Data management and integration

Larkbio has experience in the design and implementation of complex sample and data management systems.

Data has always been an important element of healthcare but it has never been more obvious than today. The hunger for analyzed medical related data to improve delivered care and to better meet quality measures is spurring a revolution in healthcare.

The data deluge also effects medical research to a great extent. Interconnecting genomic data measurements, laboratory results, demographic and lifestyle data is the topic of most leading projects.

Larkbio can handle a wide variety of data. The origin of the data can be almost anything from a simple fitness application on a smartphone to a high security medical laboratory. Our job is to integrate the data and turn it into meaningful and actionable information.

Health and fitness data management

The healthcare industry historically has generated large amounts of data, driven by record keeping, regulatory requirements and patient care. Although most of the data is still stored in hard copy form, there is a strong trend toward digitization of these data. In fact, these massive quantities of data hold the promise of supporting a wide range of medical and healthcare functions, including among others clinical decision support, disease surveillance, and population health management.

Within the global healthcare sector, there were traditionally two major types of digital data: clinical records and health research records. These are usually stored at healthcare providers and research institutions in a largely unstructured format. The proliferation of personal medical sensors, fitness trackers and smartphone applications is adding a new dimension to an already complex data landscape (51 million wearables will be sold in 2015 compared to 17 million in 2014).

The systems developed by Larkbio aggregate this multifaceted data coming from different sources and turn the individual pieces into insights.


High quality and high quantity components (sample and data) need to be managed accurately and efficiently – always considering cost effectiveness, regulatory compliances and tracking requirements.

All our data management systems share the following features:

Web based Real time data entry and easy access to contents
Physical data security
  • Redundant servers and architecture
  • Regular data backup
  • The complete database is stored encrypted at a remote, independent location
Logical data security
  • Extended authorization protocol
  • Minimal access principle: users can only access data indispensable for their tasks
  • Data encryption (including passwords)
Advanced search functions The wealth of information can be utilized through a complex data filtering system. Results are arranged and displayed in sets and subsets, creating a base for advanced statistical calculations.
Integrative data analysis Combine clinical, demographic, genetic, environmental and lifestyle data to uncover new insights
Show off your research results Effortlessly create reports with graphs, statistics and interactive maps.


Our sample and data management systems use the following modules:


  • Sample reception: Information may be imported from a range of electronic files or entered manually. Supports a range of labeling methods, including bar codes, RFID etc.
  • Sample storage: Configurable storage system with customizable hierarchies.
  • Sample tracking: Tracking aliquots, derivatives and pooled samples ensure chain of custody when materials are moved or used.


  • Clinical data: medical history, medication profile, physical examination results, laboratory results, images and medical imaging results, molecular diagnostic results etc.
  • Environmental and social factors: demographic data, family related data, diet etc.
  • Experimental results: arrays, sequences, PCR results etc.
  • Personal data: in addition to standard authentication and access control procedures, personal data is inaccessible without explicit permissions


Identify associations between the stored metadata using these techniques:

  • Filtering
  • Descriptive statistics
  • Relationship between variables
  • Hypothesis testing


Present the results of statistical queries with the help of the following tools:

  • Line, bar and pie charts
  • Histograms / scatter plots
  • Special tools designed for big data visualization