Healthcare teams face the same recurring problems across systems, platforms, and patient-facing workflows. Here are the six we see in nearly every project.
Healthcare is not an industry where you can move fast and break things. Every feature touches a patient, a clinician’s time, or a record that will eventually be audited. The margin for error is thinner than any other industry we build for and the upside of getting it right is a better clinical day for the people doing the work.
Mpiric has spent over a decade building for this reality. We’ve partnered with hospital groups, specialty clinics, pharma companies, and digital health startups across 15 countries delivering telemedicine platforms, diagnostic AI tools, patient portals, and practice management systems that hold up in production environments.
What makes us different is our discipline around clinical workflow. Before we write code, we spend time understanding how a ward actually runs, how a physician actually documents, and how your operations team responds when something breaks. That grounding is why our software gets used not just deployed.
Records scattered across EHRs, labs, imaging systems, and wearables, making unified views nearly impossible to build.
Moving from pilot telemedicine setups to enterprise-grade virtual care with reliable video, scheduling, and billing.
Getting patients to actually use portals, apps, and follow-up tools after their initial clinical visit ends.
Build robust data pipelines and scalable AI platforms. Ensure data quality, integration and reliable cloud/on-premise systems to support model development.
Every challenge above requires engineering depth across multiple services. Here are the six outcome-focused solutions we deliver for healthcare clients.
AI-assisted diagnosis, imaging analysis, and risk scoring delivered directly into clinician workflow.
Each service: title + description (150-160 chars) + 4-5 ‘what we build’ bullets + button linking to service page.
Clinical-grade AI that supports clinicians without replacing their judgment. Explainable, auditable, and built for the regulated reality of modern healthcare.
Predictive models trained on clinical data with real accountability. Interpretability, drift monitoring, and validation built into every production ML pipeline.
Patient and provider web platforms built with accessibility, strong performance, and secure integration with your existing clinical and scheduling systems.
Native and cross-platform apps for patients, clinicians, and care teams with offline support, background sync, and secure biometric authentication built in.
Custom EHR, practice management, and specialized clinical software built to fit your exact operations when off-the-shelf systems cannot deliver the result.
DTC, online pharmacy, and medical supply commerce built around prescription flows, age gating, pharmacist review, and insurance payment handling.
Healthcare SEO, local search, content, and paid media that respects advertising policies while converting real patient intent into booked appointments.
Most healthcare apps range from $80,000 for a focused MVP to $500,000+ for enterprise systems like custom EHRs or clinical AI. Adding encryption, audit logging, access controls, and secure infrastructure typically adds 20-30% over a standard app of similar complexity.
A production telemedicine platform typically ships in 4-7 months. A focused MVP with video, scheduling, basic EHR integration, and e-prescribing can launch in 3-4 months. Full-featured platforms with multi-specialty workflows and insurance integrations generally need 6-9 months.
Yes. We build integrations using FHIR R4 APIs for modern Epic and Cerner instances, HL7 v2 interfaces for legacy systems, and athenahealth’s athenaOne API. Read-only data access typically takes 3-4 weeks; bidirectional clinical documentation sync takes 8-12 weeks.
HL7 v2 is the legacy messaging standard used by most existing hospital systems. FHIR is the modern REST-based standard required for new integrations and US interoperability mandates. New products should prioritize FHIR; hospital integrations usually need both for full coverage.
We never use real patient data outside production. Our standard practice: synthetic data generation for realistic patient populations, de-identification pipelines when real data shape is required, separate cloud accounts for production and non-production, and mandatory data agreements with every vendor.
Yes. Clinical AI adoption depends on three things we build in from day one: explainability so clinicians see why a recommendation was made, override controls so the physician stays in charge, and bias auditing across patient demographics. No black boxes in clinical settings.
Yes, and this is one of our common engagements. Clean EHR migration covers schema mapping, coding normalization (ICD-9 to ICD-10, legacy drug codes to RxNorm), document migration, audit trail preservation, and a parallel-run period. Typical timeline is 4-6 months from kickoff.
Empower your healthcare business with custom software, AI integration, medical IoT solutions, and scalable digital infrastructure engineered for performance, compliance, and long-term growth. Let’s create healthcare technology that truly makes an impact.
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