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    <title>AI Health Index: Medication Safety &amp; Prescribing changes</title>
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    <description>Material product changes at Medication Safety &amp; Prescribing vendors, tracked by AI Health Index. Every entry is dated and cited to a public source.</description>
    <language>en-us</language>
    <copyright>Free to reuse with a visible link to aihealthindex.io. Terms: https://aihealthindex.io/use-this-data</copyright>
    <lastBuildDate>Thu, 24 Sep 2026 12:00:00 GMT</lastBuildDate>
    <ttl>720</ttl>
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      <title>AI Health Index: Medication Safety &amp; Prescribing changes</title>
      <link>https://aihealthindex.io/categories/medication-safety-and-prescribing</link>
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      <title>InsightRX released Gemini 2026.2, an update to the model selection engine in InsightRX Nova that adds a new general adult vancomycin…</title>
      <link>https://aihealthindex.io/changelog/insightrx</link>
      <guid isPermaLink="false">aihealthindex.io:change:6ab90c10ee3c2977980e6544</guid>
      <pubDate>Thu, 24 Sep 2026 12:00:00 GMT</pubDate>
      <category>Model / architecture</category>
      <category>Medication Safety &amp; Prescribing</category>
      <category>Clinical Decision Support</category>
      <description><![CDATA[<p>InsightRX released Gemini 2026.2, an update to the model selection engine in InsightRX Nova that adds a new general adult vancomycin model, McCarthy 2026, plus CKD-EPI eGFR versions of the published models that improved with that input. McCarthy 2026 was fit on a stratified sample drawn from 549,171 patients across 304 US organizations, scales body size on fat free mass and bends the eGFR effect on clearance at 90 mL/min/1.73 m2. InsightRX reports that the release raised individual prediction accuracy from 66% to 68%, cut bias (MPE) from 1.8% to 0.9% and cut error (NRMSE) from 28% to 27% versus Gemini 2026.1.</p><p><strong>Why it matters:</strong> Health systems using Gemini get the new model recommendations automatically, and InsightRX states the Hughes 2024 obesity model is now mostly selected only for patients with obesity. It also shows the vendor retraining and revalidating models on network data after deployment. The accuracy gains are vendor reported and modest.</p><p>Impact: Medium. Verification: Verified. Type: Model / architecture.</p><p>Evidence: <a href="https://blog.insight-rx.com/resources/continuous-learning-in-practice-three-ways-to-improve-adult-vancomycin-models">Vendor announcement</a></p><p>Source: <a href="https://aihealthindex.io/changelog/insightrx">AI Health Index change log</a>. Free to reuse with a link back. <a href="https://aihealthindex.io/use-this-data">Terms</a>.</p>]]></description>
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      <title>FDB (First Databank): FDB commercially deployed FDB Script Agent, an AI prescribing tool that converts ambient patient encounter dialogue directly into…</title>
      <link>https://aihealthindex.io/changelog/first-databank</link>
      <guid isPermaLink="false">aihealthindex.io:change:6a92e5ef75bbcbbe469c6fe8</guid>
      <pubDate>Mon, 24 Aug 2026 12:00:00 GMT</pubDate>
      <category>Product / capability</category>
      <category>Medication Safety &amp; Prescribing</category>
      <category>Clinical Reference &amp; Evidence</category>
      <category>Clinical Decision Support</category>
      <description><![CDATA[<p>FDB commercially deployed FDB Script Agent, an AI prescribing tool that converts ambient patient encounter dialogue directly into structured prescriptions. The agent applies FDB's drug terminology codification and clinical validation to what it hears, so the output is a coded medication order rather than free text, and it queues that order for clinician review rather than transmitting it. It launched first on the Tebra platform. This moves ambient listening past documentation and into order entry, which is a materially different risk surface.</p><p><strong>Why it matters:</strong> Practices can cut the manual keystrokes of prescription entry, which is one of the slowest steps left in an otherwise automated encounter. The important detail for evaluators is that FDB owns the drug knowledge base underneath the codification, so the structured output inherits an established terminology layer rather than a model's guess at a drug name and dose. Anyone assessing this should focus on the review step: what the clinician sees before signing, and what happens when the agent mishears a dose or a route.</p><p>Impact: High. Verification: Verified. Type: Product / capability.</p><p>Evidence: <a href="https://www.fdbhealth.com/about-us/press-releases/2026-08-24-fdb-moves-ambient-listening-beyond-notes-with-ai-powered-prescribing-agent">Vendor announcement</a></p><p>Source: <a href="https://aihealthindex.io/changelog/first-databank">AI Health Index change log</a>. Free to reuse with a link back. <a href="https://aihealthindex.io/use-this-data">Terms</a>.</p>]]></description>
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