Make patients’ lives better with AI


Whether in prevention, early diagnosis of illness or the right choice of therapy – text mining and machine learning, specifically natural language processing, make a significant contribution to people being provided with better medical care and advice on an individual level.

A look at the use cases in the healthcare text mining sector shows the diverse applications of our Health Discovery product:

From unstructured data sources we can derive suggestions for suitable prevention approaches or therapeutic measures – and support doctors and patients in the decision-making process. Patient cohorts can be assembled with just a few clicks – be it for feasibility studies and patient recruitment for clinical studies or for diagnostic support for rare diseases. In hospital information systems, the Health Discovery supports coding specialists in medical billing.

Aggregated patient data for the identification of disease mechanisms
Monitoring drug safety through continuous monitoring
Reducing patient recruitment times in clinical trials
Service billing efficiently and inexpensively

Use Cases

clinical research

The use of electronic patient data has a lasting impact on medical research. Routine medical data can be used to conduct retrospective observational and comparative studies. Much of the information needed in clinical research, such as diagnoses and symptoms, treatment history, functional values, etc., is often only available in free text. When this data is extracted from the texts and made available in a structured and semantically normalized form, more studies become possible because many more clinical parameters are available, and more patients can be included in the studies.

Customer Success Story:

Averbis is used by all four consortia of the Medical Informatics Initiative for the analysis of unstructured data.


The medical informatics initiative was created to close the gap between research and healthcare. All of Germany’s university hospitals have joined forces with research institutions, businesses, health insurers, and patient advocacy groups to create a framework that harnesses research findings to the direct benefit of patients. The German Federal Ministry of Education and Research (BMBF) is investing around 160 million euros in the programme through 2021.


The aim of the medical informatics initiative is to ensure that, in future, each doctor, patient and researcher has access to the information they require – while simultaneously allowing individuals to maintain control over their personal data. This will lead to more precise diagnostics, and better treatment decisions. It will yield new insights for research and medicine.

And it will improve patient care and help combat diseases more effectively. Specifically, the initiative is about harnessing the flood of data generated every single day in healthcare and research environments – to the benefit of individual patients, to better understand illnesses, and to tailor treatments to the needs of the individual. The goal is to establish a way to exchange research and care data across university hospitals. If it proves successful – with the help of funding from BMBF – it will open up entirely new horizons. The solutions that will be developed are expected to create added value across the health system.

Averbis BMBF Medizininformatik-Initiative

With the introduction of Diagnoses Related Groups (DRGs), the coding and billing of medical and nursing services has changed significantly. The documentation and coding of medical and nursing services is complex and prone to errors. At the same time, this is a very repetitive and time-consuming process since many patients with the same diagnoses are coded in the same way several times a year.


With Health Discovery, missing codes and documentation gaps can be identified quickly and accurately. It enables an automated search for diagnoses and procedures and provides corresponding documents in the texts. You no longer have to manually process large amounts of clinical data and can concentrate on the essentials of your work.

Customer Success Story:

Averbis collaborates with Compugroup and Meta IT for coding support in hospitals – for revenue assurance and MDK security.


The jointly developed software MetaText is programmed profit! Averbis Health Discovery analyzes medical documents such as doctor’s letters, pathology reports, surgery reports etc. and extracts revenue-relevant information. In doing so, the most modern methods from the field of Artificial Intelligence are combined with detailed rules and regulations in order to deliver the best possible results. In this way, we support coding specialists and medical controllers in finding out from the plethora of specifications the coding that represents the correct revenue for the services provided.


Averbis Health Discovery is fully integrated into the MetaText coding software, a solution that serves to optimally and correctly map services rendered in the DRG system, to harmonize the length of stay with the determined DRGs and to support the creation of MDK-compliant documentation by warning of possible scope for interpretation during coding. Customers’ experiences show that MetaText can significantly improve coding quality and therefore results.

Averbis Health Discovery is fully integrated into the MetaText coding software, a solution that serves to optimally and correctly map services rendered in the DRG system, to harmonize the length of stay with the determined DRGs and to support the creation of MDK-compliant documentation by warning of possible scope for interpretation during coding. Customers’ experiences show that MetaText can significantly improve coding quality and therefore results.

averbis coding and billing
Averbis Clinical Trials

Access to electronic patient records can be used to refine the inclusion and exclusion criteria for clinical trials, reduce the time and cost of fully recruiting the trial, and accelerate market access. By immediately listing the patient numbers that meet the study criteria, protocols for clinical studies can be created with direct feedback on their feasibility. Embedding NLP in electronic health record retrieval is an important step in increasing clinical trial activity for your patient population. The benefits of NLP for clinical trials make it possible to make important clinical data available, to define and identify patient cohorts more precisely, to expand your population database and to increase your value for collaborative networks.

