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Hospital surgical procedures entail the use of an extensive range of surgical instruments. The preparation and delivery of these surgical instruments go through multiple hands, including technicians who inspect, test, pack, assemble, and sterilize the instruments in the Sterile Supply Unit (SSU). These sterilized instruments are supplied to nurses who order these instruments, which are circulated in operating theatres to meet procedural requirements. Nurses may also have to fetch ad-hoc on-demand instruments in the Operating Theatres (OTs). The intelligent Surgical Instruments Management (iSIM) project hence aims to reduce reliance on manpower used in these repetitive manual tasks, to enable hospital staff to focus on higher value clinical patient care. This project consists of two parts, iSIM-Goldfinger and iSIM-Gonzales:

In iSIM–Goldfinger, robotics and automation are used to inspect, test, pack, and assemble cleaned General Surgery basic instrument sets in the SSU. These processes are performed before the packed instruments sets are sterilized.

In iSIM–Gonzales, Artificial Intelligence technology is used to predict the types of surgical instruments required for respective surgical procedures, based on patients’ medical profiles and surgeon preferences. The prediction model would also help to minimize costs spent on unnecessary holding of storage and processing of instruments. A robotic solution would aid in handling ad-hoc surgical instrument requests in the OTs, and dispense the instrument aseptically to the Scrub Nurses.


Principal Investigator: Ms Lee Chen Ee

Host Institution: SingHealth

SHARP Grant ref: 192 22 00005

 

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