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Does sorry work? The impact of apology laws on medical malpractice

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Physicians’ apologies for adverse medical events are acknowledged as a factor in patients’ decisions to litigate. Apology laws which render physicians’ apologies inadmissible in court are written to encourage patient-physician communication and to overcome the physicians’ disinclination to apologize because apologies could invite lawsuits. We present a novel model of apologies and malpractice in order to examine whether state-level apology laws have an impact on malpractice lawsuits and settlements. Using a difference-in-differences estimation, we find that apology laws could expedite the resolution process. We also find that apology laws result in the greatest reduction in average payment size and settlement time in cases involving severe patient outcomes.

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  1. A more extensive model of apologies based on signaling and asymmetric information can be found in Ho (2011). In order to keep the focus on the litigation and maintain simplicity in the current article, we abstract away from signaling by saying that an apology increases the psychic cost of suing a doctor, implicitly assuming the patients’ beliefs change.

  2. We would expect these reductions to lead to similar reductions in malpractice insurance premiums. Data on medical malpractice insurance premiums was obtained from the Medical Liability Monitor Rate Surveys from 1995 to 2005. To make prices comparable, the data cover a typical contract based on coverage for a $1 million per incident and $3 million per year cap. The impact of the apology laws on medical malpractice premiums for three specialties—internal medicine, ob/gyn, and surgery—was also assessed, but these effects were estimated to be statistically insignificant.

    This disconnect could be because medical malpractice insurance premiums are highly regulated and thus slow to respond. While total medical malpractice payments were trending downward after 2001, total insurance premiums continued to trend upward. Also, there appeared to be a great deal of inertia in insurance premiums, which often did not change from year to year. Or it could simply be that our data lack the power to identify the differences.

  3. The apology laws were initially identified using a search in Lexis-Nexis of state legal code using the words apology and malpractice. Our list was confirmed from the website of the Sorry Works Coalition, an advocacy group promoting apologies by physicians.

  4. California, Massachusetts, Florida, Tennessee, Texas, and Washington have general apology statutes that apply across all industries while the other 30 States have specific laws that only protect the statements of apology made by health care providers. The states can be first divided into two types depending on the applicability of these laws: general versus health practitioners only. We use the specification in Table 3 but we create two dummies for the general laws versus healthcare-only laws. We then perform an F-test checking whether we can group the general versus healthcare-only laws together; the F-test fails to reject the null hypotheses that these two types of apology laws have the same impact on claim frequencies and claim severity. Therefore, for the remainder of the paper, we are not going to differentiate between general and healthcare-only apology laws.

  5. In regressions not reported in the current paper, we find that political composition in the State Senate and State House has no significant explanatory power on the passage of apology laws.

  6. See, for example, the efforts of the Sorry Works Coalition.

  7. The divisions between full and partial apology laws are arguably poorly defined. A paper by McDonnell and Guenther (2008) reports eight states as having full apology laws, whereas an article by Morse (2009) reports only five states as having full apology laws.

  8. Ho and Liu (2011) offers an extension of the research here. Whereas this paper focuses on identifying the effect using difference-in-difference and then using hazard rate analysis to identify speed as the mechanism that drives our results, Ho and Liu (2011) focus on using diff-in-diff-in-diff to decompose the effects of the law by physician and patient characteristics such as age, gender of patient, field of practice, etc. to identify the locus of the effect.

  9. The online appendix can be found at Elaine Liu’s website at∼emliu.

  10. This is the amount that the patient receives and the doctor is required to pay after accounting for the probability that the patient wins.

  11. All we really need is that these are random valued functions of whether a doctor apologizes where ψi(1) first order stochastically dominates ψ_i (0) for i∈{L,J}. However a uniform distribution will make calculations more convenient.

  12. The only cost that remains is that an apology makes settlements more likely, making it easier for patients to initiate litigation since they know court costs are now less likely to be incurred.

  13. The NPDB dataset is not free of problems. It has been criticized because of a “corporate shield” loophole, through which malpractice payments made on behalf of a practitioner end up excising the practitioner’s name from the data in the NPDB. Chandra et al. (2005) compare data from the NPDB with other sources of malpractice information and while they find approximately 20% underreporting, they find that underreporting is not systematically different across states. Therefore, for our analysis, which is extracting information at the state level, there is no obvious reason why the corporate shield loophole would bias the effects of the apology legislation. It is also important to note that the NPDB dataset has been used for most recent influential studies of medical malpractice reform (Currie and MacLeod 2008).

  14. Since the finest date information we have about the case is years, we cannot use any finer definition of date (such as months, quarters) to look at the cases that took place right before the law passed and the cases that took place right after the law passed.

  15. The outcome variable only became mandatory for recording in 2004. The categories of injuries are reported by the entities that make payments to the patients.

