Challenges and Opportunities for Conversational AI in Healthcare

Exploring the future of healthcare automation in AI-driven patient care.

Healthcare is a complex industry. The challenges it presents are not easily overcome. Whether it’s hurdles to mass adoption or data privacy and security concerns, integrating conversational AI (chatbots) is one that is filled with massive challenges and opportunities for those brave enough to tackle them. This post will explore how AI is not only needed in healthcare but also how it can reshape the industry.

Conversational AI in Healthcare

Healthcare is a broken industry, filled with a myriad of different issues. Despite common misconceptions, AI won’t be a simple fix. An article on design and implementation of inclusive chatbots in healthcare, published in the National Library of Medicine found that if users do not believe that a conversational AI tool is relevant to them or capable of addressing their unique health goals, they are less inclined to interact with it. To be specific, the Conversational AI discussed in this article refers to facilitation of communication between patients and healthcare providers.

Despite the fact that AI chatbots for healthcare could be game-changing, it was also discovered that certain minority groups might regard conversational AI with suspicion, mistrust, and skepticism due to historical racism, experiences of medical exploitation, ethical concerns regarding the technology, or religious beliefs.

Even though the proliferation of virtual assistants for healthcare and healthcare automation tools is currently happening, another study by Elseveier highlights persistent concerns about conversational AI in healthcare. It showed that 82% of physicians recognize the risk of AI causing critical errors or mishaps. 

However, the industry needs a real solution. An article from Forbes shows that healthcare organizations are failing to meet the high demand for medical services. Due to limited staffing, many medical professionals are facing burnout, resulting in many choosing to leave their jobs.

This is where AI-powered patient engagement and other AI-based solutions (such as conversational AI) come into play. Conversational AI can deliver efficiency and make healthcare accessible to patients.

Challenges

Seamless Patience Care

A significant challenge is balancing backend complexity with a user-friendly interface to deliver effective patient care. From procedural nuances to medical terminology, the ability to deliver a seamless experience requires significant effort that needs to be accurate.  

For example, medication consumption varies across brands. Some brands may have consistent units while with others, their concentrations may be different. This adds another layer of complexity. This can lead to confusion about patients and requires healthcare systems to offer brand-specific guidance that is precise.

Algorithmic Bias and Fairness

With AI chatbot use becoming more popular throughout the healthcare industry, significant concerns are being raised in regard to algorithmic bias and fairness. Machine learning is the foundation of these systems. It learns from the data it’s trained on. But this presents a risk. For example, the training data could have an inherent bias or lack diversity. This would cause the chatbots to risk replicating and amplifying these issues. This could result in unequal healthcare access. 

How so?

Since machine learning (ML) is the backbone of AI systems, it learns from the data it receives and supports. This presents a risk. If the dataset has ingrained biases or is not diverse enough, chatbot models can magnify these problems andance the dangers. Addressing this challenge requires considering how these models will be used in practice, and how that informs their design.

The result?

Unequal healthcare access.

Some demographics may be underdiagnosed or misdiagnosed. These disparities highlight the importance of AI chatbots being designed and deployed with fairness and equity in mind.

Additionally, there could be imbalances in the training data. Certain classes could be represented differently. This would result in chatbot predictions and could in turn lead to the AI favoring the dominant class while putting others at a disadvantage. This is only further exacerbated by model overfitting, which prevents the ability of the chatbot to generalize beyond its initial training data. 

The imbalance in the nature of the training data can also make chatbot results biased. Some classes may not be as favorable in the eyes of the AI. This poses risks for critical healthcare decision-making related to appropriate diagnosis and treatment.

Struggling to Work Through Patient Issues

The healthcare process for patients is anything but simple. Navigation can be both a difficult and overwhelming process. Whether patients are looking into personal privacy concerns or attempting to understand their insurance coverage, the discussions are anything but straightforward.

