HIPAA and HITECH
Privacy Rule, Security Rule, and breach notification controls applied to AI training data, inference logs, and de-identified analytics. BAAs, minimum necessary, and access controls covered.
Industry
Clinical-Grade AI for Healthcare Providers, Clinics, and Payers
HIPAA, MOH, and EU MDR aligned AI for clinical decision support, patient analytics, imaging, and operations. Plus growth-stack work for clinics: local SEO, appointment chatbots, and digital marketing.
Overview
Healthcare, the practice in summary
Healthcare AI must be safe, equitable, explainable, and compliant. Magnis AI helps hospitals, clinics, group practices, payers, and life sciences companies deploy AI across clinical decision support, medical imaging, patient analytics, and operational automation while satisfying HIPAA in the United States, the Singapore MOH Artificial Intelligence in Healthcare Guidelines, EU MDR for software as a medical device, and India DPDP. For smaller clinics and multi-location groups we also deliver digital growth: appointment chatbots, local SEO, paid ads, and patient portals.
Regulatory Context
The frameworks shaping AI in healthcare
We translate each into engineering controls and audit evidence. AI in healthcare ships faster when the regulator is treated as a stakeholder from day one.
MOH AI in Healthcare Guidelines
Singapore Ministry of Health AI in Healthcare Guidelines (AIHGle) for clinical AI lifecycle, validation, and monitoring. Embedded into our delivery methodology.
EU MDR and SaMD
Software as a Medical Device classification and conformity assessment. We help you decide whether your AI is SaMD and what evidence is needed.
FDA Good Machine Learning Practice
FDA GMLP principles for AI/ML based SaMD, plus the AI/ML SaMD Action Plan. Applied to validation, monitoring, and change control.
Use Cases
High-value AI use cases for healthcare
Each use case is shipped with the governance, validation, and monitoring needed to clear internal risk review and regulator inspection.
Clinical Decision Support
Risk stratification, sepsis prediction, deterioration alerts, and triage augmentation with documented validation, fairness testing, and clinician-in-the-loop design.
Medical Imaging Intelligence
Radiology, pathology, and ophthalmology AI integration with PACS, workflow design, validation studies, and post-market surveillance.
Patient Analytics and Readmission Prediction
Population health, readmission risk, and care gap analytics with privacy-preserving feature engineering and equity audits.
Appointment Chatbots and Patient Portals
WhatsApp, web, and voice agents that book appointments, answer common questions, and triage to the right specialty. Built for clinic-scale practices and group networks.
Local SEO and Paid Acquisition
Local SEO for clinic websites, Google Business Profile optimisation, and Google Ads management for patient acquisition in service areas.
Operational and Revenue Cycle Automation
Prior authorisation, claims, denial management, and scheduling automation with measurable cycle-time and cost outcomes.
Services we deliver in Healthcare
The Magnis AI services that fit this sector
Enterprise AI Consulting
Three integrated pillars under one practice: AI Governance and Compliance, Generative AI and LLM Consulting, and Intelligent Process Automation. Designed for banks, insurers, healthcare, government, and large operators that need AI to stand up to regulators and to the board.
ExploreAI Chatbot Development
Custom chatbots grounded in your own knowledge base, integrated with your CRM and helpdesk, and tuned to your tone of voice. For websites, WhatsApp, Telegram, Instagram, and voice.
ExploreSEO Services
Technical SEO, content SEO, local SEO, Answer Engine Optimisation, Generative Engine Optimisation, and LLM Optimisation for Google, Bing, ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews. Built around measurable organic growth and AI visibility, not vanity rankings.
ExploreGoogle Ads Management
Search, Performance Max, YouTube, and Display campaigns built for cost per lead and return on ad spend. Transparent reporting, weekly optimisation, and no opaque agency fees.
ExploreMethodology
How an industry engagement runs
Four phases, each calibrated to the sector. Regulatory diagnostic comes first; deployment follows once the controls envelope is clear.
- 01
Clinical and Regulatory Scoping
Use-case definition with clinical leadership, risk classification, and regulator path. SaMD assessment where relevant.
- 02
Validation Design
Study design, comparator, performance and equity endpoints, and post-market surveillance plan agreed before model build.
- 03
Build and Validate
Model development with prospective and retrospective validation. Documentation built to support FDA, EU MDR, or MOH expectations.
- 04
Deploy and Monitor
Workflow integration, clinician training, real-world performance monitoring, and change control for model updates.
Outcomes
Measurable results
Specific, defensible outcomes from typical engagements.
- Clinical AI that clears clinical safety and IT security review the first time
- Measurable improvement in clinical outcomes or operational efficiency
- Equity audits that surface and address performance gaps before deployment
- Cost-of-care reduction in documentation, prior auth, and revenue cycle workflows
- More patient appointments booked through automated channels (for clinic-scale work)
Common questions about AI in healthcare
How do you ensure HIPAA compliance in clinical AI projects?
Through a combination of data architecture, contractual controls, and engineering. We design training and inference data flows with minimum necessary access, ensure BAAs with all subprocessors, de-identify or pseudonymise where appropriate, and instrument inference logging with role-based access. PHI never leaves the regulated boundary unless explicitly permitted.
Is our clinical AI a medical device?
It depends on intended use. Software intended to diagnose, treat, prevent, cure, or mitigate disease is typically SaMD and falls under FDA, EU MDR, or local equivalents. Software supporting clinician workflow without diagnostic intent is often outside SaMD. We run a structured classification exercise early so you know which regulatory path applies.
Do you work with small clinics and group practices, not just hospitals?
Yes. For clinics and multi-location groups our work focuses on appointment chatbots, patient portals, local SEO, Google Ads for patient acquisition, and operational automation. The HIPAA discipline applies the same way; the scale and pricing are clinic-appropriate.
How do you address bias and equity in healthcare AI?
Equity is treated as a first-class performance metric. We test performance across protected attributes, social determinants, and care settings, and we design data collection to surface and close gaps. Models that perform inequitably do not ship until the gap is addressed or explicitly accepted with mitigation.
How long does a clinical AI deployment take?
From scoping to a first validated deployment, six to nine months. Validation studies and IT security review are the rate-limiting steps. We front-load both to compress the timeline. Clinic-scale digital growth work (chatbots, SEO) ships in 4 to 12 weeks.
Scope a Healthcare AI or Growth Initiative
Book a 45-minute consultation with our healthcare team. For hospital-scale work we identify the highest-impact safe-to-deploy AI use cases. For clinics and groups we scope an appointment, SEO, and ads programme.