Transforming today’s healthcare with value-added AI

We summon a force that is autonomous but completely docile, give it a set of instructions, and then try like mad to stop it when we realize our instructions are imprecise or incomplete—lest we get exactly what we asked for in some clever, horrible way. —Brian Christian, The Alignment Problem

This raises an important question: How can we ensure that the technologies we develop truly reflect our goals?

In a world where buzzwords like artificial intelligence and machine learning often promise more than they deliver, it’s refreshing to find concrete examples where these technologies really shine. That’s where Value Generating AI comes in, which isn’t just a flashy add-on, but a transformative force driving real-world improvements.

At a recent meeting in Chicago, attendees witnessed an AI miracle in action. This generative AI technology, enabled by GPT-4, demonstrated how it can transform a doctor’s interaction with a patient into neatly structured notes within seconds. Imagine a doctor recording a patient visit on a mobile app, with the AI ​​platform adding information in real time, identifying gaps, and prompting the doctor to fill them in. After the visit is complete, the doctor reviews the AI-generated notes, edits them if necessary, and transfers them to the patient’s electronic health record. This nearly instantaneous process transforms the traditionally manual and time-consuming task of note-taking into something almost archaic by comparison.

The value proposition: AI for impact

The potential of AI in healthcare goes far beyond administrative efficiency. In this respect, the term “Value Generating AI” focuses on three main principles:

• AI for impact: AI is being used in critical areas such as improving the customer experience, aiding diagnosis and determining the best course of action for all parties involved.

• No AI for AI’s sake: Investments in AI are justified by the tangible returns they bring.

• AI on a large scale: Make AI accessible and usable for various applications both internally and externally.

A global survey identified eight key use cases for AI in healthcare, including medical imaging, virtual patient care and administrative functions. Another study from Finland found 34 use cases ranging from mobile solutions for home care to optimizing operating room efficiency. The role of AI in healthcare is not limited to futuristic scenarios, but is actively changing daily operations in diagnosis, chronic disease management and logistics optimization.

Generative AI: The new frontier

Generative AI promises significant productivity gains, better patient and provider experiences, and ultimately better clinical outcomes. This technology can reduce administrative costs, accelerate biomedical research and drug development, improve claims management, and help develop next-generation diagnostic devices. Major technology companies are partnering with healthcare organizations to apply generative AI, and investors are funneling capital into early-stage companies building on this innovative tool.

However, given the complexity and uniqueness of patient situations, much of the work in healthcare still requires human labor and judgment. Even in areas where less discretion is required, such as coding and charting, AI models have reached their limits due to the relatively small data sets available to train on. Generative AI promises to address some of these challenges, and the experimentation that is likely to increase in the coming years could unleash significant labor efficiency gains—reducing financial pressure on organizations, improving patient and provider experiences, and leading to better clinical outcomes.

The human touch: enhance, don’t replace

The introduction of generative AI in healthcare organizations will impact not only how work gets done, but also who gets it done. The roles of healthcare professionals will evolve as technology helps streamline some of their work. A human-in-the-loop approach will be critical: Even though many processes will fundamentally change, humans will continue to be essential in all areas touched by generative AI.

To enable these changes, healthcare organizations must learn to use generative AI platforms, evaluate recommendations, and intervene when inevitable errors occur. AI should complement operations, not replace them. Healthcare leaders must provide learning resources and policies to educate staff and ensure that AI-powered applications are easy for frontline staff to use without increasing their workload or diverting time away from patient care.

Strategic partnership

Most healthcare organizations will likely need to form strategic partnerships with technology companies to effectively implement generative AI. Leaders should pay attention to whether potential partners adhere to regulatory compliance requirements such as the Health Insurance Portability and Accountability Act, privacy and security standards, and whether data sharing can improve AI outcomes.

Value-generating AI in healthcare is not a distant dream, but a reality with enormous promise. By focusing on high-impact applications, ensuring a good return on investment, and scaling AI to be accessible and useful, organizations can effectively navigate the complexities of AI adoption. As generative AI continues to evolve, it will be critical to maintain a balance between technological advancements and the indispensable human touch, ultimately leading to better patient care and more efficient healthcare systems.