Is Increasing AI Adoption the Missing Key to Ending the Global Healthcare Staffing Crisis?
The healthcare industry has always been an interest of mine, perhaps because I once dreamed of becoming a doctor before discovering my deep love for technology, which led me to become a computer engineer. Throughout my degree, I have always been eager to explore how technology can partner with the healthcare industry to solve some of its most pressing challenges. In the past, I pursued this through hackathons. Now, with my growing knowledge and interest in artificial intelligence (AI), I am passionate about using it to develop solutions for some of the world’s most complex problems—challenges that may have been nearly impossible to tackle before the advent of LLMs and NVIDIA’s H100 GPU chips.
In this and future editions of TechStories, I will explore pressing issues across various industries, ideate AI-based solutions, and, where solutions already exist, share insights on how they can be improved and made more accessible. Now, let’s dive into the first edition!
Credit: Philips
The Problem
Here’s the challenge:
Projections indicate a shortage of up to 86,000 physicians in the United States (US) by 2038 (“New AAMC Report Shows Continuing Projected Physician Shortage,” 2024b). The staffing crisis extends beyond the US, with the WHO projecting a shortage of 600,000 doctors in Europe by 2030 and Africa facing a 45% increase in health worker shortages (Razza, 2024) and Chronic Staff Shortfalls Stifle Africa’s Health Systems: WHO Study | WHO | Regional Office for Africa, 2025). These numbers are alarming, and the consequences for global healthcare systems could be devastating if action is not taken quickly to slow the growing shortage of medical professionals.
How Can AI and Technology Solve This Problem?
Artificial intelligence is often presented as a revolutionary force in solving global challenges. Yet, it sometimes feels like a far-fetched promise rather than a tangible reality. However, when it comes to addressing the global healthcare workforce crisis, AI-driven solutions are already proving to be effective—delivering real impact faster than government-led, long-term policy interventions.
Imagine if AI could reduce the time doctors spend on tedious administrative tasks, freeing them to focus more on patient care. These tasks—such as documenting patient charts, making referrals, and writing reports—consume hours that could otherwise be spent treating more patients (Campbell, 2024). By automating these processes, AI can increase the number of patients doctors can see while reducing physician burnout, a significant contributor to the staffing crisis (Patel et al., 2018).
AI-powered products like Abridge and Nuance are already making headway in this area, yet their adoption within the healthcare industry remains slow. To make AI a true force for solving global problems, accelerating its adoption must be prioritized just as much as developing new solutions. Only then can these innovations really tackle the challenges they were designed to address.
Key Factors for AI Adoption in Healthcare
Technologists are responsible for meeting users where they are and guiding them toward technological solutions that can increase ease in their lives. Technologists must build bridges for people and walk with them across to ensure a smooth transition to AI-enabled healthcare.
1. Building AI Solutions That Address Industry-specific Needs
AI products must be tailored to meet the unique demands of the healthcare sector, where trust and reliability are paramount. Dr. Michelle Thompson, a family physician featured in The New York Times, uses Abridge, an AI-powered tool that summarizes, organizes, and tags doctor-patient conversations (Lohr, 2023). She noted that Abridge saved her nearly two hours per day and gained her trust because it allowed her to verify AI-generated transcripts before finalizing them (Lohr, 2023).
This highlights a critical need: AI tools in healthcare must always provide users with a clear, structured way to verify outputs before AI-generated output is fed into other systems.
To accelerate AI adoption in healthcare, I propose an AI model template framework. Similar to design patterns in software engineering, this framework would outline common AI adoption challenges—such as the fear of inaccurate outputs—and offer practical design solutions to mitigate them.
End-user involvement is key. The process would begin by identifying resistance factors and designing solutions to overcome them. The implementation of these solutions would ultimately increase adoption across the board.
2. Building Globally Adaptable AI Solutions
Healthcare staffing shortages are a global problem, so solutions must be designed for a global audience. To achieve this, in-depth research on diverse healthcare systems and cultural contexts must be conducted to ensure the adaptability of AI solutions.
The architecture of the AI product should be built with global scalability in mind, even if a company may initially launch its AI product in a single country due to financial constraints. This ensures that other regions, especially those behind in AI development, can quickly benefit from existing solutions to address healthcare workforce challenges.
Cloud-based AI solutions further bridge the gap by delivering tools to countries with weaker AI infrastructure, ensuring broader accessibility.
The Future of AI in Healthcare
With cloud-based AI solutions, a strong focus on user adoption, and a commitment to increasing AI literacy, I firmly believe that AI-powered tools—like Abridge—could dramatically improve the efficiency of healthcare systems across numerous countries.
Rather than just increasing the number of healthcare workers, the future of healthcare must focus on retaining and empowering existing medical professionals by increasing efficiency. Now, AI is here to support healthcare professionals, reduce their workload, and enhance their ability to provide high-quality patient care.
What Do You Think?
Learning through community is one of the fastest ways to grow. What are your thoughts on AI’s role in addressing the healthcare workforce crisis? I would love to hear from you. Let me know what you think in the comments, and let’s start a conversation.
Citations:
New AAMC report shows continuing projected physician shortage. (2024b, March 26). AAMC. https://www.aamc.org/news/press-releases/new-aamc-report-shows-continuing-projected-physician-shortage
Razza, M. M. R. (n.d.). Parliamentary question | Shortage of health and social care workers in Europe: need to bolster national health services and staff training | E-001566/2024 | European Parliament. © European Union, 2024 - Source: European Parliament. https://www.europarl.europa.eu/doceo/document/E-10-2024-001566_EN.html
Chronic staff shortfalls stifle Africa’s health systems: WHO study | WHO | Regional Office for Africa. (2025, February 3). WHO | Regional Office for Africa. https://www.afro.who.int/news/chronic-staff-shortfalls-stifle-africas-health-systems-who-study
Campbell, K. (2024, April 2). 5 Reasons Doctors Face Burnout More than Other Professionals. Dr.Bill. https://www.dr-bill.ca/blog/practice-management/5-reasons-doctors-face-job-burnout-more-than-other-professionals
Patel, R. S., Bachu, R., Adikey, A., Malik, M., & Shah, M. (2018). Factors Related to Physician Burnout and Its Consequences: A Review. Behavioral sciences (Basel, Switzerland), 8(11), 98. https://doi.org/10.3390/bs8110098
Lohr, S. (2023, June 26). A.I. may someday work medical miracles. for now, it helps do paperwork. The New York Times. https://www.nytimes.com/2023/06/26/technology/ai-health-care-documentation.html