This page provides you with instructions on how to extract data from HIPAA and load it into Panoply. (If this manual process sounds onerous, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)
What is HIPAA?
The Health Insurance Portability and Accountability Act (HIPAA) defines rules that American organizations must follow to securely handle and maintain Protected Health Information (PHI). To remain in compliance, organizations are required to have a signed Business Associate Agreement (BAA) from any partner organization that creates, receives, maintains, or transmits PHI. The partner must ensure that it will safeguard the PHI that passes through its systems. Businesses also have to meet a long checklist of compliance rules and practices.
What is Panoply?
Panoply provides end-to-end data management-as-a-service. It uses machine learning and natural language processing (NLP) to learn, model, and automate standard data management activities from source to analysis. It can import data with no schema, no modeling, and no configuration. Users can quickly spin up an Amazon Redshift instance and run analysis, SQL, and visualization tools just as they would on a Redshift data warehouse they created on their own.
Getting HIPAA data
You migrate PHI just as you would any other data, but you must stay cognizant of HIPAA regulations. No one but you and the data source can handle the data unless you have a BAA in place with them.
You can use any methods your data provider offers to extract data from their service. Many cloud-based data sources provide APIs that expose data to programmatic retrieval. Others allow you to set up webhooks to push event data to requesters. For data that lives in a database, you can use SELECT statements or a utility that does a mass dump of the data you specify.
Loading data into Panoply
Once you've identified all the columns you want to insert, you can use Redshift's CREATE TABLE statement to create a table to receive all of the data.
Once you have a table built, you might think that the easiest way to migrate your data (especially if there isn't much of it) would be to build INSERT statements to add data to your Redshift table row by row. Don't do it! Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, we suggest loading the data into Amazon S3 and then using the COPY command to load it into Redshift.
Keeping HIPAA data up to date
Once you've set up your data pipeline to your HIPAA data source, you can relax – as long as nothing changes. You have to keep an eye on any modifications that your sources make to the data they deliver. You should also watch out for cases where your script doesn't recognize a new data type. And since you'll be responsible for maintaining your script, every time your users want slightly different information, you'll have to modify the script. Keep in mind that HIPAA is all about rules and compliance, so you'll also have to know what HIPAA permits and proscribes, as will anyone else who works on the script.
Other data warehouse options
Panoply is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Amazon Redshift, Google BigQuery, PostgreSQL, Snowflake, or Microsoft Azure SQL Data Warehouse, which are RDBMSes that use similar SQL syntax. Others choose a data lake, like Amazon S3. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Postgres, To Snowflake, To Azure SQL Data Warehouse, and To S3.
Easier and faster alternatives
If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.
Thankfully, products like Stitch were built to move data from HIPAA to Panoply automatically. With just a few clicks, Stitch starts extracting your HIPAA data via the API, structuring it in a way that's optimized for analysis, and inserting that data into your Panoply data warehouse.