AI-Driven Engineering in Healthcare: Cutting Development Time Without Compromising Care

Key Takeaways:

1. Practical barriers have held back healthcare AI adoption so far
2. Along with care-facing solutions, AI can also expedite product development
3. Automation and efficiency can drive lower costs, better compliance and wider access to expertise
4. Experienced partners can help jumpstart AI implementation as adoption gathers pace
   

Despite its importance in saving lives and protecting wellbeing, healthcare regularly lags behind other industries in its adoption of technology. 

Part of this is down to healthcare’s position as a high regulated consumer-centric industry. Many healthcare firms tend to wait for innovations to be proven in a consumer-facing setting like retail, or in a regulated space like banking, so they can invest in new technology with confidence.

A similar approach is being taken to artificial intelligence (AI) adoption in healthcare. While new market entrants are more able to ‘start from scratch’ and experiment with healthcare AI implementation, legacy enterprises are waiting to see how the technology develops—most are only conducting smaller proof-of-concept projects for now.

However, the advent of generative AI in particular is really changing the game—not only can it act as a smart assistant in care provision, it is also expediting healthcare product development. The potential is such that large-scale healthcare AI adoption is expected within the next two years, meaning now is the time to act to avoid being left behind.

In this blog, we’ll explore AI-driven engineering in healthcare product development: the barriers to progress, the scale of transformation AI is generating, and how to turn concepts into practical reality.

The Biggest Challenges to Healthcare Technology Adoption

There is substantial interest in new technology in healthcare, including AI. Research has found that the global AI healthcare market was worth just $15 billion in 2022, but is expected to surpass $350 billion by 2032.

However, implementing AI and other new innovations is proving easier said than done, for four key reasons:

Conservative Industry Approach

As mentioned in the introduction, many healthcare firms prefer to wait for technology to be proven in other sectors before committing to adoption. But even then, there can be resistance to change when traditional methods are so embedded; furthermore, legacy systems in larger enterprises can impede progress, due to challenges around process restructuring and integration. 

Regulation and Compliance

As the healthcare industry is so tightly regulated, and with those regulations constantly evolving, any new technology has to be deployed with compliance front of mind. While the World Health Organization outlined considerations for regulating healthcare AI in October 2023, the compliance landscape remains unclear. Concerns range from IP ownership of AI-generated code, complexities around patient data privacy and confidentiality, and the need for AI to be used ethically and responsibly.

Implementation Challenges

At a practical level, implementing new technology requires substantial initial investment; the recruitment of specialized talent that can be expensive and difficult to source; and smooth adaptation and integration with existing systems, which may not have been designed with AI in mind.

Balancing Technology with Empathy

Healthcare differs from other industries in that the consumer (i.e. the patient) expects a human, empathetic touch at every turn. This requires every experience to be as positive as possible, with a personal and emotional connection between patient and care provider.

This kind of ‘warm care’ might be something as simple as holding a patient’s hand, but is something that can’t be fully replicated by technology. The challenge with AI, as a result, is to make the solutions feel more personal and human in their interactions - ideally with some emotional intelligence built in - to help build patient trust and confidence.

The AI Transformation of Healthcare Product Development 

The current approach to healthcare product development is slow and too reliant on individuals’ expertise and knowledge rather than global insights. This means development processes end up being highly manual, with inefficient testing and QA processes, which means time-to-market often takes years rather than months.

By applying AI insights and automation throughout the development process, AI-driven engineering can resolve many of these issues. Applied successfully, the benefits of AI-driven engineering in healthcare can be transformative:

Reduced Development Time

Many healthcare firms have been able to reduce their development cycle times by as much as 60%, and get new products to launch in as little as nine months. They have achieved this through a combination of:

• Automated requirements creation from customer discussions to BRDs (business requirements documents) or PRDs (product requirements documents) 
  
• Generating PRD-based wireframes and test cases 
  
• Generating code and developing integrations 
  
• Using QA tools and DevOps integration to test new solutions faster and speed up time-to-market
  

Expanded Knowledge Base

A lot of development in healthcare is primarily based on the experience and knowledge of individuals and small groups. AI can break down these expertise barriers as many best practices are automatically incorporated into solutions and platforms, including common modules such as onboarding, claims and networks. Many of the regulatory requirements around healthcare can already be covered by solutions and modules that are available in the open market, which allows development teams to focus on core work and proprietary algorithms instead.

