Ranking Significant Discrepancies in Clinical Reports

by   Sean MacAvaney, et al.

Medical errors are a major public health concern and a leading cause of death worldwide. Many healthcare centers and hospitals use reporting systems where medical practitioners write a preliminary medical report and the report is later reviewed, revised, and finalized by a more experienced physician. The revisions range from stylistic to corrections of critical errors or misinterpretations of the case. Due to the large quantity of reports written daily, it is often difficult to manually and thoroughly review all the finalized reports to find such errors and learn from them. To address this challenge, we propose a novel ranking approach, consisting of textual and ontological overlaps between the preliminary and final versions of reports. The approach learns to rank the reports based on the degree of discrepancy between the versions. This allows medical practitioners to easily identify and learn from the reports in which their interpretation most substantially differed from that of the attending physician (who finalized the report). This is a crucial step towards uncovering potential errors and helping medical practitioners to learn from such errors, thus improving patient-care in the long run. We evaluate our model on a dataset of radiology reports and show that our approach outperforms both previously-proposed approaches and more recent language models by 4.5


page 1

page 2

page 3

page 4


Identifying Harm Events in Clinical Care through Medical Narratives

Preventable medical errors are estimated to be among the leading causes ...

Improving Clinical Efficiency and Reducing Medical Errors through NLP-enabled diagnosis of Health Conditions from Transcription Reports

Misdiagnosis rates are one of the leading causes of medical errors in ho...

Public Health Informatics: Proposing Causal Sequence of Death Using Neural Machine Translation

Each year there are nearly 57 million deaths around the world, with over...

A Neural Attention Model for Categorizing Patient Safety Events

Medical errors are leading causes of death in the US and as such, preven...

Priority prediction of Asian Hornet sighting report using machine learning methods

As infamous invaders to the North American ecosystem, the Asian giant ho...

Medication Error Detection Using Contextual Language Models

Medication errors most commonly occur at the ordering or prescribing sta...

An Ensemble Approach to Automatic Structuring of Radiology Reports

Automatic structuring of electronic medical records is of high demand fo...

Please sign up or login with your details

Forgot password? Click here to reset