AI in Healthcare: Navigating the Balance Between Efficiency and Leverage

Everyone is talking about AI’s potential in healthcare.

Often seen as a cesspool of inefficiency (can you imagine spending more than 1/3 of your working time documenting your work like doctors do?), companies are rushing into healthcare to apply AI to streamline workflow and improve the perceived bottlenecks. For companies that can find a wedge into healthcare, it can prove quite lucrative given our $4.5 trillion dollar national expenditure on healthcare.

Broadly speaking, AI's potential can be split between driving efficiency and creating leverage from new information. Both approaches offer benefits, but the dynamics between them differ significantly—especially for investors.

So what do investment opportunities look like in efficiency vs leverage oriented solutions?

Efficiency: Cutting Costs and Saving Time

Efficiency broadly means doing more with the same or fewer resources. In healthcare, AI is used to streamline repetitive tasks, reduce errors, and improve operational workflows, resulting in immediate cost savings:

• Administrative automation: AI-driven tools manage complex tasks like billing, claims processing, and appointment scheduling. These tools streamline back-office operations, reduce administrative overhead, and allow staff to focus on patient care. For example, Ribbon Health uses AI to aggregate and clean massive amounts of data, helping payers, providers, and digital health companies streamline their operations and improve patient outcomes

• Clinical documentation: AI reduces the burden on healthcare professionals by automatically capturing and organizing clinical data. This decreases the time spent on paperwork, allowing clinicians to spend more time with patients. For example, Suki AI uses voice commands to capture and generate accurate medical notes, significantly cutting down documentation time.

• Diagnostic assistance: AI helps radiologists and clinicians by quickly analyzing medical images, identifying abnormalities, and suggesting potential diagnoses. This improves accuracy and speeds up diagnostic workflows, ensuring faster patient care. For example, Aidoc provides real-time AI-powered analysis of medical images, helping detect conditions like stroke, hemorrhages, and pulmonary embolisms faster.

While these innovations provide quick wins, incumbents have the advantage over time. Large healthcare systems can deploy these technologies across entire networks, achieving greater cost reductions through scale, making efficiency-driven AI tools a more natural fit for them. Investors would do well to be aware of how competitive the startup can be over the long run, lest they run into a Slack vs Teams competition.

For startups determined to dive into the efficiency space, it’s best to launch a lightweight and nimble product, and find a unique go to market strategy that allows you to scale your product quickly to many customers, and make your company’s footprint an attractive asset to acquire for incumbents who have:

1. Yet to develop similar solutions, but may eventually want to enter the same space, or

2. Have similar solutions, but have yet to target a particular subset of the TAM.

In short, build where the Goliaths literally aren’t.

Leverage: Small Inputs, Big Outcomes

Leverage is about multiplying the impact of an effort. In healthcare, AI can allow small teams or organizations to create exponential outputs, making it a key area for innovation and disruption. For example:

• AI in Drug Discovery: AI has opened new doors in drug discovery by engineering molecules from scratch. Traditional methods rely on known chemical interactions, but AI can model and predict entirely new compounds, accelerating the development of novel therapies. This technology allows for the creation of drugs that would be difficult or impossible to discover otherwise, offering massive potential to address unmet medical needs. (We admit not to be pharma experts at The Healthcare Syndicate (yet!), so for more details, check out this great article from Shawn Dimantha what an AI-native company may look like in this space)

• AI in Medical Devices: Much like how the iPhone revolutionized photography by using software to enhance hardware, AI is transforming medical devices. With AI-driven software, traditional devices like heart monitors or glucose sensors can improve their performance automatically, update patient data in real-time, and even adjust themselves for better accuracy. For example, surgical robotics can make precise, real-time adjustments during procedures, and companies like NVIDIA are leading the infusion of AI into surgical robots.

• AI in Remote Telemedicine: Though current incumbents have not found telehealth itself to have a sustainable business model, the telehealth hype cycle didn’t happen for no reason, as demonstrated by the rapid adoption during the early days of COVID. In addition to having value in focusing on specific applications, there is alot of promise in augmenting the virtual office visit with AI, offering powerful new ways to engage with patients. For example, Using cameras to capture vitals and analyze patient data in real-time, AI can provide care recommendations that are informed by a virtual interaction, rather than a physical exam. This technology extends the reach of healthcare providers, while also gathering vast amounts of new data, which can be used to refine and enhance remote diagnostics and care over time. Perhaps in the comfort of their own home, virtual visits can pick up subtle information that patients may hide in an in person visit.

A common theme across these examples is that the data needed to unlock the full potential of AI in healthcare hasn’t yet been generated. Unlike efficiency-based solutions, incumbents don’t have a built-in advantage here. Startups thrive in these areas because leverage doesn’t depend on size; it rewards innovation and insight. Importantly, these solutions don’t necessary have to make healthcare more efficient, but they must enable new insight or capabilities that wasn’t possible before.

For investors, while investing in these companies can take longer to bear fruit, a well-designed AI solution that creates novel leverage can disrupt traditional players, allowing smaller teams to achieve massive and sustainable returns from relatively minimal inputs, driving the next wave of breakthroughs in healthcare.

Closing Thoughts

Depending on your perspective, you may favor one type of play over another. While efficiency-focused solutions offer immediate returns especially if the company can grow market share quickly, leverage presents a unique opportunity for startups to make a profound impact.

Regardless of the approach, startups should focus on niching down and dominating their initial target market. By becoming the leader in a specific area, they can build traction and credibility, positioning themselves for long-term success, and investors would do well to search of these companies with such ambition.

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