Prioritizing Patient Safety and Minimizing Waste: Institutional Review of Cases and a Proposed Process for Designing a Surgical Pathology Gross-Only Examination Policy

[Ecology: The blind spot in pathology research]

INTRODUCTION

The 2015 Paris Agreement has been the first restrictive agreement in the fight against climate change. The newer generations of pathologists, who feel more anxiety due to environmental problems than their predecessors, are asked to publish research works while they are harder and harder to and in a context of demographical tensions. We wanted to measure the rise of ecology research in pathology since the Paris Agreement.

MATERIAL & METHODS

Over a ten years study period (2013-2022), we have identified via PubMed the number of articles in which forty-three terms taken from the sustainable development vocabulary appeared in ten renowned international pathology journals, selected for their SJR index from ScimagoJr and their impact factor, plus the Annales de pathologie, and compared their means of incidence between the 2013-2015 (m1) and 2016-2022 (m2) periods. The same process has been applied for "artificial intelligence", "deep learning" and "digital pathology".

RESULTS

A total of 1336 articles have been identified. Only "digital pathology" (fromm1=8,33 to m2=23,29; p=0,010) and "deep learning" (fromm1=0 to m2=10,14; p=0,034) saw their incidence rise significantly. A significant decrease has been observed with "biological" (fromm1=70,00 to m2=56,86; p=0,020).

DISCUSSION-CONCLUSIONS

Pathology reacts to trends but research in ecology has remained in the blind spot since 2015. However there seems to be an awakening as editorials, articles and communications in congress have blossomed the last two years.

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

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.