AiroStotle Detect

AiroStotle Detect platform detects disease prevalence in near-real-time and provides this data to practitioners and primary care providers. Patient diseases data is collected and stored and can be used to better understand the range of symptoms and diseases across all patient demographics and cohorts.

AiroStotle Detect platform can also be used for precision medicine, identifying early treatment responders to cancer and disease therapies. This critical data is not only vital to patients receiving treatment - aggregate treatment data connecting treatment response progress and patient cohorts and demographics can fill in substantial knowledge gaps around what kinds of patients respond to what types of therapies. Additionally, treatment side effects data is collected and can be used to understand what side effects are more common based on the certain patient populations. Early identification of adverse side effects can flag intervention requirements for treatment program and primary care providers to get help to patients earlier than ever before.

G2 Track (Medication Adherence)

G2 Track treatment and medication adherence platform collects ongoing real-time adherence and therapy progress data. This data is shared with both the patient to assist and inform them in their recovery, and with the program administrator and/or primary care provider.

Adherence data informs when patient non-adherence occurs and even understand what side effects and precursors to non-adherence are and what they occur. Interventions focusing on patient support and adherence enable better treatment progress and health outcomes for patient and lower ongoing healthcare costs for systems and providers.

Modelling and prescriptive analytics can drive better patient outcomes by identifying earlier when warning signs may exist that jeopardize therapy success and triggering support and resources to mitigate adverse outcomes.

• Early detection of cancer in higher risk individuals
  We are focused on the early cancer detection market globally using our proprietary breath tests, large data sets and advanced analytics. We believe that the cornerstone to conquering cancer is unprecedented access to its metabolic molecular data available in the raw untreated exhaled breath throughout all stages of the disease, which we enable by a routine exhaled breath analysis at the point of care using real-time cloud based analytics.

• Identification of treatment responder patient population segments Using VOC/biomarker breath diagnostic data to identify patient population cohorts who are more likely to respond to treatment medications and regimens. There is a critical need to expand the scope of precision oncology to enable precise detection, monitoring and selection of the appropriate intervention as early in the disease state as possible.

Recurrence detection in cancer survivors
AiroStotle leverages data and insight from early clinical trials to develop tests that enable clinicians to detect recurrence at a stage when intervention may have a higher chance to cure the disease.

Correlate side effects data with broader adverse health reactions and predict warning signs
Understand side effects data and the relationship between early side effects and adverse health outcomes. Enable early identification and prediction of adverse health and/or treatment reactions at population levels.

Correlate Side Effects Data with Patient Behaviors
Understand relationships between know patient behaviors and reported side effects. Attribute common patient behaviors to increases in types of side effects.

Identification of Non-Adherent Patient Population Segments
Using aggregate adherence and treatment compliance data combined with demographic, behavioral, and EHR data to identify commonalities in patient populations that struggle with medication adherence. Identify when non-adherent behaviors begin and identify their precursors.

Supporting significant health system advances through analytically-driven insights and health informatics

Population modelling with complete clinical data sets integrating key new novel data points on medication adherence, disease and diagnostic information, and side effects data will reveal insights that improve population health, population health risk and risk control, and support prescriptive modelling and services

• Point of care diagnosis information places data in the hands of physicians and primary care providers earlier

• EHR/EMR integration places application and device data and outcomes into accessible patient records for primary care providers

• Financial modelling around treatment and therapy to optimize treatment outcomes while reducing treatment costs