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Why Enterprise-Wide AI Requires Image- and Report-Based AI

aiOS, Care Coordination, Communication, Data Management, Patient Management
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Enterprise-wide AI is a relatively new term, slowly gaining recognition amongst those seeking to be on the cutting edge of healthcare’s technological revolution. Since AI has found its place in the workflows of radiologists, the novel technology continues to expand to other hospital service lines, increasing its potential to impact workflows beyond the reading room. But the question remains: how do you identify other areas in a hospital that are ripe for AI implementation while offering a solution that enhances the physician and, ultimately, patient experience? Furthermore, is there a way to combine the powers of different types of AI, such as image and report based AI, to create better care opportunities?

Beyond Imaging AI

After implementing the ability to identify suspected pathologies within medical imaging, we realized we only scraped the surface when it came to the potential for technology to impact hospital workflows.

Given the actionable and, at times, time sensitive nature of radiology findings, we opted to utilize the power of technology and AI to streamline patient treatment paths. To do this, we’ve made AI a tool to help curtail the burden of communicating findings to care teams based not only on image-based AI, but on report-based solutions. This can be integrated with the AI operating system (aiOS™), furthering its ability to orchestrate AI to the right provider at the right time with the relevant clinical context.

How Image- and Report-Based AI Work in Unison

In order to understand the scope of benefits when combining Image- and Report-based AI and how they orchestrate across the enterprise, it’s important to define these terms in the context of their application in medical practice:

  • Image-based AI: AI algorithms that analyze medical images and flag suspected findings. For example, our Pulmonary Embolism (PE) algorithm can flag suspected findings on CTPA, and propose to triage the case on the radiologist’s worklist, opening up expedited care opportunities. 
  • Report-based AI: Natural language processing (NLP) driven AI. The proprietary algorithms analyze reports and make the patients’ diagnoses actionable. For instance, once information about a DVT is added to a report, our report-based AI identifies those findings and routes them to the care team for prompt decision-making through mobile and desktop applications.

From day one, Aidoc has understood that image-based AI is crucial to prioritize urgent cases and augment radiologists, thus improving quality of care. Now, we combine the power of image-based AI with report-based solutions to not only streamline critical findings to care teams, but to ensure that findings are not lost within a report. To make report results actionable, report-based solutions power downstream benefits by preventing patient leakage, which can improve patient outcomes and create more ROI opportunities for health systems.

Aidoc manufactures medical and non-medical devices. For safety information on Aidoc’s products, please visit our quality and compliance page at aidoc.com/quality-compliance

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