According to a recent study published by the Grand View Research, the global market for AI in healthcare is growing at a compound annual rate of over 40%. Particularly in the United States, the implementation of artificial intelligence and machine learning in healthcare has shown nearly exponential growth, with the American market size reaching almost $10 Billion by 2021. Deep learning tools developed with the help of artificial intelligence have provided a vital boost to the provider organizations, reducing the patient clinical data variance and improving the outcomes.

One of the primarily affected areas in the medical industry due to the implementation of AI in healthcare is radiology practices and other imaging enterprises. Artificial intelligence and other such advanced tools have improved workflow management in imaging enterprises. While the time spent collecting and analyzing the data has been significantly reduced, the developed computational analysis tools have greatly improved clinical outcomes.

This article has summarized the implementation of AI in healthcare with a particular focus on imaging workflow management.

Smart Prioritization by AI in Healthcare Workflow

  • Automation management systems have helped several provider organizations with their workflow patterns. But they are even more helpful in the case of imaging enterprises.
  • The physicians depend on complex imaging results to determine the next course of action in the patient’s treatment plan. However, with the bulk of work in a fast-paced clinical setting, it isn’t easy to prioritize the cases.
  • AI-enabled systems designed for radiology and other imaging practices help automate the prioritization of the different imaging results according to the complexity and urgency of the situation.
  • When the entire practice is integrated on a single workflow automation platform, every required stakeholder receives the information in their systems the moment results arrive, almost in real-time.
  • If the system is allowed the necessary permissions, then the AI systems can also assign the results to the provider with available slots at the moment.
  • Prioritizing the cases according to the urgency of the test results helps the provider organizations efficiently handle their patients with the most suitable and available workforce, making workflow management much more straightforward.

Improved Clinical Outcomes for Patients

  • Artificial intelligence, machine learning, and other advanced technologies have proven to be reliable partners for improving clinical decision-making and patient outcomes.
  • This also holds true for imaging organizations. Apart from regular check-ups and their smoother workflow, AI in healthcare takes the quality of time-sensitive response to a higher level.
  • In some acute emergency cases like ischemic strokes, time is of utmost importance. The providers need to respond within a brief period to pull out the patient from the crisis.
  • The AI-enabled systems can analyze the data briefly and pass it on to the providers immediately to take the required action immediately.
  • The quick response to treat the AI-detected abnormalities in a scan can potentially save the lives of many patients without having to struggle for time.

Quantitative Measurement of Clinic Performance

  • Improving clinical performance is one of the top priorities of any imaging laboratory. However, measuring the performance is a difficult task for the management.
  • With AI-enabled integrated workflow management, the systems can quantitatively show how the providers and the entire health ecosystem are functioning.
  • The systems can help consolidate the quality of workload, quantity of results analysis, and communication within the providers in a single platform.
  • The management can scale the clinical performance with the help of the efficiency scores provided by the AI-enabled systems.
  • AI in healthcare can help scale up the imaging workflows, integrating both the patient’s treatment plans as well as the medical billing teams for the optimized reimbursements of the clinic.

Step-by-Step Working Pattern of AI in healthcare

AI works in a series of steps in different clinical setups. In imaging enterprises, the system integrates the various tools to ensure that the urgent patients get the priority first and the providers get the critical analysis within the required time.

Step 1:

The AI integrated system considers all the images and performs repeated computational analysis to determine the actual results. The automated system makes this crucial step much simpler and reduces the time usually required when done manually.

Step 2:

In the second step, once the system determines the required findings, it communicates the comprehensive results with the assigned providers of the platform. There is a much lesser time lag between the result procurement and the provider’s actions with the integrated workflow platform.

Step 3:

The system sends out the signals to the assigned providers regarding any urgency of the patient situations depending on the analysis of the findings. If it finds any specific abnormalities in the results, it automatically updates the workflow priority list for the providers to see and act.

How dependable is AI in healthcare and imaging analysis?

  • The use cases of AI and machine learning in healthcare and imaging analysis have grown significantly over the year.
  • Researchers and leading health tech startups are working to improve the predictive power of AI-enabled systems.
  • AI is capable of determining the required patterns in imaging analysis and providing the results to healthcare professionals within a short span of time.
  • At the same time, human intervention is essential to determine the preciseness of the results and what actions they should take further.
  • That being said, AI-enabled systems help the providers to accelerate the analysis patterns. They have not been deployed to replace human professionals.
  • A portion of the healthcare industry fears that any undetected bias in the used algorithms could lead to significant clinical variance. However, companies developing the systems stand by their words that the systems go through a series of trials to ensure bias-free results.

We hope this article helped you understand how AI in healthcare is improving imaging workflows. In case of any queries, connect with us in the comment section below. Please subscribe to our blog and follow us on LinkedIn, Twitter, Instagram, and Facebook for more such articles on medical billing, technology, and financial management.