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<rss xmlns:itunes="http://www.itunes.com/dtds/podcast-1.0.dtd" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:podcast="https://podcastindex.org/namespace/1.0" xmlns:media="http://search.yahoo.com/mrss/" version="2.0"><channel><title>AI for Nurses (Bedside Reality)</title><link>https://www.senseofthisshit.com</link><description><![CDATA[AI for Nurses (Bedside Reality) is a nursing AI podcast for RNs, nurse managers, and students who want tools that save shift time without adding review burden—not vendor demos. We own flowsheet and documentation AI triage, handoff summarizers, alert-fatigue realities, privacy gotchas, and when to refuse a pilot. Practical bedside AI literacy—not health-tech hype. Search AI for Nurses on Spotify and Apple Podcasts for weekly nursing AI and bedside workflow episodes. Audio for this show is produced with AI assistance. Episodes are researched, scripted, and reviewed for accuracy before release.<br /><br />Become a supporter of this podcast: <a href="https://www.spreaker.com/podcast/ai-for-nurses-bedside-reality--7167789/support?utm_source=rss&utm_medium=rss&utm_campaign=rss">https://www.spreaker.com/podcast/ai-for-nurses-bedside-reality--7167789/support</a>.]]></description><atom:link href="https://www.spreaker.com/show/7167789/episodes/feed" rel="self" type="application/rss+xml"/><language>en</language><category>Technology</category><copyright>© 2026 Let's Work This Sh*t Out</copyright><image><url>https://d3wo5wojvuv7l.cloudfront.net/t_rss_itunes_square_1400/images.spreaker.com/original/e7660c0bf358610e5dc9e4efcdfb24bc.jpg</url><title>AI for Nurses (Bedside Reality)</title><link>https://www.senseofthisshit.com</link></image><lastBuildDate>Tue, 25 Aug 2026 06:11:02 +0000</lastBuildDate><itunes:author>Jude</itunes:author><itunes:owner><itunes:name>Let's Work This Sh*t Out</itunes:name><itunes:email>ai@senseofthisshit.com</itunes:email></itunes:owner><itunes:image href="https://d3wo5wojvuv7l.cloudfront.net/t_rss_itunes_square_1400/images.spreaker.com/original/e7660c0bf358610e5dc9e4efcdfb24bc.jpg"/><itunes:subtitle>AI for Nurses (Bedside Reality) is a nursing AI podcast for RNs, nurse managers, and students who want tools that save shift time without adding review burden—not vendor demos. We own flowsheet and documentation AI triage, handoff summarizers,...</itunes:subtitle><itunes:summary><![CDATA[AI for Nurses (Bedside Reality) is a nursing AI podcast for RNs, nurse managers, and students who want tools that save shift time without adding review burden—not vendor demos. We own flowsheet and documentation AI triage, handoff summarizers, alert-fatigue realities, privacy gotchas, and when to refuse a pilot. Practical bedside AI literacy—not health-tech hype. Search AI for Nurses on Spotify and Apple Podcasts for weekly nursing AI and bedside workflow episodes. Audio for this show is produced with AI assistance. Episodes are researched, scripted, and reviewed for accuracy before release.<br /><br />Become a supporter of this podcast: <a href="https://www.spreaker.com/podcast/ai-for-nurses-bedside-reality--7167789/support?utm_source=rss&utm_medium=rss&utm_campaign=rss">https://www.spreaker.com/podcast/ai-for-nurses-bedside-reality--7167789/support</a>.]]></itunes:summary><itunes:category text="Technology"/><itunes:category text="Business"/><itunes:explicit>false</itunes:explicit><podcast:guid>a99679f2-5afc-51e3-a9ab-f4abedfcebb6</podcast:guid><itunes:type>episodic</itunes:type><podcast:funding url="https://www.spreaker.com/podcast/ai-for-nurses-bedside-reality--7167789/support?utm_source=rss&amp;utm_medium=rss&amp;utm_campaign=rss">Support the podcast!