The Center for Data Innovation recently spoke with Sven Jungmann, CEO of Aiomics, a Germany-based company that integrates an AI-model into electronic health record systems to help manage patient data. Jungmann explained how Aiomics detects inconsistencies in patients’ records and alerts hospital staff to review and complete missing or conflicting information.
David Kertai: How does Aiomics process and organize patient data?
Sven Jungmann: Aiomics works alongside hospitals’ existing electronic health record systems to structure and organize clinical data. This can include paper records like discharge notes and lab results to digital files and PDFs.
Once patient information or records are digitized, Aiomics AI system extracts key details such as diagnoses, lab results, medication lists, and physician notes. It also removes redundant details, flags inconsistencies, and identifies missing data. By consolidating and validating information from multiple sources, Aiomics ensures hospital staff can access the most accurate and complete patient data.
Kertai: How does Aiomics ensure the accuracy and consistency of patient data once it’s collected?
Jungmann: After collecting the data, Aiomics checks it for accuracy, consistency, and completeness. The system verifies that each document belongs to the correct patient and compares information across sources to detect gaps or inconsistencies.
For example, a doctor’s note might describe a patient having high blood pressure, but the official diagnosis list in their record doesn’t include hypertension. That omission can cause problems later, such as missing relevant treatments or inaccurate billing. These discrepancies occur when different departments use separate systems or documentation standards, leading to incomplete or inconsistent records. To resolve this issue, Aiomics detects these discrepancies and alerts hospital staff to review and resolve them. It also highlights missing details, such as incomplete treatment histories or absent follow-up information, so users can update the record.
Kertai: How does integrating Aiomics into the hospital environment change hospital staffs’ day-to-day work?
Jungmann: We’ve found that integrating Aiomics into hospital systems reduces staff’s administrative workload by an average of two hours per day. The system automates time-consuming tasks such as writing reports, filling out forms, and preparing compliance documentation. By handling these processes, Aiomics allows doctors to spend more time with patients. Better data organization also improves care quality. When hospitals can easily access accurate records, patients don’t need to repeat their medical histories, and hospitals can avoid unnecessary duplicate tests, saving both time and money.
Kertai: How do you make sure doctors trust Aiomics’ insights?
Jungmann: We keep doctors directly involved in reviewing and approving the system’s outputs so they can verify or edit any documentation before it’s finalized. To further ensure accuracy, Aiomics runs multiple AI models in parallel: there are models that perform the main analysis and decision-making, and there are models that monitor those models for potential errors or hallucinations. This layered design minimizes the risk of false or misleading results.
This human-in-the-loop process keeps doctors in charge of final decisions while saving them the time of manually reviewing every document. As a result, the AI system helps produce standardized, ready-to-use patient records.
Kertai: What has been the biggest challenge in scaling your system for hospitals?
Jungmann: Many hospitals are still learning how to purchase and integrate machine-learning software. They often prefer long-term fixed contracts for budgeting stability, but AI tools perform best under usage-based pricing models that charge according to system use or data volume. This approach aligns costs with real hospital activity and offers flexibility as usage grows or fluctuates. However, the difference in pricing expectations can slow procurement and adoption.
Some hospitals have also had negative experiences with earlier digital systems, such as expensive software that underdelivered or locked them into inflexible contracts, which makes them cautious about adopting new technologies. We address this by being transparent, offering fair and flexible pricing, and working closely with hospitals to demonstrate Aiomics’ real-world benefits in everyday care.
