Delivering Bad News: Market Responses to Obstetricians’ Negligence

David Dranove, Northwestern University Subramaniam Ramanarayanan, UCLA

Yasutora Watanabe, Northwestern University

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Deterring Negligence

• Markets generally punish low quality sellers• The liability system complements the market• If markets are effective, then the liability

system may be redundant• Anecdotal evidence of market responses to

negligence abound– Tylenol– Airjet– Toyota

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More on Market Responses

• Surprisingly few systematic studies of market responses to negligence

• Event studies (e.g., Prince/Rubin (2002); Dranove/Olsen (1994)) show stock price declines but do not decompose market and legal costs

• Garber/Adams (1998) examine auto liability verdicts and find no effect on sales

• Fournier/McInnes (2001) find that doctors who have malpractice claims lose FFS patients– Our paper is related to F/M but provides a theoretical

foundation that leads to a highly nuanced analysis

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Malpractice

• Substantial concern about deterring negligence in health sector

• Proposed tort reforms will reduce health spending (Dafny et al., 2010) but may lessen deterrence

• Does the present system deter negligence?– Community rated premiums– Minimal time cost– Some emotional cost– Suggests that market responses may be a critical factor

in deterring negligence

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Are Market Responses Plausible?

• In specialties such as obstetrics, lawsuits are common but not excessively so. – Most physicians are not sued during 10 years of our

data• Lawsuits are not usually publicized but are

common knowledge in medical community• Patients may learn about the associated

negligence through word of mouth– Information “network” for expectant mothers may

be particularly strong

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Sorting out the Effects

• In theory, one can sort out market from litigation effects– Statute of limitations is two years from time plaintiff “knew or should

have known” of negligence– Could identify differential demand effect in this two year window

• Our data do not indicate when plaintiff “knew or should have known”– Instead, data record date of delivery (“occurrence”)

• In practice, most lawsuits are filed within 6 months of when plaintiff “knew or should have known”– Not clear if maternity patients would change doctors in this time frame

• Thus, we cannot convincingly sort out the two effects though research is ongoing)

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Mixed Markets

• Studying demand responses in obstetrics is complicated by large presence of Medicaid

• Traditional economic models suggest that some physicians may ration access to Medicaid patients

• Thus, a demand shock (i.e., a physician is sued) may not affect the number of Medicaid patients treated by that physician

• Formal modeling reveals these nuanced effects

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Model of Patient Demand in a Mixed Market

• Monopolist seller faces demand from two types of customers– Type φ (“private customer”) displays downward

sloping demand Pφ(Qφ)– Type γ (“government customer”) displays perfectly

elastic demand at a price Pγ that is set by fiat• Seller faces a limited number of potential type γ buyers,

denoted Qγmax

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Initial Demand for “High Quality” MD

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Demand after Negligence

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Initial Demand for “Low Quality” MD

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Demand after Negligence

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Key Results

• After a “high quality” MD experiences negligence– Lose privately insured patients– Gain Medicaid patients– Exact offset

• After a “low quality” MD experience negligence– Lose Medicaid patients– Ambiguous impact on quantity of privately insured patients

(though at lower price)• Decomposing PPO and HMO effects

– Depends on relative shift of PPO and HMO demand curves– F/M find gain in HMO, suggesting relatively small demand

reduction

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What is Quality?

• Quality is a latent variable– Quality refers to whether MD is rationing access to

Medicaid patients– We use proportion of PPO/Medicaid to proxy for

quality• May introduce mean reversion bias in some

specifications– Instrument for quality to eliminate mean reversion

bias– Use counterfactual analysis to show that instrument is

effective

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Utilization Data

• Florida AHCA inpatient data• Administrative claims data similar to other state data

– Diagnostic information– Patient characteristics– Year and quarter of discharge– Unit of observation is MD/year/quarter

• “Operating” physician license number– Unique and consistent over time– Restrict attention to “high volume” (50 deliveries annually) MDs– 1418 high volume MDs account for 91 percent of all deliveries

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Litigation Data• Florida Department of Financial Services• Closed claims from 1979-2003– We begin analysis in 1994– Continue utilization data through 2007

• Detailed information about– Date of claim, date of occurrence, date of lawsuit filing, date of

resolution– Lawsuit usually occurs within two years of occurrence; filing soon

after that– Resolution of case requires another two years– Have not yet disentangled the effect of occurrence and effect of

lawsuit

• Match to AHCA using MD license number

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Methods• Estimate following model

– The subscripts p, q and y refer to the physician, quarter and year– Phys Volpqy measures the number of patients treated by physician p in quarter q of

year y– The primary predictor is an indicator that takes the value 1 for physician p in

quarter q of year y if a lawsuit has been filed against physician p prior to or in the quarter q.

– Also estimate with separate indicators for each post year– Includes full set of fixed effects for each physician, year and quarter (MD FE imply

that MDs sued prior to 1994 do not enter analysis)– Includes FE for year in which suit is filed (lawsuits in earlier years have greater

chance of resolution, which may affect characteristics of observed suits)• OLS and Poisson• Separate models for PPO, HMO, Medicaid• Interact demand effects with “quality”

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Measuring Quality

• The model shows that litigation effects depend on whether the MD rations Medicaid– Rationing occurs when PPO demand is high relative to MC– In this sense, rationing is related to quality

• Empirical implementation– Use PPO/Total patient ratio as indicator of rationing

• Statistical concern– Could suffer from mean regression– Do “high quality” MDs lose PPO patients due to chance?– Demonstrate with falsification test

• Instrument for quality with hospital PPO ratio– Instrument survives falsification test

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Falsification Test

• If there is mean reversion, then any MD having “high quality” should exhibit a similar pattern– Having high PPO/Total ratio in “pre” period should

be associated with lower ratio in post period due to random chance

• Randomly select “pseudo” lawsuits in same frequency as actual lawsuits

• Repeat regression on pseudo data, defining quality as PPO/Total

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Instrument to Eliminate Mean Reversion

• Require a variable that is correlated with a physician’s PPO/Total ratio but is not subject to mean reversion

• Use hospital’s non-maternity PPO/Total ratio for entire time period– Captures market area and general quality of

hospital– Not susceptible to before/after timing

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Financial Implications

• Consider high quality MD seeing 100 PPO, 50 HMO and 50 Medicaid patients annually

• After litigation, these figures are 90, 54, and 56• Using data from Physician Compensation Report and assuming

Medicaid pays half of PPO fee, MD earnings drop from $400,000 to $389,600.– Every year for at least 5 years

• Low quality MD with 20 PPO, 90 HMO, and 90 Medicaid patients goes from $320,000 to $300,000– Again, impact felt for at least 5 years

• Financial impact dwarfs any direct costs of litigation (legal fees/lost time)

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Discussion

• Despite community rated insurance, negligence does cost obstetricians

• Effects are nuanced and are consistent with economic theory

• Does this mean that deterrent effect of litigation is redundant?– Depends on social costs of negligence and efficiency of

courts– Given that the latter is questionable at best, our findings

suggest that tort system may be at best a marginal deterrent relative to the market