Learning from Agile Startups To Transform Large Healthcare Institutions

By 2050, the WHO predicts there will be over 35 million new cancer cases, a 77% increase from 2022. In tandem, global autoimmune diseases have risen yearly by 19.1%, and 129 million people in the US have at least 1 major chronic disease. Yet, developing a new drug takes around 12 to 15 years, costs $2.23 billion, and 85% of initial targets or preliminary drug proposals fail. 

There’s a significant problem between the rise in illnesses and the speed at which we develop new diagnostic methods, treatments, or medications. Add an aging population and increased human life expectancy to the mix, and all systems are being tested.

With this imbalance, smaller, AI-driven healthcare startups are quickly filling the gap. Unlike large institutions weighed down by legacy processes, these startups are proving that AI can reduce costs, accelerate discovery, and rethink patient care. 

It’s expected that 30% of new drugs will be discovered using AI, with AI reducing drug discovery timelines and costs by 25-50% in preclinical stages. But the question is, why are startups leading—and what can larger players learn from them?

Why Startups Move Faster with AI

Big Pharma dominates the pharmaceutical industry. They have the data, the finance, and the infrastructure. But healthcare startups have a distinct advantage when it comes to AI adoption: agility, flatter structures, and fewer legacy systems slowing them down. 

In the past, healthcare companies interested in advanced computing for drug discovery and diagnostics needed to make massive upfront capital investments—purchasing servers, hiring full-time infrastructure teams, and maintaining their own data centers. However, hyperscalers like Google, NVIDIA, and Microsoft have democratized access to advanced tools. Through cloud platforms and AI-as-a-service, startups can now “rent” computational power and specialized AI models. Startups pay only for what they use, freeing up capital for talent, R&D, and faster iteration. 

Startups can pivot quickly, experiment boldly, and deploy AI tools where they make the most immediate impact. Companies like AtomWise have demonstrated how AI can identify novel compounds with a 74% success rate in early screening, outperforming traditional methods and accelerating drug discovery. By being more “tech” than “bio,” these startups combine computational models with biological and medical expertise, shortening the early stages of drug discovery and breaking traditional paradigms. 

Navigating Regulation Without Losing Momentum

Regulation is one of the major hurdles in healthcare innovation. Large institutions often stall due to fear of missteps, slowing their ability to test and deploy new technologies. 

In fact, the Financial Times reported that in the UK’s NHS, “Patients are at risk of missing out on the benefits of new technologies and companies are being prevented from launching medical devices because of regulatory “hurdles” and “poor alignment”… Staff across the NHS lack the capacity and support needed to “test, adopt and scale innovation.” 

In contrast, startups can move forward with iterative pilots and early regulatory alignment, treating compliance as an integral part of design. They have the time and space to challenge outdated policies and accelerate regulatory change.

While larger institutions struggle with long approval chains and overburdened workflows, the COVID-19 pandemic proved just how quickly the healthcare industry can move when the stakes are high. Vaccines that once took decades to develop were created and approved in record time, without sacrificing safety. AI, advanced analytics, and lessons from the 2003 SARS outbreak gave researchers the insight they needed to zero in on the coronavirus spike protein, rapidly test hypotheses, and reengineer the “lock-and-key” mechanism the virus used to enter cells. Initial trials involving 3,000 people launched within weeks; the result was a successful vaccine and, importantly, it was a clear demonstration that Big Pharma can move with startup-like speed when urgency demands it.

From Locked Systems to Patient-Centered Innovation

Health systems often use software deployed 10–20 years ago and have physicians unaccustomed to and uncomfortable using digital workflows. This creates change management headaches and slows adoption, especially compared to smaller firms that can start fresh with cloud-native tools and modular AI integration.

Doctors, already pressed for time, often find legacy interfaces clunky and distracting. In fact, only a quarter of family physicians reported being very satisfied with their electronic health record (EHR), while another quarter reported being somewhat or very dissatisfied. Yet, successful implementations of AI-driven clinical near-conversational systems that capture notes automatically and generate reports for verification are freeing doctors to focus on patients rather than screens. 

Legacy systems aren’t just code running on outdated servers; there are also rules that keep patients sidelined from their own information. The provider–patient relationship remains highly asymmetrical. For instance, patients still struggle to consolidate their records in one place. In recent years, the Department of Health and Human Services received over 1,095 complaints of information blocking from healthcare providers, much of it tied to delays and technical restrictions. 

Startups have the advantage of building from scratch, designing systems with patients at the center. By making platforms portable, transparent, and easy to access in real time, they earn trust and speed up the adoption of AI tools. This focus on usability, data access, and seamless workflows creates a “human-first” model that gives smaller healthtech players a meaningful edge. Established healthcare institutions can learn from these projects and update their legacy systems accordingly. 

Some have already started. The Mayo Clinic’s Mayo Ventures is a notable example of internal entrepreneurship. By creating dedicated incubation units, the Mayo Clinic is conducting groundbreaking research with AI on liver disease and “is actively working to develop novel AI-assisted tools that can enable early detection [and] accurate estimation of disease severity and prognosis.” Other health systems are taking similar steps: Cleveland Clinic partnered with Microsoft to develop a cloud-first AI ecosystem, and the NHS launched its own AI Lab to accelerate adoption systemwide.

The playbook is emerging. Large healthcare systems don’t need to mimic startups, but they can borrow their speed by:

• Layering in hybrid cloud systems and APIs rather than replacing entire platforms at once.
• Embedding design-thinking teams that work alongside clinicians to reimagine workflows around usability.
• Creating AI governance boards that unite compliance officers, IT staff, and frontline doctors, helping pilots move faster without losing trust.
• Seeing data access not just as red tape, but as a strategic asset—using encryption and anonymization to innovate without ever putting patient privacy at risk.

Without these changes, large institutions risk falling behind the pace and agility of startups. But with them, they can turn scale and data into clear advantages.

AI-driven startups with their agile, modular systems and patient-centered design can accelerate drug discovery and care. But large institutions still hold the resources, expertise, and data needed for lasting impact. To keep up, these organizations must modernize infrastructure, embrace regulatory flexibility, create internal innovation units, and empower patients with ownership of their health data. By blending startup-inspired speed with institutional scale, healthcare can deliver faster, safer, and more effective solutions to urgent global challenges.