Development of a semi-automated method for subspecialty case distribution and prediction of intraoperative consultations in surgical pathology

• Main Authors: Raul S Gonzalez, Daniel Long, Omar Hameed
• Format: Article
• Language: English
• Published: Wolters Kluwer Medknow Publications 2015-01-01
• Series: Journal of Pathology Informatics
• Subjects: High reliability, information technology, intraoperative consultation, lean, surgical pathology
• Online Access: http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2015;volume=6;issue=1;spage=40;epage=40;aulast=Gonzalez

Background: In many surgical pathology laboratories, operating room schedules are prospectively reviewed to determine specimen distribution to different subspecialty services and to predict the number and nature of potential intraoperative consultations for which prior medical records and slides require review. At our institution, such schedules were manually converted into easily interpretable, surgical pathology-friendly reports to facilitate these activities. This conversion, however, was time-consuming and arguably a non-value-added activity. Objective: Our goal was to develop a semi-automated method of generating these reports that improved their readability while taking less time to perform than the manual method. Materials and Methods: A dynamic Microsoft Excel workbook was developed to automatically convert published operating room schedules into different tabular formats. Based on the surgical procedure descriptions in the schedule, a list of linked keywords and phrases was utilized to sort cases by subspecialty and to predict potential intraoperative consultations. After two trial-and-optimization cycles, the method was incorporated into standard practice. Results: The workbook distributed cases to appropriate subspecialties and accurately predicted intraoperative requests. Users indicated that they spent 1-2 h fewer per day on this activity than before, and team members preferred the formatting of the newer reports. Comparison of the manual and semi-automatic predictions showed that the mean daily difference in predicted versus actual intraoperative consultations underwent no statistically significant changes before and after implementation for most subspecialties. Conclusions: A well-designed, lean, and simple information technology solution to determine subspecialty case distribution and prediction of intraoperative consultations in surgical pathology is approximately as accurate as the gold standard manual method and requires less time and effort to generate.