</podcast:funding><item><title>AI for Nurses (Bedside Reality) main episode for 2026-07-21</title><link>https://www.spreaker.com/episode/ai-for-nurses-bedside-reality-main-episode-for-2026-07-21--74230263</link><description><![CDATA[In this episode, we cut through the hype around AI in electronic health records and handoff tools. Many features promise faster documentation yet end up adding clicks, reviews, and overrides that pull nurses away from the bedside. Jude shares a practical three-step test you can run on your next shift to see whether a tool actually saves time or simply shifts the work elsewhere. Learn how to measure clicks, compare outputs line by line, and track privacy or accuracy issues without waiting for a full pilot. Real examples show nurses spotting missing intake totals, ignored alerts, and overly broad summaries after just one or two uses.<br /><br /> Key takeaways:<br /> - Test one workflow at a time, such as the handoff summarizer, instead of adopting every new feature.<br /> - Compare AI output against your own notes to identify omissions, repeats, and context gaps quickly.<br /> - Track workflow impact and keep brief notes on errors so you can decide whether to keep using the tool.<br /> Support the show: <a href="https://www.spreaker.com/podcast/ai-for-nurses-bedside-reality--7167789/support" target="_blank" rel="noreferrer noopener">https://www.spreaker.com/podca...</a><br /><br /> 📩 Have questions or want to share your experience? Reach out at ai@senseofthisshit.com.<br /> 💛 Join Our Supporters Club ($3 a month) 💛 Ad-free listening + early episodes — help keep independent media alive. Click Here: <a href="https://www.spreaker.com/podcast/ai-for-nurses-bedside-reality--7167789/support" target="_blank" rel="noreferrer noopener">https://www.spreaker.com/podca...</a>]]></description><guid isPermaLink="false">742fa241-38ab-5d2c-8fcb-58b753baa2a8</guid><pubDate>Tue, 25 Aug 2026 06:00:11 +0000</pubDate><enclosure url="https://dts.podtrac.com/redirect.mp3/api.spreaker.com/download/episode/74230263/audio.mp3" length="3805019" type="audio/mpeg"/><podcast:transcript url="https://transcription.spreaker.com/starship/c797b41f-6a7c-4c0a-938d-25b1e884db03/c797b41f-6a7c-4c0a-938d-25b1e884db03.srt" type="application/x-subrip" language="en"/><podcast:transcript url="https://transcription.spreaker.com/starship/c797b41f-6a7c-4c0a-938d-25b1e884db03/c797b41f-6a7c-4c0a-938d-25b1e884db03.txt" type="text/plain" language="en"/><podcast:transcript url="https://transcription.spreaker.com/starship/c797b41f-6a7c-4c0a-938d-25b1e884db03/c797b41f-6a7c-4c0a-938d-25b1e884db03.vtt" type="text/vtt" language="en"/><itunes:author>Jude</itunes:author><itunes:subtitle>In this episode, we cut through the hype around AI in electronic health records and handoff tools. Many features promise faster documentation yet end up adding clicks, reviews, and overrides that pull nurses away from the bedside. Jude shares a...</itunes:subtitle><itunes:summary><![CDATA[In this episode, we cut through the hype around AI in electronic health records and handoff tools. Many features promise faster documentation yet end up adding clicks, reviews, and overrides that pull nurses away from the bedside. Jude shares a practical three-step test you can run on your next shift to see whether a tool actually saves time or simply shifts the work elsewhere. Learn how to measure clicks, compare outputs line by line, and track privacy or accuracy issues without waiting for a full pilot. Real examples show nurses spotting missing intake totals, ignored alerts, and overly broad summaries after just one or two uses.<br /><br /> Key takeaways:<br /> - Test one workflow at a time, such as the handoff summarizer, instead of adopting every new feature.<br /> - Compare AI output against your own notes to identify omissions, repeats, and context gaps quickly.<br /> - Track workflow impact and keep brief notes on errors so you can decide whether to keep using the tool.