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The European Journal of Health Economics

Erschienen in:

01.04.2012 | Original Paper

Malpractice liability, technology choice and negative defensive medicine

verfasst von: Eberhard Feess

Erschienen in: The European Journal of Health Economics | Ausgabe 2/2012

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Abstract

We extend the theoretical literature on the impact of malpractice liability by allowing for two treatment technologies, a safe and a risky one. The safe technology bears no failure risk, but leads to patient-specific disutility since it cannot completely solve the health problems. By contrast, the risky technology (for instance a surgery) may entirely cure patients, but fail with some probability depending on the hospital’s care level. Tight malpractice liability increases care levels if the risky technology is chosen at all, but also leads to excessively high incentives for avoiding the liability exposure by adopting the safe technology. We refer to this distortion toward the safe technology as negative defensive medicine. Taking the problem of negative defensive medicine seriously, the second best optimal liability needs to balance between the over-incentive for the safe technology in case of tough liability and the incentive to adopt little care for the risky technology in case of weak liability. In a model with errors in court, we find that gross negligence where hospitals are held liable only for very low care levels outperforms standard negligence, even though standard negligence would implement the first best efficient care level.

Nur mit Berechtigung zugänglich

See e.g. the famous Harvard-study [33] and the comment by Hyman [19].

See e.g. Boehm [3] for suggestions in this direction.

See e.g. Kessler and McClellan [25].

See e.g. Localio et al. [31] and Dubay et al. [12].

The problem could only have been solved by signing a complete contract, which is obviously impossible in our context, though. Consequently, Arlen and McLeod [1] use an incomplete contract approach to analyze malpractice liability.

See e.g. Cohen and Eisenberg [5] for a discussion of high liability risks in these cases.

By slight abuse of terminology, we subsequently use “treatment” and “technology” synonymously.

See our literature review below.

“Care” does not necessarily refer to a doctor’s effort during a surgery, for instance, but also to the organization of processes in the hospital. In fact, Rogers et al. [35] find in a case study of 444 malpractice claims that communication breakdowns were responsible in around one quarter of all observations. They point out that malpractice claims should be used much more intensively as information sources in finding out where malpractice comes from; see also Studdert et al. [40, p. 287].

For instance, the Hospital Quality Alliance (HQA) in the US has designed pay-for-performance programs by defining best practice based on individual and composite measures for heart failure, acute myocardial infarction and pneumonia, and there is econometric evidence that hospitals taking part in the program have higher quality improvements; see e.g. Lindenauer et al. [30] and Werner et al. [43]. The authors admit that controlling for self-selection issues is difficult, though.

See e.g. Kessler et al. [27, p. 2619] for a brief overview on factors influencing expected damages for malpractice.

See e.g. Boumil et al. [4].

See e.g. Faure and Koziol [14] and Kennedy and Grubb [22].

See e.g. National Practitioner Data Bank (NPDB) [34], and U.S. Department of Health and Human Services [42].

For an earlier comprehensive overview on the theoretical and empirical literature see Danzon [9], and for an assessment of the situation from a legal point of view see Hyman and Silver [20].

We are grateful to an anonymous referee who has pointed to this artifact of the discrete modeling that we used in a former working paper version of this article.

Assuming that the patient’s disutility from the safe technology is independent of the hospital’s care level normalized to zero is without loss of generality. All we need is that the patient’s health reacts more sensitively to the hospital’s care under the risky technology. As mentioned in the introduction, it is not crucial for our results that the hospital can perfectly observe the patient’s disutility θ; all that is required is an informative signal.

See our discussion in the concluding section.

One might wonder if non-liability costs should be part of the social cost function as part of these costs may be purely re-distributional. This will be discussed in the concluding section. Our results are qualitatively independent of whether α enters the social cost function or not, though.

As usual in the literature on liability, “costs” include both monetary terms such as treatment expenditures (x in our case) and the monetary equivalents of preference costs from the patient’s harm or from the doctors’ empathy for patients (1 + α in our case).

For x < q, the uniform distribution causes the artifact that courts could receive signals on negative care, τ < 0. This can easily be avoided by assuming instead that the probability mass is centered on zero which yields identical results.

Implicitly, we hence assume that proving causation is no issue. See our discussion in the concluding section.

As usual, gross negligence is defined as a negligence standard below ordinary negligence, i.e. as a rule where liability is only confirmed for very low care levels.

These similarities and differences of causation standards and negligence standards in a general model with asymmetric information have recently been analyzed by Feess et al. [17].

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Titel: Malpractice liability, technology choice and negative defensive medicine
verfasst von: Eberhard Feess
Publikationsdatum: 01.04.2012
Verlag: Springer-Verlag
Erschienen in: The European Journal of Health Economics / Ausgabe 2/2012
Print ISSN: 1618-7598
Elektronische ISSN: 1618-7601
DOI: https://doi.org/10.1007/s10198-010-0294-7

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