Transforming Healthcare: AI Moves Medicine Beyond the Assembly Line
By Toby Eduardo Redshaw, Ryan Vega, MD, John Sviokla, Sauraubha Bhatnagar, MD
Doctors spend twice as much time on administrative tasks as with patients, but AI can change that by reducing administrative burdens, improving diagnostic accuracy, and fostering a collaborative, patient-centered care model.
A time and motion study by the AMA showed that doctors spend twice as much time on administrative tasks—mainly filling out electronic health record (EHR) data—as they do with patients. Valuable physician time is wasted on satisfying the administrative needs of the system, not the care needs of the patient. How did we end up in this situation?
Part of this dilemma has been driven by healthcare technology and digital systems crafted under the influence of Frederick Taylor’s Scientific Management principles. Taylor, who notably shaped the modern manufacturing system through his consultancy for Henry Ford, championed a methodology that meticulously deconstructs processes into their smallest components, aligning them in a strict, linear sequence reminiscent of assembly lines. However, this approach starkly contrasts with the fluid, collaborative nature of medical practice.
Medicine thrives on the collective expertise of well-educated and highly skilled teams working together to meet the complex needs of patients. However, a manufacturing-driven approach inadvertently prioritizes procedural and payment-driven actions over holistic, patient-centered care. Viewing healthcare tasks as isolated sequences undermines the essence of medical practice and has created a healthcare ecosystem—and technology—that disproportionately focuses on billing mechanics rather than patient well-being.
Reimagining Healthcare with AI
Historically, in times of technological advancement, many of the big winners were those who moved beyond old improvement paradigms and completely rethought systems. Using AI and GenAI in the healthcare model could yield better patient outcomes. And adoption is likely because it could deliver more efficient cost structures from reduced rework, errors, etc.
In this new model, automatically and proactively applying human, machine, and information assets to meet system and patient needs could be a winning combination. By consistently harvesting vast amounts of data, the ecosystem can become evolutionarily intelligent, growing smarter and better over time by leveraging its expanding data bank. This will be further enhanced by staying up to date with proven healthcare improvements globally as they emerge.
The smart application of ‘regular’ AI and the newer GenAI allows every player from nurse practitioners, doctors, technicians to administrators to level up and enjoy the benefits of co-pilots working inside the ecosystem to improve quality, precision, knowledge, detection, prevention, speed and critical empathetic engagement.
Furthermore this intensely choreographed digital world allows for smarter/better longitudinal patient engagement.
It also improves the information quality and understanding on the patient side about their engagement, ‘assignments’, anxieties, curative regimes and lifelong health journey. It can also extend the teams expertise through technology towards the patient in urgent scenarios like trauma, mobile medical needs, remote underserved populations in rural areas / remote clinics , eldercare and even next-gen home care .
A Fresh Start for Healthcare
We are convinced that shifting our perspective from a linear sequence to a team-oriented ecosystem approach, particularly with the aid of AI and Generative AI (GenAI), can usher in significant improvements.
AI’s ability to process vast amounts of data and deliver intelligent tailored output, streamline administrative tasks, and enhance diagnostic accuracy, has the potential to center healthcare around the patient and patient communities. This not only alleviates the stress on medical professionals but increases efficiency and asset utilization. While these advancements challenge the traditional fee-for-service, production line model, adopting outcome-based payment systems could facilitate significant reforms.
Given the entrenched relationship between policy directives and financial incentives, there’s cautious optimism that regions outside the United States, such as Europe and developing countries, may progress more swiftly in this regard. Healthcare enterprises today are anchored in outcomes. If we’re open to evolving our methodologies and leveraging technology effectively, the U.S. healthcare system, too, has the potential to realize substantial benefits.
Embracing AI for Better Healthcare Outcomes
The first movers will have a huge advantage as it is hard to compete against better asset utilization, competitive cost structure and efficiency, better experience (both provider professionals and patients), and most importantly, healthcare outcomes.
Better healthcare outcomes, engagement, capabilities, and efficiency, especially in a labor market with perennial shortages, will be a winning combination for those embracing this new technology and leaving behind the manufacturing/factory paradigm.
The authors of this article bring a wealth of expertise and experience: John Sviokla, a former Harvard Business School professor and current Executive Fellow, is a renowned thought leader and co-founder of GAI Insights; Saurabha Bhatnagar, an MD teaching at Harvard Medical School, has extensive tech experience and previously served as Chief Medical Officer at UnitedHealth Group and Deputy Chief Medical Officer at the VA; Ryan Vega, also an MD, led innovation at the VA and is now head of healthcare at Vantiq, a leader in real-time AI and M2M Gen AI; and Toby Eduardo Redshaw, a globally recognized CIO and CTO with decades of experience in tech and business, has led numerous start-ups and is a regular lecturer at top business schools and advisor to prestigious forums.
