Generative AI and Healthcare: How the Industry Can Harness Disruptive Transformation

As the healthcare industry grapples with soaring administrative and medical costs and inconsistent medical outcomes, there is a pressing need to adapt to new demands around customer centricity, improved health outcomes and value-based care. Generative AI (Gen AI) is revolutionizing the sector with transformative solutions to these challenges. 

Gen AI’s ability to analyze huge volumes of medical data, replicate human action and enhance decision-making means that many operational processes can be automated and optimized, driving clinical decision-making as well as predictive and personalized care. This will allow healthcare companies to drive better experience, enhance medical outcomes and reduce costs. 

Not surprisingly, the industry’s willingness to adopt Gen AI is palpable. According to Everest Group’s Generative AI in Customer Experience Management (CXM) survey report, supported by WNS, 60-70 percent of healthcare organizations are aware of and have a vision for incorporating Gen AI. Others are already leveraging its potential, proactively pioneering its integration across the healthcare sector. 

Whether organizations take a proactive, reactive or balanced approach, examining the healthcare industry’s journey and current state reveals the vast opportunities and the issues that must be addressed to realize them. 

Overcoming Barriers for Healthcare Gen AI to Flourish

As Everest Group’s research demonstrates, the healthcare industry is generally well-prepared for Gen AI adoption across technology, data, people and processes, with robust capabilities in these areas placing the sector behind only banking and financial services in terms of readiness. However, healthcare faces inherent barriers to industry-wide Gen AI adoption, with major concerns around data privacy, bias, accuracy and security. 

Data privacy remains a prominent challenge as the vast amounts of sensitive patient information require stringent protection measures. Processes, especially those involving clinical decision-making, will require human oversight to drive accuracy and effectiveness. Regulatory complexities pose another hurdle as healthcare providers navigate numerous compliance requirements. In fact, 70 percent report regulatory compliance issues with Gen AI as a potential challenge to adoption. 

Finding the right balance of talent is also integral. Skills across a broad spectrum of roles will be required to ensure the effective deployment and use of Gen AI solutions. This will be crucial to effective model training, testing and validation – and nowhere are the stakes higher than in healthcare. At present, talent readiness represents a concern for the industry, with just 35 percent of healthcare organizations reporting sufficiency in AI engineers and less than half in data scientists and software developers to effectively deploy Gen AI models. 

Encouragingly, organizations are innovating to overcome other barriers. In response to the infrastructure, computing power and scalable architecture that Large Language Models (LLMs) demand – not to mention the technical challenges and interoperability standards – leading organizations have shifted focus toward small language models or proprietary custom models tailored to specific healthcare needs. These healthcare-specific models are expected to largely address accuracy and bias concerns.

Re-imagining Customer Experience Management in Healthcare 

By unleashing the immense capabilities of Gen AI, the healthcare industry is poised to unlock new value and accelerate the move toward value-based care. The potential use cases are myriad and far-reaching. 

Many organizations are beginning with administrative and operational use cases, such as claims processing, clinical operations and member services. For example, in clinical operations, Gen AI can automatically generate patient-doctor conversations, create post-visit summaries, update Electronic Medical Records (EMRs) and manage workflows. 

Conversational chatbots and virtual assistants powered by Gen AI represent another opportunity, offering instant communication, query resolution and real-time assistance. Research also shows that healthcare is fully seized of Gen AI’s potential in text and video generation. 

The transformative potential of Gen AI will be realized by re-imagining healthcare services. Combined with advanced analytics, it can enable tailored treatment plans, remote monitoring and timely care interventions. To achieve value-based care, Gen AI must be leveraged with other technologies to enable an end-to-end patient journey with personalized care, omni-channel communication, doctor-patient interaction and much more. For example, Gen AI can harness multitudes of clinical data along with evidence-based medical guidelines that will generate care recommendations while conversation bots provide patient-specific responses.

As it matures, Gen AI will prove pivotal in revolutionizing the healthcare industry by driving personalized and convenient patient services, enhancing operational efficiency and transforming clinical practices for improved medical outcomes. Furthermore, its convergence with emerging technologies, such as Metaverse and augmented / virtual reality, will completely transform healthcare delivery.  

Taking the Next Step Toward Transformation

While healthcare leaders have been quick to recognize the industry-changing importance of Gen AI, actions taken today will determine whether the technology’s full transformative potential is realized. 

It is imperative to develop an enterprise Gen AI strategy that explores use cases and drives large-scale impact across the value chain. Additionally, having the right data and technology infrastructure is essential to leverage Gen AI effectively. Organizations must also develop and use risk management frameworks to mitigate associated risks.

For many healthcare organizations, a combination of in-house development and collaborative partnerships represents a strong route forward. This approach enables the industry to tap into external expertise, accelerate the deployment of Gen AI capabilities end-to-end and successfully navigate past barriers to a more accessible, efficient and patient-centric industry.