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.
Once we have facilitated the most difficult part, which is extracting variables from free text (clinical characteristics, comorbidities, signs and symptoms, adverse events or outcomes), we can also combine all this unstructured information with other structured data layers (genomics, transcriptomics, proteomics and imaging) which can be sourced both from our worldwide network of hospitals and from clinical trial databases.
Savana works with its premium partners in order to offer a combined proposal:
Some healthcare providers are very good at data science, AI and even Natural Language Processing.
We simply make sure that the data they produce is harmonic with the other sites in our network, so they can jointly conduct multisite research projects.
When sites become part of the
they are invited to participate in national and international research studies contributing to evidence generation, accelerating health science and improving patient care.
Savana informs each site about the ongoing research studies in which they may participate, sponsored by private and/or public institutions. If interested and following ethics committee approval, there is no longer a requirement to complete Case Report Forms CRFs since the data is already structured and available. It is simply a matter of sharing the specific data agreed in the study protocol.
They need our platform to curate their data. The appreciate the power to unlock all of the clinical value embedded within existing Electronic Medical Records in order to self reuse it for different purposes, that range from research projects (for example through a grant) to clinical trial recruitment and even predictive modelling for management.
Even when NLP can be done by many, they appreciate that we have read more than 3 billion clinical documents, thus our engine is well trained and sometimes it’s better not reinventing the wheel.
Even when we apply a federated model where data never belongs to us, hospitals are often a pain with regards to agreements around medical records and the EMR providers usually don’t help.
What we offer to the sites is:
And the solution we provide is the interaction between clinicians and data scientists:
We invested millions and years in developing a methodology by which we can infer the variables from the EMR, keeping quality and controlling bias. The consequence is a methodology which results are replicable, thus generalizable.
We collaborate with a network of +250 hospitals across Western Europe and the Americas.
You don’t have to. You just need to go to our peer-reviewed publications, both clinical and technical, where our methodology has been scrutinized and proven.
In our publications you will also find validations of the AI models we have created.
It depends on what you understand by more complicated. If I only need one pair of shoes, it’s easier to just manufacture it. But if you need thousands of shoes, the only way is to build a factory.
If you want to generate real world evidence about a disease or a drug, you will normally want a) very granular information b) new mathematical models in order to find new associations and hypothesis. Then, this is your method. While if you want to spend millions and years in creating a registry, this is not for you.
It really depends on how deep you want to get into the information. If you want the information in 1 month, then you’d better go for a database cut. But if you want to own a dynamic registry, navigate it, query it in search for new insights,… and you can wait some months to have this, then it’s definitely worth it.
We are just enjoying the result of years of focused investment into being the best at mining medical records for real world evidence generation purposes. There is no magic in it. All we are doing is applying state of the art AI and the scientific method to clinical research.
No. The amount of information you will get will be relatively more cost-efficient than any traditional way of doing things. By far.
Of course not. You are the only one who has the clinical question and you will need to guide our team until we are sure that they understand the exact problem you are trying to solve. Aside from that, agreements with hospitals are tough, and in our experience, what works better is to convince them by approaching them together, so our collaboration will serve to accelerate the Project.
We normalize the clinical concepts according to the SNOMED CT ontology, with variables added by Savana’s internal medical staff in those cases not covered by SNOMED CT. Mapping to OMOP is also part of the process when required.
Yes. Savana is compatible with other similar platforms. Other types of repositories based on structured text or free text can be complementary to the information processed by Savana.
Savana is able to extract both structured and unstructured data (free text).
Thanks to using natural language processing techniques to extract clinical content from the free text of the electronic medical record, Savana offers doctors the opportunity to carry out research on pathologies and/or patient groups in real time and at any time, which to date has been impossible to perform. At the same time those results can be published in a scientific journal.
Savana facilitates massive and very fast extraction of clinical variables found in the free text of EMRs, which replaces the current work of manually reviewing chart by chart.
Structured data like pharmacy, laboratory or genomics also can be extracted and added to the database if required.
Clinical documents: being the company which has processed the biggest number of documents of this type worldwide; allowing our algorithms to currently be among the most trained for this purpose. Savana has been implemented in +200 sites across 16 countries for years and its use has generated abundant scientific publications, answering questions in multiple therapeutic areas.
Savana is compatible with all EMR systems, regardless of format and source. Our technology is vendor agnostic. The only limitation is that the documents are text and not images. The preferred document formats for the information extracted from EMR are CSV, JSON, XML and DB, being compatible with other data exchange formats that we will assess previously.
There is no extraordinary requirement beyond the usual ones for a healthcare provider IT (internet connection, usual operating systems, etc).
It enables the export of all the data in different formats for its use by other artificial intelligence tools, or statistical tool, such as SPSS or R.
No, the hospital has the processed information at its disposal to make the use it deems appropriate.
No. Every site must opt in or out once we have a new study protocol. That way they always keep control over their data. Of course, every hospital can also suggest a study to the rest of the network.
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