Healthcare Data Analytics
The growing adoption of cost control policies and stringent compliance regulations have made it crucial for pharma companies to effectively collect, manage and analyze data across diverse sources. Leading pharma companies are adopting data science in healthcare solutions to derive insights from a plethora of information available.
Sigmoid enables medical data analytics to gain faster insights from historical and real-time data sources like a lab/diagnostic, prescription, claims, hospital log files, IoT sensors, social media, and patient data. Our data science in healthcare solutions helps pharma companies with customized data solutions across the pharma value chain, from setting up data infrastructures to clinical trial design and providing better insights into patient behavior.
Our Medical Analytics Solutions help Pharma companies to:
Identify and integrate clinical data sources
Clean and prepare data for ML modeling
Analyze the real-world patient data
Accelerate, scale and derive faster insights
Providing End-to-End Data Solutions at each stage of the Pharma Value Chain
| Clinical and Market Research | Drug Discovery and Formulation | Trial Planning and Execution |
| Safety Surveillance | Portfolio Management | Competitive Intelligence |
| Drug Development and Manufacturing | Production Planning | Quality Control and Monitoring | Reduce Cost and Increase Drug Utilization |
| Compliance to Regulatory Warehousing Storage | Streamline Compliance and Government Decisions | Demand Forecasting and Stockouts | Network Optimization |
| Targeted Marketing | Promotion Response Modeling | Field Force Effectiveness |
| Territory Alignments and Call Planning | Forecasting Sales Results | Price Formulation |
| Patient Segmentation | Personalized Medication for Patients | Capturing Electronic Medical Records |
| Patient Query System | Prediction of Potential Patients |
Our Expertise
Drug Discovery and Development
Accelerate the process of drug discovery and development by running predictive algorithms on high volume datasets
Patient Engagement Solutions and Prediction
Identify likely target patients by building ML models and creating multiple predictive profiles for wider coverage of patients
Operational Effectiveness
Reduce operational margins by increasing efficiency of the entire manufacturing process from procurement to production
High Volume Data Aggregation
Collect, integrate and analyze high volume data from diverse sources like labs, prescriptions, IoT sensors and medical records
Promotion Response Modeling
Optimize promotional efforts by powering sales force sizing, territory alignment, call planning and incentive compensation
Clinical Trial Design
Enable effective capture and fluid exchange of clinical data to accelerate trials and reduce likelihood of manual data errors
Granular Metric Analysis
Perform granular analysis of metrics like average ingredient cost per prescription and drug utilization to increase revenue
Streamline Compliance
Quickly uncover insights to streamline governance decisions, highlight current gaps and reduce risk of compliance failures
Targeted Patient Medication
Sift through unstructured genomic data (DNA) to spot patterns and create effective, personalized medication for patients
