Evidence map›Paper›PMID 42775514›Full record

ArticleBioethics2026

The Process Is Not the Point: Moral Uncertainty and Substantive Justice in Health Care Priority Setting.

Leah Pierson

Abstract read
In one paragraph

Article in Bioethics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Leah PiersonDepartment of Medicine, Brigham and Women's Hospital, Boston, MA, USA.ORCID https://orcid.org/0000-0001-7135-8825

Funding

Forethought Foundation for Global Priorities Research
6 · The paper itself

Abstract

Health actors must ration care due to resource constraints. But even when decision makers know the relevant empirical facts about an allocation decision-including how many patients will benefit from a service, which patients will benefit, how much each patient will benefit, and so on-these facts may not provide a clear answer about how to make allocation decisions. This is because competing moral principles recommend different courses of action: for instance, maximizing health benefits may involve deprioritizing more disadvantaged groups. Facing this moral uncertainty, many ethicists and policymakers have embraced using "fair processes" that emphasize procedural justice over substantively just outcomes. This paper challenges the proceduralist consensus on two grounds. First, I argue that designing fair processes generates as much moral uncertainty as making allocation decisions directly, while also failing to address the substantive allocation question that must ultimately be answered. Second, both theory and empirical evidence suggest that fair processes favor less cost-effective interventions, risk compounding health inequities, and divert resources that might otherwise be used for health care. As an alternative approach, I argue that health actors should pursue substantive justice through Maximizing Expected Choiceworthiness (MEC)-a decision framework that aims to choose options based on their expected value across different possible moral principles, weighted by the confidence decision makers have in each principle. I demonstrate that MEC can yield practical, intuitive answers to some resource allocation questions, while providing a helpful framework for approaching others. I conclude that health actors can and should pursue substantive-rather than procedural-justice in setting health priorities.

Indexed as

Decision MakingHealth Care RationingHealth PrioritiesMoralsSocial JusticeHumansUncertainty

Identifiers

PMID42775514
PMCPMC13664643

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.