Health Care & Life Sciences

Migrating systems and devices to modern architecture and processes can transform an organizations value, improve operational efficiencies and get the most of their data strategy. Further, we can implement near real-time reporting and predictive tools to help organizations store and use information in the best way possible. 

Big Data: Population health management, personalized medicine, genomic medicine, clinical trial and decision support databases. 

Connectivity: Building the infrastructure necessary for automation and delivery of information to patients, payers and third-party providers.

Sensor Data: Aggregate and build alert and predictive models for data collected from patient devices such as wearables and monitors as well as optimizing the quality of care through the aggregation and analysis of relevant patient history from such devices. 

We build systems and develop algorithms for automated and computer-assisted analysis of medical images. Recent advances in machine learning through deep learning and GPU-accelerated computation have generated both the precision and scale necessary to identify, classify, and predict patterns in medical images of all types.

We specialize in developing technologies for cell detection, tissue segmentation and feature detection of anatomical tissue in CT, MRI and high-resolution digital pathology scans. With advanced tools such as anomaly detection, image registration, and 3D-reconstruction. We will work together to find best practices, methodologies, hardware, and data-handling pipeline for your application.

Mass Spectrometry: Identify mass spec peaks, verify and quantify target ions and add biological meaning to your data using big data and machine learning techniques.

MRI: The automated classification of MRI images and segmentation of key structures and anomalies is a necessary task to solve when faced with the increasing quantity of medical images. Recent advances in deep learning and GPU architecture make it a suitable tool for such computer vision problems.

Leverage data science to garner insights from relevant real-world healthcare data sources such as electronic medical records (EMR), claims, Rx notes, and drug interaction information to improve outcomes through better treatment patterns and smarter practices. 

Natural Language Processing: Automate entry, reduce errors, and flag inconsistencies using advanced NLP methods for unstructured and medical data. Predictively determine best treatments and optimize the quality of care through the aggregation and analysis of relevant patient history and care data.

Clinical and Drug Discovery: Optimize methods by correlating, modeling and searching patient records using machine learning to enhance drug discovery and development efforts. 

Patient Self-Reporting: Healthcare faces new challenges as individuals explore internet resources and discuss their ailments with peer communities. Leverage the rise of health apps and tele-health to connect providers with patients in the quickest and most personal way possible in real-time. Aggregate and correlate data sources, audio, text and streaming data from these interactions to get full patient perspective and maximum value. 

Building more efficient systems means examining all aspects of operations through the most complete data available. We can examine or collect information to solve the toughest business questions and make actionable recommendations with combined domain knowledge.  

Supply Chain: A strong supply chain management program addresses pricing, standardization and utilization. We can analyze categories of spending and usage by combining predictive analytics with lifetime value of equipment and reagents to identify and implement supply chain systems that meet or exceed the quality, safety and cost savings standards of the initiative. 

Health Operations: We can model outcomes, behaviors and factors of initiatives at each institution or department by identifying gaps, risks and issues with data-backed precision. Scheduling, readmittance, effectiveness and productivity of teams can be monitored. 

Payment and Revenue Benchmarking:  We can extract billing information to determine optimized solutions and methods for process automation and fraud. We can examine claims data and model reimbursements to predict which payments will be delayed. Further, we can create systems to monitor non-reimbursable paired tests that are contradictory or unnecessary.

Patient Care: Assessing whether certain initiatives and programs are translating to health outcomes is critical to value-based care. Further, we can examine prospective cost savings from operational changes.