<br /> Support the show: <a href="https://www.spreaker.com/podcast/ai-for-nurses-bedside-reality--7167789/support" target="_blank" rel="noreferrer noopener">https://www.spreaker.com/podca...</a><br /><br /> 📩 Have questions or want to share your experience? Reach out at ai@senseofthisshit.com.<br /> 💛 Join Our Supporters Club ($3 a month) 💛 Ad-free listening + early episodes — help keep independent media alive. Click Here: <a href="https://www.spreaker.com/podcast/ai-for-nurses-bedside-reality--7167789/support" target="_blank" rel="noreferrer noopener">https://www.spreaker.com/podca...</a>]]></itunes:summary><itunes:duration>271</itunes:duration><itunes:keywords>ai,alerts,artificialintelligence,bedside,bedsidereality,documentation,efficiency,ehr,handoff,healthcare,nurses,nursing,patientcare,privacy,reality,shifts,summaries,technology,tools,workflow</itunes:keywords><itunes:explicit>false</itunes:explicit><itunes:image href="https://d3wo5wojvuv7l.cloudfront.net/t_rss_itunes_square_1400/images.spreaker.com/original/e7660c0bf358610e5dc9e4efcdfb24bc.jpg"/><itunes:season>1</itunes:season><itunes:episode>3</itunes:episode><itunes:episodeType>full</itunes:episodeType></item><item><title>AI for Nurses (Bedside Reality) main episode for 2026-07-14</title><link>https://www.spreaker.com/episode/ai-for-nurses-bedside-reality-main-episode-for-2026-07-14--74075746</link><description><![CDATA[In this episode, we tackle the daily grind of alert fatigue that pulls nurses away from bedside care. From repetitive low-level warnings that bury real concerns to tools that add clicks instead of saving time, the discussion reveals how mismatched AI systems create extra work on already packed shifts. Learn a practical, no-waiting approach: pick one frequent alert, track its triggers and impact during a single shift, then log exact wording and timing to share with your charge nurse or informatics team. This focused data turns vague complaints into actionable tweaks, like delaying notifications or tying them to a second vital sign.<br /><br /> Key takeaways:<br /> - Track high-volume alerts and note whether they change your actions<br /> - Compare patterns across rooms to spot overly sensitive thresholds<br /> - Suggest limited unit tests to reduce noise without broad disruption<br /> - Use short logs of time, room, and text to drive faster conversations with support teams<br /><br /> 📩 Have questions or want to share your experience? Reach out at ai@senseofthisshit.com.<br /><a href="https://www.spreaker.com/podcast/ai-for-nurses-bedside-reality--7167789/support" target="_blank" rel="noreferrer noopener">https://www.spreaker.com/podca...</a>]]></description><guid isPermaLink="false">42dce8cb-0dc5-5709-a991-89490f76aef8</guid><pubDate>Tue, 18 Aug 2026 06:00:11 +0000</pubDate><enclosure url="https://dts.podtrac.com/redirect.mp3/api.spreaker.com/download/episode/74075746/audio.mp3" length="4345610" type="audio/mpeg"/><podcast:transcript url="https://transcription.spreaker.com/starship/af5f2d4b-2f26-49f9-b45a-2a20c2428115/af5f2d4b-2f26-49f9-b45a-2a20c2428115.srt" type="application/x-subrip" language="en"/><podcast:transcript url="https://transcription.spreaker.com/starship/af5f2d4b-2f26-49f9-b45a-2a20c2428115/af5f2d4b-2f26-49f9-b45a-2a20c2428115.txt" type="text/plain" language="en"/><podcast:transcript url="https://transcription.spreaker.com/starship/af5f2d4b-2f26-49f9-b45a-2a20c2428115/af5f2d4b-2f26-49f9-b45a-2a20c2428115.vtt" type="text/vtt" language="en"/><itunes:author>Jude</itunes:author><itunes:subtitle>In this episode, we tackle the daily grind of alert fatigue that pulls nurses away from bedside care. From repetitive low-level warnings that bury real concerns to tools that add clicks instead of saving time, the discussion reveals how mismatched AI...