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Healthcare Healthcare provider Automation + Integration

Regulatory Reporting Automation

A healthcare provider submitted regulatory reports manually through a government portal. Staff spent hours each week preparing and uploading data. The process was error-prone, and every error carried compliance risk.

Problem

Regulatory reporting required precise data in specific formats. Staff prepared the data manually, checked it against internal records, and uploaded it through the portal. The process consumed hours every week.

Errors were inevitable with manual entry. A missed field or incorrect value could trigger compliance issues. The stakes were high, and the process relied entirely on human attention to detail.

What They’d Tried

They hired dedicated admin staff for reporting. This reduced the burden on clinical staff but did not reduce errors. More people doing the same manual process still produced mistakes.

They created detailed checklists and procedures. The checklists helped but could not catch every error. Staff still had to manually prepare and validate data before each submission.

What We Did

We built automated HL7 generation with error tracking and validation. The system pulled data from internal systems, validated it against regulatory requirements, and generated properly formatted submissions.

Submissions ran nightly without human intervention. A dashboard showed status for each submission and flagged any failures immediately. Staff reviewed the dashboard instead of preparing manual reports.

Outcome

Submissions ran nightly, unattended. Staff hours previously spent on manual reporting were freed for other work. Error rates dropped dramatically. Compliance risk was reduced because the system validated data against rules consistently, every time.

The Database-First Lesson

HL7 requires precisely structured data. Automation was only possible because the data was modeled correctly first. If the source data had been messy or inconsistent, automating the submissions would have just meant submitting garbage faster. Getting the data model right was the prerequisite for everything else.

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