Technical depth and industry insight at the intersection of AI, healthcare, and pharmaceutical technology.
Formulary management has evolved from paper binders to spreadsheets to rigid databases. The next leap is intelligent systems that actively support clinical and financial decisions. Here is where the industry is heading.
Most enterprise software vendors are bolting AI onto legacy architectures and calling it transformation. Truly AI-native systems are built differently from the ground up, and the gap is becoming impossible to ignore.
With over 40 biosimilars now approved in the US, the transition from reference biologics is a formulary management challenge that touches rebates, clinical protocols, and member communication simultaneously.
The federal government publishes a remarkable amount of pharmaceutical and clinical data through free APIs. These five endpoints are essential tools for any healthcare technology team.
Each generation of large language models brings capabilities that move healthcare use cases from theoretical to practical. Here is what the latest generation makes possible that was not feasible 18 months ago.
When a health plan moves a drug to a higher tier or removes it from the formulary, some members switch, some appeal, and some leave the plan entirely. Understanding these dynamics is critical for sound formulary decisions.
Pure AI systems hallucinate. Pure rules engines cannot adapt. The hybrid architecture that combines both is emerging as the standard for regulated industries where decisions must be both intelligent and auditable.
When a health plan manages dozens or hundreds of formularies that share a common base with client-specific overrides, the data architecture determines whether the system scales or collapses under its own weight.
Metaspan data feeds are the backbone of drug pricing and classification for many PBMs and health plans. Getting the weekly ingestion pipeline right from the start saves months of technical debt down the road.
The FDA's 2022 CDS guidance created a clear path for software that supports clinical decisions without triggering medical device regulation. Understanding the four criteria is essential for any healthcare AI team.
Pharmacy benefit managers sit at the center of drug pricing, formulary design, and claims adjudication. Their technology stacks are a patchwork of legacy systems, vendor tools, and manual processes. Here is what the landscape looks like and where the gaps are.
Building AI systems that handle protected health information requires specific architectural decisions that cannot be retrofitted. This guide covers the practical engineering requirements for HIPAA-compliant AI.
The RxNorm API from the National Library of Medicine provides a free, authoritative source for drug interaction data. This technical guide walks through building a real interaction checker using the API.
Medicare Part D compliance is a moving target. Each year, CMS updates coverage requirements, formulary submission formats, and transition protocols. Technology can transform compliance from a scramble into a system.
The openFDA API provides free access to millions of records on drug labels, adverse events, recalls, and more. This guide covers the endpoints, query syntax, rate limits, and practical patterns for building on it.
For decades, formulary management has relied on manual processes, spreadsheets, and rigid database tools. AI is finally making it possible to automate the analytical work while keeping humans in control of clinical decisions.