Evidence map›Paper›PMID 36700236›Full record

ArticleJournal of pathology informatics2023

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

Jack Greenburg, Yunrui Lu, Shuyang Lu, Uhuru Kamau, Robert Hamilton, Jason Pettus, Sarah Preum, Louis Vaickus, Joshua Levy

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in Journal of pathology informatics, 2023. 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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.3field-weighted citation impact, top 10% 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, 6 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 5 institutions in 1 country.

Jack GreenburgDepartment of Computer Science, Middlebury College, Middlebury, VT, USA.
Yunrui LuProgram in Quantitative Biomedical Sciences, Dartmouth College Geisel School of Medicine, Hanover, NH, USA.
Shuyang LuProgram in Quantitative Biomedical Sciences, Dartmouth College Geisel School of Medicine, Hanover, NH, USA.
Uhuru KamauProgram in Quantitative Biomedical Sciences, Dartmouth College Geisel School of Medicine, Hanover, NH, USA.
Robert HamiltonDepartment of Pathology, Johns Hopkins Hospital, Baltimore, MD, USA.
Jason PettusEmerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Health, Lebanon, NH, USA.
Sarah PreumDepartment of Computer Science, Dartmouth College, Hanover, NH, USA.
Louis VaickusEmerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Health, Lebanon, NH, USA.
Joshua LevyProgram in Quantitative Biomedical Sciences, Dartmouth College Geisel School of Medicine, Hanover, NH, USA.
Dartmouth College · USDartmouth HealthDartmouth Institute for Health Policy and Clinical Practice · USJohns Hopkins Hospital · USMiddlebury College · US

Funding

Zhao - Proj 2P20GM130454 · NIGMS · DARTMOUTH COLLEGE · PI Li Song · 2019 to 2026
$27.2M
Relation Between In-utero Vitamin D and Immune Function in Early Childhood P20GM104416 · NIGMS · DARTMOUTH COLLEGE · PI KARAGAS, MARGARET RITA · 2013 to 2022
$23.7M
NIGMS NIH HHS P20 GM104416NIGMS NIH HHS P20 GM130454
6 · The paper itself

Abstract

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.

Indexed as

Current procedural terminologyMachine learningMisbillingNatural language processingPathology reportsWeb development

Identifiers

PMID36700236
PMCPMC9867971
OpenAlexW4315651993

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.