Privacy concerns such as “I don’t want anyone to know about this,” while also wondering “Does my insurance cover this treatment?” can be deeply emotional. Addressing these concerns requires not only accurate answers, but sensitivity as well. 

Conversations about health are often highly charged emotionally. People who turn to doctors looking for medical advice aren’t simply seeking information. They are seeking expertise, reassurance, empathy and understanding. Any automated system has to give the right answers but it also needs to make the patient feel connected on an emotional level like a human so they can trust it and have a positive experience.

Data Security and Data Privacy

Cyberattacks can target conversational AI tools. If the security of these tools is not maintained, malicious entities might get their hands on the secure and sensitive data that is stored in these AI systems. This includes information that a patient volunteers to an AI during a conversation as well as the details that the patient might reveal in the course of fulfilling an AI’s requests.

The Opportunity

Despite the challenges, there are considerable opportunities with conversational AI in healthcare.

A report from Elsevier found that 26% of clinicians say they use AI for work purposes. But an impressive 96% believe it has the potential to speed up work, especially knowledge discovery. And 88% say they think of AI as a valuable tool that can improve the quality of their work.

In another example, consider a Tele Health clinic, that operates services across the US. Now this clinic has to staff for customer support across 4 different time zones. How can such a clinic engage patients in a personalized and cost effective manner at scale?

For healthcare organizations like this Tele Health clinic, conversational AI offers a path to deliver personalized care at scale while maximizing the administrative staff’s time and expertise.

24/7 Patient Support

Healthcare providers can offer 24/7 assistance without having to worry about staff burnout, thanks to conversational AI. While conversational AI can handle much of the burden, inquiries that go beyond the normal patient routine may still require human intervention.

Improved Patient Engagement and Satisfaction

Patients can receive real-time, nuanced responses anytime, anywhere. The intelligent technology behind personalized conversational AI can understand context and intent. When a patient talks with a virtual health assistant, the interaction resembles a conversation with a live person, resulting in truly unprecedented engagement. 

Increased Efficiency for Healthcare Providers

Healthcare providers enjoy reduced average handle times, and some reports indicate that it’s down 20%. The big U.S. hospitals have seen operational efficiency gains of up to 40%. The result? More resources can be allocated to patient care. For customers leveraging 24×7 Customer platform, 70-80% of patient inquiries are answered by Conversational AI.

Streamlined Administrative Tasks

Routine and monotonous administrative tasks in healthcare can be accomplished by AI chatbots (such as 24x7Customer.com). This includes appointment scheduling, sending reminders to patients, and dealing with cancellations, insurance information, help navigating patient portals, requesting paper work etc. 

Furthermore, voice-enabled AI has the potential to make a real impact in healthcare. Doctors can easily dictate their diagnoses, treatment plans, and observations, and the AI can accurately transcribe this into text. This is beneficial in saving time and could also help in making sure that what’s written down in the patient’s record is accurate.

Improved Medication Management

The use of conversational AI to remind patients about their medications and provide dosage information can help patients avoid adverse drug events when medications are not taken as directed. This can include reminder, side effect tracking, and answering prescription related questions.

Better Chronic Disease Management

AI-powered tools can help patients in managing long-term illnesses by keeping track of important health measures and track vital signs. These capabilities facilitate ongoing communication with healthcare providers, ensuring continuous monitoring and better disease management

With a variety of new startups now tackling these healthcare challenges to improve accessibility, the potential impact of Conversational AI in transforming both patient care and healthcare operations continues to grow.

The Bottom Line: Conversational AI Is Here To Transform Healthcare 

AI is no longer science fiction. Instead, it has become a ubiquitous reality. It is now used in every facet of our society. Although conversational AI in healthcare has its fair share of risks, it also has the potential to transform the industry. From reducing costs to enhancing efficiency, conversational AI is an invaluable tool for the industry. By taking advantage of these technologies and using them responsibly (with a focus on fairness, patient privacy, and a human touch), healthcare organizations can build a system that is more equitable for all.