Our Services

We support companies developing medication, devices, and interventions to improve patients’ lives by providing:

  • Data collection using a proprietary app, QITraq™, which integrates data from passive and active measures.
  • Development of digital biomarkers strategy for early- and late-phase clinical trials, participant selection, and other purposes, with design of assessment schedules and study-specific, in-the-moment surveys.
  • Data analysis and real-world interpretation of data gathered by QITraqTM or other apps, clinician and participant ratings, and recorded interviews in order to develop digital biomarkers. Projects can include retrospective analysis of existing datasets. We conduct guided, replicable, interpretable analyses using machine learning and other modern techniques to examine issues including treatment response, participant recruitment, and prediction of drug and placebo response.

QITraq™

Next gen mobile collection.

QITraq™, our proprietary app, allows you to collect study data while integrating data from passive and active data collection, including study-specific surveys, actigraphy, geolocation, audio, participant-produced video, text, speech characteristics, natural language processing, and other sources.

Consulting

Specialized development and analysis.

QI supports clinical research for companies pursuing development of drugs and devices to help improve the lives of patients, caregivers, and other stakeholders through

  • Development of digital biomarker strategy for early- and late-phase clinical trials, real world outcome studies, commercialization, human factors and usability, health economics and outcomes research, and other study types. We can advise on assessment schedules and the design of study-specific momentary ecological assessment surveys.
  • Analysis of digital biomarker data gathered using other applications, including consumer and clinical-grade devices, as well as clinician- and participant-rated data.
  • Our approach directly addresses limitations with big data, exploratory algorithms, and implicit biases inherent to exploratory studies of issues such as the prediction of placebo response.