Healthcare leaders are keen to embrace AI, partly to keep pace with competitors and other industries, but, more importantly, to increase efficiency and improve patient experiences. However, only 77% of healthcare leaders actually trust AI to benefit their business.
While AI chatbots excel at handling routine tasks, processing data, and summarizing information, the highly regulated healthcare industry worries most about the reliability and accuracy of the data that is fed into and interpreted by these tools. Without proper usage and employee training, data breaches become additional pressing threats.
Even so, 95% of healthcare leaders plan to increase AI budgets by up to 30% in 2025, with large language models (LLMs) emerging as one of the most trusted tools. As LLMs mature, 53% of healthcare leaders have already implemented formal policies to help their teams adapt to them, and another 39% plan to implement policies soon.
For healthcare providers who want to streamline communication services with AI but are still wary of doing so, here are some recommendations for overcoming the most common obstacles.
1. Train AI With Reliable Medical Sources
While healthcare leaders may not be directly involved in AI training, they must play a pivotal role in overseeing its implementation. They should ensure that chatbot providers are training and regularly updating their AI with credible sources.
The rich, structured data captured by mandatory electronic health records (EHRs) offer vast repositories of digital health data that can now serve as the foundation for training AI algorithms. Advanced LLMs can comprehend medical research, technical analysis, literature reviews, and critical assessments. However, rather than training these tools with all the data at once, new evidence shows that focusing on a smaller number of intersections maximizes AI performance while keeping the training cost low.
2. Ensure HIPAA-Compliant Data Practices
The Health Insurance Portability and Accountability Act (HIPAA) outlines standards for protecting sensitive patient health information (PHI). To align with these regulations, healthcare leaders should ensure third-party vendors:
- Gather only the minimum amount of PHI required to fulfill the chatbot’s purpose.
- Grant access to PHI only to authorized personnel with strong password and authentication policies.
- Employ robust encryption techniques to protect PHI both at rest and in transit.
- Store necessary data on HIPAA-compliant servers with strong access controls.
- Ensure they sign business associate agreements (BAAs) to comply with HIPAA.
- Ask for their response plan for security incidents.
Healthcare leaders using these tools should regularly check access reports—a step that is also easy to automate with AI—and send alerts to management if unusual activity occurs.
Moreover, they must obtain clear and informed consent from patients before collecting and using their PHI. When requesting consent, communicate how patient data will be used and protected.
3. Well-Designed Interfaces That Improve Workflows
One of the biggest obstacles when transitioning to mandatory EHRs was the usability of the technology. Physicians were unsatisfied with the amount of time spent on clerical tasks as they adjusted to the complicated workflows, increasing their risk for professional burnout, and the chance of making mistakes that can affect patient treatment.
When working with third-party vendors, request a demo and a second opinion before selecting an AI platform or software solution. Don’t forget to ask if their product allows customization that adapts to current programs so that you can integrate the ready-to-use features that best suit your workflows.
User-centered design and standardized data formats and protocols will help facilitate seamless information exchange across healthcare technology and AI platforms. With these standards in place, AI algorithms can be meaningfully integrated into clinical care across various healthcare settings. Established protocols also help these tools perform better by facilitating interoperability and enabling access to larger, more diverse datasets.
4. Proper Usage and Employee Training
A 2024 study found that medical advice provided by ‘human physicians and AI’ was, in fact, more comprehensive but less empathic than that provided by ‘human physicians’ alone. To bridge the gap, healthcare leaders must understand AI’s capabilities and limitations and ensure proper human oversight and intervention.
Healthcare leaders can embed chatbots in their websites and patient apps to offer users instant access to medical information, assisting in self-diagnosis and health education. These tools can send timely reminders to patients to refill their prescriptions, helping patients adhere to treatment plans. They can also help classify patients based on the severity of their condition, assisting healthcare providers in prioritizing cases and allocating resources efficiently.
Nevertheless, these tools can still hallucinate, and it’s imperative that a human validator be involved in complex tasks. Work with third-party experts to define your vision for AI communication tools and create your desired workflows. Once you agree on your use cases, operational and cultural change management processes—like Kotter’s 8-step change process—offer a roadmap for onboarding employees, ultimately enhancing patient outcomes.
5. Ask the Chatbot To Catch Mistakes
No business leader wants to make mistakes, but the healthcare industry is a high-stakes environment where even minor oversights can lead to severe repercussions. Yet, even the best clinicians aren’t immune to medical errors. AI can be a powerful tool to improve patient care by catching errors and filling in the gaps.
A 2023 investigation using GPT-4 to transcribe and summarize a conversation between a patient and clinician later employed the chatbot to review the conversation for errors. During the validation, it caught a mistake in the patient’s body mass index (BMI). The chatbot also noticed that the patient notes didn’t mention the blood tests that were ordered, nor the rationale for ordering them.
This example indicates that AI can be used as a supplement to help doctors handle AI hallucinations, omissions, and errors that can be used to train and improve AI applications.
Healthcare AI exists to support doctors and nurses, simplify workflows, improve patient accessibility to care, and minimize oversights. While they can’t fully replace the empathy, intuition, and real-world experience that human healthcare providers bring to the table, these tools offer excellent analytical and time-saving benefits. When healthcare leaders take their time to ensure careful adherence to HIPAA regulations, transparent communication with patients, and proper employee training, they can implement these tools safely and confidently.
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