Category: Healthcare

MachineMetrics on AWS supports automated production monitoring and analytics, while maintaining strong security and compliance. Users achieve strong security and compliance at scale, by using underlying MachineMetrics and AWS frameworks. There’s a myth in the life sciences industry that cloud-based solutions threaten compliance due to a lack of security and stability. This level of caution can […]

The FAIR data principles were first proposed in a seminal paper published in 2016 in the Journal Scientific Data. It was written by a group of international experts in data management and curation. To address the challenges that the research community is facing, they proposed FAIR Principles as a framework for making data more discoverable, […]

Today, healthcare organizations rely on a combination of automated and manual processes to compose, review, and classify patient safety reports. These reports are entered manually by front-line clinicians into the RL Datix reporting system. This entry includes both discrete data points as well as a free-text narrative. Although the data collection process may begin with […]

New draft guidance from the Food and Drug Administration (FDA) opens the door for medical devices with AI/ML to learn and improve after the product comes to market. The guidance, required by Congress in late 2022 as part of the fiscal year 2023 omnibus bill, addresses a long-standing challenge with the approval or clearance of […]

Every hour, healthcare and life sciences organizations continuously generate large amounts of structured and unstructured health data from multiple systems, applications, and devices. Secure exchange and use of this data using interoperability standards such as Fast Healthcare Interoperability Resources (FHIR) can lead to better clinical decisions, clinical trials, and operational efficiency. To drive this transformation, […]

As healthcare and life sciences customers work to advance clinical research, and realize personalized healthcare and precision medicine for patients, they face varying compliance, regulatory, and security requirements as well as disparate data that is siloed across multiple applications and organizations. These customers increasingly need to unlock access to quality data and leverage privacy-enhanced multi-party […]

Blog is guest authored by Professor Ken Butcher, Medical Director of the New South Wales Telestroke Service and Andres Diaz-Pinto, Senior Deep Learning engineer at NVIDIA Annotating medical images accurately and at scale is a key prerequisite for training Artificial Intelligence (AI) models, which are later used for diagnostic support in a clinical production environment. […]

Amazon Web Services, Inc. (AWS) has increased maximum sizing of global references per second (GRefs/s) for Epic on AWS customers.  AWS now supports operational database workloads of up to 42 million GRefs/s.  This represents a 61% GRefs/s increase from the previous AWS GRefs/s sizing announcement.  This step-change in scalability, delivered on the R6in instance, represents […]

Blog guest authored by Shawn Stapleton, PhD, global Data Science and Innovation lead for Philips Properly de-identified electronic health record (EHR) data is imperative to curate data sets for use in creating insights into population health. Being able to automate this incredibly manual and time-consuming process would speed up your health informatics innovations and time-to-market. […]

To our many customers and partners who work in Life Sciences, welcome to 2023, a year that I expect to be the turning point in how data science and machine learning (ML) accelerate development of new life-saving therapies. (I’ll share 2023 thoughts on Healthcare in an upcoming post.) The first reason I’m so optimistic is […]