One of Colombia’s most prestigious health entities embarked on a transformative project: migrating to a new central hospital system. This project, led by the IT department, was vital for the institution’s future. However, they encountered a critical obstacle: the new system’s provider delivered “clean” versions, devoid of any data. To test and validate progress, the entity’s team needed to populate each new version with thousands of clinical and administrative records—a task that was simply impossible to perform manually.
Client: A prestigious health sector entity in Colombia.
Challenge: To populate a new hospital system with test data for each vendor delivery, a manual process that was impossible to execute due to the volume and need for precision.
Solution: STELA RPA bots that read records from files and loaded them into the new system via its graphical interface, acting as a bridge between incompatible systems.
The entity’s IT team faced a dilemma. To ensure the quality of the new hospital platform, it was essential to perform exhaustive tests after each vendor delivery. But these deliveries arrived as an empty shell. The task of loading the thousands of records needed to simulate a real environment was monumental.
Performing this process manually was unfeasible for two main reasons: the enormous volume of records required and the extremely high risk to data quality. A single typo in a clinical record could invalidate an entire battery of tests. Without a safe and agile way to populate the data, the entity was forced to limit the number of deliveries and test cycles. This meant a reduced ability to control development quality and a much higher risk of problems being detected late, jeopardizing the project’s timeline and budget.
The solution was to use STELA RPA bots as a “bridge” between the old and new systems. Since the technologies and data models of both systems were incompatible for a direct database migration, STELA offered a simpler and safer alternative: interacting with the new system through its graphical user interface, just as a user would.
The automated process was as follows:
What the entity valued most was the security and precision of the bots. Unlike a person, a bot does not make typos or mix up patient data—a critical factor when handling sensitive clinical information.
The implementation of STELA not only solved the problem but also radically changed the project’s methodology. The most impactful benefit was the ability to have a test environment fully populated with 8,500 records in just 10 hours. This agility allowed them to switch from an original plan of 2 or 3 data loads for the entire project to monthly deliveries and test cycles, facilitating much more rigorous progress tracking and incremental testing.
The efficiency metrics were conclusive:
| ASPECT | STELA (10 Bots) | MANUAL PROCESS (10 People) |
|---|---|---|
| Total Records Loaded | 8,500 | 8,500 |
| Total Time Employed | 10 hours | 850 hours (12 days) |
This comparison demonstrates a time saving of 88% and a 97% reduction in errors compared to the manual process. STELA became a strategic asset, allowing the IT team to continuously control the project’s quality and guarantee data integrity every step of the way.