A pathology order interface based on the transmission of texts obtained from pathology requisitions and just-in-time construction of HL7 messages.

Background: A pathology order interface using Health Level 7 standards (HL7) generally has an HL7 client program that gathers information from the clinical electronic medical record system, packages the information in the form of HL7 message, and sends the message using secure communication protocols to an HL7 interface engine located on the pathology side. We describe an alternative approach that transmits the texts obtained from requisitions, with subsequent just-in-time construction of HL7 messages.

Materials And Methods: The order interface is between a dermatology clinic EMR and pathology information system.

Using Timestamp Data to Assess the Impact of Voice Recognition on the Efficiency of Grossing Biopsies.

Context.—: Studies on the adoption of voice recognition in health care have mostly focused on turnaround time and error rate, with less attention paid to the impact on the efficiency of the providers.

Objective.

Computational Algorithms that Effectively Reduce Report Defects in Surgical Pathology.

Background: Pathology report defects refer to errors in the pathology reports, such as transcription/voice recognition errors and incorrect nondiagnostic information. Examples of the latter include incorrect gender, incorrect submitting physician, incorrect description of tissue blocks submitted, report formatting issues, and so on. Over the past 5 years, we have implemented computational algorithms to identify and correct these report defects.

Construction and Utilization of a Neural Network Model to Predict Current Procedural Terminology Codes from Pathology Report Texts.

Background: At our department, each specimen was assigned a tentative current procedural terminology (CPT) code at accessioning. The codes were subject to subsequent changes by pathologist assistants and pathologists. After the cases had been finalized, their CPT codes went through a final verification step by coding staff, with the aid of a keyword-based CPT code-checking web application.

Pathology report data extraction from relational database using R, with extraction from reports on melanoma of skin as an example.

Background: Different methods have been described for data extraction from pathology reports with varying degrees of success. Here a technique for directly extracting data from relational database is described.

Methods: Our department uses synoptic reports modified from College of American Pathologists (CAP) Cancer Protocol Templates to report most of our cancer diagnoses.

Artificial Intelligence for Pathologists Is Not Near--It Is Here: Description of a Prototype That Can Transform How We Practice Pathology Tomorrow.

Context: Pathologists' daily tasks consist of both the professional interpretation of slides and the secretarial tasks of translating these interpretations into final pathology reports, the latter of which is a time-consuming endeavor for most pathologists.

Objective: To describe an artificial intelligence that performs secretarial tasks, designated as Secretary-Mimicking Artificial Intelligence (SMILE).

Design: The underling implementation of SMILE is a collection of computer programs that work in concert to "listen to" the voice commands and to "watch for" the changes of windows caused by slide bar code scanning; SMILE responds to these inputs by acting upon PowerPath Client windows (Sunquest Information Systems, Tucson, Arizona) and its Microsoft Word (Microsoft, Redmond, Washington) Add-In window, eventuating in the reports being typed and finalized.