Healthcare technology often fails for two reasons: it underestimates the complexity of clinical workflows, or it prioritizes regulatory requirements only when they become blockers.
Our healthcare-focused digital engineering practice builds HIPAA-compliant healthcare data platforms and solutions that are architected to comply, integrated with clinical workflows by design, and validated in real operational environments.
AI extraction of diagnoses, medications, procedures, and outcomes from unstructured clinical notes and discharge summaries.
Appointment scheduling, care plan communication, and post-discharge follow-up, integrated with EHR systems.
EHR data integration analytics platform for aggregating EHR, claims, lab, and wearable data into a unified patient record.
AI-assisted PA request generation and status tracking, reducing clinician time on administrative workflows.
ML models identifying high-risk patients for proactive intervention using claims, vitals, and social determinants data.
CV models for AI radiology imaging diagnosis, facilitating quality, anomaly flagging, and imaging workflow optimization.
Automating coding, claims processing, and denials management to accelerate reimbursement and reduce administrative leakage.
Aggregating cross-continuum data to identify high-risk cohorts and drive proactive, value-based intervention strategies.
Streamlining adverse event detection and regulatory reporting through intelligent intake and case processing workflows.
PHI is encrypted at rest (AES-256) and in transit (TLS 1.3) across all pipeline components, no exceptions.
Role-based access with field-level permissions and comprehensive audit trails for all PHI access events.
HIPAA Safe Harbor and Expert Determination de-identification for data used in AI training and analytics.
All cloud infrastructure is configured to meet Business Associate Agreement requirements, documented, and auditable.
PHI never leaves the specified geographic boundaries, which are critical for state-specific regulations and hospital system requirements.
Mid-size health system automated PA workflows for 8 speciality departments, 4.2 hours saved per clinician per week.
Clinical NLP pipeline processing 15,000 discharge summaries per month for a regional hospital network.
Intelligent appointment reminder and rescheduling platform deployed across 22 outpatient clinics.
Revenue cycle AI identifying undercoded encounters across a 6-hospital system.
Traditional EHR systems trigger on single-variable threshold rules, for example, a drug interaction flag or an abnormal lab result, and are prone to alert fatigue. Conversely, the EHR data integration analytics platform uses multivariate models trained on longitudinal EHR data to identify complex risk patterns (sepsis onset, readmission risk, deterioration trajectories) and surfaces prioritized alerts with the supporting evidence presented alongside the recommendation. This reduces false-positive alert volume while improving sensitivity on genuinely actionable clinical signals.
Our healthcare predictive analytics platform integrates with major EHR systems, including Epic, Cerner (Oracle Health), Meditech, and Allscripts, via FHIR R4, HL7 v2, and secure APIs. We also support SMART on FHIR app deployment within Epic and Cerner workflows. Lab and medical device data are ingested via HL7 messaging and direct integration with laboratory information systems. All integrations are designed to preserve data provenance for audit and regulatory purposes.
For AI clinical decision support systems that qualify as Software as a Medical Device (SaMD) under FDA guidance, we support regulatory submissions, including the 510(k) and De Novo pathways. This is provided with documentation aligned to the FDA's AI/ML action plan and predetermined change control protocols. All data pipelines are engineered to meet HIPAA's technical safeguards, with assured encryption at rest and in transit, access controls, audit logging, and Business Associate Agreement frameworks. Custom model development follows ISO 13485 quality management principles for medical device software, and all production models include performance monitoring to detect drift against validated baselines.
By training on roughly two years of patient history, these AI models can predict who will return to the hospital within 30 days with far greater accuracy than the traditional checklist methods doctors have used for years. But it requires access to discharge summaries, diagnosis codes, medication reconciliation records, and ideally prior utilization history.
AI drug discovery platforms target identification, molecule generation, and ADMET property prediction, reducing early-stage discovery timelines from years to months by computationally screening vast chemical spaces before any wet-lab work begins. In clinical trial design, AI is applied to patient cohort identification using real-world evidence from EHRs and claims data, adaptive trial design modeling, and site selection optimization. Pharmacovigilance teams use NLP to automate adverse event extraction from unstructured clinical notes and regulatory submissions, significantly reducing the manual effort of post-market safety monitoring.
Clinical AI models must be interpretable to the clinicians who use them, both for regulatory approval and practical adoption. We attach SHAP-based feature importance explanations to every model prediction, surfacing the key clinical factors (e.g., elevated creatinine, recent hospitalisation, medication non-adherence signals) that drove the risk score in plain language within the clinical workflow. Model performance is monitored continuously across patient subgroups to detect differential performance or bias. All production models are documented with model cards, reviewable by clinical governance committees.
Since 1999, SunTec has been a trusted business process and software engineering partner for technology companies worldwide. Our team of over 1,500 full-time employees delivers innovative services every day to help our clients save time and money.
