund (Si

)

` Adjust 2011 shares to align with 2010 end-of-year estimated 

S'2011 = S2011+q(E2010-S2010

)

where

S'i = adjusted share in year iq = adjustment weight (0

≤

q

≤ 1)

Ei = estimated actual 2010 collections share through end 

` Apply 2011 President’s Budget to each adjusted share to est

R lt esu

s
Forecasting Future Collections

Summary of Results (Illustrative Only)

$6,000$5,500

ars)

ICFM ForecastPresident's Budget

ars)

$4 500$5,000

lions of Dolla

ons of dolla

$4,000$4,500

llections (Mil

ctions (millio

$$3,500

Co

Collec

$3,000

FY 11 FY 12 FY 13 FY 14 FY 15 FY 16 FY 17 FY 18 FY 
Summary

ICFM Capabilities

 Is a single integrated system Is a single, integrated system Employs available data Is transparent Is transparent Produces internally consistent results Incorp g orates realistic ceilings on collections Appropriately incorporates historical collections perfor Incorporates unique features of local stations (populat

demographics, economic conditions, etc.)

 Supports investigation of alternative scenarios
Summary

Future Enhancements

 Enhance ability to represent impacts of policy change

health reform)

 Improve treatment of economic conditions and insuran

Improve treatment of economic conditions and insuran

 Incorporate fourth component of MCCF (long-term ca

model

 Provide for forecast updates to end of current year Correlate year-to-date collections performance to key 

(e.g., Days to Bill)

 Develop ICFM user interface