Flask Clinical Monitoring

Automated remote monitoring.

Metrics that you and your clinop teams trust.

Composition (No. 1) Gray-Red, 1935, Piet Mondrian, The Art Institute of Chicago

Automated remote monitoring 24×7

Flask Data remote monitoring enables you to compute automated metrics  in near-real-time for GCP compliance monitoring.  Metrics for protocol compliance, safety and data quality that R&D, clinops team and sites can trust.

You can create metrics from any item of clinical data collected by the Flask data collection systems.. Metrics are automatically computed by scanning data flows every 10’; flask clinical monitoring metrics can be aggregated, graphed, analyzed or used as alerts.

Clinical operations teams can subscribe to alerts, prioritize by context and take action. Patients can be subscribed to alerts to reinforce protocol compliance triggered by thresholds.

Cost-effective GCP compliance

In the past, there were unconnected islands of alerts, queries and batch reports from data managers. Trials were conducted on fixed schedules of visits to sites physically or virtually. Today – the velocity, variety and volume of data collected in decentralized trials is 5-10X greater than in traditional study models.  

Traditional study monitoring based on queries, SDV, and data management reports, cannot keep up.  Traditional monitoring is slow and expensive.

Flask Data remote monitoring enables you to automate measurement of missing data, safety, patient compliance and investigator compliance.  You can track quality, safety and patient compliance with real-time data tools. Timing and size of study is no longer an issue.  You can monitor GCP compliance of 3,000 patients and 300 sites as easily as 30 patients and 1 site.

Flask Data remote monitoring enables you to define your own metrics and thresholds for alerts and trends.  The Flask Data engineering team can work with you  to define protocol-specific operational, compliance, safety, and data quality measures in order to assure GCP compliance. For example:

  • Adherence – Patients that comply with or deviate from the protocol.
  • Missing data – missed treatment, visit or any item of interest on the schedule of events for the study.
  • Safety-related deviations – over/under dosing, early treatment, late treatment, missing treatment.

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