How Generative AI is Transforming Patient Care

Artificial intelligence (AI) holds the potential to transform businesses across almost every industry thanks to its ability to analyse and learn from vast amounts of data, and healthcare is no different. Long wait times, complex medical jargon and a lack of personalised care can leave patients feeling frustrated and confused. AI, however, has the power to change this. Generative AI, a specific branch of AI, goes beyond analysis, creating new content from realistic images to complex reports. Ultimately, by leveraging generative AI, the healthcare industry can become more efficient and patient-centric, empowering both healthcare workers and patients to make informed decisions and improve the quality of care.

Current Challenges in Patient Care

The healthcare industry is one of the major successes of modern times. However, its success has driven an exponential growth in demand. While people are living longer than ever before, they are surviving with significant health conditions and increasingly complex needs. Meanwhile, healthcare systems face rising costs and a workforce that is struggling to meet its patients’ needs. The result is that patients often face considerable wait times, with the need for specialist appointments potentially delaying critical diagnosis or treatment. In addition, there is a growing demand for more personalised care. Not only does this give individuals more choice and control over how their care is planned and delivered, it ultimately results in better patient outcomes. However, the need to offer more with less only adds to the strain. Without major structural and transformational change, healthcare systems will struggle to remain sustainable.

The Potential of Generative AI

Generative AI is a type of deep-learning model that can learn from raw data to produce statistically probable outputs when prompted, instead of just making predictions about existing datasets. At a high level, generative AI models contain a coded version of their training data and then draw from it to create outputs that are similar but not identical to the originals. These models have been used for years in statistics for numerical data. However, they can now also be used for images, speech, 3D models and other complex types of data.

Generative AI has become increasingly popular across various industries. Most people will now know about generative AI programs such as OpenAI’s ChatGPT and the AI image generator DALL-E. However, while these open-source applications are easily accessible, the uses of generative AI are extremely far-reaching. The ability to produce new content within seconds can transform how people do things and could be a game-changer for patient care. By leveraging generative AI to create personalised treatment plans, translate complex medical information and bridge the gap to specialists, it has the potential to empower both patients and healthcare providers.

The Mercy Health and Microsoft AI Collaboration

An exciting long-term collaboration between Mercy Health, a leading healthcare organisation, and Microsoft is leading the way in using generative AI for patient care. The partnership aims to use generative AI to meet some of the industry’s most pressing challenges, giving physicians and healthcare workers more time to care for their patients and improving patient outcomes.

Through the partnership with Microsoft, Mercy plans to use Azure Open AI service to make several immediate improvements to its healthcare services:

  • AI-Assisted Communication – by generating clear and concise summaries of complex medical information tailored to individual patient needs, patients will be able to better understand their lab results, diagnosis and treatment options,, leading to more informed discussions with their healthcare provider.
  • Appointment Scheduling – when taking patient calls, not only will AI be able to help schedule appointments, but the AI solution will provide recommendations for follow-up actions to ensure patients’ needs are met in a single interaction.
  • Internal Chatbot – healthcare workers at Mercy will be able to quickly find information about policies and procedures. By facilitating them to find the information they need, they will have more time to focus on patient care.

Of course, this is just the beginning. Mercy is exploring many uses of AI and plans to launch multiple new use cases within the coming months. These applications will help perform real-time clinical decision-making that will ultimately improve patient care. Moreover, by partnering with Microsoft Cloud, the healthcare organisation has a trusted and comprehensive platform where it can securely centralise and organise data. With a centralised AI-powered data platform, healthcare workers at Mercy can leverage smart dashboards to tap into secure data insights, reducing patient delays and improving productivity. 

The Future of Generative AI in Healthcare

The collaboration between Mercy Health and Microsoft is just one example of the potential of generative AI in healthcare. There are many more applications that could transform the industry, including:

  • Drug Discovery – analysing huge volumes of genetic information and molecular structures. This could accelerate the discovery of new drugs and pave the way for personalised medicine approaches.
  • Medical Imaging Analysis – assisting radiologists by analysing medical scans (X-rays, MRIs) and identifying potential abnormalities. This could lead to faster, more accurate diagnoses and potentially reduce the workload for human specialists.
  • Mental Health Support – leveraging AI-powered chatbots to provide initial mental health screenings, offer emotional support, and even guide users towards appropriate resources. This can be particularly valuable in areas with limited access to mental health professionals, bridging the gap in care delivery.
  • Proactive Health Management – analysing a patient’s health data and lifestyle habits to predict potential health risks. This could lead to personalised preventative measures and early intervention strategies.
  • AI-powered Surgical Assistants – creating 3D simulations of surgeries to aid surgeons in planning and performing complex procedures with greater precision.

Of course, amongst all these innovations, there are important ethical considerations. Ensuring data privacy and security will be paramount. In addition, careful monitoring and mitigation strategies will be needed to prevent AI algorithms from perpetuating biases in healthcare data and to avoid discriminatory outcomes. However, by addressing these concerns and fostering responsible AI, generative AI has the potential to transform the industry, making healthcare delivery more efficient, personalised and patient-centric.