Time, date and format

  • Date: Thursday 26 May, 2022
  • 12pm PT
  • Location: Breakout room to be confirmed

Overview

The use of AI within a radiology setting has potential to dramatically improve patient outcomes and reduce cost, but without a strong business case facilities struggle to adopt solutions.

While the case for AI within a radiology department is well served by diagnostic enhancement, radiologist productivity and modality throughput arguments, providers are often challenged to measure the value downstream in the patient care cycle, through contribution to the referring department or wider health system economics.

Delivered as part of AIMed’s Global Summit, this focus group discussion aims to explore how health systems and hospitals build business models for the adoption of AI in radiology that drives benefits downstream in the referring department (e.g. neurology, oncology, pulmonology).

For example, certain AI applications:

  • Detect conditions (Intracranial hemorrhage, ischemic stroke, fractures, tube misplacement, lung nodules) that can elevate the patient automatically within the radiology workflow, improving patient diagnosis and time to treatment with downstream benefits to the patient and health system
  • Add value for the referrer through a quantitative report that can also be used to inform better quality of care by the referrer or adjust the patient’s risk stratification. These reports may also be used as a communication tool with patients to engage them further, with potential to increase perception of value and expand market share for that disease type (e.g. MS Plaque quantification, COPD, ILD)
  • Process retrospective image data for certain conditions, undetected in the original scan, that should trigger follow up (Calcium scoring to determine CV health, Spine micro fractures to determine bone density issues, lung nodules and lung densities etc). AI has shown that it can identify patients who can then have a follow up exam with their primary care physician to begin treatment. When this occurs, patient outcomes are expected to improve, but the payer can increase their annual amount they received from CMS in their capitated plan (Medicare Advantage C)
  • Identify patients that are past due on their follow-up radiology exam. This increases department revenue, but also can have tremendous impact on patient care

Moderated by Tom Osborne, CMIO at VA Palo Alto Health Care System, this focus group will also consider how such value propositions would be measured in the healthcare environment, and product enhancements to a platform that could reduce the load on the various stakeholders within the health system to carry out this assessment.

Desired outcomes

  • Build an understanding of how a provider-based insurance (ACO) could leverage AI for risk re-stratification to maximize payments under Medicare Advantage C or similar capitated models
  • Assess the value of enhanced reporting for certain clinical conditions to both improve patient outcomes and offer opportunities for competitive differentiation of the health system
  • Develop a model of how background population screening could be used to generate additional revenue for a hospital or health system through management of previously undiagnosed conditions
  • Understand the metrics that would be required, and the systems which would generate that information, in the wider healthcare IT infrastructure.

Participants

  • Moderator:
    • Thomas Osborne, Director, National Center for Collaborative Healthcare Innovation; Chief Medical Informatics Officer, VA Palo Alto Health Care System
  • Blackford participants:
    • Ben Panter, CEO
    • Tom Shearer, VP Sales
  • AIMed participants:
    • TBC