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World's largest AI-based healthcare research network

Savana is a unique company at leveraging AI to generate global and
deep Real World Evidence, thus enabling a data-driven healthcare

World's largest AI-based healthcare research network​

Savana is a unique company at leveraging AI to generate global and deep Real World Evidence, thus enabling a data-driven healthcare

These are some of our Publications

These are some of our Publications​

ERS - publications logo
An association between the severity of coronavirus disease 2019 (COVID-19) and the presence of certain chronic conditions has been suggested.
Journal of Clinical Medicine logo
Patients with Chronic Obstructive Pulmonary Disease (COPD) have a higher prevalence of coronary ischemia and other factors that put them at risk

Generating RWE across 14 countries and 5 languages​

Generating RWE across 14 countries and 5 languages​

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Healthcare providers

We help early adopters in the challenge of reinventing healthcare systems:

Instead of collecting data manually, we are using NLP and Machine Learning to reuse healthcare providers’ data for research.

This way of generating evidence is much more achievable, negating the need for multiple intermediaries.

Savana’s data modelling is set up following the OMOP Common Data Model. Complying to OHDSI standards to enable efficient analysis and reliable evidence.

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These days there are countless companies doing RWE. And they all throw the same messages, even when no one really knows what they mean.

Statements like:

Improving patient outcomes with better insights.

End-to-end data platform for collecting and connecting the information.

Better, more and affordable real-world evidence at scale.

Information processing, analytics & research platform.

Accelerating health research through automatic data capture.

Connected intelligence for real-world analytics.

Lately, they have also started to place a few cool buzzwords, such as #AI, #MachineLearning and #NaturalLanguageProcessing.

But as YOU know. Medicine is not simple.

And at the end of the day, a predictive value in real and prospective patients ends up unmasking everything. 

And that cognitive-computing-analytics-Alpha-power-project is not going to improve the quality of your evidence generation nor is it going to do anything for the patients.

In fact, all these groups have good intentions; they ultimately want to communicate two ideas which are totally right:

01. Artificial Intelligence:

Artificial Intelligence is mathematics supported by computation, which, when used effectively, can generate analysis that go beyond classical statistics.

  • In addition, today we have technology that we did not have 10 years ago and that allows us to handle enormous amounts of information.
  • If you manage all this well, with a good clinical question and with order, you can make a leap in the quality of the evidence.

02. Electronic Medical Records:

Electronic Medical Records are an immense source of valuable clinical information which formerly you could only extract partially, manually, slowly and at great expense. While now there is technology which allows for its extraction at scale.

These two statements are true because some things have changed:

AI and machine learning techniques have exploded.

AI and machine learning techniques have exploded.

Some people think that AI is about an almighty computer with which you can talk and will give you all the answers about all the patients, or an infinite database automatically generated. But in short, what it is and what we now have is a tool which allows us to make real progress towards finding associations and new variables

Natural Language Processing is very robust now and it lets us extract that circa 80% of the information which is narrative free text.

Natural Language Processing is very robust now and it lets us extract that circa 80% of the information which is narrative free text.

And of course now we have Electronic Medical Records (not paper), with file formats and standards that are getting better at talking to each other.

And of course now we have Electronic Medical Records (not paper), with file formats and standards that are getting better at talking to each other.

The consequence is that traditional registries have been outperformed.

And so have observational studies, manual chart reviews, claims databases, ICD codes…

Because now we can extract the complete information from medical records.

Not just the claims, or just a few hundreds of variables that someone decided to register. Now we can have much more realistic and flexible databases.

And even better, we can update them directly from the information systems at the points of care, without intermediaries. 

Likewise Biotech has improved exponentially, so have RWE possibilities.

And the timing is perfect, because precisely RWE is more demanded than ever for decision making.

But what is the problem with this new AI approach to RWE?

Very simple. It is not research-grade. All these players have a tech angle, but not a science angle. 

If you request information from the same site several times, you will get different variables. It’s not robust. It’s not validated.

That’s why Savana’s absolute focus for 8 years has been on developing a methodology through which AI tech meets science standards, controlling bias and missing data.

This way, the generation of information from medical records is replicable and accurate.

This way, the generation of information from medical records is replicable and accurate.

In other words, if we create the same pragmatic registry several times, we will get the same variables. And we do it without using complicated AI technicalities as an excuse to lower the standards.

And since we have strategies to harmonize this automatic extraction among sites, countries and languages, the information gets generalizable at an international level.

And once we meet the scientific standards, we can start enjoying the richness of information.

We can get so much deeper into the information that, since we needed a name to express the amount of insights that we were finding.

We called this Deep RWE.

We called this Deep RWE.

