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!

Can switchover disruption halt the adoption of AI in healthcare?

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!

Generative AI in healthcare: feasible and lower-risk use cases

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.

Use cases for private payers

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

  • automatically summarize data
  • optimize scheduling
  • generate care plans

Member services

  • create call scripts
  • generate custom coverage summaries
  • deploy chatbots for members
  • suggest doctors based on parameters, like location or condition

Corporate functions

  • automate accounting and HR processes 
  • generate reports
  • provide coverage updates for policyholders

Claim management

  • draft responses to appeals
  • aggregate data for claims 
  • generate summaries of claims
  • ensure accurate and timely processing of claims

Marketing and sales

  • analyze customer data and feedback
  • deploy and improve sales chatbots for consumers

Use cases for hospitals

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

  • summarize discharge information
  • provide recommendations to patients based on their medical history and medical literature
  • improve documentation accuracy

Clinical operations

  • create post-visit summaries
  • extract value from unstructured notes 
  • generate educational materials
  • analyze patient data to predict potential health issues
  • generate schedules

Corporate functions

  • streamline IT operations
  • generate documentation like contracts and reports
  • improve talent acquisition

Customer care

  • generate personalized care instructions
  • analyze customer feedback
  • autogenerate notifications

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.

How to adopt gen-AI in healthcare

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:

  • Prioritize the applications of AI. Start by prioritizing AI applications based on their value, focusing on those that align with your organizational goals and offer significant benefits.
  • Assess the AI tech stack. Evaluate the available AI tech stack – LLM models, APIs, and other tech infrastructure you currently use and identify any gaps or enhancements needed. Verify if your data infrastructure can handle the required data volume for AI applications, and ensure support for popular machine learning frameworks like TensorFlow or PyTorch.
  • Consider data security, bias and fairness, regulatory compliance, and accountability of gen-AI-powered platforms. It will help you establish trust, and mitigate ethical risks while complying with industry regulations and fostering confidence among stakeholders.
  • Build out your gen-AI capabilities or products. Start with a proof of concept development and proceed with multiple iterations, refining and enhancing the solution based on insights gained during each phase. 
  • Scale AI implementations. Finally, scale your AI product gradually to continuously enhance AI capabilities for improved patient care and operational excellence.

Now, for a better understanding of how AI can be put into action, let’s consider our recent project.

Modeso case study: Using AI for manufacturing clear aligners

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.

Bring AI to your healthcare project

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.