Healthcare
Solutions
Ampr
Mapping healthcare data can be tedious, so we’ve trained machines to do it while giving you complete visibility and control.
Hakkoda’s ML Classification engine is trained on large healthcare data sets and processes data through three layers of machine learning.
Ampr addresses key healthcare use cases
Save Thousands of Hours
Manually integrating complex healthcare data sets can take hundreds or thousands of hours and leads to costly errors.
Our solution applies three layers of machine learning to accelerate integration and reduce time-consuming manual work.
Dynamic Patient 360
Ampr stitches together your numerous data sources into a single patient view.
Intelligently interpreting relationships between interactions, patients, and physicians, providers gain a 360-degree view of each patient that adapts as your systems change.
Mergers & Acquisitions
Realizing the value of an acquisition requires integrating its data. Unfortunately, it is a messy and time-consuming process, especially in healthcare.
Ampr brings the power of machine learning to integration, lowering cost and increasing speed and accuracy.
Modernizing Data Sharing
Sharing data internally, between organizations, and the Center for Medicaid and Medicare Services (CMS) is a big challenge.
Ampr and Snowflake simplify it. Snowflake enables secure, compliant data sharing, and Ampr maps the shares to your data sets.
Looking Glass
To ensure price transparency in the Centers to Medicare and Medicaid Services (CMS), healthcare providers need to make pricing publicly available. However, how can you do this for large, semi-structured data sets?
Although Snowflake has amazing support for storing structured files, large price transparency files often exceed the supported size. Our solution is Looking Glass, an accelerator designed to restructure large price transparency files in a fully structured reusable model.
Looking Glass addresses key healthcare needs
Efficient Resource Use
Looking Glass doesn’t require excessive infrastructure and only consumes resources while a new file is being restructured to ensure customers only pay for resources they are using.
Improves Speed
The reusable model accelerates the consumption of the semi-structures price transparency by providing a consistent model for flattening data according to CMS guidelines.
A Practical Solution
This accelerator is cloud-provider agnostic, meaning it lives on the same cloud platform as your external stage and restructures files into a size supported by Snowflake.
Meeting Healthcare Goals
Looking Glass’ end goal is to enable consumers to best meet their needs by providing tools and support that helps them comply with price transparency while maintaining costs.
Snowflake Optimization
Solutions
HakkodaRM
HakkodaRM is a resource monitoring and observability service built for Snowflake customers.
You can use it to monitor your compute and storage resources, optimize resource utilization, and discover insights to run your Snowflake environment smoothly.
HakkodaRM can detect anomalies in your environments and takes action.
HakkodaRM compliments Snowflake’s credit quota
Time-Series Warehouse Monitor
HakkodaRM learns from historical data and automatically detects when your warehouse is about to go over its credit limit. It also accounts for seasonality and trends, ensuring credit usage spikes don’t catch you off guard.
Inefficient & Slow Query Detection
Get notified if daily queries take longer than expected. See which queries could benefit from a technical deep dive to transform them to run more efficiently.
Optimize your Snowflake compute and credit usage by improving the quality of the SQL you use every day.
Task & Stream Transparency
Get alerts if tasks are missed or falling behind. Learn why your tasks aren’t running correctly or efficiently.
Visualize all your metrics with PowerBI or Tableau.
Interactive Alerts
Save time. Respond to alerts in Slack, Teams, email, or ServiceNow, without logging into Snowflake.
Need help? Hakkoda engineers are on-call to assist you based on your subscription level.