Evidence map›Paper›PMID 33799361›Full record

ArticleHealthcare (Basel, Switzerland)2021

Incorporating Portfolio Uncertainty in Decision Rules for Healthcare Resource Allocation.

Pedram Sendi, Amiram Gafni, Stephen Birch, Stephen D Walter

Abstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
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

4 authors.

Pedram SendiInstitute for Clinical Epidemiology, Basel University Hospital, CH-4031 Basel, Switzerland.
Amiram GafniCentre for Health Economics and Policy Analysis, Department of Health Research Methods, Evaluation, and Impact, McMaster University, Hamilton, ON L8S4K1, Canada.ORCID 0000-0001-8279-8172
Stephen BirchCentre for the Business and Economics of Health, University of Queensland, St Lucia, QLD 4072, Australia.
Stephen D WalterDepartment of Health Research Methods, Evaluation, and Impact, McMaster University, Hamilton, ON L8S4K1, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cost-effectiveness analysis is widely adopted as a means to inform policy and decision makers in setting priorities for healthcare resource allocation. In resource-constrained settings, decision makers are confronted with healthcare resource reallocation decisions, e.g., moving funds from one or more existing healthcare programs to fund new healthcare programs. The decision-making plane (DMP) has been developed as a means to graphically present the results of reallocating available healthcare resources when healthcare program costs and effects are uncertain. Mapping a value function over the DMP allows the analyst to value all possible combinations of net costs and net effects that may result from reallocating available healthcare resources under conditions of uncertainty. In this paper, we extend this approach to include a change in portfolio risk, stemming from a change in the portfolios of funded healthcare programs, as an additional source of uncertainty, and demonstrate how this can be incorporated into the value function over net costs and net effects for a risk-averse decision maker. The methodology presented in this paper is of particular interest to decision makers who are risk averse, as it will help to better incorporate their preferences in the process of deciding how to best allocate scarce healthcare resources.

Indexed as

cost-effectiveness analysisopportunity costsresource allocationrisk aversionuncertainty

Identifiers

PMID33799361
PMCPMC8000383

What OpenQuestion holds

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LicenceCC BY
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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.