# KastHunt Consulting: AI\-native healthcare technology for clinics, founders, and health systems\. From live PoC to full\-scale delivery, with clinical credibility\.

> AI\-native healthcare technology built for clinics, founders, and health systems\. From live PoC to full\-scale delivery, with clinical credibility and a delivery guarantee\.

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## Posts
- [How to Choose an AI Clinical Scribe for Multi\-Site Providers](https://kasthunt.com/choose-ai-clinical-scribe-multi-site-providers/): A practical 2026 guide to choosing an AI clinical scribe across multiple sites, covering EHR integration, governance, safety, regional compliance, rollout and vendor evaluation\.
- [Best AI Clinical Scribe Platforms in 2026: 15 AI Medical Scribes Compared for Clinics, Hospitals and Multi\-Site Groups](https://kasthunt.com/best-ai-medical-scribes-2026/): Explore 15 AI medical scribe platforms in 2026 and compare EHR integration, deployment, pricing, governance, multi\-site support and regional availability for clinics and hospitals\.
- [Why Clinicians Preferred AI Notes Even When They Hallucinated More Often](https://kasthunt.com/ai-scribe-automation-bias/): One study found reviewers preferred ambient AI\-generated clinical notes in 47% of comparisons versus 39% for physician\-authored notes, despite higher hallucination rates\. The finding shows that clinician preference and note accuracy are different measures and both need to be evaluated separately\.
- [Who Is Accountable When an AI Scribe Gets a Note Wrong?](https://kasthunt.com/ai-scribe-accountability/): When an AI scribe gets a clinical note wrong, responsibility does not automatically fall on one person or organisation\. This article examines current guidance on clinician review, supplier obligations, health\-system governance and the separate question of legal liability\.

- [How Accurate Are AI Medical Scribes? What the Studies Actually Measured](https://kasthunt.com/how-accurate-are-ai-medical-scribes-what-the-studies-actually-measured/): AI medical scribe accuracy cannot be reduced to one percentage\. Studies measure different things—from transcription hallucinations and omissions to semantic agreement, note quality and clinician review\. This blog explains why the numbers differ and what healthcare teams should actually evaluate before deployment\.

## Categories
- [Clinical AI](https://kasthunt.com/category/clinical-ai/)
- [AI in Medicine](https://kasthunt.com/category/ai-medicine/)
- [Data Governance](https://kasthunt.com/category/data-governance/)
- [Healthcare Productivity](https://kasthunt.com/category/healthcare-productivity/)
- [NHS Technology](https://kasthunt.com/category/nhs-technology/)

## Optional
- [Sitemap index](https://kasthunt.com/sitemap_index.xml)