New alliances in the healthcare sector
Healthcare systems worldwide are facing major restructuring driven by increasing regulation, record-level public debt, and shrinking budgets. Traditionally separate stakeholders in the healthcare industry—such as hospitals, insurers, and pharmaceutical companies—are increasingly forming alliances. They are seeking ways to improve the quality of healthcare delivery and patient safety while reducing costs.
Drug development is one example. It is becoming increasingly expensive, while productivity in research and development is declining. Pharmaceutical companies are therefore looking for ways to develop medications in a more cost-efficient and targeted manner.
A shortage of study participants
Access to clinical data plays an important role in this context. It supports feasibility assessments for study protocols and the identification of potential participants for clinical trials. Recruiting suitable participants and identifying trial sites are among the main reasons why clinical studies are delayed.
Currently, around half of all clinical trials do not reach the required number of participants. Approximately 80 percent of studies fail to meet their original recruitment targets and timelines. A delay of just one day in bringing a drug to market can cost a pharmaceutical company up to USD 8 million.¹
Big Data: the growing data volume in healthcare
The analysis of routine medical data presents typical Big Data challenges. These include questions around how to efficiently collect, standardize, and semantically analyze increasing volumes of patient data.
Over the past ten years, the amount of information in healthcare has grown rapidly, driven by advances in genome sequencing, the adoption of electronic health records, the generation of data for clinical trials, social media, and medical devices—including smartphones, smartwatches, and fitness trackers. This development requires modern text mining and data mining tools to derive meaningful insights from these data sources.
AI to support efficient clinical research
Our solutions support access to large, harmonized patient populations and facilitate processes related to participant identification for clinical research. Using our AI platform, medical documents can be analyzed and searched based on diagnoses, symptoms, prescriptions, specific findings, and many other entities.
Heterogeneous patient data—both structured and unstructured—can be harmonized and normalized. This allows patient cohorts to be assembled efficiently for use cases such as feasibility studies, participant identification, support for rare disease analysis workflows, or assistance with medical coding processes.
In this way, Averbis supports collaboration between pharmaceutical companies and hospitals by enabling the structured use of existing data resources. This can support participation in industry-sponsored clinical studies and strengthen competitiveness in the allocation of research funding.