Using Willingness to Pay to Evaluate Hospital

Mergers: Results from 16 Mergers

Presented by Rich Lindrooth

Co-authors: David Dranove Mark Satterthwaite

Northwestern University

This research was funded by Robert Wood Johnson’s HCFO

Initiative

Using WTP to Evaluate Mergers

• Technique was refined in Capps Dranove and Satterthwaite (Rand 2002)

• WTP is a measure based on the value health plan enrollees place on inclusion of a hospital in a managed care network.

• Direct theoretical link between WTP estimates and hospital prices

Key Institutional Details

• Health plans assemble networks

• Health plans negotiate with local hospitals• Traditional competition models do not apply• Must instead invoke bargaining models

• Health plans market networks to local employers

• Must provide geographic coverage coincident with where employees live

• Services that employee are likely to need

• Hospitals that are most attractive to networks bargain for the highest prices

Computing WTP

• Hypothetical WTP calculation• MCO considering adding hospital X to

network• Hospital X is a leader in CABG, but is

not conveniently located• What is a typical enrollees WTP to have

access to this hospital?

Example continued

• Typical enrollee considers future medical needs• Most likely (e.g., 90%) they will remain healthy: WTP =

0• Small chance (e.g. 9.9%) of requiring hospital for routine

needs• Local hospital will do just fine• WTP to have access to X if routine problem arises = $1000• Or for our purposes 9.9%*1000=$99

• Very small chance (e.g. 0.1%) of requiring CABG• Hospital X is best in town• WTP to access X if CABG required = $20,000• Or for our purposes 0.1%*20000=$200

- Overall WTP to have access to X = $299. - This is the maximum amount that X can “squeeze” out

of the negotations (on a per patient basis)- This will take the form of higher overall prices.

, (Diagnosis Need CABG)

1,..., , ..., | CABG

/ | CABG

Interimi k

Interim

Interim

WTP

EU G k J

EU G k

Three possible health states for person i: Torn Knee ligament, Need a CABG, Healthy

Torn Ligament (A) CABG (B)Healthy

1 2ˆ ˆ ˆ, , ...,i i iJP P P 1 2, , ...,i i iJP P P

, (Diagnosis Torn Ligament)

1,..., , ..., | Torn Ligament

/ | Torn Ligament

Interimi k

Interim

Interim

WTP

EU G k J

EU G k

WTPij = 0

,

,1

({ }) ({ / })

(Diagnosis | Demographics ) (Diagnosis )

i k i i

DInterim

i i kd

WTP EU G EU G k

PR d WTP d

Interim Conditions

Interim Choice Probabilities:

i(Torn Ligament | Demographics )PR i(CABG | Demographics )PRProbabilities of Each Condition

( )k ik iWTP N WTP f X dX Aggregate Over The Population

Mathematically:

, ,

, ,

( ) ( / )

1 1ln ( , , ) ,

1 , , ,

EAk Y Z

i i i i i iY Zk i i i

V G V G kW G N E

N f Y Z dY dZ ds G Y Z

Key limitations

• Do not observe prices• Do not observe MD information

• Omitting MD does not bias the model • MD choice of hospital reflects patient preferences• Analyst could infer that the MD “ran the show”

when in fact the MD was deferring to the power of the hospital

• Assumes network is formed to fully reflect employee preferences

A look at 16 mergers

• We use a sample of 16 mergers occurring in 1995-2000:

• Bakersfield, CA (2)• Buffalo, NY (3)• Daytona, FL (4)• Denver, CO (2 total; use 1)• Jacksonville, FL (2, one de-merger)• Rochester, NY (4)• Seattle (Use as a control. 1 hospital switched

systems in 2000)• Milwaukee (Market too tumultuous over time

to model)

Example: Daytona Market

• Ownership in many of the markets was tumultuous during this period

• Many mergers, some de-mergers, and some exits.

• The following maps trace merger activity in Daytona from 1995 until 2000.

• Each color represents a system.• A white dot is a independent hospital

Data

• Patient-level data• the Healthcare Cost and Utilization

Project State Inpatient Database (HCUP-SID)

• Hospital Characteristics• American Hospital Association Annual

Survey (AHA)• Hospital Financial Data

• CMS Medicare Cost Reports (MCR)

Estimating the Effect of the Mergers on Net Inpatient

Revenue

• Step 1: Calculate WTP• Value a hospital/system brings to a

network• Step 2: Estimate effect of change in

WTP on Net Inpatient Revenue

• Step 3: Measure Percent change in Net revenue:

Change in Net Revenue/Pre-merger Net Revenue

Calculating WTP

• Estimate WTP for independent hospitals and system combinations one year prior to the first merger in the market.

• Prior to system consolidation calculate the sum of the independent hospital’s WTP

• After system consolidation use the system WTP • The difference reflects the change in WTP is

solely a result of the merger

WTP Estimates

Bakersfield Year # of hospitals Pre-WTP Post-WTP % ChangeMerger 1 1997 3+1 4,468 5,127 14.7%Merger 2 2000 1+1 1,591 1,724 8.3%

BuffaloMerger 1a 1996 2+1 13,871 14,573 5.1%Merger 1b 1998 3+2 14,573 17,623 20.9%Merger 2 1997 1+1+1+1 11,720 14,170 20.9%

DenverMerger 1 1997 1+1 13,805 14,555 5.4%

DaytonaMerger 1a 1999 3+1 8,696 9,885 13.7%Merger 1b 2000 4+1 9,885 11,595 17.3%Merger 2 1998 1+1 10,390 11,118 7.0%Merger 3 1996 1+1 1,012 1,053 4.1%

