AI is making its way into various industries, including healthcare. It can make administrative tasks in healthcare easier and faster and even spot diseases early, improving diagnosis and helping save lives. For example, ChatGPT helped diagnose a rare disease – a task where 17 doctors failed. However, just like Electronic Health Records (EHR) took years to be adopted by healthcare professionals, it will probably take a lot of time until valuable AI applications become commonplace in this industry.
In this article, we’ll discuss why adopting AI might be a struggle for the healthcare sector and list the emerging gen-AI use cases that allow healthcare companies to put innovations into practice much sooner. Let’s dive in!
In economics, the term “switchover disruption” is used to describe potential challenges associated with adopting new technologies. In healthcare, this process can be illustrated by the adoption of EHR – an electronic version of a patient’s medical history.
Though computer-based patient records were identified as an essential technology for healthcare back in 1991, medical institutions started implementing EHR only after the U.S. government allocated billions of dollars for its adoption in 2009.
Why did a technology that aimed to improve the quality of care and reduce costs have to wait almost two decades to be implemented?
The answer is simple ‒ switchover disruption. High initial expenses for software, hardware, staff training, and workflow redesign ‒ all these factors prevented medical institutions from actively introducing EHR into their practice.
The adoption of AI in healthcare may face the same fate, as it is inevitable to escape disruptions during the transition process. What’s more, based on a new Pew Research Center survey, 60% of adult Americans would feel uncomfortable if their healthcare provider relied on AI to do things like diagnose disease and recommend treatments.
This concern sounds valid as people tend to trust other people and not robots. At the same time, it might indicate that the adoption process for AI in healthcare might be even slower than that for EHRs… But not necessarily.
Generative AI is revolutionizing the way businesses work and it’s unfortunate that healthcare might be slow to catch on. But here’s a way for healthcare to avoid the switchover disruption and start making the most of AI technology even this year, in a way that both healthcare professionals and patients will feel good about. Wonder what this way is? Let’s explore!
To minimize disruption during the switchover and shape public opinion positively, healthcare innovators should establish trust with both patients and healthcare professionals. People have to understand that AI is intended to assist rather than replace human doctors, and it does not compromise individuals’ rights. Building this trust is a gradual process, which can’t be achieved in a week or month.
If you are considering adopting gen-AI, begin with use cases that entail a relatively lower switchover disruption. For example, you can use AI for administrative purposes. Once AI effectiveness is proven, you can progress to incorporating gen-AI capabilities into clinical applications.
Let’s see how medical organizations, namely private payers and hospitals, use AI in their operations and what for.
Private payers in healthcare refer to non-governmental entities. These can be private insurance companies providing health insurance coverage, healthcare management organizations, or pharmaceutical companies. By integrating AI into operational workflows, such institutions can enhance efficiency, leading to improved customer service and satisfaction. Here are some applications of AI tech by private payers.
Healthcare management
Member services
Corporate functions
Claim management
Marketing and sales
When it comes to hospitals, gen-AI technology can improve almost any aspect, starting from diagnostic accuracy and treatment planning to operational efficiency, resource allocation, patient care coordination, and even preventive measures. The versatile application of gen-AI holds the potential to revolutionize healthcare delivery, ultimately enhancing overall hospital performance and patient outcomes. Here’s how AI can improve the way hospitals operate.
Continuity and quality of care
Clinical operations
Corporate functions
Customer care
These are general cases briefly explaining how healthcare institutions can use AI. But what should a healthcare executive start with to successfully adopt this technology? Here are some basic steps to begin with.
Undoubtedly, gen-AI is capable of transforming the healthcare industry, improving operational efficiency, speeding up workflows and enhancing diagnostic accuracy.
However, launching the transformation process with gen-AI in healthcare involves a strategic approach. Here are the steps you can take to leverage AI:
Now, for a better understanding of how AI can be put into action, let’s consider our recent project.
As a software development company, Modeso partners with different organizations seeking to develop custom software solutions and integrate AI capabilities. One of our projects is Xflow, a custom cloud-based platform that digitally connects every stage of clear aligner manufacturing, allowing dental clinics and labs to manage the entire manufacturing process in-house – from scan to design and fabrication. We built this platform for Dental Axess, a global integrator of CAD/CAM and dental imaging solutions.
The platform’s core functionality revolves around integrating with third-party systems such as 3D scanners and design software to consolidate data and automate workflows.
Xflow incorporates AI capabilities that play an important role in the clear aligner design process. The AI algorithms take charge of refining the 3D scans uploaded to the cloud, addressing rough edges, and creating a polished foundation. As a result, the end product produced using the Xflow platform is of high quality and visually appealing.
The incorporation of AI-guided automation in generating printable 3D scans has reduced the need for manual labor and decreased errors. Essentially, integrating AI into the workflow automation platform has not only streamlined the production of clear aligners but has also enhanced its economic feasibility, representing a significant advancement in the field.
Though gen-AI is only entering the stage, its capabilities are already undeniable. In the hands of experienced engineers, AI has the potential to substantially enhance healthcare operations and improve the quality of patient care. But if you have doubts about adopting the technology, don’t rush to create an AI-powered robotic surgery system. For starters, consider automating specific administrative processes to assess their efficiency, and if it proves to be effective, move on.
And in case you need help from a professional software development company, Modeso would be glad to offer our services. Feel free to contact our team to discuss your project.
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