There is significant upside potential for provider systems to employ the power of Generative AI, yet few have developed a strategy. Harnessing Generative AI in Healthcare is a pragmatic guide for healthcare organizations to utilize the potential of Gen AI to transform their processes and workflows.
From our client experience, an integrated Gen AI strategy to transform business domains (such as Clinical Operations or Revenue Cycle Management) has the potential to drive a 15% to 40% improvement in bottom-line economic results for a provider. This is a 20% to 60% lift relative to the improvement from adopting traditional approaches to AI.
Driving Meaningful Value
There are four areas where Gen AI can drive meaningful incremental value:
- Content Generalization & Personalization: Develop personalized communications to providers, payors, regulators, and patients, leveraging vast amounts of information to create simple summaries in near real-time, as well as providing content for individual care and treatment.
- Patient Engagement: Provide 24/7 support to nurses, care managers, and other healthcare professionals by automating routine tasks such as prior authorization, and augmenting more difficult ones, such as optimizing referrals, thereby dramatically increasing staff productivity.
- Virtual Health Assistant/Co-Pilot: Support the engagement of patients during onboarding and post-discharge in areas such as medication adherence, summarizing health plan benefits and post-discharge summaries.
- Clinical Diagnosis and Imaging: Enhance medical imaging interpretation, automate part of the process, and reduce the burden on radiologists, thereby supporting the clinical diagnosis process for providers.
Key Elements of a Successful Gen AI Transformation
There are five key elements to a successful Gen AI transformation: AI Strategy & Governance, Data, Technology, Talent, and Adoption.
- AI Strategy & Governance: It's important to have a clear and aligned AI strategy that focuses on the most impactful and feasible business domains, and that balances the need for innovation with the appropriate governance protocols to ensure the trustworthiness, ethics, and privacy of Gen AI. Identifying one or two business domains, such as Clinical Operations or Revenue Cycle Development, can be the best way to get started with Gen AI.
- Data: Data plays a large role as a differentiator in Gen AI. AI companies investing in gathering more specialized healthcare data to train healthcare-specific models are outperforming non-industry specific Large Language Models (LLMs). Large healthcare organizations can train and heavily tune LLMs directly on their proprietary data, using it as a differentiator in the creation of new custom LLMs specific to their organization and patient populations.
- Technology: AI models and systems have become more complex and demanding of hardware resources, requiring cloud platforms and specialized processors to run large neural networks. Organizations should first adopt solutions from outside partners who can provide the AI infrastructure and integration, before developing their own custom LLM solutions.
- Talent: With the move towards AI-driven solutions, there is a shift from traditional IT roles to more specialized AI roles such as Machine Learning Engineers, Data Scientists, Data Engineers, Cloud and DevOps Engineers, and User Experience Designers. These roles are crucial to ensuring the technology is integrated into existing architecture and user workflows.
- Adoption: Ensure adoption of Gen AI solutions by integrating them into existing workflows and user interfaces and by building organizational muscle and momentum to scale up the Gen AI transformation. A 'last mile' first approach is suggested, where the integration of AI into business processes requires close collaboration between domain experts and technical teams.
Getting Started in the Next 90 Days
To get started with Gen AI adoption in the next 90 days, determine the business domains with the highest potential impact and assess your data environment. Target two to three domain MVPs to prove the value and test the operating model. By starting now, you will be significantly further along than most provider organizations.