Bridging Healthcare and AI: An Interview with Miguel Rebelo, Data Scientist on Innovation and the Future of Health Data
03/03/2025Miguel Rebelo, Data Scientist at Promptly, has a diverse background in biochemistry, biotechnology, and computational statistics. His research journey began with ovarian cancer studies in a lab at IPO, followed by in silico analysis at CIBIO-InBIO, and later at I3S, where he investigated the genetic architecture of Alzheimer’s disease.
After pursuing a second MSc in Computational Statistics, he transitioned into machine learning engineering. Now at Promptly, he integrates healthcare, data science, and engineering to drive innovative AI solutions.
Introducing the Interviewer
Can you share your background and what led you to data science in healthcare?
I’ve always had broad interests, making it difficult to choose just one path. I pursued a degree in Biochemistry and later an MSc in Biotechnology because they encompassed multiple scientific disciplines. My first internship at IPO involved wet lab experiments for ovarian cancer research. Later, at CIBIO-InBIO, I encountered computational biology, which sparked my interest in in silico research. At I3S, I was finally able to merge healthcare and data science by studying Alzheimer’s disease genetics.
Despite unsuccessful PhD funding attempts, I pursued another MSc in Computational Statistics, which led me to machine learning engineering. Joining Promptly feels like completing the puzzle—bringing together healthcare, data science, and engineering
🔹 AI and Data Harmonization in Healthcare
How is AI transforming healthcare, particularly in health data?
AI is revolutionizing healthcare by automating processes, improving predictive analytics, and extracting insights from vast data sources. It structures unstructured data, detects patterns, and enhances decision-making, making healthcare more efficient and data-driven.
What is data harmonization, and why is it crucial in healthcare?
Data harmonization ensures that information from different sources—EHRs, claims data, registries, and wearables—can be integrated and compared. Inconsistent formats and terminologies often hinder effective data use, and harmonization enables seamless research, clinical applications, and policy-making.
How does Promptly use AI for data harmonization, and what are the main challenges?
Promptly employs AI to automate data harmonization, mapping different formats and terminologies into standardized frameworks. A major challenge is dealing with inconsistencies in how healthcare providers record data. AI, particularly machine learning and natural language processing (NLP), helps resolve these variations.
Can you provide an example of how AI-powered solutions help clients with fragmented data?
AI at Promptly is a continuous journey rather than a product. We help clients scale and solve both old and new challenges. A great example is a pilot project where we structure unstructured data in a private and scalable manner for next-generation evidence studies. Traditionally, clinically relevant data is buried in lengthy unstructured texts, requiring manual extraction. Instead of relying on interns to sift through logs, we now automate this process, allowing researchers to focus on meaningful analysis.
🔹 Data Standardization & Interoperability
How does AI streamline data standardization across various sources?
AI automates data mapping, detects inconsistencies, and ensures compliance with global health data standards. This reduces manual effort and accelerates the integration of new data sources into existing systems.
How does Promptly address interoperability challenges in healthcare?
Interoperability remains a major hurdle as different systems often lack seamless communication. Promptly leverages AI to bridge data silos, enabling organizations to share and analyze health data efficiently while maintaining security and compliance.
What role do global standards like FHIR play in Promptly’s work?
FHIR (Fast Healthcare Interoperability Resources) is essential for structuring and exchanging health data. Our solutions align with these standards to ensure smooth data integration and cross-system collaboration.
🔹 Innovation & the Future of Health Data
Where do you see the biggest opportunities for AI in health data analytics?
AI’s potential lies in predictive modeling, personalized medicine, and real-world evidence generation. It can analyze patient data to predict disease risks, optimize treatments, and improve overall healthcare efficiency.
How is AI unlocking new insights from real-world data?
By analyzing fragmented and unstructured data, AI enhances pharmaceutical research, clinical trials, and population health management. It identifies trends and correlations that were previously difficult to detect.
What is a common misconception about AI in healthcare?
A widespread misconception is that AI will replace human decision-making. In reality, AI acts as an assistant, augmenting the expertise of clinicians and researchers rather than replacing them.
What excites you most about Promptly’s work in AI and data science?
The most exciting aspect is how we are advancing AI-driven data integration, allowing healthcare organizations to fully leverage their data for better patient outcomes.
🔹 Last Thoughts
What advice would you give to healthcare organizations looking to adopt AI?
Start with high-quality data. AI is only as good as the data it processes, so investing in data governance and standardization is crucial.
Any final thoughts on AI’s role in healthcare?
AI will continue to transform healthcare, but success depends on collaboration between data scientists, clinicians, and policymakers to ensure ethical, secure, and effective implementation.
To learn more about how Promptly is revolutionizing health data with AI, reach out to our team! 🚀
