Real work.
Documented clearly.

The Problem

Clinical workflows depended on two Electronic Health Record systems that didn't share data cleanly. Staff were re-keying information, reconciling mismatched records, and building manual workarounds — introducing risk and slowing patient-facing work.

The Approach

I designed and implemented integration services between TouchWorks and IntelleChartPro, mapping data models across systems and building reliable, observable data flows. The work included clear ownership boundaries between systems, defensive handling for partial data, and a tested path for recovery when upstream systems misbehaved.

The Outcome

Clinical data moved between systems reliably, reducing duplicate entry and the reconciliation burden on staff. Downstream reporting and patient workflows became meaningfully more trustworthy because the source of truth was clear at every step.

The Problem

When in-person intake became unsafe overnight, the provider needed a way to capture patient information remotely without disrupting downstream clinical systems. Existing paper and in-office workflows assumed the patient and staff were in the same room.

The Approach

I helped deliver a remote patient intake workflow on a compressed timeline: a web-facing intake experience, backend services to validate and route submissions, and integrations that landed the data cleanly in the systems clinicians already used. Scope was ruthlessly prioritized to what actually enabled care to continue.

The Outcome

Patients could complete intake safely from home, staff received structured information ahead of visits, and the provider maintained continuity of care through a period when most workflows were being reinvented in real time.

The Problem

Application code was in source control and shipped through a pipeline, but database changes were still hand-applied. Environments drifted, deployments required a specific person to be on the call, and rollbacks were an anxiety event rather than a routine.

The Approach

I brought the database under source control alongside the application, defined a repeatable schema change and deployment process, and wired it into the existing Azure DevOps pipelines. Every environment reached the same state through the same automated path, with a clear review trail for schema changes.

The Outcome

Schema drift went away. Deployments stopped depending on a single person's presence, and the team could trust that what shipped to production had already been proven through the earlier environments.