Real-time analysis of de-identified patient populations at the individual site level, and aggregated at the study/protocol, consortium or network level. Users can instantly re-evaluate recruitment strategies and examine investigator selection based upon objective real-time data, all of which translates into significant savings in time, cost, and resources.
Faster and More Efficient Trials
Decrease costs associated with lengthy study timeline extensions resulting from slower than expected patient recruitment. By reducing the number (or %) of non and under-performing study sites, Sponsors can more reliably predict enrollment and the number and types of sites required to meet or exceed projections.
A Noticeable Leap Forward
Potential subjects from all participating sites are evaluated against protocol eligibility criteria and receive a PMI Score. Data is then visually displayed to the user at the site level; de-identified data is aggregated and displayed at the Sponsor level, enabling faster access to relevant information.
Further stratify selected patients based upon overall health risks posed by co-existing illnesses. This provides a deeper understanding of how inter-current illnesses might impact trial participation and outcomes.
Address Trial Complexity
With the realization of personalized medicine, protocol criteria are becoming more complex. Patient iP’s cloud-based platform is the ideal solution for multi-variant data analysis for protocol modeling and site feasibility. This may be further aggregated for big data visibility
Value at Every level of development
Real-time patient data informs objective site selection. Sophisticated analytics address complex questions of study design and protocol feasibility. Network and/or university aggregated de-identified data may be used to model decisions for drug, indication or patient population evaluation.