Faster Customization

AI tools can make product modification processes resemble human interactions, where models understand, read and learn from behaviors and suggest improvements accordingly. This enables continual refinement of models, and consequently the ability to introduce new features quickly, adapt to new market trends, and respond to customer needs.

Reduced Costs

Automating processes and reducing the level of manual effort required in development saves on labor costs, especially as highly skilled developers can be hard to find and expensive to recruit and retain. Additionally, automation speeds up development cycles, which can lower cost-per-interaction or cost-per-claim, and ultimately deliver a stronger return on investment (even if the initial investment is higher).

Stronger Compliance

Healthcare bodies already understand the value of technology in data privacy and patient confidentiality, from encryption and anonymization to stringent access controls. AI-backed security can help reinforce these measures, while making AI algorithms transparent and auditable can also support the ethical use of AI that doesn’t promote biased decision-making.

Improved Patient Outcomes

Ultimately, the whole point of healthcare is to drive better outcomes for patients. The ability of AI to expedite and improve diagnostics; process medical data including images with higher accuracy; and inform personalized treatment plans can all contribute to more positive results for all. What’s more, being able to do so at a greater scale means clinicians can improve the quantity of people they’re able to care for, as well as the quality of the care itself.

Ciklum’s Implementation Strategy for AI-Driven Engineering in Healthcare

There is still time to innovate and gain a first-mover advantage when implementing AI-driven engineering in healthcare. For example, according to McKinsey, only 29% of healthcare organizations say that they have fully implemented generative AI (as of the first quarter of 2024). However, if you include those who have started to test AI functionality, this figure rises to 72% - so now is the time to accelerate AI plans to keep pace with the rest of the industry.

To ‘jump-start’ this development, working with an expert partner is the quickest and most effective way to access vital solutions, skills and expertise. At Ciklum, we provide a comprehensive lab-tested framework for AI-driven product engineering, with a proven track record of reducing development time from years to months.

This is based on a deep understanding of both AI technology and healthcare-specific requirements; the ability to customize and hyper-personalize solutions while leveraging global best practices; and a ‘no stone unturned’ approach to use case experimentation, tool evaluation and implementation, and technology mapping across product lifecycles.

As a result of our complete support from discovery to launch, healthcare organizations like yours are already benefitting from:

	The right tools applied to the right development phases
	Rapid experimentation and a ‘fail forward’ methodology
	Greater agility in the marketplace
	Custom adaptation for individual client needs
	Proven mapping of AI technology across the product engineering landscape

In Summary: The Time for Healthcare Technology Adoption is Now

With large-scale healthcare AI implementation expected within the next couple of years, organizations that don’t start adapting now risk being left behind. This is especially the case for healthcare bodies with legacy systems, who have more work to do compared to new players who can technologically start fresh.

The key to success is to balance technology and humanity: maintaining the human touch in care provision, and using AI to enhance human capabilities rather than replace them. In the years ahead, this will mean focusing on proprietary value rather than basic functionality, and ensuring that AI is developed ethically and with a core focus on delivering more efficient care and better outcomes for patients.

Working with a partner like Ciklum gives you the best possible chance of achieving that success, and jumpstarting your AI journey. We already have a framework for automating the entire product engineering landscape through AI technologies, and a fully-fledged lab that experiments with new tools and use cases across healthcare and other industries.

Our agility means we’re perfectly positioned to help you scale your AI implementations, going from small experiments to large-scale rollouts that make tangible differences to patient outcomes. Talk to us today to find out more on our unique approach to product engineering.