Healthcare and Life Sciences Industry - Incident Detection and Response Alarming Best Practices

Healthcare and Life Sciences

Introduction

Healthcare and life science organizations are reinventing how they collaborate, make data-driven clinical and operational decisions, enable precision medicine, and decrease the cost of care. To help healthcare and life science organizations achieve business and technical goals, AWS for Healthcare & Life Sciences provides an offering of AWS services and AWS Partner solutions used by thousands of customers globally.

Common Healthcare and Life Sciences Workloads:

Medical imaging

Medical imaging in healthcare spans diagnostic medical imaging, digital pathology, dental imaging, and related applications. Medical imaging systems enable workflows anchored in the generation, storage, and analysis of medical imaging study data.The data is commonly generated by medical imaging hardware, like CT scanners, magnetic resonance imaging (MRI) machines, and ultrasound devices, and may be stored in picture archiving and communication systems (PACS) or vendor neutral archives (VNA).

• Medical imaging system reference architecture

Healthcare interoperability

Healthcare interoperability refers to health data exchange between information systems like electronic healthcare records (EHR), pharmacy systems, diagnostic imaging systems, laboratory systems, and claims systems. With interoperability, health data can be communicated between electronic systems, between organizations, and across geographical boundaries with standardized protocols.

• FHIR (Fast Healthcare Interoperability Resources) enabled interoperability architecture

Healthcare analytics

Healthcare delivery systems, payors, and service providers use analytics for a range of purposes, such as revenue cycle management, quality management, and process improvement. These entities generate, analyze, and exchange large volumes of data. The health data processed spans a diverse set of domains, including clinical, finance, supply chain, human resources, research and more. The volume and variety of data processed by analytics is steadily increasing, making extensible, elastic, cloud-based architectures increasingly attractive.

• Healthcare analytics reference architecture

Machine learning for healthcare

Artificial intelligence/machine learning (AI/ML) is being applied to a growing set of problems across healthcare, such as prioritizing treatments, predicting health outcomes, guiding provider workflows, and streamlining revenue cycle operations. A key strength of AI/ML technology is the ability to continually learn from real-world data and improve performance over time. However, healthcare applications of AI/ML pose unique problems, including regulatory oversight, design control obligations, and interpretability requirements imposed by stakeholders. Machine learning development is often performed in concert with traditional analytics and can leverage elements of the infrastructure described in the Healthcare analytics scenario.

• Machine learning for healthcare reference architecture

Research and development

AWS solutions support researchers’ need for increased computing power and various options for handling complex data at higher rates of production and consumption. Service Workbench on AWS helps IT teams to provide secure, repeatable, and federated control of access to data, tooling, and compute power that researchers need. With Service Workbench, researchers no longer have to worry about navigating cloud infrastructure. They can focus on achieving research missions and completing essential work in minutes, not months, in configured research environments. With Service Workbench on AWS, researchers can quickly and securely stand up research environments and conduct experiments with peers from other institutions. By automating the creation of baseline research setups, simplifying data access, and providing price transparency, researchers and IT departments save time, which they can reinvest in following cloud best practices and achieving research reproducibility.

• Service Workbench on AWS reference architecture

Recommended Metrics to Monitor

We recommend using the below metrics to create and configure alarms based on the above sample architectures and advise to follow the Practices for Observability from the AWS Well-Architected, Operational Excellence Pillar located here.