Healthcare software development & AI engineering for providers, payers, and health-tech companies

HIPAA and HITECH govern PHI handling in the US, alongside state privacy laws, the 21st Century Cures Act's information-blocking provisions, and payer-specific data-sharing agreements. Projects involving AI also need clear data governance for model training and audit logging.

Yes — AI can draft prior authorization requests with supporting clinical documentation and track payer responses, which reduces manual review time, though final approval decisions typically still involve human review on both provider and payer sides.

Generative AI produces content — clinical notes, summaries, draft letters. Agentic AI takes multi-step actions autonomously, like verifying eligibility across systems or following up on a denied claim, often incorporating generative AI as one step within a broader workflow.

Predictive models analyze clinical history, social determinants, and utilization patterns to flag patients at high risk of readmission at discharge, allowing care teams to prioritize follow-up outreach for those patients.

It depends on the use case, but common components include FHIR-compliant data pipelines, PostgreSQL or MongoDB for structured/document data, vector databases like Qdrant for semantic search over clinical text, and cloud infrastructure (AWS, Azure, or GCP) configured for HIPAA compliance.

It depends on whether the need is a commodity function (scheduling, standard billing) — typically better bought — or a workflow specific to your organization's clinical or operational model, which usually justifies custom development.

Computer vision supports radiology triage prioritization, wound assessment tracking in remote monitoring programs, and operating room utilization analysis, generally as decision-support tools rather than autonomous diagnostic systems.

ROI typically shows up as reduced scribe or transcription costs, faster documentation-to-billing cycle times, and improved clinician retention tied to reduced after-hours charting burden.

Bias mitigation requires diverse and representative training data, ongoing model performance monitoring across patient subgroups, and keeping AI systems in a decision-support role with human clinical oversight rather than autonomous decision-making.

SMART on FHIR is a framework for building applications that can securely launch from within an EHR and access patient data through standardized FHIR APIs, commonly used for clinical decision support and specialty-specific tools.

RPM software ingests data from connected devices (blood pressure cuffs, glucometers, wearables), applies clinical thresholds to flag concerning readings, and routes alerts to care teams through a dashboard integrated with clinical workflows.

This includes encryption at rest and in transit, signed business associate agreements with cloud providers, access controls and audit logging, secure architecture review, and data governance policies covering PHI handling throughout the system.

Payers use AI for claims pre-adjudication checks, automated eligibility verification, prior authorization decision support, and fraud/anomaly detection, generally alongside human review for final determinations.

Pilots for well-scoped use cases like documentation assistance or eligibility automation often move from pilot to production within a few months, while broader Agentic workflows involving multiple systems typically take longer due to integration and validation requirements.

Look for demonstrated experience with HL7/FHIR standards, a track record of passing hospital or payer security reviews, clear approach to HIPAA compliance and data governance, and a delivery process that includes clinical or compliance stakeholders rather than working in isolation.