EVERSANA’s Advanced Data & Analytics Support Real World Evidence (RWE) Research
Advanced Analytics
Areas of Research:
Weighted comparator cohorts using entropy balancing
Target & Control cohort development using diagnosis, drug and procedure codes, based on knowledge of clinical manifestation
Weighted comparator cohorts using entropy balancing
Longitudinal patient journey; patient journey using mutual information
Machine-learning and predictive modeling to find undiagnosed or rare disease patients (without ICD-10 codes), to predict treatment switch, and risk of disease progression
Cost of illness studies identifying individual cost contributors
2-step ML based HEOR identifying risk factors and model association
In Rare Disease, EVERSANA is Leading the Way
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+25Experience with Different Disease States
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7Exclusive Rare Disease Products In Market
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4Active Commercial Engagements in Cell and Gene Therapy
Advanced Analytics Case Studies
Identifying Rare Disease HCPs with Omnichannel Targeting
How A Machine Learning Model Identified Potential Switch Targets Generating ~80% of New Patients For the Brand
Finding APDS Patients Using Predictive Models
Utilizing Machine Learning to Predict Patients Without a Specific ICD Code
Assess MPS II Diagnosis and Treatment Referral Pathways and Identify Potential Patients via Predictive Modeling
Machine Learning and Algorithmic Network Mapping in Rare Diseases
Predictive Modeling for Treatment of Relapsing-Remitting Multiple Sclerosis
Understanding First-Line Treatment and Factors Leading to Treatment Switching via Machine Learning
Assessing the Telehealth Treatment Landscape and Building Predictive Models to Identify Patients/Providers Most Likely to Use Telehealth
Strengthen the Robustness of a Machine Learning Model by Incorporating EMR Data
Identifying Potential High-Risk Factor Arrhythmia Patients Using Predictive Modeling
A Robust Approach Combining Clinical, Demographic and Social Determinants of Health Data
Scoring and Segmenting Key Opinion Leader Physicians With Innovative and Customizable Machine Learning Techniques
Influence Mapping for Healthcare Providers in Oncology
Improving an AATD Predictive Model Using EMR Data
Strengthen the Robustness of a Machine Learning Model by Incorporating EMR Data
Use of Machine Learning to Identify Gastroparesis Patients Suitable for Nasal Spray Metoclopramide
Utilizing Supervised and Unsupervised Learning, With Hierarchical Patient Embeddings to Build Predictive Models
Predictive Modeling for Treatment Switching in Paroxysmal Nocturnal Hemoglobinuria Patients
Designing Machine Learning Models From Both Patient and Physician Perspectives