Evidence map›Paper›PMID 36812575›Full record

ArticlePLOS digital health2022

Cancer tracking system improves timeliness of liver cancer care at a Veterans Hospital: A comparison of cohorts before and after implementation of an automated care coordination tool.

Yapei Zhang, Catherine Mezzacappa, Lin Shen, Amanda Ivatorov, Alexandra Petukhova-Greenstein, Rajni Mehta, Maria Ciarleglio, Yanhong Deng, Woody Levin, Steve Steinhardt and 6 more

Open access · goldAbstract read
In one paragraph

Article in PLOS digital health, 2022. 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
0.6field-weighted citation impact, top 36% of its field
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, 4 citations in OpenAlex.

  1. Article
  2. Deploying a national clinical text processing infrastructure.Journal of the American Medical Informatics Association : JAMIA · 2024
    Article
  3. The future of hepatology.Hepatology (Baltimore, Md.) · 2023
    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

16 authors at 4 institutions in 2 countries.

Yapei ZhangDepartment of Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America.
Catherine MezzacappaDepartment of Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America.ORCID https://orcid.org/0000-0001-7181-6902
Lin ShenDepartment of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America.ORCID https://orcid.org/0000-0001-8101-4129
Amanda IvatorovYale University, New Haven, Connecticut, United States of America.ORCID https://orcid.org/0000-0002-4396-3865
Alexandra Petukhova-GreensteinDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, United States of America.
Rajni MehtaVA Connecticut Healthcare System, West Haven, Connecticut, United States of America.
Maria CiarleglioYale Center for Analytical Sciences, Yale School of Public Health, New Haven, Connecticut, United States of America.ORCID https://orcid.org/0000-0002-6591-3674
Yanhong DengYale Center for Analytical Sciences, Yale School of Public Health, New Haven, Connecticut, United States of America.
Woody LevinVA Connecticut Healthcare System, West Haven, Connecticut, United States of America.ORCID https://orcid.org/0000-0001-6866-1815
Steve SteinhardtVA Connecticut Healthcare System, West Haven, Connecticut, United States of America.
Donna ConneryVA Connecticut Healthcare System, West Haven, Connecticut, United States of America.
Michael PineauVA Connecticut Healthcare System, West Haven, Connecticut, United States of America.
Ifeyinwa OnyiukeDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, United States of America.
Caroline TaylorDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, United States of America.
Michal G RoseDepartment of Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America.
Tamar H TaddeiDepartment of Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America.ORCID https://orcid.org/0000-0002-6500-1739
Yale University · USVA Connecticut Healthcare System · USBrigham and Women's Hospital · USHarvard University · US

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Training Program in Investigative HepatologyT32DK007356 · NIDDK · YALE UNIVERSITY · PI MICHAEL H NATHANSON · 1986 to 2026
$7.4M
NCATS NIH HHS UL1 TR001863NIDDK NIH HHS T32 DK007356
6 · The paper itself

Abstract

introductionHepatocellular carcinoma (HCC) requires complex care coordination. Patient safety may be compromised with untimely follow-up of abnormal liver imaging. This study evaluated whether an electronic case-finding and tracking system improved timeliness of HCC care.

methodsAn electronic medical record-linked abnormal imaging identification and tracking system was implemented at a Veterans Affairs Hospital. This system reviews all liver radiology reports, generates a queue of abnormal cases for review, and maintains a queue of cancer care events with due dates and automated reminders. This is a pre-/post-intervention cohort study to evaluate whether implementation of this tracking system reduced time between HCC diagnosis and treatment and time between first liver image suspicious for HCC, specialty care, diagnosis, and treatment at a Veterans Hospital. Patients diagnosed with HCC in the 37 months before tracking system implementation were compared to patients diagnosed with HCC in the 71 months after its implementation. Linear regression was used to calculate mean change in relevant intervals of care adjusted for age, race, ethnicity, BCLC stage, and indication for first suspicious image.

resultsThere were 60 patients pre-intervention and 127 post-intervention. In the post-intervention group, adjusted mean time from diagnosis to treatment was 36 days shorter (p = 0.007), time from imaging to diagnosis 51 days shorter (p = 0.21), and time from imaging to treatment 87 days shorter (p = 0.05). Patients whose imaging was performed for HCC screening had the greatest improvement in time from diagnosis to treatment (63 days, p = 0.02) and from first suspicious image to treatment (179 days, p = 0.03). The post-intervention group also had a greater proportion of HCC diagnosed at earlier BCLC stages (p<0.03).

conclusionsThe tracking system improved timeliness of HCC diagnosis and treatment and may be useful for improving HCC care delivery, including in health systems already implementing HCC screening.

Identifiers

PMID36812575
PMCPMC9931271
OpenAlexW4289261283

What OpenQuestion holds

Textmetadata
LicenceCC0
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.