Customer Success Story:

Through its global network of 150 clinics, our partner TriNetX provides current medical data at patient level. This enables supportive, curative and preventive therapies to be developed for the disease. The TriNetX NLP service powered by Averbis utilizes sophisticated algorithms to extract clinical facts from physician notes, clinical and pathology reports, links them with other Electronic Medical Record (EMR) data, and makes the combined data available for assessing study feasibility, protocol design, site selection, and subsequent identification of patients for clinical trials.

The TriNetX NLP service powered by Averbis mines unstructured data, such as measurements and observation on ECOG performance status for oncology, NYHA classification, Ejection Fraction, and Corrected QT Interval for cardiac studies. NLP also collects information from clinician notes for patients whose hospital medical records may be incomplete due to visits to multiple healthcare facilities. Extracted data is subsequently mapped to standardized clinical terminologies that can be easily analyzed by researchers using the TNX platform.

TriNetX chose Averbis as NLP partner for the following reasons:

Deep experience in healthcare

Unparalleled accuracy across data domains

Comprehensive understanding of multiple languages

averbis healthcare nlp
medical-records averbis

Medical errors are among the most common causes of death in Western countries. One of the reasons being the lack of relevant information at the right time. In the meantime, medical knowledge doubles every 100 days. Doctors can no longer keep all knowledge about diseases, therapies, drugs, and their interactions in mind. The use of information technology is therefore indispensable in hospitals and medical practices. However, existing information systems are often isolated and health data cannot be systematically evaluated. A solution is provided using artificial intelligence in combination with NLP (Natural Language Processing): large amounts of health-related information from different sources and formats can be linked and analyzed. This knowledge can be made available wherever medical decisions have to be made.

Customer Success Story:

Averbis Health Discovery was successfully applied to help diagnosing rare neurologic diseases. Diagnosis of rare neurogenetic disorders is often challenging, particularly adult-onset presentations, with long diagnostic delays and misdiagnosis. As therapies become available, it is increasingly important to identify patients with rare neurologic diseases.


This multicenter project on ten rare neurogenetic diseases was run at six German University medical centers. Averbis Health Discovery structured medical data by ranking documents according to probability of disease, based on disease-specific lists of weighted signs and symptoms. Existing electronic medical records from the Department of Neurology of each center, corresponding to 10 years of activity, were screened, and patients ranked by probability of having the respective disease.

An experienced team of physicians reviewed the data for the top ranked patients and those without a confirmed diagnosis were contacted for testing for the respective disease.

In the pilot phase, 4 patients with Pompe disease and 4 heterozygous NPC1 mutation carriers were identified in Munich. More than 400.000 datasets from four centers were analysed for three diseases:

Niemann-Pick type C disease, Pompe disease and Fabry disease. Four novel Pompe patients and 3 heterozygous NPC1 or NPC2 mutation carriers were identified, who had not previously been diagnosed.

What our customers say

Prof. Dr. Hans-Ulrich Prokosch
Universitätsklinikum Erlangen
The complex process of anonymizing medical documents is significantly shortened by using Health Discovery. This enables large quantities of free medical texts to be made available for medical research.
Johannes Drepper
TMF e.V.

Averbis Health Discovery is a valuable contribution to data protection-compliant, networked medical research.

Prof. Dr. Kurt Marquardt
Universitätsklinikum Gießen und Marburg

Health Discovery is an effective tool to prepare textual information for research quickly and in accordance with data protection regulations.

Dr. Martin Richter
Klinische Landesregisterstelle des Krebsregisters Baden-Württemberg

Averbis Health Discovery helps us to slim down our work processes and reduce the burden on our documentalists. This saves us valuable time we can invest in other activities.

Marc Bliem
MetaIT GmbH
Managing Director

All hospitals should receive appropriate remuneration for their services. In order to adequately take into account coding-relevant information from free-text routine data such as doctors’ letters or laboratory reports, we use the AI software Health Discovery from Averbis. We are pleased that we were able to integrate the best text mining solution in Germany into our MetaKIS product. Our customers are thrilled.

Read more about our research projects
Make patients’ lives
better by AI