Many articles have addressed how artificial intelligence (AI) can improve supply chain management, from automating repetitive tasks to demand planning. A recent webinar on AI and the future of healthcare got me thinking about a few additional applications that could further supply chain’s role in fostering a more value-based health system. The webinar was presented by IIA Healthcare, an AI-driven technology company in the hiring and recruiting space, and featured two experts:
- Former IBM executive and AI expert Beth Rudden, CEO of Bast AI
- Boston Children’s Hospital AI engineer, Dinesh Rai, MD
Rai explained how generative AI applies algorithms to large sets of data to identify patterns and predict what comes next. That could be the next word in an application like ChatGPT or, if we consider supply chain opportunities, determining the supplies required to meet a specific patient need or diagnosis.
Rudden added that while machines can consume and analyze far more data than any one person could, humans add the specific context and emotional intelligence, both critical to translating medical knowledge into actual care and healing. For example, a highly trained oncologist may understand the kind of cancer a patient has and how to treat the disease, but he may struggle to explain things to a Vietnamese grandmother in a manner that builds trust and compliance. Beyond medical information, AI can help the doctor and team communicate effectively. The care team can then provide a healing measure of compassion.
That got me thinking about how AI might help supply chain professionals when sourcing culturally sensitive products and services. For example, the appropriateness of the food and personal hygiene products provided in a patient’s room can have a significant impact on whether a patient feels seen and valued.
Rai described how AI can help match a patient’s social needs with the specific resources available to them, such as eligibility for food assistance. This is essentially what supply chain does: matches resources to meet specific needs. That made me wonder: could AI query the myriad products and vendors in different categories to find those that meet specific needs beyond cost and efficacy, such as those provided by a certified diverse supplier or that lower the carbon footprint?
AI’s translational capabilities can also support workforce development, including efforts to hire more individuals from disadvantaged communities. For example, Rudden says it could be used to help a mother on government assistance identify that she has skill sets that can be applied in a working environment, such as negotiating with vendors to manage cash flow. AI could also be used to retrain individuals by transferring knowledge in a manner that best matches individual trainees’ unique backgrounds and experience and how they learn.
AI could also address the long-held desire to identify products the same way, e.g., using the unique device identifier (UDI) across multiple databases and processes. Rai says AI can write queries to create more consistency across multiple databases. That, in turn, could help identify when products are the same, even if not identified as such, and assign the UDI, facilitating a more holistic view into the role specific products play in lowering the cost of care, improving quality and outcomes, and/or optimizing reimbursement.
Rudden sees the potential for each of us having our own personalized AI that could, for example, analyze the results of a medical test in the context of everything else happening in and around us (physiologically, emotionally, socially, etc.). The challenge, she says, is not the technology involved, but rather how we – as humans – view and manage data. Beyond inaccuracies and incompleteness, she says we need to solve for data sovereignty, including patient rights to their own data. If we want to address the whole person, and meet their clinical and social needs, patients must be comfortable providing the data. And there is no better way than respecting their rights.
