Digitizing data from the lab

Laboratory data is a central component of modern medical practice and is usually primarily associated with health data and blood values. However, laboratory values are also collected and processed in research, quality assurance and various industrial applications. In all cases, their precise collection, analysis and use is fundamental to achieving a successful result.

Laboratory values - definition and areas of application

Laboratory values refer to data obtained from analyses and tests carried out by laboratories. These results can come from biological, chemical or physical samples and are indispensable in many industries. We have collected some examples. 

• Medical diagnostics - Results from blood tests, tissue analyses and other clinical tests are crucial for the diagnosis of diseases, reliable findings and the planning of treatments. This data also forms the basis for preventive measures.
• Pharmaceutical research - In the development of new drugs, precise laboratory values play an important role in the evaluation of efficacy and safety. Reliable data is needed to investigate the correct dosages and the tolerability of substances.
• Environmental analysis - Laboratory values are used to monitor the quality of water, air and soil and to assess ecological risks. These analyses are crucial for compliance with legal requirements, internal compliance standards and environmental protection.
• Industrial applications - In the manufacturing industry, chemical and physical tests are used to guarantee and control product quality. Automated laboratory analyses can help to optimize production processes and avoid product recalls.
• Quality assurance in the food industry - To ensure the consistent quality of the food produced, regular laboratory checks are carried out on food and beverages. This ensures the quality of the product and the safety of consumers.

Example - Interpreting a complete blood count with AI

Interpretation of the AI

Thrombin time: 16.5 sec
→ at the lower standard edge (Standard: 16.0-20.0)

Total protein: 8.2 g/dl
→ slightly increased (Norm: 6.0-8.0)

BUN (urea nitrogen): 10.3 mg/dl
→ slightly increased (Standard: 6-25, but at the upper edge) (Note: still within the reference range, but tends to be conspicuous in the context of creatinine).

Creatinine: 0.98 mg/dl
→ at the upper standard edge (Norm: 0.50-1.20)

Use cases for the digitalization of laboratory values

The automation of the selection and digital processing of laboratory values can be a decisive advantage in a variety of application scenarios. The digitalization of laboratory values not only offers the opportunity to optimize and automate existing processes - it also enables the development of new services that can only be implemented thanks to the new technological possibilities. 

• Optimization of clinical diagnostics - Automated and precise evaluation of laboratory results reduces processing time and helps to make informed decisions on patient treatment quickly. By digitizing the values, patients can also read, view and manage their health data easily and independently.
• Increased efficiency in research and development - In research institutes, large amounts of data can be efficiently collected and analyzed, which makes it easier to identify trends and new findings. This increases the availability of data for innovations and significantly accelerates research cycles.
• Quality management in the industry - Automated systems and digital data ensure precise monitoring of laboratory results and contribute to compliance with quality standards. Solid quality controls are crucial to ensure long-term success and customer satisfaction.
• Innovative solutions - Digitally retrievable laboratory values open up a wide range of possibilities for developing new services and solutions. In the healthcare sector, for example, apps that enable the individual selection, evaluation and management of health data for patients.

Digitizing laboratory values with artificial intelligence (AI)

The automated selection and digitalization of laboratory values has become increasingly efficient and precise thanks to the use of artificial intelligence (AI). Thanks to technologies such as Medical Named Entity Recognition (NER) technologyFor example, medical terms and data can be recorded in a specialized and precise manner and converted into structured information. Such intelligent technology is particularly powerful as it can be trained on a wide range of industry-specific terms and terminology, allowing the precise extraction of relevant information from unstructured data sources.

The AI-supported systems are able to recognize laboratory values from different formats, classify and split multi-page document collections and process and digitize the data obtained. These solutions support companies in various industries and enable high-quality data to be obtained. This data in turn enables the creation of a digital knowledge database that makes information accessible and enables the sustainable optimization of processes.

Functions for digitizing laboratory values

• Data extraction - AI-supported technologies analyze documents such as PDF reports, images of laboratory results or electronic formats and extract relevant information. 
• Data evaluation and retrieval - After extraction, algorithms can be used to analyze the data in order to identify significant trends and deviations. Internal queries also make the extracted data accessible and searchable.
• Data integrity - When collecting and storing data, the quality and security of the information obtained is of the utmost importance. Both are guaranteed by Data integrity ensured.
• Integration into existing systems - The automated systems can be seamlessly integrated into existing company systems, ensuring a smooth flow of data.
• Machine learning - The continuous learning process enables AI models to optimize the accuracy of their data extraction over time. As a result, the quality of the data collected increases steadily.
• Scalability - AI solutions are flexible and can be adapted to the growth of your company. Seasonal load peaks can also be scaled and absorbed.

Digitizing laboratory values pays off

The digitalization of laboratory data is crucial for efficiency and quality in modern medical practice, research and industry. These processes not only enable the precise collection, analysis and use of data, but also promote the acquisition of valuable knowledge that is essential for well-founded decisions and innovative solutions.

By automating data processing, companies can not only optimize existing processes, but also develop new business models and offerings. Artificial intelligence (AI) plays a key role here by enabling the efficient extraction and analysis of laboratory data, thereby creating central sources of knowledge that make relevant information quickly accessible.

With technologies such as Medical Named Entity Recognition (NER) technology, specific terms can be precisely identified and converted into structured data. This ensures a high level of data integrity and helps companies to comply with quality standards and implement optimized processes.