Make Patients’ Lives Better With AI
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.
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.
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.
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.
Clinical studies are the key to medical progress. The use of NLP to determine patient populations is an important step towards obtaining more precise case numbers and more complete medical information about patients. Health Discovery enables the identification of larger patient cohorts, the refinement of inclusion and exclusion criteria, reduces the time and cost of recruitment, and thus increases the value of clinical trials significantly.
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.