  16. Another way to construct the state-level dataset is by the total number of settlements/payments made in a given year. Our goal is to analyze the impact of apology laws, which intend to encourage practitioners to apologize and communicate more openly with their patients. The impact on the settlement is hinged upon the apology. While the model in Section 3 cannot distinguish the timing of the apology, the apology is likely to be most effective soon after the incident occurs, not a few years later. Therefore, we aggregate it by the year of incident instead of the year of settlement.

  17. We adjust the payment by CPI. Therefore, all payments are in Y2000 dollars.

  18. We have excluded all cases that occurred in 2008 since only less than 100 cases which occurred in 2008 had been settled by 2009.

  19. The other law measures for which we have controlled the timing in our study include the existence of non-economic cap, punitive cap, laws on full information disclosure, joint and several liabilities, and collateral source rule. The information on the existence of the laws (excluding information disclosure laws) is from the annual produced by the American Tort Reform Association (2009). The information on the disclosure laws is from Gibson and Del Vacchio (2006).

  20. Legally, it is unclear whether apology laws would apply to cases that have occurred before the law passed.

  21. Here, we abuse notation a bit, equating the idea of probability of settlement with speed of settlement.

  22. Population, age, and racial composition data are retrieved from census data. Data on number of physicians per state was retrieved from the American Medical Association.

  23. The coefficient in Column 3 is at the borderline of the 10% level of significance. The magnitude of coefficient does not change from Column 1 or Column 2, but rather the standard errors have gone up.

  24. One might think, using the aforementioned logic, that the dependent variable should be the cases that occurred in 2002 in column 1, those that occurred in 2003 in column 3, etc. However, we would only have 21 observations in each regression and would not be able to capture any general state or year trends.

  25. There are nine categories of injuries in the NPDB, which we group into three categories for the ease of analysis and presentation (see Table 2 for subcategories).

  26. The severity of injuries is only available for cases reported after 2002. For a similar analysis grouped by the size of payment, see the Online Appendix on the author’s website at∼emliu.

  27. From this dataset we can observe that it is true that cases involving more severely injured patients usually take longer to resolve than insignificant injury cases.

  28. We have also attempted a maximum likelihood estimate of the unconditional hazard ratio using a proportional hazards model that accounts for right truncation from Finkelstein et al. (1993). However, due in part to their model being weakly identified, the procedure does not converge.

  29. Regressing the same specification on different payment size quantiles finds that the law has the largest effects on the 3rd quantile and no effect on the 1st and 4th quantile. The lack of effect on 4th quantile payments could be due to the fact that apologies are likely to be less important in cases worth millions of dollars, or that the largest cases take many years to resolve and thus cases of this size have yet to be resolved in most states in which apology laws have been passed.

  30. Note that once the health outcome is realized, doctors will apologize deterministically. p a , therefore, represents the ex ante probability that the doctor will apologize. We include this expression for ex ante probability of apology since it will be useful for discussing moral hazard and welfare in the next section.


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Correspondence to Benjamin Ho.

Additional information

The authors wish to thank Ori Heffetz, Amitabh Chandra, Ren Mu, Christine Durrance and Emily Owens as well as seminar participants at Stanford University, Cornell University, University of Houston Center for Public Policy, Hong Kong University, Brigham Young, National Taiwan University, the ALEA Annual meetings and ASHE for helpful comments.



1.1 Proof of proposition I

1.1.1 Part (a) and Part (b)

The patient decides to settle if the benefit of settling, S, is greater than the benefit of going to trial, \( J\left( {h,a} \right) - {c_P} - {\psi_J}(a). \) The probability of settling is given by:

$$ {p_s} = \Pr \left[ {S > \left[ {J\left( {h,a} \right) - {c_P} - {\psi_J}(a)} \right]} \right] $$

The doctor proposes a settlement S to minimize her expected payment:

$$ Expected Payment = E\left[ {{p_s}S + \left( {1 - {p_s}} \right)\left( {J\left( {h,a} \right) + {c_D}} \right)} \right] $$

Minimizing the doctor’s malpractice costs from Eq. 2 using the probability of settlement given by Eq. 3 yields the optimal settlement offer:

$$ S* = J(h,a) - \frac{{{c_P} + {{\overline \psi }_J}a - {c_D}}}{2} $$

Doctors offer a higher settlement when their costs of going to court are higher and a lower settlement when the costs the patient faces are higher. The optimal settlement probability by the patient shows that the patient is more likely to settle as the costs of going to court rise:

$$ p_s^{*} = \frac{{{c_P} + {{\overline \psi }_J}a + {c_D}}}{2} $$

The patient’s probability of litigating, p L , is then given by the probability that the expected malpractice payment is greater than the psychic cost of litigating:

$$ p_L^{*} = \Pr \left[ {E\left[ {p_s^{*}S* + \left( {1 - p_s^{*}} \right)\left( {J\left( {h,a} \right) - {c_p} - {\psi_J}(a)} \right)} \right] > {\psi_L}(a)} \right] $$

Using the assumption that psychic costs follow a uniform distribution and that we have an interior solution, we can reduce this probability to:

$$ p_L^{*} = \left[ {J\left( {h,a} \right) - {c_p} + p{{_s^{*}}^2}} \right] - {\overline \psi_L}a. $$

Again, \( {\overline \psi_L}a \) is the additional psychic cost for the patient to sue if the physician apologizes. Consistent with the empirical evidence (Sloan and Hsieh 1995), the probability of litigation given in Eq. 8 is increasing with more serious health outcomes, decreasing in the costs of going to trial, but increasing in the probability an early settlement is reached.