  • Deep RWE basically incorporates richer clinical characteristics into high-validity virtual registries.
  • That offers an opportunity to understand populations across clinical criteria while also supporting care pathway enhancement.
  • It is possible to pragmatically achieve high visibility into cohorts of interest, interventions, and clinical and financial outcomes.
  • By engaging health systems directly, we go to the source for the highest quality phenotype data.
  • Moving literally from hundreds of variables into tens of thousands of them, allows us to query the databases with disruptive questions about biomarkers.

This approach is extremely flexible, as you can go with one single specific clinical question or with a whole approach to a disease, retrospectively (for example 5 years) and prospectively, with updates of information at the requested frequency.

It's like having a dynamic registry, but without having to create it.

It's like having a dynamic registry, but without having to create it.

Imagine a CRF with an "undo" button that allows you to change the variables as you need it. That's possible now.

Imagine a CRF with an "undo" button that allows you to change the variables as you need it. That's possible now.

Applying high-validity Virtual Registries
across 16 Therapeutic Areas in 14 countries

An international oncology study of Artificial Intelligence applied to electronic medical records:

This is a unique collaborative study between the Head and Neck Cancer International Group (HNCIG) and Savana.

The first of its kind for head and neck cancer study, HNC-TACTIC is a multi-language, multi-center, retrospective, real-world evidence study analyzing Electronic Medical Records (EMRs).

The study aims to describe patients with head and neck squamous cell carcinoma (HNSCC) in a real-world setting.

 

What we do:

  • Sometimes the simplest way can also be the best one.
  • And the best way is not a cut of a database, nor a group of preselected variables, nor a certain number of patients. It’s not that. And neither a very costly registry.
  • The best way we can imagine is to retrieve the actual complete information about what is happening at the points of care.
  • It’s having access to the complete medical records information.

Every single patient. Every single variable.
The most realistic data source possible.

Every single patient. Every single variable.
The most realistic data source possible.

  • In order to get this, you basically need:
    • A combined team of data scientists and clinicians with experience in research (in our case, lead by oncologists).
    • A system able to retrieve information from any healthcare provider (as long as they have electronic medical records -EMR-, paper doesn’t work).
    • Natural Language Processing, because 80% of the variables and outcomes are going to be in the clinical narratives’ free text.

This system is exactly what we created.

This system is exactly what we created.

Discover how
we can help:

We tested our medical, scientific and technological capabilities through the following Reality Checks:

01 - Major bleeding events and recurrence of venous thromboembolism in anticoagulated cancer patients:

WHAT

  • An AI predictive model for prediction of bleeding and thromboemobolism among anticoagulated cancer patients.

WHY THIS WORK IS REMARKABLE 

  • Because through the analysis of a multicentric 2,893,208 patients population, it created one of the first AI algorithms for this purpose. The methodology included techniques that prevented bias and class imbalance.

WHAT WAS THE CONSEQUENCE 

  • Medical Oncologists now have an available predictive model for deciding about anticoagulation.

WHAT THIS WORK DEMONSTRATES

  • The RWE generated through NLP applied to EMR, in combination with a Machine Learning approach, facilitates the generation of predictive models in cancer patients, with direct clinical impact.

02 - Rheumatoid arthritis:

WHAT

  • Prevalence and comorbidities of rheumatoid arthritis-associated interstitial lung disease.

WHY THIS WORK IS REMARKABLE 

  • Because the analysis of a multicentric population using Natural Language Processing showed remarkably similar results to those recently obtained through a traditional manual data collection.

WHAT WAS THE CONSEQUENCE 

  • Using this study, awareness is being raised among rheumatologists about the need to consider testing for interstitial lung disease in rheumatoid arthritis populations, given that its prevalence is higher than previously known.

WHAT THIS WORK DEMONSTRATES

  • Natural Language Processing applied to EMR looks like an agyle way to obtain reliable epidemiological information, without the effort of manual data collection.

Visit more Reality Checks:

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Scientific institutions also benefit from our approach

This partnership will accelerate the use of data from de-identified EMRs to monitor disease progression and outcomes in UK patients hospitalised with COVID-19 as part of the international Big COVIData study.

This partnership will unlock the clinical data from de-identified, free text in Electronic Medical Records to establish a predictive model to identify patients who may have COPD but have not yet been diagnosed and medications that are proving positive outcomes while being affordable to all.

This is part of our healthcare provider network

involving +200 sites

Savana is supported by
European Union Next Generation funds:

We send a weekly email for you to start making friends with AI. 

It's not for those who want to generate evidence by traditional means, filling registries by hand and using logistic regression. This is for those who want to leverage machine learning in order to generate evidence in a more automated way and with higher granularity of variables.

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