EVALUATE HEALTH DATA, PROMOTE PATIENT SAFETY
Health care providers are faced with the great challenge of improving the quality of treatment in the healthcare sector and increasing patient safety, while at the same time reducing health care costs. Smart data analysis of medical data is a promising way to permanently change research and development in the healthcare sector. Aggregated patient data can contribute to the identification of disease mechanisms, reduce the time needed to recruit patients for clinical trials, improve monitoring of drug safety by conducting continuous surveillance, and enable plausibility checks of medical activities to be performed efficiently and cost-effectively.
HEALTH DISCOVERY – BIG DATA ANALYSIS FOR MEDICINE
Health Discovery is a 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. Health Discovery enables meaningful predictions for diagnoses and therapeutic procedures. Patient groups can be assembled with just a few mouse clicks — be it for feasibility studies and patient recruitment for clinical trials, for diagnosis support of rare diseases, or to support medical coding specialists in creating medical service bills.
Use-Cases Health Discovery
Automatic Coding in the
Evaluation of Physician Reports
Health professionals use Health Discovery to collect and analyze reports from hospitals and medical specialists in a semi-automatic manner. By recognizing the patient’s name, it is possible to automatically sort reports into electronic patient files. The physician receives a quick overview of all relevant information such as diagnoses, medications, and therapy recommendations. Thus, doctors save valuable time, which they can dedicate to the treatment of patients instead.
in State Clinical Cancer Registers
in State Clinical Cancer Registers
The German Cancer Prevention and Registration Act (KFRG, 2013) has led to the establishment of clinical cancer registries throughout Germany. They aim to collect all cancer patients who have been treated at a particular facility, within a particular healthcare network, (or ideally) in a defined catchment area. A number of state cancer registries use Health Discovery to automate data collection and to extract relevant information from pathology reports.
This extracted information includes tumor type, diagnosis, morphology, topography, TNM and grading, as well as tumor-specific receptor status. The main objectives of this assessment are quality assurance and representation of the quality of results of the treatment of cancer patients as a whole.
for Rare Diseases
There are over 7,000 rare diseases. More than 350 million patients worldwide are affected. It takes an average of seven years for a rare disease to be correctly diagnosed. Since therapies now exist for more and more rare diseases, patients lose valuable time during which the disease progresses and the quality of life decreases.
Starting with a few clicks!
In this video, we’ll show you how to configure Health Discovery as a text mining and machine learning platform in a few minutes minutes to analyze large volumes of medical documents for diagnoses, symptoms, prescriptions, special findings, and more.
In collaboration with several neurological university hospitals, Health Discovery has been optimized to support the diagnosis of rare diseases. The software analyzes patients’ clinical histories and looks for phenotypes and findings that speak for a rare disease. A complex ranking method determines the sensitivity and specificity of the characteristics, and proposes candidates to the physician who are suspected to suffer from a rare disease. Candidates will then be invited to continue testing in order to exclude or confirm the suspicion. Several patients with various neurological diseases have already been diagnosed as a result.
WOULD YOU LIKE TO SEE A DEMO?
We would be glad to present our products to you and create a demonstration based on your selected data repositories.