Aidoc Staff

3 Applications of AI in Healthcare

The digital era has ushered in a great deal of personalization, powered by artificial intelligence (AI) algorithms that tailor our digital experiences to a t. As we continually feed the algorithms with unique inputs, we further deepen our personalized experiences. Though the AI phenomenon is normally discussed in the context of social media outrage or Netflix suggestions, a lesser known application of AI is in the domain of healthcare, where the novel technology is making inroads that impact patient treatment paths. In this blog post, we’re going to highlight some of the key applications of AI in healthcare as it relates to its benefits, both in the clinical and administrative sense. 

1. Clinical Decision Support

There are multiple types of AI in healthcare. Sometimes, it’s referring to automated call centers at large health systems that direct callers to the right department, others can be voice to text note narration. In this instance, we’re talking about AI and its capacity to work as a clinical decision support tool. What does that mean?

Let’s look at how AI can impact cardiovascular workflows, for example. More specifically, let’s discuss AI for pulmonary embolism (PE). AI tools can now work as diagnostic aids, flagging suspected positive pulmonary embolism findings for radiologists and prioritizing their worklist, ensuring that patients in need are being identified and triaged more quickly. 

Not only that, but some AI tools go as far as to set extra parameters. In PE cases, for instance, once a suspected positive PE case is flagged, members of the pulmonary embolism response team, designated by the hospital, are put into a group chat and given instant access to images, removing communication barriers and can coordinate any necessary follow-up care for the patient. This application of AI in healthcare doesn’t only help identify the patient/s in need, but gives care teams the tools to make decisions on clinical care paths expeditiously.

2. EHR and Data Management

Healthcare is inundated with data, but it’s not necessarily being used. While there are countless variables to which we can attribute this problem, AI is poised to offer a unique solution, collating data from disparate sources and giving care teams actionable insights. Consider the types of AI that can be involved:

  • Imaging AI: Algorithms that are built to flag suspected positive findings. These algorithms are heavily trained on diverse data sets to flag suspected findings for a series of pathologies. Depending on the algorithms training, pathologies can be flagged on different imaging modalities.
  • Natural Language Processing (NLP): This type of AI can parse through mountains of text, pulling data from human language and making decisions based on that information. Think about transcribing clinical notes or dictation software, for example.

Learn about more types of AI and their applications in healthcare through the infographic below:

Infographic showcasing applications of AI in healthcare through different types of AI that can be used in healthcare environments.

Through the varying applications of AI in healthcare, physicians can quickly have access to patient records and imaging simultaneously, giving them a comprehensive picture of the patient’s state, equipping them with the confidence to make the best treatment decision.

3. Administrative Workflow Enhancement

The usefulness of AI in healthcare is not only limited to the clinical domain, but also the administrative spaces. In the effort to mitigate workloads, AI offers a potential to reduce burdensome, administrative tasks that take time away from the main goal: providing optimal patient care. Take the earlier example of streamlining communication for example. With AI tools to connect relevant physicians based on a suspected positive pathology, you are removing hours of administrative time making phone calls, sending text messages and other time-consuming tasks to coordinate patient care. AI also has the potential to help greatly with scheduling.

In Conclusion

Implementing AI in healthcare is no small feat. Hospitals are striving not only to increase their bottom lines, but to continually provide a high level of quality care while often understaffed and under-resourced to do so. Healthcare AI companies and vendors strive to fill the gaps in multiple ways:

  • Offering clinical decision support tools
  • Managing the data tsunami
  • Enhancing administrative workflows

Through these means, AI powered healthcare is no longer a thing of a 70s sci-fi novel, but a reality that health systems are continually embracing.

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Aidoc Staff