The healthcare industry is riddled with administrative inefficiencies that diminish patient care quality, inflate costs, and lead to a colossal waste of human resources. At the heart of the problem lie redundant processes that primarily sit in the front and back office.
Back office processes including billing procedures and documentation processing are financial drags on enterprise healthcare systems because they are time-consuming, labor-intensive, and prone to error. Meanwhile, patient inquiries can surge and significantly slow down operators in the front office.
Like enterprises in other industries, many healthcare organizations embrace AI as the silver bullet that will address these inefficiencies. AI point solutions can streamline some processes, but relying on them exclusively could potentially exacerbate inefficiencies.
In this blog post, we will dive into why healthcare leaders are turning to AI, the risks of flawed approaches organizations may take in deploying it, and a practical framework for effectively applying AI in enterprise.
AI Adoption Accelerating in Healthcare
As recently as March 2022, experts questioned whether healthcare was taking too long to adopt AI compared to other industries. AI adoption in healthcare has accelerated rapidly since, matching the increasing availability of the technology in the market.
Now, more than half of healthcare executives report deploying AI to tackle workforce challenges according to a recent survey. The survey found a pattern in healthcare organizations generally investing in AI in this sequence: solutions for the backoffice, then clinical operations, then clinical care.
Prioritizing back office first can be seen as a “low risk, high reward” approach by healthcare executives compared to the relative risk of applying AI for clinical care. Plugging AI into a step within revenue cycle management, for example, can provide fast cost savings through freeing up an internal resource.
Other AI investment is taking place in the front office. Over 27% of healthcare executives pointed to conversational AI as the type of model they deploy.
When asked for what use case, most executives said that they use chatbots. This indicates a knowledge gap between the leaders surveyed and the wide array of additional use cases conversational AI has to offer.
Risks of Flawed Approaches to Applying AI
AI is a massive opportunity space in heathcare to be sure, but organizations in the industry are in danger of diminishing ROI or creating even more operational headaches if they don’t apply AI effectively. Two flawed approaches are:
- Overrelying on point-solutions
- Leaving humans out of the loop
Point-solutions like chatbot tools can quickly handle high volumes of customer inquiries in the front office, but in healthcare specifically, the risk of providing inaccurate or unhelpful information is much greater. Chatbots need to be trained on specially curated, human-in-the-loop data to ensure they can answer patients’ questions about billing and care.
Most chatbots in the market today are out-of-the-box, and lack the specific alignment needed to provide patients with truly helpful answers to these inquries. A poorly trained chatbot can alienate patients and harm trust in healthcare providers.
With staff in short supply, healthcare organizations will be tempted to try to apply AI to encompass an entire process. But humans need to stay in the loop.
According to the survey referenced above, 78% of healthcare executives are actively or planning on applying AI to make revenue cycle management more efficient. They need to define a proper framework for combining AI and human labor in this critical process, or they risk cascading impacts along the revenue cycle due to errors.
That framework needs to include:
- Defined steps that are best performed by AI and those best performed by human agents.
- Coordinated handoffs of data between electronic health records, AI, RCM platforms, and human agents.
- Distinct points in the cycle where human agents QA outputs delivered by AI.
AI Enablement for Healthcare Enterprises Made Simple
Applying AI effectively in enterprises is complex. Invisible makes it simple.
Through our platform, we bring world-class AI models and our expertise from building them into leading enterprises to tackle their greatest challenges. For healthcare, we turn highly coordinated processes like revenue cycle management into ordered, repeatable steps on our platform that are performed seamlessly by AI and our global workforce.
Processes and steps in our platform are 100% configurable, and we integrate with any system. This allows us to design custom processes involving both AI and people, coordinate data between systems like EHRs and RCM platforms, and minimize errors through human QA along the way.
For Headway, a mental healthcare company, we use this approach to help match patients with mental health providers at an 8x faster processing speed than previous vendors. We also lowered the cost of the process by 37% vs. their internal team.
Using our experience in training world-leading AI models, we train healthcare chatbots, too. To optimally align conversational AI models, we hire domain experts in fields like medicine to create AI training datasets.
Ready to make healthcare simple? Get in touch.
Digital Marketing ManagerAndrew Hull
Andrew is the Digital Marketing Manager for Invisible. Based in Salt Lake City, he is deeply interested in researching and documenting the emerging trends in AI development and application, and how new technologies are enabling the potential of human innovators.
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