Creating AI that Gets into Clinics: The Human-Centered Approach

Remember handwritten memos? Those brief notes that once served as the main way to send messages in hospitals during the 80s and 90s? They are still used in some departments today. As old-fashioned as they seem, these simple memos have endured because they keep interruptions to a minimum.

When doctors at a major metropolitan hospital in New York tried replacing handwritten memos with texting, issues arose. Texting made it too easy to contact colleagues, and doctors were interrupted constantly with non-urgent messages and chatter. The old memos had an unexpected advantage—they forced doctors to be concise and deliberate with their communication. Many returned to the old method. Similarly, the transition from paper to electronic health records (EHRs) was very slow, especially among senior physicians set in their ways. Careful change management helped ease the switch.

These examples show a common challenge when new technology enters healthcare: adoption depends heavily on human factors like workflow, habits, and communication style. As AI tools—from simple algorithms to virtual assistants—become more common, a focus on human-centered design is crucial for smooth integration. In fact, recent surveys indicate that many clinicians do not fully understand how AI works. Without involving end users, even advanced AI risks being rejected.

So, how can AI solutions be designed so that both providers and patients accept them?

The Downsides of a Tech-First Approach in Healthcare AI

The need for consumer focus in healthcare is often discussed in industry reports. Many early healthcare solutions, including some AI applications, were developed by technology companies without enough input from frontline healthcare workers. This tech-first method often fails to fully meet the needs of both providers and patients or to fit into clinical routines.

Consider EHRs once more. They were mainly built for billing, not to help doctors deliver better care. The result? EHR systems that are clumsy and time-consuming for clinicians to use—leading to information overload, hidden data, and endless pop-ups and alerts. EHRs have become a major factor in physician burnout, with over half of doctors now showing symptoms.

The lack of human-centered design in healthcare AI leads to issues such as confusing interfaces, AI suggestions that do not match real-world settings, the spread of biases, and other potential negative effects. This emphasizes the need for AI that truly supports both patients and doctors beyond merely offering accurate diagnoses.

Poor user interfaces can confuse users and add friction to daily routines. Many tools end up clashing with the everyday practices of healthcare providers, increasing their stress.

AI recommendations that do not account for the complexities and uncertainties of real clinical contexts may seem unreliable. Algorithms that lack explainability can appear as a black box.

Moreover, biases from clinical data used to train these systems can be passed on. For instance, one well-known hospital algorithm once favored certain patients simply because more had been spent on their care. This can lead to automation bias, where clinicians rely too heavily on AI without proper evaluation.

There is also the risk of unexpected negative outcomes, such as shifts in data patterns or errors due to unforeseen factors. Continuous monitoring is necessary to catch problems before they derail the system.

Beyond accuracy, it is important to consider how AI truly benefits patients and doctors in everyday practice—something that simple statistics may not capture.

Healthcare AI must be designed and implemented with caution through extensive user testing, bias audits, and a focus on patient needs.

A New Direction for Healthcare

Thus far, it is clear that technology-first methods in healthcare AI can fall short. So what is the remedy? A design thinking approach. But what does human-centered AI in healthcare really mean? What core principles should shape its creation and use?

For healthcare AI, design thinking means gathering teams that include patients, clinicians, and administrators to develop solutions. It is about keeping end user needs central to the process. It means building AI to support clinical work rather than replace human judgment. It also means ensuring that patient priorities—such as privacy, trust, and clarity—are maintained throughout the system's life. Some key ideas include:

·       AI as a Support Tool: Systems should be built to enhance human skills and decision-making, not to replace them. Clinicians should remain in control of key choices.

·       Patient Focus: Protecting patient data and ensuring transparency and trust should be top priorities, allowing patients to guide how AI is used in their care.

·       Inclusive Design Teams: Involving patients, clinicians, and administrators ensures diverse perspectives shape effective solutions. For example, AI systems in imaging have performed better when developed with input from healthcare workers, leading to tools that improve scheduling and aid diagnostics.

·       Continuous Improvement: Regularly collecting user feedback after deployment helps refine AI systems so they adapt to changing needs.

·       Seamless Workflow Integration: AI tools must be designed to fit smoothly into clinical and operational routines without disrupting current processes.

While these guidelines offer a broad view, applying human-centered design well means paying attention to details. Teams must consider edge cases as they develop solutions. For example:

·       How will AI affect communication habits? Could it promote more asynchronous coordination?

·       How can a balance be struck between standard procedures and individual customization?

·       How should AI handle complex patient conditions, data inconsistencies, or rare illnesses?

·       How might workflows evolve as AI becomes more integrated?

The Key to Human-Centered Design

A study by the CDC covering 24 projects found that design thinking in healthcare led to solutions that were more effective, efficient, and user-friendly than traditional methods.

User adoption and trust are vital for any new technology. For instance, early use of design thinking at a renowned medical center resulted in many small yet important improvements, showing that involving healthcare professionals in the design process can significantly increase their readiness to use AI tools. This approach also helps avoid common pitfalls, ensuring that AI truly benefits both patients and clinicians. Think of design thinking as a process of brainstorming, prototyping, and testing with end users in the lead. This ensures that AI is refined until it meets the actual needs of its users. Early planning for change management and training often resolves issues before they arise.

Design Thinking in Action

Design thinking also supports greater personalization for individual patient needs, constraints, and preferences. The collaborative and iterative nature of this approach can help reduce long-term costs that might otherwise come from major redesigns later. Essentially, human-centered design offers a flexible process to create AI systems that are ready for the future and can adapt as user needs change over time.

Building a Human-Focused Healthcare AI

In summary, design thinking produces healthcare AI that works well for people, not the other way around.

Consider the challenges faced by many busy clinicians who struggle with heavy documentation and outdated records. Surveys reveal that a large number of doctors feel overwhelmed, largely due to administrative tasks and information overload. The idea is to use AI to lessen these burdens by quickly providing relevant patient histories, monitoring treatment progress, and flagging potential issues. Such a tool should rapidly summarize current clinical guidelines and help create personalized care plans based on each patient’s situation.

The outcome is that clinicians feel more empowered, gaining more time to build meaningful patient relationships and offer better care. Moreover, continuous feedback from users allows the AI system to improve over time. This represents AI designed to assist healthcare professionals, built with human needs in mind. Many providers wish all their tools worked as simply and effectively.

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Conclusion 

Ultimately, the success of healthcare AI comes down to putting people first. The key is to involve end users from the beginning by forming cross-disciplinary teams, studying clinical routines, and co-creating solutions that reveal hidden needs. Rapid prototyping then allows ideas to be quickly tested and refined based on real feedback.

This human-centered approach results in healthcare AI that feels natural rather than disruptive. Tools are developed to support existing workflows rather than complicate them. Importantly, involving stakeholders in the process builds trust and a sense of ownership in the final product.

Exciting opportunities lie ahead as this collaborative method unlocks the full potential of healthcare AI. Areas like process automation, personalized medicine, and population health management continue to benefit from these efforts. In short, by putting people first, AI becomes a valuable asset rather than a burden. The human element is more important than ever.