Ruchi Thanawala

AI-Assisted Surgical Training

By Ruchi Thanawala, M.D.
Ruchi Thanawala

AI can support device innovation and surgical training, but it requires data and collaboration.

Artificial intelligence (AI) has been in development since the 1950s, and its use in health care has presented inspiring possibilities for surgical advancement and post-op care. Pioneering robots like the Da Vinci surgical system may now be over 20 years old, but it’s AI’s capability to reinvent and reinvigorate surgical approaches that makes it such an asset to the healthcare industry.

So far, AI has proven its capabilities to exceed human functioning in drug discovery, symptom analysis, screening and diagnosis. From diagnosing breast cancer at a higher rate than multiple human pathologists to the machine learning (ML) robot that identified an ingredient commonly found in toothpaste that can fight malaria-based parasites, AI continues to surprise. Now, AI is being deployed in surgical training to accelerate learning and help remove biases from education.

However, without captured data, quality devices and well-informed representatives, the possibility for the technology to make a difference dwindles. Let’s explore how AI can help both medtech representatives and educators innovate surgical devices and training.

Sharing Knowledge for Better Training and Outcomes

The operating room (OR) is, simultaneously, a very complex and simple place. While, on occasion, surgeons may use many tools to achieve a relatively straightforward goal, at other times, a complicated result may be produced with a fairly elementary device. Since circumstances may vary from case to case, even when doing the same operation, surgeons need to be well informed on the tools they are using so they are best placed to respond and adapt to unpredictable situations.

However, much of the valuable data on how medical devices are used is currently lost. Approximately 25% of resident cases often go unlogged or underreported. In addition to poor case logging, feedback from the OR is often anecdotal and not currently tracked or shared easily between representatives, institutions and the consultants responsible for using the devices.

The valuable knowledge of how a medical device performs in the OR currently only exists among a limited number of people, and innovations in the operating room occur far faster than can be downloaded into a textbook.

Using AI tools to help capture case log data in an online surgical learning platform means better access to information from the OR and resident skills labs. Hospitals can then share back information with representatives on the frequency of use and application of the devices with feedback from the surgeons themselves. Representatives receive clear, accurate and data-driven information to improve their devices.

For hospitals, having AI aggregate OR data on the application and frequency of device use assists in surgical training. Educators with a comparative overview of student learning curves can quickly identify where residents might need extra support and practice. Additionally, teaching faculty can see where they might need to make overall course improvements, for example, if multiple learners are falling behind in a specific area.

Datasets are then anonymized to show residents their individual progress compared to their peers on local and national scales. Residents with access to the platform not only have a historical record of all their case logs but can see their longitudinal development. AI further accelerates learning as the platform can integrate with additional educational materials that automatically update for each logged case.

AI-powered systems enable automation, making it easier and more likely that teaching faculty will participate in evaluative feedback. Unlike oral or informal feedback or reporting, personalized online platforms tailor learners’ journeys to their needs and pace of learning. Digital records are then readily available to consulting surgeons who can easily access resident case histories and objectively verify skills to speed residents’ progression in the OR.

An online platform that protects learner and patient information while safely and securely identifying learning opportunities for residents and representatives alike can help pair the right representative with the right learners and ensure that the best devices are available for each surgeon. Likewise, manufacturers that listen and respond to feedback on devices will increase their chances of surgeons adopting upgraded devices into their practices.

The Future of Health Care Is Smart

Medical device representatives working in tandem with educating surgeons are critical to applying new technologies successfully in the OR. A knowledgeable representative can be vital in facilitating skills labs for residents learning how to use a device and in the OR for surgeons using the device for the first time in a different application.

Good partnership relationships between medical device representatives and surgeons are bi-directional in their benefits. New AI tools give representatives a greater understanding of how surgeons use devices, allowing for more informed and fruitful conversations.

The future of healthcare is smart. Right now, devices have embedded RFID (Radio Frequency Identification) chips to locate them wirelessly in hospitals, but much more is possible. Smart hospitals of the future will have medical devices that talk to each other, helping to relay patient information, administer correct medicine doses and notify doctors of changes in patient health. AI tools that collect data on device usage in the OR can provide the building blocks of knowledge that smart hospitals will require.

Highly connected devices can make hospitals more productive and efficient, with AI technologies supporting doctors in all stages of diagnosis, treatment and post-op care. Collecting surgical training and medical device use data through AI-powered platforms will help hospitals better know their teams (and the skill sets available), streamlining data flows and providing insights for device development.

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Ruchi Thanawala