Combining these results allows us to write the closed form solution for the expected malpractice payments net of costs for patient and doctor:

$$ \begin{array}{*{20}{c}} {{\text{Net}}\;{\text{Gain}}\;{\text{for}}\;{\text{Patient}}:{{{\Pi }}_P}\left( {h,a} \right) = p_L^{*}\left[ {J(h,a) - {c_P} + p{{_s^{*}}^2}} \right]} \\ {{\text{Net}}\;{\text{Cost}}\;{\text{for}}\;{\text{Doctor}}:{{{\Pi }}_D}\left( {h,a} \right) = p_L^{*}\left[ {J(h,a) + {c_D} - p{{_s^{*}}^2}} \right]} \\ \end{array} $$

Finally, consider the doctor’s incentives to apologize. The doctor will apologize for all health outcomes where ΠD(h, 1) < Π D (h, 0)Footnote 30:

$$ {p_a} = { \Pr }[h \in \left\{ {h:{\Pi_{\text{D}}}\left( {h,1} \right) < {\Pi_D}\left( {h,0} \right)} \right\}] $$

From Eq. 6 we can calculate the difference in settlement probabilities after an apology to see that settlements increase in the event of an apology:

$$ {\left. {p_s^{*}} \right|_{{a = 1}}} - {\left. {p_s^{*}} \right|_{{a = 0}}} = \frac{{{{\overline \psi }_J}}}{2} $$

However, the effect of an apology on the likelihood of initiating litigation depends on the relative effect of the apology on the psychic costs which makes litigation less attractive, with the effect of the apology on settlement probabilities and judgment payments which makes litigation more attractive. From Eq. 8 the effect of an apology on probability to litigate is given by:

$$ {\left. {p_L^{*}} \right|_{{a = 1}}} - {\left. {p_L^{*}} \right|_{{a = 0}}} = J\left( {h,1} \right) - J\left( {h,0} \right) + \left( {\frac{{{{\overline \psi }_J}}}{2}} \right)\left( {{c_P} + {c_D} + \frac{{{{\overline \psi }_J}}}{2}} \right) - {\overline \psi_L} $$

The effect of an apology on the probability to litigate is increasing in the effect on judgment sizes—J(h, 1) − J(h, 0)—and decreasing in the psychic costs an apology imposes, \( {\overline \psi_L} \). Perhaps more interestingly, apologies make patients more likely to litigate when the costs of going to court (both actual and psychic) are higher due to the fact that one deterrent to litigation is the threat of having to pay high court costs, and apologies reduce the likelihood of going to court in the event of litigation.

1.2 Proof of proposition 2

1.2.1 Part (a)

We can see from Eq. 6 that the apology law reduces the expected payment in case of an apology, Π D (h, 1), but has no effect on expected payments when no apology is made, Π D (h, 0), so the set of health outcomes for which the doctor would apologize, {h D (h, 1) < Π D (h, 0)}, must be larger than before the laws were passed.

1.2.2 Part (b)

From Eq. 8, a patient decides to initiate litigation if the expected benefit from litigation outweighs the costs of litigation. Apology laws reduce judgment sizes which decreases the benefits of litigation; and thus, the probability that the patient litigates decreases.

1.2.3 Part (c)

From the probability of settlement given in Eq. 6, the likelihood of settlement is always higher in the event of an apology. Since apologies are more frequent, we expect more settlements.

1.2.4 Part (d)

It can be seen from Eq. 5 that settlements are smaller in the event of an apology (which are now more common) and smaller still after a law reduces J(h, 1).

1.2.5 Part (e)

Since the laws increase settlement, reduce probability of litigation, reduce both judgment and settlement sizes, then we see from Eq. 2 that malpractice payments net of costs made by the doctor must also go down.

1.2.6 Part (f)

Given symmetric information and risk neutral parties, the welfare implication of the law is unambiguous: since we assume that doctor effort is unaffected, the only effect of litigation is a transfer from the defendant to the plaintiff that imposes a deadweight loss from the cost of litigation (c P  + c D ). Thus the reduced likelihood of litigation and judgment means that the law increases welfare.

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Ho, B., Liu, E. Does sorry work? The impact of apology laws on medical malpractice. J Risk Uncertain 43, 141–167 (2011).

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