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Institutional design and moral conflict in health care priority-setting

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Abstract

Priority-setting policy-makers often face moral and political pressure to balance the conflicting motivations of efficiency and rescue/non-abandonment. Using the conflict between these motivations as a case study can enrich the understanding of institutional design in developed democracies. This essay presents a cognitive-psychological account of the conflict between efficiency and rescue/non-abandonment in health care priority-setting. It then describes three sets of institutional arrangements—in Australia, England/Wales, and Germany, respectively—that contend with this conflict in interestingly different ways. The analysis yields at least three implications for institutional design in developed democracies: (1) indeterminacy at the level of moral psychology can increase the probability of indeterminacy at the level of institutional design; (2) situational constraints in effect require priority-setting policy-makers to adopt normative-moral pluralism; and (3) the U.S. health care system may be in an anti-priority-setting equilibrium.

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Notes

  1. Efficiency has other, less prototypical forms as well, such as satisficing forms. Thus, a health care system that, for example, declines to provide unusually cost-ineffective treatments can be usefully understood as promoting efficiency, even if the system does not thereby maximize population health.

  2. Fojo and Grady (2009, 1045) reported that a drug called cetuximab could provide this health gain at a cost of about $80,000 (about $115,000 in 2024 dollars).

  3. For ease of exposition, the essay hereafter mostly omits the qualification about developed democracies. The reader may find it helpful to keep in mind that, if only because policy-makers in non-democratic regimes are less accountable to the public, they can more easily side-step goal conflicts by fiat.

  4. More precisely, this essay discusses a variant of CEA called “cost-utility analysis” (CUA). Collapsing the distinction between CEA and CUA is innocuous here, although the distinction matters in some more specialized contexts.

  5. The QALY is a construct that combines quantity and quality of life into a single measure by weighting the former by the latter. It ranks health states on a scale of zero (dead) to one (perfect health). To take two toy examples, suffering from rheumatoid arthritis (but being in perfect health otherwise) may receive a score of 0.90, and suffering from depression (perfect health otherwise) may receive a score of 0.85. The QALY allows a policy-maker to compare health gains across different disease areas. For example, a person who lives for one year in perfect health, for one year with rheumatoid arthritis, and for one year with depression gains 2.75 QALYs ((1.0 • 1) + (0.9 • 1) + (0.85 • 1) = 2.75).

  6. One World Health Organization working group (2014, 22) has deemed it “unacceptable” for a country such as Kenya to provide public funding for dialysis because “[m]oney spent on dialysis could instead save 300 times as many healthy life years if spent on tuberculosis control.” However one judges this argument’s merits, it is a moral argument.

  7. For a given individual and a given medical treatment, CEA is intrinsically interested only in the magnitude and duration of the treatment effect. It is not intrinsically interested in considerations such as the patient’s age or the quality of the patient’s life prior to treatment. As Nord (1999, 21–22) has noted, the specialists who first applied CEA to the health care context in the 1960s and 70s simply assumed a utilitarian social welfare function and thus stipulated that the social or aggregate value of a medical treatment was equal to the sum of the health gains that it produced in the people who received it. More recently, Nord (e.g., 2015) has proposed ways of integrating distributive moral considerations into the formal structure of CEA.

  8. Earlier exponents of similar views include, for instance, Eddy (1991), Gustavsson and Tinghög (2020), and Weale (2016).

  9. In presenting this hypothesis, I join the many researchers in psychology who have argued that the concrete-abstract distinction partly explains aspects of human moral mentation or behavior (e.g., Aguilar et al. 2013; Conway and Gawronski 2013; Kogut and Ritov 2015; Kvaran et al. 2013; Redelmeier and Tversky 1990).

  10. Much research in psychology, and especially in psycho-linguistics, suggests that abstract representation is generally more difficult and more effortful than is its concrete equivalent. For a discussion of some of this research, including citations, see Schwanenflugel (1991).

  11. Consider here that “opportunity cost neglect” seems to be a general feature of human cognition (e.g., Frederick et al. 2009). At least one research team (Persson and Tinghög 2020) has reported that even people with experience in health care priority-setting exhibit it.

  12. Consider here Jonsen’s (1986, 174) statement that “[e]ven the most evangelical utilitarian would find it difficult to expunge the rule of rescue from the psychological dynamics of technology assessors.”

