Session ID: 
270

Data Science Reduces Anatomic Pathology Reporting Errors

2:30pm - 3:30pm Thursday, February 14
Orlando - Orange County Convention Center
W311E

Description

The term pathology reporting errors used in this context is to contrast with the term interpretation errors. Errors in pathology reports, such as typographical (voice recognition) errors, paraffin block designation errors when brought to the attention of the pathologists are generally obvious. However, they can be elusive to catch. Despite this, these errors are usually not as harmful as the interpretation errors but they are still potentially harmful when they cause misunderstanding by the clinicians. Also, performing additional studies without including the results in the pathology report can be a compliance issue. While the open source language R is traditionally used in data science, particularly in numerical computation, and while the anatomic pathology report is in the format of text, the application of data science approach turns out to be an effective way to detect pathology reporting errors.

Learning Objectives: 

  • Define pathology reporting errors and describe the characteristics of these errors
  • Describe data science and tools of data science
  • Describe how data science is used to reduce pathology reporting errors and to enforce reporting uniformity

Speaker(s): 

Pathologist,
Dahl-Chase Pathology Associates

Audience: 

Data Scientist
Physician, CMO, CMIO
Quality Professional

Level: 

Introductory