Enhancing patient engagement with GenAI

GenAI: an evolution to enhance patient engagement

Generative AI (GenAI), a subset of artificial intelligence, holds the potential to transform patient engagement in healthcare. By using algorithms that use a large amount of data and large, pre-trained models to generate new content, such as text, code, images, video, and audio, it can generate new data that mirrors the input data on which it's trained.

GenAI opens several possibilities for enhancing patient engagement, including:

Personalised communication

GenAI can analyse a patient’s medical history, preferences and behaviour patterns to generate personalised messages, reminders and health tips. 

This level of personalisation can significantly improve the patient’s engagement with their health management, and it ensures the advice given is relevant and actionable for the patient.

For example, a patient with diabetes might receive personalised meal plans and exercise routines based on their lifestyle and glucose levels. This not only makes the health management process more engaging but also ensures that the advice is relevant and actionable for the patient.

Virtual health assistants

GenAI-powered virtual health assistants can provide patients 24/7 access to medical advice. They can increase patient engagement through:

• answering patient queries

• providing medication reminders

• even guiding patients through symptom checkers.

Virtual health assistants can be particularly useful for patients with chronic conditions who require constant monitoring and timely medication. They can also provide immediate help in emergencies, ensuring the patient gets the right advice at the right time.

Tailored health education

By understanding a patient’s health condition, literacy level and learning style, GenAI can generate easy-to-understand educational materials. These can help the patient understand their condition better and engage more actively in managing it.

For example, a patient newly diagnosed with hypertension might receive simple and clear educational content about the condition, its causes, symptoms and management strategies. 

Predictive analytics

GenAI can predict future health trends based on a patient’s current health data. These predictions can help patients proactively engage in managing their health.

For instance, by analysing a patient’s health data, GenAI might predict an increased risk of heart disease. This can prompt the patient to make lifestyle changes — such as adopting a healthier diet and regular exercise — that can prevent or delay the onset of disease.

Understanding the challenges these opportunities bring

GenAI holds immense potential to transform patient engagement in healthcare, empowering patients to take an active role in their health management.  This also includes the important requirement, but seen as an opportunity, for healthcare professionals to review outputs and manage inputs. 

Other challenges to consider when planning implementation, include: 

Data privacy

Patient healthcare information is particularly sensitive, making data security paramount. Addressing data privacy concerns is a significant challenge when implementing GenAI in healthcare.

Lack of transparency

GenAI models, especially language learning models (LLMs), often operate as a ‘black box’, making it difficult for healthcare professionals and patients to understand how the AI arrived at a particular output.

Trust

Gaining trust from doctors and patients for implementing GenAI is another hurdle. The healthcare community needs to be confident in the AI’s accuracy, consistency and safety.

Biases in AI algorithms

Training data that lacks diversity and fails to represent different patient populations can perpetuate biases. This hinders equitable treatment, worsening healthcare disparities.

Regulatory and legal framework

The regulatory and legal framework governing the use of this technology is taking shape, especially through the new EU AI Act. 

It's time to think about the implications within your organisation, and how they  will factor into decision making.

Human in the loop

Given that GenAI can produce incorrect responses, you need healthcare practitioner facilitation and monitoring — what’s known as having a ‘human in the loop’ —  to ensure any suggestions are beneficial to patients.

Verification systems

Developing digital layers for quantifying clinical confidence is another challenge when implementing GenAI in healthcare.

Conclusion: the revolution comes with responsibilities

GenAI is poised to revolutionise patient engagement in healthcare, offering a myriad of benefits. These advances can empower patients, making them active participants in their health management — and ultimately improving health outcomes.

The implementation of GenAI in healthcare isn’t without significant hurdles that need to be overcome, however to fully harness the power of GenAI in healthcare, it’s crucial to navigate these challenges carefully. Taking a Responsible AI approach can help organisations address these challenges and PwC have a comprehensive approach to Responsible AI use: https://www.pwc.com/gx/en/issues/data-and-analytics/artificial-intelligence/what-is-responsible-ai.html

As you move forward, it’s your organisation’s responsibility to ensure GenAI is used in your processes in a way that is ethical, equitable and beneficial for all. Incorporating trust by design is key to your successful adoption of GenAI.