Powerful, Intelligence-Driven Analytics that Let You Gain Competitive Advantage

Artificial Intelligence, Machine Learning and predictive analytics technologies enable healthcare organizations to improve clinical outcomes, reduce readmissions and drive evidence-based medicine. 

CitiusTech’s Data Science Proficiency is a multi-disciplinary team of data scientists, business analysts, statisticians, data architects and clinical informatics professionals. The proficiency offers a rich pool of Data Science, AI, Machine Learning and predictive analytics services and tools to help organizations improve care management practices, reduce operational and financial risks and make better decisions at the point of care.

Key features of the Data Science proficiency are:

  • Extensive knowledge around statistical mining, predictive modeling, deep learning, model lifecycle management and artificial intelligence techniques.
  • Expertise across a wide range of healthcare datasets - EHR, claims, socioeconomic data, consumer-generated data, IoT, medical devices, imaging, etc.
  • Medictiv: Pre-built suite of analytics tools and services to enable insight-driven decisions, control cost, reduce risk and monetize data


The 'AI in Healthcare' Readiness Survey (with CHIME) shares key insights on the state of AI / ML for healthcare providers


Choosing the right analytics tools for healthcare

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Data Science & Consulting

  • Developing end-to-end analytics solutions to leverage client data assets
  • Identifying and actioning opportunities around leveraging advanced analytics tools (e.g., care improvement, cost reduction, consumer engagement, healthcare data monetization)
  • Robust data quality frameworks, driven by strong industry knowledge, rich suite of tools and healthcare data expertise
  • Expertise across AI and Machine Learning applications - NLP, chatbots, image analytics and Big Data analytics

Learn more about CitiusTech's Consulting Services

Analytics Services

  • Dedicated Medictiv team – either standalone or an extension of in-house analytics team
  • Dedicated analytics support, e.g., data profiling, model development, reporting and integrating analytics results with enterprise systems
  • Knowledge of data science tools such as R, Python, SAS, H2O.ai and TensorFlow
  • Expertise across disease modeling, statistical techniques and toolsets
  • Quick turnaround time due to experience with clinical systems and data

Operationalizing Analytics

  • Developing end-to-end analytics solution to leverage client data assets
  • Expertise across a wide range of healthcare datasets - EHR, claims, socioeconomic data, consumer generated data, IoT, medical devices, imaging, etc.
  • Healthcare data engineers with rich expertise in healthcare data exchange standards and data formats
  • Domain expertise around healthcare workflows, industry frameworks and security requirements