The healthcare industry is undergoing a major transformation, driven by artificial intelligence (AI), its subset generative AI (GenAI) and data modernization. While AI has long been a buzzword, its adoption in healthcare has been gaining momentum over the last few years. This shift is not just about improving operational efficiencies but also enhancing patient outcomes. From large hospital systems to smaller providers, organizations are exploring how AI can be integrated into their workflows. But as these technologies continue to evolve, healthcare organizations face both exciting opportunities and significant challenges in implementing AI solutions.

The growing appetite for AI in healthcare

Healthcare organizations are increasingly recognizing the importance of AI and GenAI in transforming their operations. Many organizations had expressed skepticism about AI’s role in healthcare, often viewing it as too complex or not suitable for their needs. However, as the technology has matured and demonstrated clear benefits, especially with the meteoric rise of GenAI in November 2022, that skepticism is rapidly diminishing.

“Today, AI and GenAI are top priorities for healthcare organizations worldwide, with many already in the process of implementing AI-driven solutions to enhance everything from back-office operations to clinical decision-making,” confirms Shantanu Baruah, EVP, Healthcare at HCLTech.

The shift is evident across all sizes of healthcare organizations. Larger hospital systems, particularly those with research arms, tend to be more advanced in their AI adoption. In contrast, smaller providers and clinics are still in the early stages of exploring AI use cases.

Regardless of maturity, all healthcare organizations share a common challenge: preparing their data for AI.

Data modernization: The critical foundation for AI

A key challenge healthcare organizations face in their AI journey is modernizing their data infrastructure.

“Many healthcare providers still operate on legacy systems that are not equipped to handle the demands of AI. These systems often store data in silos, making it difficult to access and use for AI applications. As a result, organizations are increasingly focusing on data modernization efforts to ensure that their data is AI-ready,” says Dr. Sandesh Prabhu, Global Head-Life Sciences and Healthcare Practices at HCLTech.

Modernizing data involves several key strategies:

1. Data governance and quality: Many organizations are establishing or strengthening their data governance programs to ensure high-quality, reliable data. This is essential for AI to function effectively, as AI models rely on clean, accurate and well-structured data to make decisions.
2. Breaking down data silos: Healthcare organizations are working to break down silos between different systems, such as Electronic Health Records (EHRs), and ensure that their data can be integrated across platforms.
3. Leveraging new technologies: Organizations like Databricks are emerging as valuable resources for healthcare organizations to modernize their data infrastructure. These platforms can help organizations store, manage and prepare their data for AI applications.

AI in healthcare: Internal vs patient-facing use cases

As healthcare organizations dive deeper into AI adoption, there is a clear trend of starting with internal-facing AI use cases before expanding to patient-facing ones. This approach allows organizations to gain quick wins, improve operational efficiencies and minimize risk. Common internal AI use cases include:

• Operational efficiency: AI is used to automate routine administrative tasks such as scheduling, billing and supply chain management
• HR and workforce management: AI is also being used to streamline hiring, training and staff scheduling processes

However, healthcare organizations are quickly recognizing the potential of AI in patient-facing applications. These use cases often start small but hold the promise of transforming the patient experience:

• Clinical decision support: AI is being used to assist healthcare professionals in making better decisions by analyzing patient data and recommending treatment options
• Patient navigation and appointment scheduling: AI-powered chatbots and virtual assistants are helping patients schedule appointments, navigate healthcare systems and get answers to common questions
• Patient education: AI is also used to generate personalized educational materials for patients, helping them understand their conditions and treatments

While these patient-facing applications are still in their early stages, they are expected to grow rapidly as healthcare organizations recognize their potential to improve patient care and satisfaction.

The challenge of implementing AI safely and responsibly

One of the most critical aspects of AI adoption in healthcare is ensuring that AI systems are safe, ethical and transparent. Healthcare organizations must consider how to implement AI responsibly, especially given the sensitive nature of healthcare data.

Responsible AI involves:

• Establishing governance structures: Many organizations are still in the early stages of developing formal AI governance structures. This includes creating AI ethics committees, appointing AI leaders and setting up policies and guidelines for AI use
• Transparency and accountability: Organizations need to ensure that their AI systems are transparent and accountable. This involves ensuring that AI models are explainable and that they can be monitored for biases and errors
• Risk management: Healthcare organizations are also focusing on managing the risks associated with AI, including data security and patient safety concerns

“As the AI landscape continues to evolve, healthcare organizations must stay vigilant about these ethical considerations to maintain trust and avoid negative consequences,” says Baruah. 

AI readiness: The need for education and training

As AI continues to play an increasingly prominent role in healthcare, organizations must invest in education and training for their staff. AI literacy is becoming as important as cybersecurity training, and healthcare organizations are beginning to recognize the need for robust AI training programs.

Key steps for building AI literacy in healthcare organizations include:

• Developing AI literacy programs: These programs should be aimed at both technical staff and non-technical personnel to ensure that everyone understands how AI works and how it will affect their work
• Leadership buy-in: For AI initiatives to succeed, leadership teams must be well-versed in AI and its potential. This requires ongoing education and training for executives and decision-makers
• Continuous learning: As AI technology evolves rapidly, organizations must foster a culture of continuous learning to keep their teams updated on the latest developments

The role of strategic partners in healthcare AI adoption

Strategic partners play a critical role in helping healthcare organizations navigate the complexities of AI adoption. Companies like HCLTech can support healthcare organizations in several ways:

• AI consulting: Helping healthcare organizations select the right AI use cases, build their AI strategy, and identify suitable vendors
• Data modernization: Assisting healthcare organizations in modernizing their data infrastructure to make it AI-ready
• Providing sandbox environments: Creating safe environments where healthcare organizations can test AI solutions before deploying them at scale

“Healthcare organizations are increasingly looking for partners who can offer end-to-end AI solutions, from data modernization to implementation and governance,” says Prabhu.

An AI-driven future in healthcare

The healthcare industry is at a pivotal moment in its AI journey. While many organizations are still in the early stages of AI adoption, the potential for AI to transform healthcare is immense. Data modernization and AI readiness are essential for healthcare organizations to unlock the full potential of AI, and as the industry matures, responsible AI practices will become increasingly important. Strategic partners who can help organizations navigate these challenges will be key to ensuring successful AI implementation and improving patient outcomes.

As AI and GenAI continue to evolve, the healthcare industry must remain adaptable, invest in training and prioritize ethical considerations to ensure that the benefits of AI are realized while maintaining patient trust and safety.