How can AI help radiologists perform higher-level tasks

With the quantity, diversity and complexity of medical imaging and its associated data, radiologists have an opportunity to use artificial intelligence (AI) to enhance their ability to drive problem-solving and learning applications in patient care. Artificial intelligence technology can be used to help radiologists become more productive, efficient and focused on their patients. There is a tremendous amount of attention being paid to the clinical uses of AI. However, AI is also in its infancy for radiology operations and services, where it promises to have just as great of an impact, particularly in redefining how radiologists will share and interpret data in the future.

AI-driven healthcare solutions can enable radiology departments to become stronger and more productive than ever before, with more visibility into their operational issues, from equipment maintenance to scheduling to post-imaging follow-up. It all starts with data, which delivers actual information and insights into what is happening. Then, AI and predictive analytics offer further foresight, enabling staff to better respond to what is likely to happen.

 

Still, these capabilities do not stand to replace the responsibilities of clinical or technology professionals. What they do, however, is provide these individuals with greater information, layered alongside their years of experience, enabling them to work more efficiently, improving operations and ultimately enhancing the patient experience.

 

From an equipment maintenance standpoint, AI makes zero-unplanned-downtime a possibility. By collecting data from all imaging machines, service and bioengineering professionals can better predict if a system is going to have unplanned downtime or may otherwise need preventive maintenance. For example, technology managers may find that every three months a machine consistently displays the same error and slows clinical workflow, although a regular software patch update can prevent it. Having the right data can enable teams to predict such an occurrence and issue patches proactively, to avoid downtime, maintain workflow, and provide a better experience for the staff and their patients.

Another operational example is how AI can aid in better, smoother patient scheduling. AI can help with revenue capture because it can predict whether a particular patient is going to show up for his or her appointment with significant accuracy. With this type of intelligence, radiology departments can better anticipate and proactively call the anticipated no-show patients to provide reminders or double-book that time slot. In this way, AI can help address the operational impact of lost revenue that typically occurs with radiology appointments due to no-shows or last-minute cancellations.

The first step to using AI technology is collecting appropriate data and making sure that it is complete, clean and organized with a consistent nomenclature. This provides a comprehensive picture of radiology operations. For instance, if we are to predict no-shows and cancellations, we need to accurately capture all the potential factors that might affect no-shows, such as: patient demographics, imaging study type, the chronology of the scheduling process, and the no-show/cancellation event itself. With the right data captured, the AI solutions are easier to deploy.

The next step is ensuring high-quality information. When making decisions that may affect how a healthcare organization operates and cares for patients, it is important to ensure that decision-making is based on sound interpretation of the best possible data. While we recognize that in the real world no data is perfectly accurate, consistent or complete, it’s promising that AI methods can also tolerate a level of imperfection that is realistic. In fact, one can even employ these methods to detect imperfections in the data themselves and to suggest strategies to improve data acquisition. Once data is standardized, AI-driven medical insights are more accurate and provide greater value.

The third step is simply remembering that implementing new AI technology into a busy and hardened clinical workflow can be challenging and will require an investment in time and resources to properly train staff on new ways of working. It’s critical to implement workforce training to help hospital staffers understand how to use and interpret data in new ways. Beyond using the data and outputs to make a significant impact on day-to-day operations, it is necessary to explain to staff why the changes are being made and how they will help to evolve operations. Sharing how AI technology can help them and assuring them that it is by no means a replacement for the valuable work they do, can be an impactful and empowering message. The change management element is, and will continue to be, the most significant challenge for AI adoption in healthcare operations.