  13. This does not mean that all specialists tend toward efficiency or that all laypersons tend toward rescue/non-abandonment.

  14. For example, in U.S. tort law, many specialists (e.g., judges, lawyers, legal economists) believe that the primary purpose of awarding punitive damages is to incentivize optimal deterrence, but most members of the public reject this view and believe that the primary purpose of doing so is to punish, in the retributive sense of that word (e.g., Sunstein et al. 2000).

  15. For a more detailed description of PBAC’s work, see, e.g., Whitty and Littlejohns (2015, 130).

  16. For a more detailed description of NICE’s work, see, e.g., Charlton (2020).

  17. Although NICE aims to produce more rather than fewer QALYs at a given cost, it is more accurate to state that the agency satisfices rather than maximizes in this regard (Rumbold et al. 2017, 113–14). For example, NICE “makes no effort to identify and prioritise those interventions that offer the least cost per QALY gain out of all those interventions that might be funded” (ibid., 113). The basic point here applies to health technology assessment agencies in other countries as well. No such agency maximizes QALYs in the strict sense of that word.

  18. The four drugs were bevacizumab, sorafenib, sunitinib, and temsirolimus. According to one researcher, NICE relied on the following ICERs: bevacizumab: £171,000 per QALY; sorafenib: £103,000 per QALY; sunitinib: £72,000 per QALY; temsirolimus: £103,000 per QALY (Raftery 2009, 271).

  19. I am grateful to an anonymous reviewer for emphasizing this point.

  20. Charlton (2022) has reported that another institutional design that NICE oversees—the Highly Specialised Technologies Programme, by which the agency evaluates drugs for very rare and very debilitating conditions—also sacrifices a degree of efficiency in a way that honors rescue/non-abandonment.

  21. For a more detailed description of IQWiG’s work, see, e.g., Wright et al. (2017, 72–73).

  22. More recently, IQWiG (2023, 112) seems to have moved away from EFA. For ease of exposition, however, this essay describes EFA in the present tense.

  23. The following description of EFA draws on Gandjour (2011).

  24. Consider here Kieslich’s (2012, 378) statement that “cost effectiveness considerations play a secondary role in health priority setting in Germany.”

  25. Consider here one research team’s (Wright et al. 2017, 74) statement that “the Efficiency Frontier offers the opportunity to make cost-effectiveness analysis. work in the German context without having to reveal willingness to pay.”

  26. One research team that interviewed German health policy-makers (Klingler et al. 2013, 275) attributed to them the view that “German methods for [health technology assessment] which rely on ad-hoc thresholds that have not to be set in advance, are less visible and therefore perhaps less politically problematic than fixed thresholds.”

  27. Consider here Luyten’s and Denier’s (2019, 162) statement that “explicit [cost-effectiveness] thresholds can prove to be counterproductive for sensible policy-making because people may simply ‘[not] want to know.’”

  28. Consider here Daniels’s and Sabin’s (2002, 2) statement that “no democratic society we are aware of has achieved consensus on. distributive principles for health care.”

  29. I am grateful to an anonymous reviewer for prompting me to make this point.

  30. This is not to deny that priority-setting policy-makers can apply in their work discrete ideas from one or another normative-moral theory.

  31. Consider here Jonsen’s (1986, 173) statement that “[m]odern health planners. . are not utilitarians in theory. But they are, to some extent, utilitarians in practice.”

  32. For a broader argument to the effect that restricted consequentialism does not accurately describe the work that priority-setting policy-makers do, see Wilson (2017, 159–60).

  33. The foregoing is not a criticism of Hausman, who does not argue that restricted consequentialism provides a general descriptor of the work of priority-setting policy-makers.

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Acknowledgements

I am grateful to Howard Eichenblatt and to the anonymous reviewers for this journal for valuable comments about this article.

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  1. Wachtell Fellow in Behavioral Law and Economics, University of Chicago Law School, 1111 East 60th Street, Chicago, IL, 60637, USA

    Philip Petrov

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Petrov, P. Institutional design and moral conflict in health care priority-setting. Med Health Care and Philos (2024). https://doi.org/10.1007/s11019-024-10201-2

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