Analytics for Contract Research Organizations

• Historical analytics of similar-type trials (e.g., enrollment rates and timelines, the number of sites and countries) to set realistic goals and study footprint.
• Selecting trial sites based on user-defined criteria (e.g., patient demographics diversity, key risk index, disease prevalence, therapeutic area expertise).
• Visualizing site distribution on maps.
• Forecasting site- and country-specific enrollment rates.
• Predicting staff, inventory, budget, and other resource needs.
• What-if analysis to assess the impact of different enrollment scenarios, geographical site variation, etc.

• Continuous monitoring of trial progress KPIs (e.g., screen failure rate, drop-out rate, last patient last visit, study milestones).
• Root cause analysis (e.g., for protocol deviations, patient attrition).
• Benchmarking projected enrollments vs. cumulative actual enrollment with notifications on enrollment rates falling below the defined threshold.
• Tracking enrollment rates at a granular level (e.g., by cohort, site, and week).
• Monitoring patient disposition and adherence to trial protocols (e.g., medication intake, device usage, follow-up visit attendance).
• Resource utilization tracking with alerts on budget overruns, staff shortages, and more.

For preclinical and Phase I – Phase III trials

• Tracking clinical pharmacology parameters during drug and biotech product trials (e.g., Cmax, Tmax, AUC for pharmacokinetics; DLTs for toxicity).
• Tracking medical device trial metrics (e.g., error types, failure rates).
• Comparing results across different study stages and groups (e.g., animal models vs. humans, medication vs. placebo).
• Adverse events alerting and root cause analysis (e.g., establishing a correlation between adverse events and concomitant medications).
• Identifying trends in clinical findings (e.g., dose-response relationships, biomarkers specific to a certain disease state).
• Predictive analytics (e.g., to forecast treatment outcomes).
• What-if modeling (e.g., to simulate device performance under different conditions, build ADME simulations).

• Monitoring long-term product impact on health outcomes and overall well-being based on the analysis of data from EHR/EMR, patient-reported outcomes, online reviews, and other RWE sources.
• Comparing product usage outcomes across different participant groups.
• Identifying patterns in RWE data (e.g., usage outcomes specific to a population group, cases of off-label use).

• Real-time alerts on adverse events and side effects.
• Automated adverse event reporting in compliant formats.

• Monitoring laboratory KPIs (e.g., turnaround time, test accuracy, cost per test, error rate).
• Quality control for laboratory processes with calculation of QC compliance rates.
• Identifying cases of unnecessary testing.
• Laboratory inventory demand forecasting.
• Predictive equipment maintenance.
• Biostatistical analysis (e.g., sample size calculations, power analyses).

• Monitoring participant safety metrics (e.g., percentage of reported adverse events per period, percentage of complaint ICFs) to ensure compliance with the relevant regulations like CFR Title 21 and Title 45 for the US.
• Tracking submissions to regulatory bodies like IRB and ethics committees with notifications on submission deadlines or approval expirations.
• Alerting on compliance risks (e.g., frequent protocol deviations, late adverse event reporting).
• Tracking training completion rates for staff on Good Clinical Practice (GCP) and other relevant guidelines.
• Automated generation of compliance reports in the established formats.

• Predicting investigational product (IP) demand for efficient allocation across study sites.
• Recommendations on optimal reorder points and IP quantities based on the analysis of usage rates, expiration dates, and current inventory levels.
• Identifying root causes of wastes and returns.
• Logistics and transportation analytics (e.g., monitoring the temperature of transported IPs, suggesting cost-efficient delivery routes).
• Scenario modeling to assess the impact of different events on supply chain performance (e.g., patient drop-out, supplier change).

• Monitoring financial KPIs (e.g., operating cash flow, revenue growth rate).
• Cost and revenue attribution (e.g., by client, project, department).
• Customer profitability analysis.
• Payroll analytics with compensation vs. performance comparison.
• Tax analytics (e.g., to get insights into tax liabilities under different jurisdictions).
• Predictive analytics (e.g., to forecast cash flow, study-specific costs).
• Financial modeling to support budget and other strategic planning aspects.

• Analysis of patient-generated health data (PGHD) from wearables, sensors, and patient apps.
• Segmenting participants (e.g., by demographics, condition, genetic profile) to identify gaps in trial population and disease prevalence.
• Identifying likely-to-enroll subject groups and participants with high drop-out probability.
• Analyzing how social determinants of health (SDOH) impact recruitment, retention, and trial outcomes.
• Tracking participant engagement metrics (e.g., visit attendance rates, communication response rates, participant-initiated contact, peer referral rates).
• Monitoring quality-of-life metrics (e.g., sleep, health perceptions, general life satisfaction, symptoms).

• Tracking customer management KPIs (e.g., churn rate, CLV, contract renewal rate).
• Customer segmentation (e.g., by company size, therapeutic area, location).
• Churn prediction and attrition drivers identification.
• Identifying high-value clients and pinpointing cross- and up-selling opportunities.
• Analyzing customer satisfaction based on feedback and survey data.