AI Procurement Checklist for Healthcare CIOs
A practical checklist for evaluating AI vendors and AI projects in healthcare — the questions to ask before money moves and the red flags to watch for in vendor responses.
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Thoughts on AI adoption, software architecture, and building compliant systems for regulated industries.
A practical checklist for evaluating AI vendors and AI projects in healthcare — the questions to ask before money moves and the red flags to watch for in vendor responses.
Most AI pilots in financial services do not fail technically. They stall in the gap between an interesting demo and a production system that risk and compliance can sign off on.
The specific questions that separate AI vendors who can support a HIPAA workload from vendors who say they can. A practical guide for healthcare buyers in early evaluation.
If your AI system cannot answer these five questions in seconds, it is not audit-ready — and that gap will surface at the worst possible moment.
An honest look at Tampa Bay's tech ecosystem in 2026 — talent, infrastructure, industry verticals, coworking, and what makes the market different from coastal tech hubs.
A practical, architecture-level guide to HIPAA compliant app development. Covers technical safeguards, PHI data flows, audit logging, encryption, BAA obligations, and common mistakes that cause compliance failures.
How to design AI document analysis pipelines that hold up under HIPAA, SOC 2, and legal review. Extraction, RAG, accuracy thresholds, hallucination mitigation, and the architectural decisions that determine whether your system passes audit.
A practical guide to custom healthcare software development — covering use cases, HIPAA requirements, integration complexity, and what distinguishes successful projects.
Architecture guidance for SaaS founders building on AWS — covering multi-tenancy, auth, data isolation, and the decisions that are expensive to change later.
A technical comparison of FHIR and HL7 v2 for engineering teams building healthcare integrations. Covers data models, interoperability use cases, EHR compatibility, and implementation considerations.
Practical AI use cases for small businesses — from document processing to customer support automation. No machine learning expertise required.
A technical guide to fintech software development — covering regulatory frameworks, security architecture, payment processing, and the engineering patterns that matter in financial services.
A practical guide to DynamoDB data modeling — covering single-table design, access pattern planning, GSIs, sparse indexes, and the patterns that prevent expensive rework.
A practical guide to AWS Amplify Gen 2 for production applications — authentication, data modeling, custom resolvers, and the limitations to know before you build.
A technical checklist for SaaS founders preparing for SOC 2 Type II. Covers access controls, logging, encryption, change management, and vendor oversight — written for engineering teams.
An honest comparison of offshore and onshore software development — covering cost, quality, communication, IP risk, compliance considerations, and when each model works.
A practical guide to evaluating and selecting a software development partner — covering technical due diligence, contract structure, engagement models, and red flags to watch for.
Modern patterns and practices for building fast, maintainable Next.js 16 applications with React 19, Server Components, and the App Router.
How to integrate AI into healthcare, legal, and compliance-focused systems while maintaining security, auditability, and regulatory compliance.