Patient-Centered Radiology: How AI will Improve the Patient Experience

Patient-Centered Radiology: How AI will Improve the Patient Experience

Patient-centered radiology represents a holistic approach to medical imaging that aims to consider and prioritize patients’ individual needs and concerns. It goes beyond the technical aspects of taking and interpreting images, aiming to provide a positive and seamless experience for patients. Artificial intelligence (AI) can play a major role in improving the patient experience in radiology. 

For example, AI can be used to:

  • Streamline administrative workflow: AI can automate tasks such as radiology exam protocoling and scheduling, submitting prior authorization requests to payors, and managing inventory of medical supplies such as contrast agents. These streamlined operations reduce delays and enhance efficiency, resulting in a more convenient experience for patients.

  • Reduce image acquisition times: AI has the potential to significantly decrease the duration of imaging procedures, such as MRIs, without compromising image quality. This means shorter wait times, potentially less claustrophobia, and faster diagnoses, offering patients a more convenient and efficient experience with radiology.

  • Provide patients with more information about their condition: Radiology reports are typically dictated using specialized medical terminology directed towards their target audience, the referring clinicians. With the 21st Century Cures Act mandating patient access to their medical records, it becomes imperative to communicate medical findings in layperson terms, especially benign imaging findings which may provoke undue patient stress and anxiety. AI can assist by automatically populating the report with phrases that provide context and overall more closely involve the patient in their radiologic care. 
  • Accurate translation of reports into different languages: AI-powered translation tools can translate radiology reports into various languages, making these critical documents accessible to patients from diverse cultural backgrounds. This ensures that language barriers do not impede patient understanding and informed decision-making, truly engaging the patient in a meaningful manner.

  • Answering patient questions: Chatbots and AI-driven customer support can respond to patients’ inquiries and provide assistance before, during, and after imaging exams. This extra layer of support enhances patient comfort and satisfaction throughout the radiology process.

Patient-centered AI applications have the potential to significantly reduce friction in radiology processes. This improvement enables patients to actively engage in their care and elevates their satisfaction levels. Notably, patient satisfaction surveys are pivotal metrics used by government bodies to evaluate hospital performance. Moreover, AI is evolving to the point where it can handle administrative tasks like appointment scheduling and prior authorization requests, leading to more efficient and cost-effective radiology practices in the future.

Although these patient-centered applications of AI in radiology are promising, there is a concern that the inevitable siloing of these applications into fragmented products and platforms with limited cross-compatibility leading to increased friction and barriers to widespread adoption of these tools. Integrated radiology AI platforms address this concern effectively in orchestrating and consolidating multiple distinct software into one unified ecosystem. One crucial advantage of these integrated platforms is the capability to reside within the secure confines of a hospital’s server infrastructure, positioned behind the client’s firewall. This strategic placement provides robust protection for sensitive patient health information (PHI) against cybersecurity threats. In this manner, integrated platforms not only enhance compatibility and workflow efficiency but also prioritize PHI protection, making them an attractive choice.

AI is a powerful tool that can be used to improve the patient experience in radiology in a variety of ways, including reducing wait times, improving diagnosis accuracy, personalizing treatment plans, and providing patients with more information about their condition. As AI technology continues to advance, we can anticipate even more innovative ways to use AI to improve the care of patients who need radiology services.

Picture of Varun Danda

Varun Danda

Varun Danda, MD, is an integrated interventional radiology resident at the Icahn School of Medicine at Mount Sinai, NY. He has a passion for medical technology, focusing on artificial intelligence, augmented reality, and medical devices in radiology.

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