Globally, the way patient clinical data is collected, organized, and used is changing rapidly with health workers migrating to a digital format. However, in Mexico, only about 50% of healthcare professionals use electronic health records.

“Many are still handled in Excel or, in some very extreme cases, even on paper,” says Arturo López Pineda, EXATEC of Engineering in Computer Technologies, Morelia Campus, at Tec de Monterrey, and director of Amphora Health, a data science and software development company.

The company was founded in 2020 with the goal of not only digitizing medical information, but also turning that data into accurate clinical predictions or decisions.

To do this, they use artificial intelligence (AI), data science, and machine learning.

One of their products is Vaquita EHR, an electronic clinical record system designed with AI to improve the experience of healthcare professionals and reduce the day-to-day administrative burden.

In addition to scheduling appointments and sending prescriptions, it allows for the recording of all the patient’s clinical information, including studies, images and tests performed, so that their entire medical history is available with just a click.

“The free version of our platform offers many more features than platforms like Doctoralia,” says López Pineda.

From Big Data to Applied Science

In addition to Vaquita EHR, they have Beluga Science, a platform that centralizes and organizes large volumes of clinical data according to international standards, such as the OMOP Common Data Model.

This tool allows researchers and professionals to explore epidemiological patterns and treatment outcomes without needing programming skills.

“Beluga helps to build trust because the data has a traceable path from the beginning,” López Pineda points out.

This approach has profound implications for personalized medicine and translational research: by structuring data from real populations and facilitating visualizations, it accelerates the generation of evidence capable of guiding better treatments and health policies.

“We have built models to predict diabetes complications, which is one of the most important health problems in Mexico, for example” says López Pineda.

To develop these products, they use different types of AI, such as natural language processing (NLP), which can read information exactly as humans write it, with errors and spelling mistakes, and then organize it.

“There are many medical acronyms, and there are formats that only apply to certain institutions,” says López Pineda.

Since its inception, Amphora Health has prioritized ensuring that its technologies meet the highest standards of security, privacy, regulatory compliance and Mexican legislation on the protection of sensitive data.

The information gathered by the company is able to empower both hospitals and clinics, as well as research teams, to advance in diagnoses, treatments and preventive models based on solid scientific evidence.

The Future of Amphora Health

An example of how their services can be applied to research is their collaboration with the Mexican Social Security Institute (IMSS), which seeks to enhance joint projects in critical areas such as diabetes, hypertension, chronic kidney disease, cancer, and infectious diseases.

“Being such a large institution, IMSS has massive amounts of data,” says López Pineda.

In the scientific field, Amphora Health’s presence is reflected in recent publications in collaboration with academic institutions and research centers.

The studies range from genomics of familial hypercholesterolemia in Mexican populations to epidemiological analyses of chronic kidney disease, demonstrating the potential of data analysis tools to generate critical knowledge for contemporary medicine.

Looking ahead, their goal is to migrate Vaquita EHR from outpatient care to the hospital setting. They also aim to expand Beluga Science so that it can store and analyze high-quality data from millions of patients, including those from other Latin American countries.

Thus, the company is interested in creating an ecosystem where health big data, AI, and collaborative research can converge to sustainably improve clinical outcomes.

By enabling in-depth analysis of real and accessible data for researchers and healthcare professionals, this company lays the foundation for a more predictive, personalized, and equitable medical science in Latin America and the world.

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