Evidence map›Paper›PMID 21676022›Full record

ArticleThe Milbank quarterly2011

How health care organizations are using data on patients' race and ethnicity to improve quality of care.

Ruth Thorlby, Selena Jorgensen, Bruce Siegel, John Z Ayanian

Abstract read
In one paragraph

Article in The Milbank quarterly, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–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

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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  8. A Scoping Review of Approaches to Improving Quality of Data Relating to Health Inequalities.International journal of environmental research and public health · 2022
    Article
  9. Article
  10. Potential harms associated with routine collection of patient sociodemographic information: A rapid review.Health expectations : an international journal of public participation in health care and health policy · 2019
    Review
  11. Article
  12. Article
  13. Assessing race and ethnicity data quality across cancer registries and EMRs in two hospitals.Journal of the American Medical Informatics Association : JAMIA · 2016
    Article
  14. Article
  15. Article
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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

4 authors.

Ruth ThorlbyNuffield Trust, 59 New Cavendish Street, London W1G 0AN, UK. ruth.thorlby@nuffieldtrust.org.uk
Selena Jorgensen
Bruce Siegel
John Z Ayanian

Funding

HARVARD CLINICAL AND TRANSLATIONAL SCIENCE CENTER (UL1)UL1RR025758 · NCRR · HARVARD MEDICAL SCHOOL · PI NADLER, LEE MARSHALL · 2008 to 2011
$91.4M
NCRR NIH HHS 1 UL1 RR 025758-01NCRR NIH HHS UL1 RR025758
6 · The paper itself

Abstract

contextRacial and ethnic disparities in the quality of health care are well documented in the U.S. health care system. Reducing these disparities requires action by health care organizations. Collecting accurate data from patients about their race and ethnicity is an essential first step for health care organizations to take such action, but these data are not systematically collected and used for quality improvement purposes in the United States. This study explores the challenges encountered by health care organizations that attempted to collect and use these data to reduce disparities.

methodsPurposive sampling was used to identify eight health care organizations that collected race and ethnicity data to measure and reduce disparities in the quality and outcomes of health care. Staff, including senior managers and data analysts, were interviewed at each site, using a semi-structured interview format about the following themes: the challenges of collecting and collating accurate data from patients, how organizations defined a disparity and analyzed data, and the impact and uses of their findings.

findingsTo collect accurate self-reported data on race and ethnicity from patients, most organizations had upgraded or modified their IT systems to capture data and trained staff to collect and input these data from patients. By stratifying nationally validated indicators of quality for hospitals and ambulatory care by race and ethnicity, most organizations had then used these data to identify disparities in the quality of care. In this process, organizations were taking different approaches to defining and measuring disparities. Through these various methods, all organizations had found some disparities, and some had invested in interventions designed to address them, such as extra staff, extended hours, or services in new locations.

conclusionIf policymakers wish to hold health care organizations accountable for disparities in the quality of the care they deliver, common standards will be needed for organizations' data measurement, analysis, and use to guide systematic analysis and robust investment in potential solutions to reduce and eliminate disparities.

Indexed as

Data CollectionDelivery of Health CareEthnicityHealthcare DisparitiesHealth Services ResearchHealth Status DisparitiesHumansMedical RecordsNational Academies of Science, Engineering, and Medicine, U.S., Health and Medicine DivisionPractice Management, MedicalQuality ImprovementQuality of Health CareUnited States

Identifiers

PMID21676022
PMCPMC3142338

What OpenQuestion holds

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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.