The Future of Healthcare Apps Depends on AI-Assisted Development

Digital health apps have faced serious headwinds in recent years amid dried-up funding and macroeconomic challenges. New digital products angling to make inroads in today’s markets must do so on tighter budgets. But patients and providers don’t want to wait years for promising software to mature. Their needs demand a go-to-market strategy that includes efficient software development.

There’s sunlight on the horizon, though. Gen AI is quickly proving to be an indispensable tool for software teams that can help organizations build apps smarter and faster. This isn’t just a shiny object among dozens of other Gen AI use cases. It’s a shift toward modernized app development – and one on which the future of digital health apps depends.

I’ll explain why in this piece. But first, a closer look at the efficiency paradox that has confounded digital health app development for years.

Traditional software development has an efficiency paradox

It’s no secret that today’s patients and providers expect user-friendly, empathy-driven software. But building that software efficiently often feels like balancing three legs of a stool: cost, time, and quality. Cut any one leg, and the others will quickly collapse. 

That’s because there’s a natural limit to how fast traditional software development methods can go – and throwing more developers at a project doesn’t always yield faster results. Sometimes, it creates bottlenecks.

For decades, digital health leaders have scratched their heads trying to crack this efficiency paradox. Today, we finally have a solution in Gen AI. In the next section: how this tech is helping modern software teams do more with less.

Gen AI can help software teams achieve hyper-efficiency

With AI assistance throughout the entire software development lifecycle, teams can experience what I call “hyper-efficiency”: a 10x productivity boost from end to end.

And the biggest efficiency gains aren’t limited to coding. Gen AI can fast-track:

• discovery and ideation.
• product design.
• backlog management.
• code comprehension, reconciliation, and maintenance.
• testing and quality assurance.
• deployment and release management.

Take a problem-oriented mindset to Gen AI implementation and messaging

If AI-assisted software development is so promising, why haven’t more digital health leaders embraced it?

The simple answer is caution. Though Gen AI feels embedded in many modern workplaces, it’s still relatively new tech – and that newness has made healthcare leaders wary of overeager adoption. In particular, they’re concerned about Gen AI’s potential to hurt patient trust, compromise patient data, worsen clinical outcomes, and harm critical processes like drug R&D.

But software development is one of the safest ways to take advantage of Gen AI in healthcare today. The key is to start small. Choose a platform that is not mission critical. Then, focus on augmenting practices with a low regulatory burden. Being subject to regulatory considerations (e.g., for HIPAA or GDPR) can reassure patients that their data is treated seriously. This way, orgs can reap the efficiency benefits without increasing compliance or clinical risks.

Want to integrate Gen AI into your own software development practices? As you identify low-risk starting points, I recommend carefully targeting specific bottlenecks that could benefit from this tech. As your team grows more comfortable with Gen AI, you can incorporate it into other parts of the software development lifecycle to achieve true hyper-efficiency.

Along the way, make sure internal stakeholders can see exactly how Gen AI is solving specific problems. Just think about the rise of ambient listening software: hospital leaders were more likely to embrace it when they saw how the tech could help physicians focus more on patient interactions instead of note-taking and paperwork.

Digital health leaders can take a similar approach to Gen AI in software development. It’s important not just to focus on the immediate business impact, though. Keep each product’s end users in mind as well. With faster app development, healthcare systems can more quickly deploy much-needed tools that improve patient care and provider productivity. And with messaging that’s grounded in this future vision, you can get even the biggest skeptics on board.

It’s time to be bullish on Gen AI

The future of healthcare apps depends on leaders who are willing to embrace change and are able to balance the risk-reward model of new technology. AI-assisted software development can help teams achieve faster delivery, lower costs, and higher-quality results – all key to meeting patients’ and providers’ digital health expectations.

But this future isn’t automatic. It requires digital health leaders to be bullish on Gen AI’s ability to transform software development. That confidence will inspire the whole organization to buy in.

Photo: tadamichi, Getty Images

Luiz Cieslak is an SVP at CI&T a global digital specialist. CI&T’s Life Sciences and Healthcare team partners with pharmaceutical companies, consumer healthcare firms, and medical device manufacturers to create better experiences for patients and healthcare professionals.

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