Patient Analytics
Patient analytics
Unlocking actionable insights and enabling better commercial decisions by combining patient interaction and journey with AI/ML-driven analytics.
Combine real-world data, therapeutic expertise, and
AI/ML-driven analytics
The value of patient analytics is driven by both an increasing need for more granular insights along the patient journey and an increasing supply of real-world data. AI/ML-powered analytics can help identify patient behaviors and patterns, drivers and barriers of these behaviors to influence and inform key decisions, both commercial and medical.
Equipped with deep insights and AI/ML-recommended actions, companies can achieve patient analytics excellence to transform the lives of people by ensuring the right treatment for the patients, at the right time.
Leverage Axtria’s patient analytics capabilities that bring together a deep domain/therapeutic experience, understanding of structured and unstructured patient-level data, and data science techniques to reveal unexpected relationships.
Integrating information from diverse sources for mapping the comprehensive patient journey and more…
Axtria brings an extensive knowledge of data sources and a depth of experience in analytics. Our approach addresses these issues using statistical techniques and triangulation across multiple data sources.
RWE Data
- Electronic medical records (EMR)
- Longitudinal prescription data (LRx)
- Medical claims data
- Hospital and lab data
- Oncology data (SEER)
Traditional Sources
- Medical team expertise
- Chart audits
- Product sales
- Quant surveys
- Patient focus groups
- Desk research
- KOL interviews
- Disease state reports
Axtria’s solution offerings
Patient journey
Derive insights related to patient diagnosis, treatment, health outcomes, and their associated costs to inform decisions and actions.
Patient finder
Find the right patients at the right time (prior to switch or discontinuation to take corrective actions, showing characteristics similar to other patients with a relevant confirmed diagnosis, are currently on a first-line therapy and will probabilistically move to a second line therapy after three months, etc.)
Patient-centered customer segmentation
Drive payer and provider segmentation to identify high-value opportunities to guide ‘where to play’ and ‘how to win’ decisions, to focus on high-value segments.
AI/ML-driven analytics
Identify complex patterns in data and deliver greater insights (improving drug adherence using ML-based interventions (message alerts and targeted/relevant content), treatment pathway recommendations using predictive models, etc.)
Flow models
Capture key dynamics in diagnosis, treatment, and management-role played by providers at each stage, leverage and leakage points, choice of regimen at each line of therapy, etc.
Patient data BI
Self-service access that cuts through the complexity of patient-level data, with the ability to create intuitive visualizations that deliver engaging and timely insights.
Market sizing
Market sizing and evaluation in various product situations, such as products with multi-indications, used for specific populations, requiring a diagnostic test, etc.
HEOR
Facilitates cost and benefit tradeoffs and establish outcomes evidence.
Clinical trials
Supports trials via data-based patient recruitments, site selection, and cohort studies.
Overcoming barriers to patient adherence
Poor drug adherence is associated with adverse health outcomes, higher healthcare costs, and lost brand revenues. Patient adherence to a therapy was a persistent problem in a rare cardiovascular condition which has no cure. Although the treatment improves symptoms, decreases healthcare costs, significantly slows progression, and reduces the risk of early death, about 45% of patients discontinue drug usage in less than 90 days of prescription.
To overcome the barriers to patient adherence, Axtria deployed various steps and designed machine learning algorithms. This helped to identify patient risk adherence, implementation of algorithms for continuous scoring, and impact analysis to estimate the impact of the patient intervention program.
Download Case Study / $15 million incremental revenue with AI/ML-enabled patient adherence
Customer success stories
Automated Email Campaigns Support The Patient Assistance ...
Patient Flow Analysis For A Global Oncology Leader
Targeted Field Operations With Predictive Analytics For ...