ML Predictive Excellence: 8-Wk Proof of Concept
Drive healthcare outcomes for clinical insights, supply chain reliability, and administrative processes by leveraging Pariveda's Healthcare-focused ML rationalization and prototyping offer.
Machine learning, predictive analytics, and NLP adoption are rapidly rising in the nation’s top-ranked hospitals and healthcare organizations. These capabilities have the potential to help the healthcare industry unlock its “dark data” (the huge datasets that are beyond the scope of human capability). The analysis of this data can deliver key clinical insights, supply chain reliability, streamlined administrative processes, or even advanced diagnosis, improving quality and efficiency while lowering costs.
Pariveda's deep expertise delivering custom machine learning solutions in healthcare on Azure products such as Azure for Health Cloud, Azure API for FHIR, Azure Data Lake, Storage, Cosmos DB, Kubernetes Service, Data Analytics, and Azure Machine Learning can help realize these outcomes. Our process will educate key business leaders on these applications, illuminate many opportunities across the business, and focus on the single highest-value outcome for your organization. From there, we rapidly design, deploy, and experiment to demonstrate the feasibility and deliver a custom-tailored solution in Microsoft Azure to realize process or experience improvements.
Pariveda’s tried-and-true delivery methodology ensures the most valuable outcomes are achieved, from ideation and conceptualization to reality.
Ideation - Identify highest-value clinical or operational outcomes.
Review and Prioritize - Review outcomes with Executive Leadership, prioritize, and select concepts to research further.
Research and Prototype - Engage all parties to formalize dependencies. Develop mock-ups and/or prototypes using the Azure toolkit
Validate and Field Test - Fully functional clinical or operational prototype for a subset of customers or users.
Release - Production launch of product/service after incorporating lessons learned.