Interview with Javier De Oca, Co-Founder and CEO of IOMED

Hello Javier - please introduce yourself and your role.

“I’m Javier De Oca, the Co-founder and CEO of IOMED. I’m responsible for overall business management, leading the team, as well as sales and business development. I have over 10 years of experience in the healthcare industry and I’m passionate about using technology to improve patient care. I’m also a strong advocate for diversity and inclusion in the workplace. 

“I believe that IOMED has the potential to make a real difference in the world and I’m excited to be a part of the team that’s making it happen.”
 

What led you to this industry?

“Upon completing my BBA from ESADE, I initially ventured into Marketing and Sales, specialising in African international markets. My co-founder, Gabriel Maeztu, a medical doctor, introduced me to his innovative idea and brought me on board from the get-go. My father's role as a surgeon and my mother's role in hospital pharmacy provided me with invaluable insights into the critical aspects of healthcare - early diagnosis, treatment efficacy and patient safety. These experiences fueled my passion for healthcare and inspired me to contribute to IOMED's mission of enhancing patient outcomes."
 

What is IOMED?

“IOMED was founded in 2016 and we develop AI-powered technology for healthcare data activation. Our Natural Language Processing system is capable of standardising both structured and unstructured healthcare data, adding value to healthcare organisations and leading the path to a global data-driven healthcare ecosystem.
“Initially, IOMED began deployment in hospitals in the Barcelona, Spain region, but our success quickly drove us to expand throughout Spain. Today IOMED has grown into one of the European leaders in the digital health space, with our headcount surpassing 50 employees in less than six years.”

Javier De Oca, co-founder and CEO of IOMED

How are you helping hospitals structure data with AI?

“Our platform leverages the power of AI and Natural Language Processing (NLP) to unlock valuable healthcare data from both structured and unstructured sources, including human-written records. This data is then standardised into the OMOP Common Data Model and can be stored on any hospital’s in-house system, enabling a comprehensive understanding of healthcare information while maintaining data protection and security.
“By structuring healthcare data with AI, we are enabling more healthcare professionals and organisations to make informed and precise decisions, and paving the way for more collaborative and evidence-based medicine.”

How is IOMED helping to eliminate narrow data samples & highly inefficient processes with its cutting-edge NLP (Natural Language Processing) engine?

“At IOMED, we address two pervasive issues in healthcare: the constraints of limited data samples and the bottlenecks caused by outdated data processing methods. To tackle these challenges, we employ state-of-the-art Artificial Intelligence (AI) and Natural Language Processing (NLP) technologies. These technologies are capable of mining crucial information from electronic health records (EHRs), which are digital versions of patients' medical histories.

“What sets our approach apart is our ability to extract not just numerical or coded data but also the invaluable free-text narratives often found in clinical records. These narratives can contain essential details about diagnoses, treatments and patient responses that are not captured in structured data fields. By incorporating this unstructured data, we can create a more comprehensive and representative data sample, thereby overcoming the limitations of narrow data sets.

“To make this data usable and interoperable, we standardise it using the Observational Medical Outcomes Partnership (OMOP) Common Data Model. This model is widely recognized for its ability to facilitate the sharing and analysis of healthcare data. By converting diverse data into this standardised format, we eliminate the need for manual data mapping and transformation, which are both time-consuming and error-prone processes.

“The automation of these processes through our NLP engine significantly accelerates data collection and preparation. This efficiency gain frees healthcare professionals from administrative burdens, allowing them to allocate more time and resources to patient care. It also enables quicker data analysis, which is crucial for timely decision-making in various healthcare applications, from clinical research to patient treatment plans.”

Tell us about your partnerships and expansions?

“We currently have more than 50+ strategic partners in the healthcare sector. These include hospitals and medical institutions, regulatory organisations, healthcare technology companies, as well as researchers and academics. We are actively expanding with new partnerships across Europe so that more organisations can harness the power of our technology. 

“Additionally, we are part of the OHDSI network and actively working to structure all medical data into OMOP CDM, allowing us to closely collaborate with this community and contribute to significant advancements in healthcare research. So far, IOMED has processed data from 21m medical records and standardised it into the OMOP CDM Model.”
 

What do the next 12 months hold for you and the company?

“Over the next 12 months, we are focused on expanding into new markets such as Portugal and Italy, and continuing to grow our platform and team.”
 

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