Make Patients’ Lives Better With AI

Health Discovery is the text mining and machine learning platform for analyzing large amounts of patient data.

Health Discovery at a glance

Health Discovery is the healthcare text mining and machine learning platform for analyzing large amounts of patient data.

With Health Discovery, medical documents can be analyzed and searched for diagnoses, symptoms, prescriptions, special findings and other criteria. Heterogeneous patient data in a structured and unstructured form is harmonized, the content analyzed using text mining and natural language processing and made searchable using a uniform interface.



Medical Understanding

Health Discovery understands the world of medical terminology, and that in different languages. We offer 50+ different NLP annotators that can be used out-of-the-box to extract various medical facts such as diagnoses, medication, laboratory values, etc.



AI-Applied Healthcare

We are leaders in applying the latest deep learning technology to healthcare. We combine it with the performance of the world’s most powerful text mining engine on the market – UIMA Ruta.



Making data usable

Health Discovery transforms unstructured “dirty” patient data into structured high-quality data. It maps all findings to semantic codes from medical terminology based on our curated terminology with millions of medical terms.



Get insigths

Health Discovery gives insights into large sets of patient records and identifies hidden relations in near-real time. It supports diagnosis and therapy, finds larger and better fitting patient selections and automates billing in hospitals.

Health Discovery enables meaningful predictions about diagnoses and therapy courses. Supported by natural language processing, patient cohorts can be compiled with just a few clicks – be it for feasibility studies and patient recruitment for clinical studies, for diagnostic support for rare diseases or to support medical coding specialists in medical billing.

Use Cases

Real World Studies

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, many studies become possible in the first place, as many more clinical parameters are available, and more patients can be included in the studies. Learn more.

Automated Coding & Billing

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. Learn more.

Informed Clinical Decision Making

Medical errors are among the most common causes of death in Western countries. One of the reasons is 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 by the use of 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. Learn more.