1. 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

2. 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

3. 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

4. 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?

5. 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.

6. Three possible health states for person i: Torn Knee ligament, Need a CABG, Healthy Torn Ligament (A)CABG (B)Healthy WTP ij = 0 Interim Conditions Interim Choice Probabilities: Probabilities of Each Condition Aggregate Over The Population

7. Mathematically:

8. 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

9. 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)

10. 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

17. 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)

18. 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

19. 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

20. WTP Estimates

21. HHI (Just for reference)

22. 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.

23. 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

24. System Fixed Effect Results

25. 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

26. Effect of mergers on net inpatient revenues

27. 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%

28. 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

29. 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.