<?xml version="1.0" encoding="UTF-8"?><oembed><type>video</type><version>1.0</version><html>&lt;iframe src=&quot;https://www.loom.com/embed/ca120823bd8e41fb90ed23c223eae27e&quot; frameborder=&quot;0&quot; width=&quot;2730&quot; height=&quot;2047&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>2047</height><width>2730</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>2047</thumbnail_height><thumbnail_width>2730</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/ca120823bd8e41fb90ed23c223eae27e-e6f5792204591ce8.gif</thumbnail_url><duration>537.896</duration><title>Using AI to Fill Burdensome Medical Forms</title><description>In this walkthrough, I demonstrate how to use Doximity&apos;s generative AI to bootstrap the form-filling process — no fancy EHR integration required.

The example form is New York State&apos;s CDPAP, the form used to get patients home health aide services. I upload the form directly into Doximity and write a simple prompt: fill out the attached form using the patient information provided below, and flag anything you can&apos;t complete due to missing data. I paste in de-identified patient notes from a prior visit and let the AI run.

The output is solid. It pulls the primary diagnosis, secondary diagnoses with ICD codes, a description of the current condition, the treatment plan, functional limitations, dietary needs, home therapies, and contributing factors — essentially the clinical meat of the form. I then ask it to update the form with the proper ICD codes based on the primary diagnoses, and it returns accurate codes that I spot-check against a separate AI tool.

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