Seeding a healthcare app with Snaplet Seed is easier than manual seeding and SQL scripts

Seeding your healthcare app: pain or pleasure?

See how Snaplet Seed beats manual seeding and handwritten SQL scripts without causing bloodshed

Imagine you’re a developer working on a healthcare application. You need good data to test your app and make sure it will work correctly when launched into the real world. It can’t be any data, it needs to be realistic data that resembles the different complexities in healthcare. There are many examples of scenarios where relationships and dependencies are of great importance within this industry. For instance, physician types should match certain illnesses, and certain illnesses should match certain population types, genders, ages etc.

How would you get good data? Considering the sensitivity of medical information, a dump of production data certainly isn’t feasible. You are left with manual seeding, SQL scripts, or fake data. While manual seeding methods and SQL scripts offer some control, both approaches often struggle to handle intricate data relationships and become a headache to maintain as applications grow and data needs expand.

In this video, Snaplet developer Khaya, highlights the limitations of manual insertion and handwritten SQL scripts and introduces an alternative solution: Snaplet Seed. Watch the video to see how Snaplet Seed makes data generation headaches a thing of the past.

Now that you’ve watched the video, let’s explore in some more detail some of the intricacies of data seeding, and how Snaplet Seed navigates complex data relationships and dependency issues to give you highly realistic production-like data you can use to code or test against.

Navigating complex data relationships

As noted above, the relationships in healthcare data can become quite complex. Patient records are linked to doctors, hospitals, and medical histories, creating a complex web of dependencies. Ensuring data integrity across these interconnected tables manually, is time-consuming and error-prone. For instance, inserting a patient record requires corresponding entries in doctor and hospital tables to maintain referential integrity. Not only does it require an in-depth knowledge of the relationships that exist, it also requires understanding of the data itself. For example, you would need to be knowledgeable about categorization of illnesses based on age ranges to distinguish between the severity of certain illnesses and risk factors in specific age groups. Sounds painful, doesn’t it?

Addressing dependency issues

Dependency management is critical in database seeding. Let's continue with our healthcare example. Suppose you are populating data for medical procedures. Each procedure record depends on various factors such as patient demographics, diagnosis, and treatment plans. Without automation, managing these dependencies becomes a daunting task prone to inconsistencies. For example, patients suffering from Psychosis would require different treatments to someone with Sinusitis or Gingivitis. Specifically, having realistic data means that the dependencies are properly linked: a condition like Psychosis would exist on a table with other related information, for example, treatment. If you’re manually creating this data you’d fail to create the dependencies and links, and your tests and development would be unrealistic. If you’re looking for real data integrity, simply seeding your database with “blah blah blah” or generic fake data is not ideal. Snaplet Seed automatically takes care of these relationships while considering business logic.

Business logic integration

Incorporating business logic into your database seeding scripts is essential for maintaining data integrity and supporting application functionality. As you seed your healthcare app, you may encounter the following business logic considerations:

  • Data validation: Ensuring fields are not null, validating data formats, and enforcing valid values.
  • Referential integrity: Verifying foreign key relationships to maintain data consistency.
  • Unique constraints: Checking for duplicates in unique fields like patient IDs or usernames.
  • Date logic: Ensuring coherence in dates, such as admission dates preceding discharge dates.
  • Patient privacy (HIPAA Compliance): Safeguarding sensitive information to comply with regulations.
  • Error handling: Logging errors and preventing insertion of inconsistent data.

Snaplet Seed automatically considers these business logic scenarios and puts it into practice without you having to specify anything.

The importance of determinism

Apart from automatically taking care of business logic, Snaplet Seed  also generates deterministic data. This means that when you use the same input parameters, you'll always get the same output. Why does this matter? Let's consider this scenario:

Imagine you're working on testing a new algorithm to predict patient readmission rates based on factors like demographics and medical history. To evaluate the algorithm effectively, you need a dataset that accurately represents your patient population. With deterministic data generation, you can generate consistent datasets for testing. Each time you run the seeding process with the same input parameters, you'll obtain identical datasets, ensuring reproducibility in your experiments.

Expanding data requirements

As your application evolves, so does its data requirements. Imagine your healthcare app is expanding to include telemedicine services. Seeding the database now involves not just patient and doctor records but also scheduling appointments, managing prescriptions, and handling payment transactions. Maintaining these evolving data needs seems dreadful without a tool like Snaplet Seed .

Manual data refinement

It's important to note that while Snaplet utilizes AI to generate data, we don’t always get it right on the first try. However, thanks to machine learning, the more you use Snaplet, the better we become at generating data accurately. To improve our predictions, you have the ability to manually edit the generated data, providing your own constraints and logic where needed, which means that each time you use Snaplet, our data predictions and generation capabilities improves.

In conclusion, seeding databases for applications, especially in sensitive domains like healthcare, presents numerous painful challenges. Manual methods and handwritten scripts fall short in handling complex data relationships, dependency issues, and evolving data requirements. Fortunately, Snaplet offers a simpler solution by automating the data generation process, while ensuring determinism and providing flexibility and customization. With the help of Snaplet Seed, you can maintain data integrity, support application functionality, and adapt to your application's evolving needs.

Snaplet Seed's aim is to make your data seeding experience less painful, more efficient, more reliable and scalable, so that you can focus on solving problems and building robust, innovative applications. If you haven’t tried out seed, give it a go now. We’d love your feedback and if you have any requests or ideas for improvement, tell us, and we might even send you some beautiful butthole swag!

For more info on Snaplet Seed, see the links below:

A quick look at Snaplet Seed

Need data to seed your testing database? Just Snaplet Seed it

Unearthing the power of seed data in database management

Snaplet Seed plays right with Playwright

Snaplet Seed has got your end-to-end

Almarie Stander
May 3, 2024