Evidence map›Paper›PMID 37890905›Full record

ReviewThe Lancet. Digital health2023

Disparity dashboards: an evaluation of the literature and framework for health equity improvement.

Jack Gallifant, Emmett Alexander Kistler, Luis Filipe Nakayama, Chloe Zera, Sunil Kripalani, Adelline Ntatin, Leonor Fernandez, David Bates, Irene Dankwa-Mullan, Leo Anthony Celi

Abstract readReview
In one paragraph

Review in The Lancet. Digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

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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

10 authors.

Jack GallifantLaboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA. Electronic address: jgally@mit.edu.
Emmett Alexander KistlerDivision of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Luis Filipe NakayamaLaboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Ophthalmology, São Paulo Federal University, São Paulo, Brazil.
Chloe ZeraDepartment of Obstetrics, Gynecology and Reproductive Biology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Sunil KripalaniDepartment of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
Adelline NtatinDepartment of Health Equity, Beth Israel Lahey Health, Boston, MA, USA.
Leonor FernandezDepartment of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
David BatesDivision of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA.
Irene Dankwa-MullanMerative & Center for AI, Research, and Evaluation, IBM Watson Health, Cambridge, MA, USA; Department of Health Policy and Management, Milken Institute School of Public Health, George Washington University, Washington, DC, USA.
Leo Anthony CeliLaboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA; Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Biostatistics, Harvard T H Chan School of Public Health, Boston, MA, USA.

Funding

Critical Care Informatics: Ethical considerations around the use and sharing of health-related dataR01EB017205 · NIBIB · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI CELI, LEO ANTHONY G, MARK, ROGER GREENWOOD · 2014 to 2021
$3.8M
Critical Care InformaticsR56EB017205 · NIBIB · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI CELI, LEO ANTHONY G, GHASSEMI, MARZYEH · 2018 to 2023
$783k
NIBIB NIH HHS R01 EB017205NIBIB NIH HHS R56 EB017205
6 · The paper itself

Abstract

The growing recognition of differences in health outcomes across populations has led to a slow but increasing shift towards transparent reporting of patient outcomes. In addition, pay-for-equity initiatives, such as those proposed by the Centers for Medicare and Medicaid, will require the reporting of health outcomes across subgroups over time. Dashboards offer one means of visualising data in the health-care context that can highlight essential disparities in clinical outcomes, guide targeted quality-improvement efforts, and ultimately improve health equity. In this Viewpoint, we evaluate all studies that have reported the successful development of a disparity dashboard and share the data collected and unintended consequences reported. We propose a framework for systematic equality improvement through incentivisation of the collecting and reporting of health data and through implementation of reward systems to reduce health disparities.

Indexed as

Health EquityAgedDelivery of Health CareHealth FacilitiesHumansMedicareQuality ImprovementUnited States

Identifiers

PMID37890905
PMCPMC10639125

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.