Data Science & AI
Use Case Development
Get help to take you from ideation through documentation of relevant Data Science use cases
Process large data sets to create complex visualizations
Powerful Data Models
Build powerful data models on top of data warehouses using the best tool set
Natural Language Processing (NLP)
Build NLP techniques to power more human-relevant interactions
Build and deploy a machine learning platform to scale through and process enormous data sets
Robotic Process Automation (RPA)
Streamline processes and improve operations while working on large data sets
Wearable Technology Integration with EMR to Improve Patient Care
With the advancement of wearable technology, physicians are deeply interested in understanding patient behavior and the practice of self patient care in the treatment of chronic medical conditions. Wearable devices provide clinicians with access to more pertinent information to improve treatment methods, save cost and guarantee positive health outcomes.
Disease Diagnostics and Early Disease Prevention
Through the application of deep learning and big data processing techniques, practices can analyze big data to detect early symptoms of chronic diseases and better care or patients, in turn, managing the patient population needs of the community they serve.
Population Health Management
Patient healthcare data is scattered across various sources which include wearables, social media, social determinants of health (SDOH) and others. Effective population health management involves data analysis related to patient geography, health ailments and more.
Healthcare practices can leverage the analysis of data using best-practice driven data sciences to manage operations to keep healthcare costs to the minimum. Using the power of big data analytics, it is possible to optimize the use of healthcare resources, guarantee positive patient outcomes and minimize unnecessary hospital readmissions.
- Data Analytics:
- For processing big data streams (Apache Spark, Apache Apex)
- Machine Learning / Deep Learning:
- Building powerful data models on top of big data (Apache MXNet, Keras/TensorFlow, Azure ML, PyTorch)
- Natural Language Processing (NLP):
- Building NLP to power more human-relevant interactions (NLTK, Stanford NLP, spaCy)
- Robotic Process Automation (RPA):
- UiPath, Eggplant, taskt