Adapting Technical Tools to Meet Clinical Needs: Lessons Learned
One of the most critical insights we have gained throughout the development of digital health solutions for neurodegenerative diseases: ALS (Amyotrophyc lateral sclerosis) and MS (Multiple Sclerosis) is the importance of adaptability in clinical tools. As patient conditions evolve, so should the tools we use to support their care.
In projects such as BRAINTEASER, this adaptability isn’t just an ideal—it’s a necessity. Here are 4 lessons we have learned to help you adapting clinical tools to effectively support both healthcare experts and patients.
1.Flexibility in Follow-up Care Matters
Standardized follow-up schedules are crucial, but every patient is unique. In our experience, offering the possibility of unscheduled follow-up visits within our clinical tools proved to be essential. By allowing healthcare professionals to schedule a visit outside of the normal timeline when they observe a change in the patient’s condition, we better ensure timely interventions. This helps clinicians respond to unpredictable changes in ALS or MS more effectively, providing care that adapts to patient needs in real-time.
2.Data Visualization for Quick Decision-Making
We also learned the value of visual cues and streamlined data access for supporting healthcare professionals. For example, color-coded indicators for smartwatch synchronization make it easy to see briefly whether there are issues that need addressing—green for recently synced, yellow for minor delays, and red for significant issues. These visual elements are simple, yet they significantly reduce the time spent navigating detailed records, allowing clinicians to focus more on care rather than data management.
3.Predictive Insights with AI Integration
AI models are powerful tools that can offer predictive insights into disease progression. However, their true value lies in presenting these predictions in a way that is meaningful and actionable for clinicians. Integrating color-coded risk graphs helps in conveying complex information—such as survival probability or the need for non-invasive ventilation—in an accessible manner. This reduces uncertainty and aids healthcare providers in making informed decisions at critical junctures.
4.Continuous Feedback Loop is Key
Finally, adaptability is an ongoing process. Tools must evolve continuously through direct feedback from users—in this case, healthcare professionals and patients. Incorporating their suggestions led us to refine many aspects of our tools, from enhancing data visualization to creating more intuitive workflows for clinicians. The lesson here is that successful digital health tools are not static; they must be refined continuously through real-world experiences and feedback.
Adaptable clinical tools create an environment where healthcare is more responsive, patient-centered, and ultimately more effective. Ensuring that our technology aligns with the evolving realities of chronic conditions like ALS and MS makes a tangible difference in care quality.
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Written by María Riquelme Orozco, Telecommunications Engineer, Researcher at Life Supporting Technologies – Universidad Politécnica de Madrid.
