How AI is Revolutionizing Surgical Efficiency & Patient Safety

By Harshil Goradia, Chief Technology Officer, Censis

Artificial intelligence, or AI, is growing in adoption across nearly every industry. Healthcare is no exception. It is estimated that AI in the healthcare market will reach more than $187 billion USD by 2030, registering a CAGR of 38.5% from 2024 to 2030, according to a report by Grand View Research.

As AI continues to revolutionize healthcare, its potential in the surgical environment is becoming increasingly evident. By addressing critical operational challenges, AI can enhance precision, efficiency and patient outcomes. For example: staffing shortages and high turnover in Sterile Processing Departments (SPDs) contribute to burnout, reduced productivity, and quality concerns. These issues are compounded by the constant pressure to expedite operating room and instrument turnover to meet growing case volume and demand. At the same time, ensuring instrument processing is high quality and meets exacting standards to prevent the risk of surgical site infections (SSIs), makes reliable solutions more urgent than ever. 

Technology as the Solution

Technology has proven to be a powerful enabler in addressing healthcare challenges and elevating the standards of patient care. Generative AI and Large Language Models (LLMs) have been a game changer in this regard. Innovations like Retrieval Augmented Generation (RAG) empower LLMs to access information outside of their training data, which can lead to more context-sensitive and accurate responses. As an example, doctors can now use an AI voice-based assistant to scribe notes and generate summaries while discussing patient diagnosis, AI powered chatbots can provide immediate support and information to patients and also help with routine administrative tasks. In SPDs, using computer vision models, AI copilots can be leveraged to make it easier for technicians to get information on various instruments and ensure high productivity and quality of SPD operations. These advancements make it easier to utilize AI for retrieving context-sensitive information and generating rich content in any area, automating mundane administrative tasks, and freeing up time for healthcare professionals to focus on providing high quality care to patients. Additionally, emerging technologies such as Bluetooth-based real-time location services are gaining traction, enabling hospitals to automatically track movements of surgical trays throughout the facility.

There is still some way to go before we see large scale adoption of these tools across the surgical processes. However, as concerns around accuracy and privacy of information are addressed through responsible AI practices and ongoing advancements in automation, LLMs and RAG, adoption and trust is expected to accelerate.  This moment will create a flywheel effect, driving greater efficiency and innovation and further increasing adoption, ultimately benefiting the entire healthcare ecosystem.  

The Impact of AI on Surgical Safety and Efficiency

AI is already having an impact on many core challenges facing SPDs. Staffing shortages can be addressed through AI-based assistants, creating a digital workforce that can augment the physical workforce, helping to alleviate burnout and reduce mundane manual time-consuming tasks. Bots can operate 24X7 and provide contextual information quickly and easily from multiple sources of data, leading to productivity and quality improvements of 5-10 times. In addition, users can now interact with their data in more natural language-based user interfaces, allowing them to execute their jobs in an easier way. 

A key advantage of AI in this space lies in the ability to deliver advanced data analytics with actionable insights.  This empowers hospital functions including SPDs to enhance efficiency, quality and patient safety, ultimately improving patient care. By shifting from descriptive analytics to predictive, SPDs can forecast instrument usage to eliminate inefficiencies and optimize processes. Additionally, AI improves inventory management across the supply chain, ensuring resources are utilized effectively and reducing waste. 

AI’s Role in Perioperative Planning and Decision-Making

Perioperative planning is becoming more complex as the volume of cases increases, making it increasingly difficult to make decisions based on all the available information. Data volumes have increased significantly in the last few years due to high adoption of electronic health records (EHRs), detailed capture of patient information, collecting data from multiple sources such as lab results, surgical devices, etc. Machine Learning algorithms can analyze these large data sets and generate insights to assist perioperative leaders in preoperative evaluation, medical history review, surgical planning and postoperative care planning. AI will not replace human expertise in perioperative planning, but rather serve as a force multiplier. 

How AI Improves Management of OR Resources

Today, planning and coordinating resources required to support the surgical schedule is largely a manual task. Everyday schedulers, service leads, sterile processing leaders, and technicians spend time reviewing the ever-changing case schedule and matching material requirements with physical assets. Traditional software has helped automate parts of this process but has struggled to bridge the gaps between systems and resource optimization. AI offers promise to solve these problems in the same way a human would, and therefore a way to automate these parts of the resource planning process.

For example, AI can help anticipate case demand, improve team readiness, and avoid costly inefficiencies. This enables more accurate forecasting, better alignment of resources, and fewer delays in the OR. 

In addition to achieve world-class operations SPD and OR KPIs include eliminating non-compliance events, minimizing tray errors, ensuring all cases run on schedule, achieving 100% sterilization of instruments and availability of trays at the right place at the right time. AI will play a significant role in trying to achieve these north star metrics.  

The Future of AI in the OR

The goal of adoption of any new innovative technology in healthcare and perioperative fields should aim to deliver excellent patient outcomes and the highest levels of care. AI has significant potential to accelerate these goals but its success hinges on developing high-quality, domain-specific models that are easy to use, safe, and secure. Achieving this will require strong collaboration between technology providers, SPD, OR and surgical personnel to drive meaningful impact across the entire perioperative loop. 

In the next few years, you can expect to see growing investments in AI, automation and other technologies to improve the standard of patient care. Virtual Health Assistants, automated AI powered tracking of surgical instruments through RFID and smart sensors, leveraging data to provide deep predictive and prescriptive insights, predictive maintenance of equipment, staff scheduling and workflow optimization are some of the key areas where AI will have a significant impact. 

At the same time, mitigating any data privacy and security concerns, ensuring compliance with regulations, creating confidence and trust amongst healthcare professionals, and improving data quality and standardization will be crucial in driving mass adoption of these tools across healthcare and perioperative functions. By doing so, AI has the potential to transform the way surgical operations are planned and executed in the future.  

– Harshil Goradia is the Chief Technology Officer at Censis, a leader in surgical asset management solutions that is solely focused on improving perioperative efficiency and quality. He joined Censis in March 2024 from his previous role as a Senior Director, Data Science and Engineering at The FORT, which is the AI and Innovation Center of Excellence at Fortive (parent company of Censis). Prior to Fortive, Harshil served in various technology roles of increasing complexity at Arrow Electronics, Oracle and Infosys.