Balancing innovation and risk: The role of generative AI in healthcare

The paradigm shift being experienced across the pharma industry with the advances in machine and deep-learning techniques, artificial intelligence solutions (AI), especially Generative AI holds significant promise in dispensing and improving patient care. Be it the promise of personalized interactions, offering solutions for the entire patient journey, rather than just treatment or delivering more intuitive and human-like conversations with patients and caregivers, Gen AI can have transformative effects. 

Use cases of gen AI for patient engagement 

Enhance productivity

Gen AI models with their advances and deep machine learning capabilities can analyze data patterns, and generate images, text, audio, as well as synthetic data. This works in tune with the automation capabilities allowing professionals to streamline backend operations and administrative tasks such as appointment scheduling, medical billing and claim processing, record keeping, data entry, data extraction etc.

Generate useful insights

Gen AI can analyze vast data sets, be they structured (such as health data) or unstructured (such as physician notes) and provide insights that can improve patient outcomes. These data sets allow healthcare providers to identify trends in patient behavior, and response to treatment and therapy. These tools can also help accelerate and enhance drug development and clinical trials, enabling providers to deliver personalized treatment plans based on the patient’s medical history, demographics, genetic makeup and even lifestyle choices. 

Action drivers

Generative AI can help create a more engaged and supported patient community through patient engagement solutions and advanced health tech tools such as virtual health assistants (VHAs) and chatbots. By breaking information barriers and improving communication, Gen AI can help set the stage for more supportive and accessible healthcare.

Most organizations have only scratched the surface of what gen AI can do for them. But, while the potential benefits of Gen AI in pharma are immense, several challenges pose significant barriers for companies that are ready to embrace generative AI for reshaping patient care experiences. 

Current efforts do not meet patient expectations

Despite the advancements, large language models (LLM) can still occasionally generate inaccurate information because of insufficient data. Many gen AI models operate on a “black box” model, which can make incomprehensible mistakes, making it difficult for patients and caregivers to come to a definite conclusion. Furthermore, many patients may show reluctance in accepting AI insights, preferring traditional human interaction over digital models for critical decision-making. 

Data silos

Gen AI needs to train on large data sets to produce outputs that are fit enough for patient use. And while organizations may have established a data management infrastructure, silos and disparate data sources can impact the integrity of the information. Furthermore, gen AI models may often reflect underlying bias in the data, thus producing skewed and inaccurate insights that fail to cater to a diverse patient group. While it has only been in the recent few years that providers have begun focusing on patient engagement solutions and improving patient experiences in healthcare, the adoption of gen AI models holds immense promise.