Published in Surgical Innovation.

The surgical count is the primary method to account for and manage surgical instruments, needles, and sponges during operative procedures. However, a miscount, when there is a discrepancy between counted and deployed instruments, is estimated to occur in roughly 1 in 140 cases. These events often result in significant costs to hospitals and patients per year due to lost operating room time and secondary imaging procedures. An estimated 1 in 70 miscounts result in a retained surgical instrument, resulting in patient harm, as well costly reoperation and litigation. Given the high cost of OR time and the current burden of counting procedures, it is clear that amore accurate and less labor-intensive counting system is needed.

In recent years, there is tremendous growth in machine learning technology applied to healthcare. Computer vision techniques are now applied across many medical domains and are most visible in the context of minimally invasive surgery and endoscopic surgery. A literature search of computer vision studies on the detection or localization of surgical instruments outside of the surgical field highlighting only 4 studies that provide insight into both the feasibility and challenges of utilizing existing computer vision techniques to build a system that can perform the surgical count.

Submitted to AORN Journal

Having a retained surgical item (RSI) is a feared complication of surgery, referring specifically to an object (e.g., supply, instrument, or equipment) being unintentionally left inside of a patient. It is estimated that there are over 2,000 RSIs per year in the United States. Sponges are the most common RSI. Retained sponges can be discovered immediately after surgery or months (or even years) later. In response to the frequency of this complication, several technologies, including data-matrix-coded sponges (DMS) and radiofrequency, aim to maintain a proper count of laparotomy sponges to avoid RSIs. One example of a data-matrix-coded sponge counting system is the SurgiCount Safety-Sponge System (Stryker, Kalamazoo, MI). Although several studies indicated that the use of DMS or radiofrequency labeling significantly reduces the frequency of retained sponges, it is crucial to consider the possibility of errors and associated downstream effects, as illustrated in this report.

Published in Surgical Innovation.

The Stanford Biodesign Faculty Fellows program was established in 2014 to train Stanford Medical and Engineering faculty in a repeatable innovation process for health technology translation while also being compatible with the busy clinical schedules of surgical faculty members. Methods. Since 2014, 62 faculty members have completed the fellowship with 42% (n = 26) coming from 14 surgical subspecialties.

This eight-month, needs-based innovation program covers topics from identifying unmet health-related needs, to inventing new technology, developing plans for intellectual property (IP), regulatory, reimbursement, and business models to advance the technologies toward patient care.

Results/Conclusion: Intake and exit survey results from three years of program participants (n = 36) indicate that the fellowship is a valuable hands-on educational program capable of improving awareness and experience with skill sets required for health technology innovation and entrepreneurship.