Evidence map›Paper›PMID 34985912›Full record

ArticleJCO clinical cancer informatics2022

Chasm Between Cancer Quality Measures and Electronic Health Record Data Quality.

Anna E Schorer, Richard Moldwin, Jacob Koskimaki, Elmer V Bernstam, Neeta K Venepalli, Robert S Miller, James L Chen

Erratum issuedAbstract read
In one paragraph

Article in JCO clinical cancer informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Observational
  4. Article
  5. Review
  6. Review
  7. Article
  8. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Anna E SchorerOncology Analytics, Atlanta, GA.ORCID 0000-0001-6215-4054
Richard MoldwinCollege of American Pathologists, Northfield, IL.ORCID 0000-0001-5859-8488
Jacob KoskimakiCancerLinQ, American Society of Clinical Oncology, Alexandria, VA.
Elmer V BernstamThe University of Texas School of Biomedical Informatics at Houston and Division of General Internal Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, TX.ORCID 0000-0001-7643-791X
Neeta K VenepalliDepartment of Medicine, The University of Illinois, Chicago, IL.
Robert S MillerCancerLinQ, American Society of Clinical Oncology, Alexandria, VA.ORCID 0000-0003-0869-6316
James L ChenDepartments of Internal Medicine and Biomedical Informatics, The Ohio State University, Columbus, OH.ORCID 0000-0002-4095-5202

Funding

Convalescent Plasma to Limit Coronavirus Associated ComplicationsUL1TR003167 · NCATS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI KARP, DANIEL D, MCPHERSON, DAVID D · 2019 to 2023
$45.3M
Harnessing the power of CTSA-CDRN data networks: Using social determinants of health, frailty and functional status to identify at-risk patients and improve risk adjustmentU01TR002393 · NCATS · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI SHIREMAN, PAULA K · 2018 to 2021
$3.1M
CDC HHSNCATS NIH HHS U01 TR002393NCATS NIH HHS UL1 TR003167
6 · The paper itself

Abstract

purposeThe Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) requires eligible clinicians to report clinical quality measures (CQMs) in the Merit-Based Incentive Payment System (MIPS) to maximize reimbursement. To determine whether structured data in electronic health records (EHRs) were adequate to report MIPS CQMs, EHR data aggregated by ASCO's CancerLinQ platform were analyzed. MATERIALS AND

methodsUsing the CancerLinQ health technology platform, 19 Oncology MIPS (oMIPS) CQMs were evaluated to determine the presence of data elements (DEs) necessary to satisfy each CQM and the DE percent population with patient data (fill rates). At the time of this analysis, the CancerLinQ network comprised 63 active practices, representing eight different EHR vendors and containing records for more than 1.63 million unique patients with one or more malignant neoplasms (1.73 million cancer cases).

resultsFill rates for the 63 oMIPS-associated DEs varied widely among the practices. The average site had at least one filled DE for 52% of the DEs. Only 35% of the DEs were populated for at least one patient record in 95% of the practices. However, the average DE fill rate of all practices was 23%. No data were found at any practice for 22% of the DEs. Since any oMIPS CQM with an unpopulated DE component resulted in an inability to compute the measure, only two (10.5%) of the 19 oMIPS CQMs were computable for more than 1% of the patients.

conclusionAlthough EHR systems had relatively high DE fill rates for some DEs, underfilling and inconsistency of DEs in EHRs render automated oncology MIPS CQM calculations impractical.

Indexed as

Electronic Health RecordsNeoplasmsAgedData AccuracyHumansMedicareQuality Indicators, Health CareUnited States

Identifiers

PMID34985912
PMCPMC9848533

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.