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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.
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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.
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.
Artificial Intelligence is mathematics supported by computation, which, when used effectively, can generate analysis that go beyond classical statistics.
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.
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.
And so have observational studies, manual chart reviews, claims databases, ICD codes…
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.
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.
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.
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.
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.
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
WHY THIS WORK IS REMARKABLE
WHAT WAS THE CONSEQUENCE
WHAT THIS WORK DEMONSTRATES
WHAT
Prevalence and comorbidities of rheumatoid arthritis-associated interstitial lung disease.
WHY THIS WORK IS REMARKABLE
WHAT WAS THE CONSEQUENCE
WHAT THIS WORK DEMONSTRATES
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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.
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