Development of an interactive web dashboard to facilitate the reexamination of pathology reports for instances of underbilling of CPT codes.

Current procedural terminology Machine learning Misbilling Natural language processing Pathology reports Web development

Journal

Journal of pathology informatics
ISSN: 2229-5089
Titre abrégé: J Pathol Inform
Pays: United States
ID NLM: 101528849

Informations de publication

Date de publication:
2023
Historique:
received: 27 11 2022
accepted: 03 01 2023
entrez: 26 1 2023
pubmed: 27 1 2023
medline: 27 1 2023
Statut: epublish

Résumé

Current Procedural Terminology Codes is a numerical coding system used to bill for medical procedures and services and crucially, represents a major reimbursement pathway. Given that pathology services represent a consequential source of hospital revenue, understanding instances where codes may have been misassigned or underbilled is critical. Several algorithms have been proposed that can identify improperly billed CPT codes in existing datasets of pathology reports. Estimation of the fiscal impacts of these reports requires a coder (i.e., billing staff) to review the original reports and manually code them again. As the re-assignment of codes using machine learning algorithms can be done quickly, the bottleneck in validating these reassignments is in this manual re-coding process, which can prove cumbersome. This work documents the development of a rapidly deployable dashboard for examination of reports that the original coder may have misbilled. Our dashboard features the following main components: (1) a bar plot to show the predicted probabilities for each CPT code, (2) an interpretation plot showing how each word in the report combines to form the overall prediction, and (3) a place for the user to input the CPT code they have chosen to assign. This dashboard utilizes the algorithms developed to accurately identify CPT codes to highlight the codes missed by the original coders. In order to demonstrate the function of this web application, we recruited pathologists to utilize it to highlight reports that had codes incorrectly assigned. We expect this application to accelerate the validation of re-assigned codes through facilitating rapid review of false-positive pathology reports. In the future, we will use this technology to review thousands of past cases in order to estimate the impact of underbilling has on departmental revenue.

Identifiants

pubmed: 36700236
doi: 10.1016/j.jpi.2023.100187
pii: S2153-3539(23)00001-9
pmc: PMC9867971
doi:

Types de publication

Journal Article

Langues

eng

Pagination

100187

Subventions

Organisme : NIGMS NIH HHS
ID : P20 GM130454
Pays : United States

Informations de copyright

© 2023 The Author(s).

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Auteurs

Jack Greenburg (J)

Department of Computer Science, Middlebury College, Middlebury, VT, USA.

Yunrui Lu (Y)

Program in Quantitative Biomedical Sciences, Dartmouth College Geisel School of Medicine, Hanover, NH, USA.

Shuyang Lu (S)

Program in Quantitative Biomedical Sciences, Dartmouth College Geisel School of Medicine, Hanover, NH, USA.

Uhuru Kamau (U)

Program in Quantitative Biomedical Sciences, Dartmouth College Geisel School of Medicine, Hanover, NH, USA.

Robert Hamilton (R)

Department of Pathology, Johns Hopkins Hospital, Baltimore, MD, USA.

Jason Pettus (J)

Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Health, Lebanon, NH, USA.

Sarah Preum (S)

Department of Computer Science, Dartmouth College, Hanover, NH, USA.

Louis Vaickus (L)

Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Health, Lebanon, NH, USA.

Joshua Levy (J)

Program in Quantitative Biomedical Sciences, Dartmouth College Geisel School of Medicine, Hanover, NH, USA.
Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Health, Lebanon, NH, USA.
Department of Epidemiology, Dartmouth College Geisel School of Medicine, Hanover, NH, USA.
Department of Dermatology, Dartmouth Health, Lebanon, NH, USA.

Classifications MeSH