Evidence map›Paper›PMID 25681260›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2015

Metrics and tools for consistent cohort discovery and financial analyses post-transition to ICD-10-CM.

Andrew D Boyd, Jianrong John Li, Colleen Kenost, Binoy Joese, Young Min Yang, Olympia A Kalagidis, Ilir Zenku, Donald Saner, Neil Bahroos, Yves A Lussier

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. POPDx: an automated framework for patient phenotyping across 392 246 individuals in the UK Biobank study.Journal of the American Medical Informatics Association : JAMIA · 2023
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. 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

10 authors.

Andrew D BoydDepartment of Biomedical and Health Information Sciences, University of Illinois at Chicago, Chicago, IL, USA University of Illinois Hospital and Health Science System, Chicago, IL, USA boyda@uic.edu.
Jianrong John LiDepartment of Medicine, University of Arizona, Tucson, AZ, USA The University of Arizona Health Sciences Center, Tucson, AZ, USA.
Colleen KenostDepartment of Medicine, University of Arizona, Tucson, AZ, USA The University of Arizona Health Sciences Center, Tucson, AZ, USA.
Binoy JoeseUniversity of Illinois Hospital and Health Science System, Chicago, IL, USA Department of Medicine, University of Illinois at Chicago, Chicago, IL, USA.
Young Min YangDepartment of Biomedical and Health Information Sciences, University of Illinois at Chicago, Chicago, IL, USA.
Olympia A KalagidisDepartment of Biomedical and Health Information Sciences, University of Illinois at Chicago, Chicago, IL, USA.
Ilir ZenkuUniversity of Illinois Hospital and Health Science System, Chicago, IL, USA.
Donald SanerThe University of Arizona Health Sciences Center, Tucson, AZ, USA Biomedical Informatics Service Group, Arizona Health Science Center, University of Arizona, Tucson, AZ, USA.
Neil BahroosUniversity of Illinois Hospital and Health Science System, Chicago, IL, USA.
Yves A LussierUniversity of Illinois Hospital and Health Science System, Chicago, IL, USA Department of Medicine, University of Arizona, Tucson, AZ, USA The University of Arizona Health Sciences Center, Tucson, AZ, USA Department of Medicine, University of Illinois at Chicago, Chicago, IL, USA Biomedical Informatics Service Group, Arizona Health Science Center, University of Arizona, Tucson, AZ, USA Department of Pharmaceutical Sciences, University of Illinois at Chicago, Chicago, IL, USA Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Cancer Center, Tucson, AZ, USA The work was completed in part at The University of Illinois Lussier.y@gmail.com.

Funding

VITAMIN AP30CA023074 · NCI · UNIVERSITY OF ARIZONA · PI Dan Theodorescu · 1985 to 2026
$110.2M
CTSA INFRASTRUCTURE FOR PEDIATRIC RESEARCHUL1RR029879 · NCRR · UNIVERSITY OF ILLINOIS AT CHICAGO · PI MAZZONE, THEODORE · 2009 to 2011
$11.0M
Center for Clinical and Translational ScienceUL1TR000050 · NCATS · UNIVERSITY OF ILLINOIS AT CHICAGO · PI AZAR, DIMITRI T, MERMELSTEIN, ROBIN J. · 2012 to 2014
$9.8M
Semantic Approaches to Phenotypic Databases AnalysesK22LM008308 · NLM · UNIVERSITY OF CHICAGO · PI LUSSIER, YVES A · 2004 to 2006
$468k
NCATS NIH HHS UL1 TR000050NCATS NIH HHS UL1TR000050NCI NIH HHS P30 CA023074NCI NIH HHS P30CA023074NCRR NIH HHS 1UL1RR029879-01NCRR NIH HHS UL1 RR029879NLM NIH HHS K22 LM008308NLM NIH HHS K22 LM008308-04
6 · The paper itself

Abstract

In the United States, International Classification of Disease Clinical Modification (ICD-9-CM, the ninth revision) diagnosis codes are commonly used to identify patient cohorts and to conduct financial analyses related to disease. In October 2015, the healthcare system of the United States will transition to ICD-10-CM (the tenth revision) diagnosis codes. One challenge posed to clinical researchers and other analysts is conducting diagnosis-related queries across datasets containing both coding schemes. Further, healthcare administrators will manage growth, trends, and strategic planning with these dually-coded datasets. The majority of the ICD-9-CM to ICD-10-CM translations are complex and nonreciprocal, creating convoluted representations and meanings. Similarly, mapping back from ICD-10-CM to ICD-9-CM is equally complex, yet different from mapping forward, as relationships are likewise nonreciprocal. Indeed, 10 of the 21 top clinical categories are complex as 78% of their diagnosis codes are labeled as "convoluted" by our analyses. Analysis and research related to external causes of morbidity, injury, and poisoning will face the greatest challenges due to 41 745 (90%) convolutions and a decrease in the number of codes. We created a web portal tool and translation tables to list all ICD-9-CM diagnosis codes related to the specific input of ICD-10-CM diagnosis codes and their level of complexity: "identity" (reciprocal), "class-to-subclass," "subclass-to-class," "convoluted," or "no mapping." These tools provide guidance on ambiguous and complex translations to reveal where reports or analyses may be challenging to impossible.Web portal: http://www.lussierlab.org/transition-to-ICD9CM/Tables annotated with levels of translation complexity: http://www.lussierlab.org/publications/ICD10to9.

Indexed as

International Classification of DiseasesClinical CodingHumansInternetUnited Statesfinancial analysesICD-10-CMICD-9-CMmedical informaticsnetwork patternspatient cohort

Identifiers

PMID25681260
PMCPMC4457110

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.