标签：#Health

Building a Scalable, HIPAA‑Compliant Healthcare Document Processing Pipeline in .NET & Azure Quick Answer A deep dive into architecting a production‑grade Healthcare Document Processing Pipeline—covering AI extraction, FHIR integration, vector search, and compliance at scale. In my experience, the biggest cost is not the AI model, but the orchestration that turns raw scans into audit‑ready FHIR resources. The right mix of services can reduce latency by 30‑50% while keeping the bill below 10% of the raw compute budget. Choose services that expose a BAA and native hybrid search (Azure Cognitive Search) to avoid a second compliance layer. Prioritize deterministic scaling (Container Apps + Aspire) over elastic serverless when real‑time SLAs are tight. Version your embeddings; treat the vector index as a first‑class contract. HIPAA‑Ready High‑Volume Document Ingestion When a health system starts ingesting thousands of paper‑to‑digital documents per day, the naïve “scan‑and‑store” approach quickly becomes a compliance and performance nightmare. The real challenge is to produce HIPAA‑ready, FHIR‑compliant, low‑latency data that can be consumed by downstream clinical decision support or billing systems. Compliance is not a checkbox; it’s a series of audit trails that must survive a 30‑day retention policy and survive a forensic review. In production, the cost of a single PHI exposure can exceed the annual budget of the entire platform. Real‑World Example Consider a mid‑size hospital that receives 25,000 inpatient discharge summaries, 8,000 lab reports, and 12,000 imaging PDFs every month. Each document is a mixture of scanned images, PDFs, and legacy forms. The billing team needs structured diagnoses and procedure codes within 30 seconds to avoid claim denials, while the analytics team wants similarity search for rare disease cases in the last 12 months. The pipeline must: Extract structured entities with ≥95% accuracy. Redact PHI in transit and at rest. Provide audit logs

Luxury water can contain different minerals and taste noticeably different. But a remote source, alkaline pH, and high price do not necessarily mean better hydration.

Michael Polansky — better known publicly as Lady Gaga's partner and a former top deputy to Sean Parker — has quietly spent years building an AI-driven startup that keeps living human skin tissue alive for weeks outside the body to discover new skincare compounds, and is only now going public about it.

I founded Krasyn, an outpatient EMR with an AI scribe inside it. Krasyn has run a working outpatient clinic's real patient records since March 2026, so what our scribe drafts ends up in charts that real clinicians sign. This post covers two things we shipped in August: a published benchmark of how faithful those drafts are to the transcript, and Note Check, a tool that reads any scribe's note against its transcript and lists what the transcript does not support. Why a fluent note is the problem A faithful note and a note with one invented blood pressure look the same on the screen, and the clinician who signs it owns every sentence. Published evaluations put ambient-scribe hallucination at about 1 to 3 percent of notes. A March 2026 analysis of 71,173 AI-drafted and finalized note sections found a confirmed edit in 5.8 percent of them. The drafting got automated. The checking did not. I wanted a number for our own scribe that I could defend, with the definitions printed next to it. A benchmark without definitions is marketing. The unit: a clinical assertion We measure at the level of a clinical assertion, one atomic statement about the patient that could be true or false on its own. "Denies fever, chills, and nausea" is three assertions. A measurement and its value are one. Hedging is kept verbatim. Every assertion gets exactly one label against the transcript: Supported: the transcript says it, or it is a faithful paraphrase or clinical translation. Inferred: not stated, but a reasonable clinical inference with a basis in the transcript. Tracked separately because it is the contested category. Unsupported: no basis in the transcript at all. Contradicted: the transcript says the opposite, including a symptom the patient denied, a treatment the clinician declined, or another person's symptom attributed to the patient. Hallucination rate is unsupported plus contradicted over all assertions. Coverage is measured separately against key facts per case, because a note tha

After two years of testing, these are the electric toothbrushes that impressed WIRED staffers the most.

When the biotech company Insilico Medicine used its computer models to propose a promising drug for pulmonary fibrosis, it enthusiastically claimed in a press release that the molecule had been “discovered by” its generative AI platform. Insilico leads a pack of companies using AI to rapidly come up with drug ideas humans might never think…

The UK government is facing calls to cancel a sprawling health care contract with Palantir. The region of Greater Manchester insists it can do a better job itself.

An effort to transform the world of sports through steroid use doesn't exactly seem to be bearing financial fruit.

From granola to guacamole, a wide variety of foods have been recalled lately over salmonella risks. Experts say common-sense precautions go a long way.

The vaccine reportedly thwarted the cancer's return and spread.

With smarter gym tracking, new health alerts, and offline Gemini, the Pixel Watch 5 fine-tunes a winning formula—for a price.

She opposes abortion, is wary of peptides, supports limiting vaccines, vexes MAHA.

It's the data, stupid.

For a healthtech verification link, the usual SendGrid vs Resend vs Postmark debate starts too late: the best alternative transactional email API is the one that leaves reviewable evidence after delivery. Short answer: choose a transactional email API only after a small evaluation proves API sending, controlled templates, verified-domain operation, suppression handling, and retrievable delivery records; Infrai is a practical option when those basics matter more than SMTP migration or webhook-driven automation, while teams that require either of those should keep a provider that supplies them. That result sounds less exciting than a feature matrix. Good. A verification message is part of an account-control path, so the useful output of a provider experiment isn't a polished welcome email. It's an evidence packet that connects one signup, one approved template revision, one domain configuration, one send request, and one later delivery record without placing health data in the message or logs. My first pass at this decision would be deliberately small: one synthetic recipient, one expiring link, one correlation ID, and no production data. I don't promote the notebook experiment until the evidence can be checked mechanically. The catch is that a provider can pass the send test while failing the operating model because an auditor cannot reconstruct what happened later. Reliability begins with five linked artifacts Start with five claims and demand an artifact for each. The API accepted a send. The rendered body came from the approved template revision. The sending domain was verified and DKIM could be rotated. A suppressed recipient wasn't treated as a normal send. Finally, a delivery record could be pulled into the team's own evidence store. Google also expects senders to authenticate mail, so domain work is part of the experiment rather than a launch-week chore. For a reviewer, those records need to form one understandable chain: the synthetic signup created a correla

US healthcare is broken. Under RFK Jr., the research agency working to fix it is, too.

Save $250 off the Garmin Fenix 8, plus discounts on other popular fitness trackers from the brand.

At what point did you start to think you were an engineer? An engineer. What a lofty,...

The scanning and bloodwork health startup founded by Spotify's founder will officially launch in New York in about a month.

Research shows heat amplifies the dangers of pesticides.

Struggling with burnout? Procrastination? Reaching your goals? Let me share a few methods that help...