Return on investment for artificial intelligence in corporate imaging is a multi-faceted topic that includes efficiency, accuracy, patient outcomes and financial considerations. In radiology, the rise of AI has promised to revolutionize the field by increasing diagnostic precision and improving patient care.
However, the financial aspect of AI adoption is complex, especially due to the current lack of direct reimbursement for AI applications in medical imaging. Nevertheless, AI can indirectly contribute to ROI by increasing the efficiency of imaging providers and supporting functions that deliver greater provider productivity and better staff efficiency – ultimately improving outcomes and reducing the overall cost of healthcare.
Dawn Kram, principal consultant, EI and AI, at The Gordian Knot Groupand a colleague will discuss this complex topic at HIMSS25 in Las Vegas in March in a session titled: “The ROI of AI in Enterprise Imaging.”
Cram has more than 30 years of healthcare experience in clinical technologies, IT systems management, and leadership in enterprise and departmental imaging systems, clinical information systems, and medical imaging software development.
She has extensive experience orchestrating all phases of system and application development, strategic and tactical planning, multidisciplinary interoperability, integrations and implementations. Its mission to realize cost-effective, scalable systems that effectively support physician workflows and application interoperability has helped vendor organizations and product vendors envision and implement improved applications and platforms for imaging systems.
We sat down with Cram to discuss AI ROI in enterprise imaging and got a preview of her HIMSS25 session.
Q. Why is the topic of AI ROI and enterprise imaging important today?
A. This topic is especially relevant and timely as healthcare increasingly integrates AI technologies to improve diagnostic accuracy, improve patient care, and eliminate tedious workflow steps. In the field of medical imaging, especially radiology, the transformative potential of AI is clear, although financial challenges, such as the lack of direct reimbursement for AI applications, complicate financing.
Nevertheless, AI can indirectly improve ROI by increasing the efficiency of imaging providers, leading to higher productivity, better workforce efficiency, and lower healthcare costs.
We will provide valuable insights into identifying cost-benefit opportunities and methods for calculating ROI when implementing different AI technologies in business imaging and for different personas. Understanding the additional costs of running AI is equally important and requires consideration to determine the true cost of ownership.
By understanding financial dynamics, healthcare organizations can make informed decisions about AI investments, maximizing benefits and managing costs effectively.
During the session, we aim to provide participants with practical tools, tips and strategies to help justify AI investments and achieve sustainable improvements in their imaging operations, even without immediate reimbursement. Every workflow, process, and delivery of healthcare improvement has an associated ROI.
Q. What types of AI will you cover during your HIMSS25 session?
A. We will discuss both clinical artificial intelligence such as pathology detection algorithms and process AI such as robotic process automation – in the context of business imaging. By automating routine and repetitive tasks, AI allows physicians to focus on more critical aspects of patient care, improving diagnostic precision and patient outcomes.
Additional cost benefits can be achieved when deploying process AI for support functions such as patient scheduling. AI can also be used to streamline imaging workflows, reducing the time required for image analysis and supporting more efficient clinical decision making.
AI can analyze vast amounts of imaging data correlated with clinical data such as labs or even genomics. It can identify patterns and anomalies that could be missed by the human eye or take significantly longer to assess. This can help detect and treat diseases earlier, ultimately leading to significantly better patient outcomes and lower healthcare costs overall.
While the application of AI in radiology is prevalent, we will also discuss other imaging specialties within the enterprise and how AI can benefit their diagnostics and workflows. For example, ophthalmology can use AI in the screening and diagnosis of retinal diseases, deploying algorithms that can analyze fundus images for signs of diabetic retinopathy or macular degeneration.
Dermatology, wound care and other photo production specialties can use apps with built-in AI to identify the body part being imaged, analyze lesions or wound size and shape, and assist in the early detection of skin cancer or possible infections.
Q. What is one of the key insights you see as HIMSS25 participants leave your session and log in when they get home to their organization?
A. One of the most important insights is the importance of using responsible AI, which is crucial for achieving any ROI. Today, AI is still inherently stupid and dependent on the people who create it. Prior to tendering, it is necessary to validate how an algorithm is created, trained and tested.
There are many factors to consider when determining whether the AI has been responsibly developed by software manufacturers. This includes whether diverse and representative data sets have been used to reduce bias and ensure fair patient care that performs reliably across demographic groups and regardless of the purchasing device manufacturer.
A critical aspect in determining responsible clinical AI is compliance with regulatory standards designed to ensure the safety, efficacy, and reliability of AI algorithms used in diagnostic imaging. Organizations can have greater confidence that an FDA-approved AI algorithm has undergone rigorous testing and validation processes, so that the algorithms intended to help physicians analyze images or provide diagnostic insights meet certain quality and safety standards before they are deployed in the clinical environment.
By adhering to these regulations, manufacturers can help build trust between healthcare providers and patients, making AI technologies safe and effective for use in medical practice.
Some additional considerations when determining responsible AI development include quality management and ongoing monitoring capabilities. Clinical AI must be continuously evaluated to ensure that the algorithms maintain their performance over time and adapt to new data, clinical scenarios and variances.
This means ensuring that compatible monitoring mechanisms exist and are implemented to detect and address any issues that arise during deployment of the AI and over time as imaging technologies and diagnostics evolve.
This session will enable participants to return to their organizations with a better understanding of how to advocate and implement AI technologies that are not only innovative, but also ethical, transparent, and provide high standards of patient care.
Cram’s teaching session, “The ROI of AI in Enterprise Imaging,” is scheduled for Tuesday, March 4 at 2 p.m. at HIMSS25 in Las Vegas.
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