JacksonvilleMerger 1 1997 3+1 21,959 27,164 23.7%Demerger 1 1999 3 27,164 21,959 -19.2%Merger 2 1999 1+1 3,849 4,093 6.3%

RochesterMerger 1 1998 3+1 35,284 36,153 2.5%Merger 2 1996 1+1 3,014 3,101 2.9%Merger 3 1999 1+1 6,930 7,213 4.1%Merger 4 2000 1+1 1,964 2,463 25.4%

HHI (Just for reference)

Bakersfield Year# of hospitals

in merger Pre-HHI Post-HHI ChangeMerger 1 1997 3+1 4,796 4,853 57Merger 2 2000 1+1 4,853 4,853 0

BuffaloMerger 1a 1996 2+1 1,488 1,694 206Merger 1b 1998 3+2 2,146 3,201 1,055Merger 2 1997 1+1+1+1 1,694 2,146 452

DenverMerger 1 1997 1+1 2,047 2,463 416

DaytonaMerger 1a 1999 3+1 3,920 4,230 310Merger 1b 2000 4+1 4,230 4,794 564Merger 2 1998 1+1 3,762 3,920 158Merger 3 1996 1+1 3,739 3,762 23

JacksonvilleMerger 1 1997 3+1 2,831 4,269 1,438Demerger 1 1999 3 0Merger 2 1999 1+1 2,831 2,840 9

RochesterMerger 1 1998 3+1 2,545 2,611 66Merger 2 1996 1+1 2,530 2,545 15Merger 3 1999 1+1 2,611 2,614 3Merger 4 2000 1+1 2,614 3,250 636

Do changes in WTP due to consolidation increased net

inpatient revenue?

• Regress net inpatient revenue:• WTP, WTP*Yrs since merger indicator• Hospital fixed effect• Hospital payer mix• Bedsize, • Total admissions, and• Control for whether the facilities were

combined.

Details of regression

• Unit of observation: Entity• Independent hospital or system

• 6 years of data• Unbalanced, some hospitals don’t

report in some years• If one hospital in the system doesn’t

report• Drop the system observation for the year

System Fixed Effect Results

WTP 3909.923** 8800.893*** 6649.468*** 4606.421**

(1937.421) (2654.161) (2383.316) (2143.295)

WTP*Second Year of Merger 155.476 234.834 340.953(341.553) (303.028) (299.064)

WTP*Third Year of Merger 1110.533* 324.111 657.558(660.681) (625.334) (499.414)

WTP*Forth Year of Merger -78.326 -1096.700 -255.871(933.719) (829.123) (606.881)

WTP*First Year of Second Merger -4024.480*** -2732.502*** -2642.907***(990.239) (905.473) (894.974)

WTP*Second Year of Second Merger -376.645 1370.700 403.522(1269.217) (1141.233) (1009.329)

WTP*First Year of De-merger 409.136 974.265(1058.589) (983.494)

WTP*Second Year of De-merger 1244.982 1359.085(1303.386) (1143.716)

R-squared 0.36 0.37 0.61 0.65Controls None Year FE All Controls All Controls

Results (in words)

• WTP coefficient is statistically different than zero with 95-99% confidence

• Though not significant if only time trends in the model without WTP*Merger year interactions

• Magnitude of the coefficient varies from about $3900 to $8800.

• Favored specification indicates an increase of about $6,650 per unit of WTP

MSA Change in WTPIncrease in Net inpatient Revenue

Premerger net inpatient revenue

Percent Increase

BakersfieldMerger 1 658.6 $4,379,723 $178,039,547 2.46%Merger 2 132.8 $883,080 $18,509,967 4.77%

BuffaloMerger 1a 702.3 $4,670,295 $353,153,430 1.32%Merger 1b 3049.6 $20,280,040 $335,892,953 6.04%Combination $24,950,335 7.07%Merger 2 2450.5 $16,295,958 $511,717,061 3.18%

Denver Merger 1 749.9 $4,987,035 $311,633,985 1.60%

Daytona Merger 1a 1189.0 $7,906,963 $230,319,971 3.43%Merger 1b 1709.9 $11,371,128 $228,534,594 4.98%Combination $19,278,091 8.37%Merger 2 728.1 $4,841,599 $208,648,796 2.32%Merger 3 41.7 $277,145 $63,230,045 0.44%

JacksonvilleMerger 1 5204.3 $34,608,462 $499,638,496 6.93%Merger 2 243.8 $1,621,071 $306,650,559 0.53%

RochesterMerger 1 868.6 $5,776,257 $416,377,613 1.39%Merger 2 87.4 $581,163 $172,894,372 0.34%Merger 3 283.3 $1,884,231 $505,253,995 0.37%Merger 4 499.1 $3,318,776 $40,754,389 8.14%

Total 18,599 $123,682,924 $4,381,249,771 2.82%

Effect of mergers on net inpatient revenues

Conclusions

• Only four mergers led to an increase in net inpatient revenue greater than 5%.

• However, this is a very conservative estimate• The denominator revenue number includes all

payers.• If the increase was due to solely private payers

then the denominator should include only private payer’s revenue.

• For example if 50% of the net revenues were private then the average % increase would be 5.6%

Conclusions

• Shows promise for prospective merger analyses

• R-squared was much lower than what is observed in a single market.• Not surprising given unmeasured

variation across markets

Caveats

• Would prefer to have data on private profits rather net inpatient revenues.

• We ignore any efficiencies (or inefficiencies) that result from mergers.

• The prospective WTP estimates do not always coincide with the realized post-merger estimates (a problem with any prospective measure).

• The second mergers during the period perform worse (may be due to small sample of second mergers). There are other factors at work that need to be addressed in future research.