Imaging AI Use Case #3: Improve Quality of Care
We’ve already discussed how artificial intelligence is helping radiologists streamline study reading and enhance study efficacy in parts one and two of this blog series. Now we’ll highlight how the efficiency and accuracy gains from imaging AI are driving improved outcomes for patients.
False positive and false negative diagnostic errors lead to higher healthcare costs stemming from:
- Treatments for more advanced diseases due to earlier missed diagnosis
- Overused or unnecessary diagnostic tests
- Malpractice claims, of which diagnostic errors are the leading cause in radiology
- Treatments for diseases or conditions patients do not have
The American College of Radiology highlights the lack of incentives for optimal care for both radiologists and referring physicians under the current Imaging 2.0 radiology practice model. With the push towards the Imaging 3.0 model and transition to value-based imaging care, the ACR hopes to do away with unnecessary imaging that results in suboptimal care, higher healthcare costs and higher radiation doses.
Unnecessary diagnostic imaging is a prevalent issue in the current volume-based radiology landscape, with an estimated $12 billion spent on unnecessary medical imaging every year in the United States. Sources of wasted imaging include:
- A lack of recent prior imaging, sometimes due to limited access to health information exchanges
- Not following evidence-based appropriateness criteria at the time of order entry or interpretation
- The use of defensive medical decision making
- Patient-driven demand for imaging tests
According to the American Association for the Surgery of Trauma, consistent classification of imaging findings contributes to shorter hospital stays and lower healthcare costs. So how do you get there?
Add AI to Your Enterprise Imaging Strategy
Intelligence and machine learning are often the last piece in the enterprise imaging puzzle and one that many health systems have yet to put in place. Adding AI to your existing enterprise imaging platform streamlines study prioritization and interpretation, and it’s easier to add than you might think.
The SilverBackTM Workflow Engine is an open platform that runs any medical imaging AI app in real time, allowing HMOs, ACOs and insurers to manage large populations effectively. Simply download any application that fits your needs (see two great lists of imaging AI apps here and here).
Risk Stratifying Patients: With machine learning, clinicians can more accurately deliver personalized risk assessments to develop tailored, patient-specific care management plans. Automated, personalized risk stratification vastly improves upon the use of conventional imaging, biomarkers and population-based risk scoring to optimize care and outcomes.
Creating Better Decision Support Pathways: AI algorithms can analyze images for disease indicators at an unprecedented rate of speed and accuracy, helping providers improve diagnostic confidence and identify appropriate, personalized plans of care. The FDA’s draft framework encourages the development of clinical decision support technology to assist healthcare providers in leveraging digital tools to improve decision making for better patient outcomes.
Improving Patient Care: Quality care improvement is at the top of every healthcare organization’s objective list. With more diagnostic information, fewer misdiagnoses and earlier, more accurate disease detection, higher quality, more personalized medical care is achievable.
Leading Edge Technology for AI Imaging
DataFirst supports the American College of Radiology’s Imaging 3.0 Initiative by delivering systems that enable AI applications to work across modalities, optimizing practice management and patient care.
If you’re ready to take action, DataFirst can help.
Connect with us online or call 800-634-8504 to get started.