Quality Management Systems

Aggregated data sets and new operational clinical trial technologies enable study teams to develop innovative methods for designing robust quality systems. Annex Clinical fuses quality management frameworks with computer data science to maximize operational efficiency while mitigating study risk. Generate high quality data to improve overall study efficiency.

Proper quality management systems require a structured setup.  Leverage quality management expertise with data science to create identifiable risk targets during study setup.

2. Generate Risk-Based Monitoring (RBM) plans

Incorporate analytical risk factors into RBM plans, specify measurement methodologies, and define Corrective Action / Preventative Action plans (CAPAs) during deviations, and differentiate between monitoring roles.  Generate a comprehensive RBM plan that requires clinical operations groups to evaluate the study’s protocol, study sites, vendor selections, and IT system capabilities.

3. Transform risks into analytics

In collaboration with study teams, convert qualitatively identified risks from RACTs into measurable quantitative data points for rapid risk identification during centralized monitoring.  Create analytical risk targets.

4. Achieve RBM Technology Feasibility

There are many RBM technologies in the market.  Conduct a comprehensive feasibility assessment to determine the right RBM technology for your clinical trial.


Converge Risk Analytics

Exceptional clinical trial quality management systems require not only identifying risks, but also the enabling of operational workflows through robust implementation models.

1. Incorporate risk targets into data capture systems

Embed pre-identified analytical risk targets into the Case Report Form (CRF) design.  Include additional data points to enable multidimensional risk analysis.  Easily identify risk targets.

2. Establish positive feedback systems

Establish positive feedback systems by designing and incorporating quality control and workflows measures into cutting edge technologies and setup techniques to establish feedback systems to automatically improve data quality at sites.  Efficiently improve quality, minimize study staff exposure.

3. Computerized Monitoring

Proper quality management requires advanced data analysis and interpretation techniques involving both data science and clinical operations expertise.  Without this technique, monitoring analyses can omit critical observations in data quality.