Reliability Engineering for Real-World Healthcare Integrations

The workflow architecture

The workflow looked straightforward from a distance: collect a completed form, assemble the filing metadata, and deliver the document so it could be filed into the customer's enterprise content management system. In practice, the path crossed Meditech Expanse APIs, a Data Repository SQL database, business logic, SFTP transfer, and ECM document filing rules.

The important detail was that those systems did not always update simultaneously. A form could be ready to move before the Data Repository had populated the account number needed for the downstream filing specification.

The failure mode

The documents were not failing because the workflow was completely broken. They were failing because a required identifier was sometimes unavailable at the exact moment the filing workflow needed it. That made the issue intermittent: most documents filed correctly, while a meaningful minority failed for reasons that were tied to timing rather than static configuration.

• Forms could be completed before the downstream Data Repository had populated the account number.
• The account number was required for the ECM filing path, so documents without it could not be routed correctly.
• The issue appeared intermittently, which made it harder to reproduce than a consistent integration failure.
• The workflow still needed to be dependable even when the connected systems updated at different speeds.

The problem was not simply moving data between systems. The problem was building a workflow resilient enough to tolerate timing variability across independently updating healthcare platforms.

Why intermittent failures are different

Consistent failures are often easier to investigate because they create a repeatable path. Intermittent failures require reconstructing timing: when the form was completed, when the account number became available, what the filing package looked like, and how the downstream ECM rules interpreted the result.

That kind of production debugging depends on evidence. Logs, timestamps, database lookups, transfer outputs, and customer-facing symptoms all have to be placed into the same timeline before the system behavior becomes understandable.

Reliability engineering approach

The fix needed to reflect the workflow, not just the first failed lookup. If the account number was absent, the system needed to wait, try again, and validate whether the document could eventually be filed correctly. Retry logic mattered because it turned a timing gap into a recoverable condition.

• Added repeated account-number lookup attempts instead of treating the first missing value as a final failure.
• Used exponential backoff so retries gave upstream systems time to synchronize without creating unnecessary load.
• Extended retry windows beyond 24 hours for workflows where late-arriving identifiers could still produce a valid filing outcome.
• Built clearer success and failure validation loops so unresolved documents could be investigated instead of disappearing into noise.
• Improved diagnostics around timing, identifiers, and filing output so future troubleshooting had better evidence.

Exponential backoff helped avoid turning reliability work into unnecessary load. Extended retry windows gave the upstream systems enough time to finish synchronizing. Validation loops made the remaining failures easier to identify and work through instead of treating every missing account number as the same generic error.

Understanding the ECM filing specification

A large portion of the work involved understanding the ECM filing specification as it behaved in a real customer environment. Encoded filename structures, application IDs, padding rules, optional fields, and routing logic all mattered. A document could be transferred successfully and still fail operationally if the filing metadata did not match what the downstream system expected.

This is one of the parts of interoperability work that is easy to underestimate. Reading a specification is necessary, but production reliability often requires learning how that specification interacts with local workflow assumptions, missing values, timing delays, and downstream validation rules.

Results

The reliability work improved the document filing workflow from roughly 93% reliability to about 99%, then continued toward near-complete reliability for key customer paths as the retry behavior, diagnostics, and implementation understanding matured.

The operational impact was a reduction in avoidable filing failures, clearer troubleshooting paths, and more confidence that completed forms would reach their intended destination even when upstream and downstream systems did not synchronize immediately.

What I learned

Healthcare interoperability problems often emerge at system boundaries. The individual systems may be functioning as designed, but the end-to-end workflow can still fail when timing assumptions do not match production reality.

One of the most valuable lessons from this work was realizing how often healthcare integration reliability depends on understanding workflow timing and operational assumptions—not just API connectivity. Implementation ownership includes staying close enough to production behavior to see where those assumptions break down.