Analytics for Medical Labs

• Monitoring operational KPIs (e.g. turnaround time, cost per test, volume of unnecessary tests, billable tests versus performed tests, staff productivity).
• Asset utilization analytics (e.g., tracking equipment-related metrics like accuracy and lifespan; recommendations on optimizing instrument usage).
• Predictive equipment maintenance.
• Environmental analytics (e.g., tracking lab’s temperature and humidity indicators, providing insights into waste production and disposal practices).
• Tracking safety incidents to prevent their recurrence.
• Predictive analytics (e.g., for test utilization, staffing needs).
• What-if models for efficient operational planning (e.g., simulation of staff workload and responsibility allocation models).

• Monitoring quality control samples (e.g., mean and standard deviation of QC results, out-of-control events).
• Sample error segmentation by time, type, patient location, and other parameters to identify error root causes (e.g., improper equipment calibration).
• Benchmarking test results against internal and external quality control standards.
• Insights into sample processing efficiency (e.g., how sample volumes and types, test complexity, and equipment availability affect turnaround time).
• At-risk sample alerts.

E.g., for clinical, public health labs

• Monitoring clinical KPIs (e.g., clinical correlation rates, interpretation rates).
• Automated test result interpretation (e.g., hematology analytics, microbiology analytics, genetics analytics).
• Benchmarking test results against the established parameters (e.g., individual results vs. the established reference intervals).
• Identifying patterns in historical data (e.g., age- and sex-specific diagnostics thresholds, indicators of flu outbreaks in epidemiological data).
• Matching patient-specific test results with relevant medical history data (e.g., medications intake, medical images) for more accurate diagnosing.
• Medical image analysis (e.g., histopathology slides, IHC images, X-rays, CT scans).
• Predictive analytics (e.g., to forecast disease progression based on test results and demographics data).

E.g., for clinical trial and R&D labs, CROs

• Choosing patients by the required criteria (e.g., demographics, disease characteristics) to assist study sponsors in determining patient eligibility for clinical trials.
• Preclinical data analytics to evaluate safety and efficacy in animal models before advancing to human trials.
• Tracking trial parameters (e.g., pharmacokinetic metrics like Cmax and Tmax).
• Comparing results across different groups (e.g., high IP dosage vs. low dosage, medication vs. placebo).
• Patterns identification (e.g., dose-response relationships).
• Alerts on adverse events and root cause detection.
• Predictive analytics (e.g., to forecast side effects).
• What-if simulations (e.g., for ADME modeling).

• Real-time tracking of inventory levels with automated reordering and shortage alerts.
• Tracking expiration dates of reagents and consumables with notifications on soon-to-expire items.
• Identifying inventory management bottlenecks and their root causes (e.g., protocol inefficiencies that lead to reagent waste).
• Comparing suppliers by performance and other required factors.
• Inventory demand forecasting.
• Scenario modeling (e.g., to estimate inventory needs in case of certain protocol changes).

• Real-time tracking of delivered sample location and condition (e.g., temperature, humidity) with alerts on delays and state changes.
• Insights into package quality (e.g., tracking contamination rates during transportation).
• Monitoring transportation KPIs (e.g., on-time performance, delivery costs, fuel efficiency rates).
• Real-time suggestions on optimal delivery routes and locations (e.g., pinpointing the most suitable lab in terms of distance and required testing equipment).

• Tracking financial KPIs (e.g., revenue and cost per test, cash flow from operations, ROI).
• Cost segmentation (e.g., by category, time period) to identify cost drivers.
• Benchmarking financial performance against historical results and industry peers.
• Profitability analysis (e.g., break-even analysis, profitability analysis for different service lines and test types).
• Budget variance analysis.
• Grant and fund management analytics (e.g., funding score tracking, grant budget monitoring).
• Forecasting and financial modeling (e.g., to predict revenue; to perform sensitivity analysis under different variables like changes in supply costs or test volume).

• Monitoring customer KPIs (e.g., customer acquisition cost, customer lifetime value, churn rate).
• Customer segmentation (e.g., by organization type and location for B2B customers like healthcare providers and pharmaceutical companies; by demographics and disease type for B2C customers).
• Customer journey mapping.
• Identifying cross-selling and upselling opportunities.
• Satisfaction analytics based on data from surveys, feedback, and reviews.

• Tracking employee performance metrics (e.g., error rates, turnaround time, output volume).
• Comparative analytics (e.g., employee performance vs. laboratory goals, performance vs. compensation).
• Recommendations on employee-specific workload optimization.
• Analyzing the effectiveness of recruitment channels and strategies.
• Employee satisfaction and attrition analytics.
• Identifying skill gaps and suggesting relevant training programs.
• Analyzing the effectiveness of practices for promoting inclusion and diversity.
• Forecasting staffing requirements based on current projects, trends, and seasonal variations.

• User-friendly dashboards with clear visuals and capabilities for drilling up and down, slicing and dicing.
• Scheduled and ad hoc reports creation with automated submission to the relevant parties.
• Dashboards adapted to role-specific needs (e.g., lab managers, researchers).
• Automated creation of reports for regulatory bodies in accordance with the relevant guidelines (e.g., quality control, synoptic, environmental, safety reports).