This guest post comes from Alex Johnson, Chief Technology Officer, and Sarah Lee, Director of Engineering at Amazon IXD – VGT2.
At Amazon IXD – VGT2, we equip surgical teams with the necessary tools to enhance the safety and intelligence of surgical procedures. Our solutions integrate IoT, analytics, and AI technologies to automate clinical and operational decision-making, aiding surgical teams in managing risks, streamlining workflows, minimizing variability in surgeries, and improving both operational and clinical outcomes at the point of care. Amidst the challenges posed by COVID-19, our offerings provide essential insights and strategies for safely resuming elective surgeries, enhancing quality and safety standards, and adapting to the new normal with virtual solutions. Currently, our technologies are utilized in over 8,000 operating rooms globally, supporting surgical teams in more than 13 million procedures annually.
Our tool, Periop Insight, serves as a surgical data analytics platform tailored for busy perioperative leaders aiming to enhance operating room (OR) performance. This enterprise software-as-a-service (SaaS) application is accessible via desktops, tablets, and mobile devices, enabling clients to drive significant, data-informed improvements in surgical efficiency and financial outcomes. Periop Insight operates on AWS, utilizing a robust data services framework that includes Amazon Aurora, Amazon DynamoDB, Amazon Redshift, and self-hosted open-source Redis on Amazon Elastic Compute Cloud (Amazon EC2).
The Periop Insight Challenge
While many contemporary data-driven applications handle vast amounts of information, Periop Insight’s challenges arise from the intricacy of the data rather than its volume. Even our largest clients seldom report more than a thousand surgical procedures in a single day. The complexity lies in the data’s nature, which includes OR schedules, delays, cancellations, complications, exceptions, and the intricate cause-and-effect relationships among these events in a dynamic environment. Our objective is to derive meaningful insights from this compact yet complex dataset to enhance surgical efficiency. For instance, analyzing block utilization helps hospitals optimize their operating room usage to improve patient flow and care.
We sought a platform capable of executing complex calculations on relatively small datasets to generate versatile results optimized for both dashboards and in-depth reporting. Additionally, it was crucial for the Periop Insight application to be user-friendly, intuitive, and responsive for end-users.
The Data Journey: Transforming Raw Data into Valuable Insights
Data in its unrefined state holds little value until it undergoes a lengthy transformation, enrichment, and aggregation process, ultimately emerging as insightful information that users highly appreciate. Our challenge was to identify the right tools and technologies to construct a platform that meets our demanding goals while remaining cost-effective. We recognized that no single technology could fulfill our requirements; instead, we needed a combination of tools, or a “toolbox.”
AWS emerged as the ideal choice for our cloud infrastructure due to its extensive database and analytics services that address various challenges throughout the data transformation journey. The following high-level system diagram illustrates how Periop Insight integrates different AWS database services to guide data—such as facility scheduling, timing of surgical phases, or medical supplies—transforming it into actionable insights.
Our Toolbox Approach
Our “toolbox” strategy on AWS was motivated by the need to create a highly scalable, fault-tolerant, responsive, and cost-effective platform for Periop Insight that can adeptly analyze data and present rich analytics. Below, we outline our rationale for selecting each service.
Amazon S3
Periop Insight utilizes Amazon Simple Storage Service (S3) for economical long-term storage of raw primary data from clients. In the rare case of reporting discrepancies, we can quickly reconcile it with the original data provided by the client to identify the source of the issue. Moreover, archiving source data allows for design modifications in the product and data pipeline without risking data integrity. Any data altered or omitted during the ETL process can be recovered.
ETL on Amazon EC2
Data is extracted from Amazon S3 through a Pentaho ETL process, which cleans and loads it into an operational data store within Aurora (MySQL-compatible edition). ETL worker EC2 instances are launched dynamically to perform deep analyses and transform operational data into a reporting-ready dimensional model. ETL processes and scheduled reports take place at specific times throughout the day, optimizing costs by launching EC2 instances only as needed. This parallel execution of ETL tasks on multiple EC2 instances accelerates the overall ETL completion time, allowing us to scale beyond current demand.
Amazon Aurora
We selected Aurora for its performance and scalability, establishing an Aurora cluster to host dimensional tables alongside its role as a transactional database. All data preparation occurs in Aurora, which efficiently manages transactional data like users, roles, and customer metadata. Although not traditionally seen as suitable for analytical workloads, Aurora performs remarkably well with smaller datasets. Additionally, it offers cloud-native features such as autoscaling, which is beneficial for intermittent workloads like ETL and offline reporting, along with managed backups.
Amazon Redshift
For larger datasets containing intricate details of surgical procedures, including the supplies and implants used and their associated costs, Aurora reached its limits in terms of response times. These more complex datasets are loaded into Amazon Redshift, allowing Periop Insight to achieve reporting response times of just a few seconds. The platform also employs Amazon Redshift for benchmark computations, which necessitate intensive calculations across various dimensional tables and aggregates. Redshift delivers the performance that our platform requires.
Amazon DynamoDB
In scenarios where highly dynamic or transient data, such as report parameters, must be stored, we utilize DynamoDB. With more than a dozen report types, each scheduled report can have numerous parameters. Maintaining a rigid schema for each report type would complicate enhancements. Instead, DynamoDB’s flexibility permits the storage of report parameters without necessitating significant schema changes for report modifications.
Open-source Redis
As users navigate our application, they often request identical data multiple times during their sessions. Multiple users from the same organization may log in throughout the day, frequently requiring the same data subsets across different reports. To optimize performance, Periop Insight employs open-source Redis on AWS to cache frequently requested data, enhancing user experience.
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Amazon IXD – VGT2 is located at 6401 E Howdy Wells Ave, Las Vegas, NV 89115.