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    <title>DEV Community: Jamie Likernon</title>
    <description>The latest articles on DEV Community by Jamie Likernon (@interestng).</description>
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      <title>DEV Community: Jamie Likernon</title>
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      <title>[Boost]</title>
      <dc:creator>Jamie Likernon</dc:creator>
      <pubDate>Sun, 01 Mar 2026 00:29:39 +0000</pubDate>
      <link>https://dev.to/interestng/-469g</link>
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  &lt;a href="https://dev.to/interestng/how-i-unified-3-fragmented-medical-apis-into-a-single-python-sdk-5di7" class="ltag__link__link"&gt;
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      &lt;h2&gt;How I Unified 3 Fragmented Medical APIs Into a Single Python SDK&lt;/h2&gt;
      &lt;h3&gt;Jamie Likernon ・ Mar 1&lt;/h3&gt;
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      <title>How I Unified 3 Fragmented Medical APIs Into a Single Python SDK</title>
      <dc:creator>Jamie Likernon</dc:creator>
      <pubDate>Sun, 01 Mar 2026 00:28:00 +0000</pubDate>
      <link>https://dev.to/interestng/how-i-unified-3-fragmented-medical-apis-into-a-single-python-sdk-5di7</link>
      <guid>https://dev.to/interestng/how-i-unified-3-fragmented-medical-apis-into-a-single-python-sdk-5di7</guid>
      <description>&lt;p&gt;I built &lt;strong&gt;MedKit&lt;/strong&gt; because medical data is notoriously difficult to work with. If you want to correlate a drug's FDA label with its latest clinical trial phases and related research papers, you usually have to juggle three different APIs, handle idiosyncratic JSON schemas, and deal with inconsistent identifier types.&lt;/p&gt;

&lt;p&gt;MedKit is a &lt;strong&gt;unified Python SDK&lt;/strong&gt; that transforms these fragmented sources (OpenFDA, PubMed, and ClinicalTrials.gov) into a single, programmable platform.&lt;/p&gt;

&lt;p&gt;Key Features:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Unified Client&lt;/strong&gt;: One MedKit() client to rule them all. No more multiple API keys or manual correlation.&lt;br&gt;
&lt;strong&gt;Clinical Synthesis&lt;/strong&gt; (med.conclude()): Aggregates data to give a "snapshot" verdict on a drug or condition, including an evidence strength score (0.0–1.0).&lt;br&gt;
&lt;strong&gt;Interaction Engine&lt;/strong&gt;: catch drug-drug contraindications using cross-label mentions (brand vs generic).&lt;br&gt;
&lt;strong&gt;Medical Relationship Graph&lt;/strong&gt;: Visualize connections between drugs, trials, and research papers as a knowledge graph.&lt;br&gt;
&lt;strong&gt;Intelligence Layer&lt;/strong&gt;: Natural language routing (med.ask()) to query data in plain English.&lt;br&gt;
&lt;strong&gt;Why Use It&lt;/strong&gt;? Most healthcare developers spend 80% of their time just cleaning and joining data. MedKit handles the plumbing (caching, schema normalization, relationship mapping) so you can focus on the analysis or the application logic.&lt;/p&gt;

&lt;p&gt;Tech Stack: Python (Sync/Async), Disk/Memory caching, and a provider-based architecture for easy extensibility.&lt;/p&gt;

&lt;p&gt;I'd love to get your thoughts on the med.conclude() synthesis logic, other features and what other providers (e.g., pharmacogenomics) you'd find useful.&lt;br&gt;
More informative description on my github repo.&lt;br&gt;
Repo: &lt;a href="https://github.com/interestng/medkit" rel="noopener noreferrer"&gt;https://github.com/interestng/medkit&lt;/a&gt; PyPI: pip install medkit-sdk&lt;br&gt;
I really appreciate any support towards this post, and stars/follows on my github repo!&lt;br&gt;
Looking forward to your feedback!&lt;/p&gt;

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