Evidence map›Paper›PMID 36245086›Full record

ArticleJournal of the National Cancer Institute2023

The Enhancing Oncology Model: Leveraging improvement science to increase health equity in value-based care.

Tej A Patel, Bhav Jain, Ravi B Parikh

Abstract read
In one paragraph

Article in Journal of the National Cancer Institute, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Review
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  8. Understanding Multi-Level Factors Impacting Digital Health Literacy in the Deep South of the United States.International journal of environmental research and public health · 2024
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  10. Review
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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

3 authors.

Tej A PatelDepartment of Health Care Management, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0002-0882-2960
Bhav JainHarvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID 0000-0003-2648-4894
Ravi B ParikhPerelman School of Medicine, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0003-2692-6306

Funding

NIH HHSVA
6 · The paper itself

Abstract

The Oncology Care Model (OCM), launched in 2016 by the Centers for Medicare and Medicaid Services, was the first demonstration of value-based payment in oncology. Although the OCM delivered mixed results in terms of quality of care and total episode costs, the model had no statistically significant impact on remediating racial, ethnic, and socioeconomic disparities among beneficiaries. These deficits have been prominent in other aspects of US healthcare, and as a result, the Institute for Healthcare Improvement has advocated for stakeholders to leverage improvement science, an applied science that focuses on implementing rapid cycles for change, to identify and overcome barriers to health equity. With the announcement of the new Enhancing Oncology Model, a continuation of the OCM's efforts in introducing value to cancer care for episodes surrounding chemotherapy administration, both policymakers and providers must apply tenets of improvement science and make eliminating disparities in alternative payment models a forefront objective. In this commentary, we discuss previous inequities in alternative payment models, the role that improvement science plays in addressing health-care disparities, and steps that stakeholders can take to maximize equitable outcomes in the Enhancing Oncology Model.

Indexed as

Health EquityMedicareAgedHealthcare DisparitiesHumansMedical OncologyUnited States

Identifiers

PMID36245086
PMCPMC9905958

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.