Innovative technologies such as artificial intelligence (AI) and machine learning are already helping healthcare systems lower costs and achieve better outcomes. Large volumes of image data—combined with faster and more complex computer processing capabilities—have opened the possibilities for machine learning and pattern matching to be applied in medical imaging.
AI is currently being used both for efficient workflow management and imaging analysis. The two uses reinforce one another, as AI-enabled image analysis supports better clinical insights, reduces clinical variance, and aids in study prioritization. Focusing on higher-priority cases helps radiology practices improve customer satisfaction and reduce patient wait times.
Meanwhile, improved data access and AI-driven worklist prioritization help care providers collaborate more effectively, improving labor utilization and increasing daily productivity.
When AI is coupled with a full enterprise imaging platform that can be standardized across an organization, it can also help maximize the value of an IT investment while helping to reduce overall IT costs. Such innovative solutions provide flexibility to meet evolving clinical needs.
3 Key Benefits of Using AI in Enterprise Imaging
With AI as a driver, provider organizations can realize three key benefits:
- Faster, better patient outcomes
- Automated study prioritization
- Measured and improved performance
Faster, Better Patient Outcomes
For many time-sensitive conditions, such as acute ischemic strokes, the time to treatment is a critical determinant of clinical outcomes. Before treatment is given, CT scans must be initiated within 25 minutes of the patient’s arrival in the ER. Scan interpretation must be completed within 45 minutes.
AI technology can perform thorough image analysis for specific clinical findings, such as analyzing CT head exams for anomalies related to intercranial hemorrhages. When radiologists are alerted to an AI-detected acute abnormality, they can respond much more rapidly to potentially life-threatening cases.
Automated Study Prioritization
Prioritization is important in any work environment, but even more so in a fast-paced clinical setting. AI-enabled systems drive efficient worklist prioritization, as they continually communicate the results of image analyses. AI can also help ensure that such studies are automatically assigned to the most appropriate available physician.
Measured and Improved Performance
By consolidating all tasks—quality, communication, and interpretation—in one unified worklist, an AI-driven workflow intelligence solution can help measure and improve productivity, drive accurate and efficient imaging, and prove the overall value of the enterprise imaging department to the entire health system.
Further, a comprehensive workflow solution can offer significant range that can include Radiology-ED communication, mammography imaging review, technologist QA and even an anonymous peer review to help ensure that quality is always a priority focus.
A Primer on AI-Enabled Workflow Prioritization
There are three basic steps to AI-enabled workflow prioritization. First, the system performs an image analysis for specific clinical findings. Next, it communicates the results and sends out alerts for high-priority studies that show abnormalities. Third, it assigns a higher priority on the interpretation worklist to studies with noted abnormalities.
The system assigns priority to studies in the workflow based on patient location, sub-specialty, procedure type, complexity, eligibility, service-level agreement (SLA), escalation, age, and more. The most urgent tasks are placed at the top of the worklist, so it is easy for users to manage the clinically urgent tasks first. Instant messaging, notifications, email, and text tools help improve collaboration across the enterprise.
But Does AI Do It All?
AI technology allows significant improvements in computer-aided detection (CAD). For example, in mammography, CAD image analysis identifies and presents areas of tissue density as image overlays in the radiologist’s image viewer. Additional tools are being developed to identify more complex patterns that may indicate the presence of specific findings such as intercranial hemorrhage, C-spine fracture, or pulmonary embolism.
New learning-based AI prediction models are being trained to identify patterns that are not readily apparent by human clinicians. These algorithms produce more accurate results than existing risk models, especially when used in predictive, pre-diagnosis discovery of disease.
The latest artificial intelligence tools demonstrate state-of-the-art results in the precise detection of the specific elements they were trained to recognize. At the same time, radiologists are naturally much better than computers at seeing and interpreting the whole picture—and recommending the appropriate course of action. The best role for AI-enabled imaging technology today lies in the consistent daily support these tools can provide radiologists.
Making a Change
Change Healthcare Workflow Intelligence™ is a vendor-neutral, flexible rules engine that offers AI-based decision-support capabilities. The platform incorporates studies from multiple PACS or facilities into a single worklist, streamlining quality and interpretation workflow management.
Change Healthcare provides the experience and trust that healthcare organizations rely on for enterprise imaging solutions. Our innovative AI solutions are setting the pace for the next level of technology-enabled clinical assistance.
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