Harnessing Generative AI in Healthcare: A Pragmatic Guide to Accelerate Impact

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What is Gen AI?

Gen AI represents a significant advancement in the field of artificial intelligence and fundamentally changes how business processes are designed, executed, and optimized. While machine learning (ML), natural language processing (NLP), and computer vision (CV) have been used in healthcare over the past decade, those instances have been limited and, in many cases, have represented expensive investments that did not always result in lasting value or transformative change for many early adopters.

What is new – and what has been the primary catalyst for the recent explosion of interest in Gen AI – are the large language models (LLMs) that emerged in the early 2020s. LLMs have proven to be exceptional at understanding human writing, speech, and images and represent a subset of AI capable of generating human-like logic and output based on massive training datasets. LLMs, trained on publicly available data, debuted in December 2022 with the release of ChatGPT, which became the fastest-growing consumer technology application ever to reach 100M users in six weeks . For the first time in history, humans can interact with computers as they would another human.

Focus on Business Domains to Move Forward

In healthcare, Gen AI has the potential to fundamentally change existing processes and workflows. We see four areas where Gen AI can drive meaningful incremental value. 

* Content Generation and 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.

* Virtual Health Assistant/Co-pilot: 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.

* Patient Engagement: 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.

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Contributors:

Liam Bouchier , Vice President, Data & AI, Impact Advisors

Joe Christman , Vice President, AI & Digital Platforms, Chicago Pacific Founders

Victor Collins , Associate Director, Data & AI, Impact Advisors

Brian McCarthy , Board member of Impact Advisors and Operating Partner, AI & Digital Platforms, Chicago Pacific Founders