The value of digital health technology rises and falls on the quality of data that it produces. Inadequate biosensors, invalid measurement methods, faulty algorithms, and poor quality control can all render data useless. Medical applications require clinically validated data, but even casual health and fitness wearables must have approximate accuracy in order to be useful. We’ve written about the growing use of artificial intelligence to convert biometric data to actionable strategies and treatments.

Austin-based and aptly-named Litmus Health has built a clinical data science platform to collect, evaluate, and interpret raw biometric data for clinical trials. Billing its system and service as “research-ready infrastructure for real-life data,” Litmus offers measurement and analytical expertise. Litmus collects data from wearables, smart devices, and home sensors. Using client protocols, Litmus has iOS and Android apps that generate patient surveys with validated questions. The Litmus platform includes a Study Hub in which clients can view the overall study progress and view in-progress data trends. When the all the data is in, the Analytics Hub organizes and evaluates the data, analyzes relationships and correlations, and interprets the results for meaningful insights and action steps.

Originally the chemical test to determine acidity or alkalinity, “litmus test” now is commonly used to describe decisive indicators. Litmus Health’s clinical data science platform is designed to assist and legitimize study results and product tests with a validated infrastructure service. If the company successfully establishes itself as a trustworthy data handling and analysis entity for digital health studies, having Litmus Health process data could be a clinical Good Housekeeping Seal.