The healthcare industry is leveraging conversational artificial intelligence to improve patient engagement. These conversational AIs are based on large language models (LLMs), which provides a window to level up patients’ interactions with the health care systems as these framework offers customized and efficient support.
The Role of Conversational AI in Healthcare
So, how does these systems comprehend human intent. It is possible due to natural language processing (NLP) and machine learning (ML) algos that fuel the conversational AI. It is because of these powerful tools that the computers can understand, interpret and respond to human language. These tools are also used to drive advancements in other verticals like customer service, healthcare and automation.
If we talk about healthcare alone, the conversational AI assists in following ways:
- Offering round the clock assistance to information and support.
- Making it easier to book appointments via intuitive and user-friendly interfaces.
- Reducing manual efforts in admin tasks and making it automatic thus, freeing up healthcare staff for more complex duties.
- Providing flexible patient communication, they can leave and engage in conversations at their convenience.
- Sending timely reminders for appointments and maintaining medication schedules.
- Bridging the communication gap between patients and providers.
- Delivering mental health assistance with privacy and supportive environment.
Benefits of LLMs in Patient Engagement
LLMs can substantially improve the functionalities of conversational AI, the combination offers following benefits:
- Customized engagements: LLMs can generate customised visual content by analysing health insights including past treatments, history of medication received, number of doctor visits and tests performed along with the medical care received. For the accessibility of diverse population, LLMs can also generate customised output in regional languages. Accordingly, the models are trained on providing tailored responses and asking follow-up questions.
- Refined interactions: Since LLMs are trained on datasets, which includes wide range of books, articles, websites and other forms of written communication, they are efficient to communicate in contextually rich conversations. This helps the patient to have a two-way communication, which means, they can ask question again depending upon the upon the clarity of the response or if they need further explanation.
- Improved efficiency: LLMs can automate administrative tasks – like directing patients to appropriate resources and summarizing medical conversations for clinicians – with less operational costs, this will give more time for healthcare staff to focus on clinical decision-making and other critical tasks that require human expertise. LLMs can also take care of manual phone calls and interactions, round the clock and handover to human agent for complex decision making.
- Patient-centric approach: LLM powered AIs provides more patient-centric approach as the system is designed in a way to prioritise their needs and requirements. From customised medical suggestions to enhanced communication, the AI models are their constant support. Along with these benefits, patients can also opt for seamless healthcare payments and claims. In this way, the technology is not only making the entire process easier and faster for both patients and staff, but also empowering patients.
LLMs based Conversation AI is like a smart personal assistant, who listens to queries and generate relevant responses, in real time and these levels-up the interaction game from patient’s side. Healthcare recipient receives customised and accessible support consequently, this form of engagement improves healthcare accessibility and patient empowerment.
Ethical Considerations
The integration of LLMs into healthcare gives both sides – patient as well as health care professional – a boost, while at the same time, it does raise several ethical concerns that need to be taken care of, like:
- Data Privacy: It is very important to create a layer of safety over patient data. There is no denying of the fact that these data are used to train and apply LLMs to produce effective communication strategies but it is equally important to use data de-identification (masking personal identifiers), federated learning (models trained across decentralized devices) and secure pipelines (to protect data & process from unauthorized access or breaches).
- Bias and Neutrality: LLMs can reflect biases, which could be present in the original source or its their training data sets and this can lead to inequitable outcomes for specific patient populations. So, it becomes critical to have diverse training datasets as inputs along with routine evaluations and engagement of diverse teams of researchers & developers to actively identify and mitigate biases during development.
- Explainability Matters: The decision-making process is not fully transparent and hence, it’s not easily understood, which raises questions about transparency. Therefore, to set realistic expectations, users must know what the AI models can do and what are their limitations. So, to catch errors and make ethical decisions, human supervision is quite significant.
- Regulatory Oversight: There must be a governing body that oversee the regulatory framework and evaluation standards. And to ensure ethical practises, developers and healthcare providers must be held accountable for the AI model’s output along with the responsible deployment of these LLMs.
By addressing challenges related to data privacy, bias, transparency and regulatory compliance the healthcare vertical can harness the potential of conversational AI and eventually improve the treatment results and lessen health inequalities.
Takeaway
In a nutshell, conversational AI in healthcare doesn’t mean that it will eclipse the role of humans, rather, it will be used as an advanced tool alongside with human supervision. These models can provide data driven insights, which will inform the health care professionals in formulising better tailor-made treatment plans, which otherwise would have been quite challenging.
The models have bandwidth of scaling to larger datasets with more complex tasks hence, they can provide consistent support across huge population segments at the same time. Again, this is un-achievable considering a human into the equation. If integration of these models into broader healthcare systems is established it will lead to efficient patient care and enhanced and timely decision making across the vertical.
Now the question that comes to my mind is, how can we make sure that as AI changes the healthcare landscape, it serves to empower us instead of creating issues with trust or fairness?
Sources: Leveraging Large Language Models for Patient Engagement & Achieving health equity through conversational AI