</itunes:subtitle><itunes:summary><![CDATA[In this episode, we tackle the daily grind of alert fatigue that pulls nurses away from bedside care. From repetitive low-level warnings that bury real concerns to tools that add clicks instead of saving time, the discussion reveals how mismatched AI systems create extra work on already packed shifts. Learn a practical, no-waiting approach: pick one frequent alert, track its triggers and impact during a single shift, then log exact wording and timing to share with your charge nurse or informatics team. This focused data turns vague complaints into actionable tweaks, like delaying notifications or tying them to a second vital sign.<br /><br /> Key takeaways:<br /> - Track high-volume alerts and note whether they change your actions<br /> - Compare patterns across rooms to spot overly sensitive thresholds<br /> - Suggest limited unit tests to reduce noise without broad disruption<br /> - Use short logs of time, room, and text to drive faster conversations with support teams<br /><br /> 📩 Have questions or want to share your experience? Reach out at ai@senseofthisshit.com.<br /><a href="https://www.spreaker.com/podcast/ai-for-nurses-bedside-reality--7167789/support" target="_blank" rel="noreferrer noopener">https://www.spreaker.com/podca...</a>]]></itunes:summary><itunes:duration>300</itunes:duration><itunes:keywords>ai,ainurses,alertmanagement,alerts,bedside,bedsidecare,chargenurse,fatigue,healthcare,hospital,informatics,nurses,nursing,patientcare,practicalai,reality,shift,technology,tracking,workflow</itunes:keywords><itunes:explicit>false</itunes:explicit><itunes:image href="https://d3wo5wojvuv7l.cloudfront.net/t_rss_itunes_square_1400/images.spreaker.com/original/e7660c0bf358610e5dc9e4efcdfb24bc.jpg"/><itunes:season>1</itunes:season><itunes:episode>2</itunes:episode><itunes:episodeType>full</itunes:episodeType></item><item><title>Navigate Flowsheets Handoffs Alert Fatigue: Save Shift Time Not Review Burden</title><link>https://www.spreaker.com/episode/navigate-flowsheets-handoffs-alert-fatigue-save-shift-time-not-review-burden--72922827</link><description><![CDATA[In this episode, we cut through the hype to examine what actually trims minutes from a nurse’s shift versus what piles on extra review work. From flowsheets that demand repeated clicks and double-checks to handoffs stretched by bloated summaries and alert fatigue burying real priorities, the focus stays on bedside-tested realities rather than polished vendor demos. We explore how AI suggestions in flowsheets can speed documentation only when they pull directly from monitors and learn unit patterns, while poorly sourced pulls create new errors to catch. Handoff tools shine when they generate short, change-focused summaries under one page, but longer repeats often cost more time than a quick verbal exchange. Alert filtering helps by grouping urgency and removing duplicates from the same trend, yet risks hiding items nurses still need to see.<br /><br /> Key takeaways:<br /> - Time one flowsheet template with AI suggestions on versus your usual method to measure real savings after a few uses.<br /> - Request handoff drafts limited to new orders and abnormal values only, then edit as the outgoing nurse.<br /> - Track alerts for one shift to spot which ones need action and test context-based grouping.<br /><br /> 📩 Have questions or want to share your experience? Reach out at ai@senseofthisshit.com.<br /> 💛 Join Our Supporters Club ($3 a month) 💛 Ad-free listening + early episodes — help keep independent media alive. Click Here: <a href="https://www.spreaker.com/podcast/ai-for-nurses-bedside-reality--7167789/support" target="_blank" rel="noreferrer noopener">https://www.spreaker.com/podca...</a>]]></description><guid isPermaLink="false">a3daabce-852f-403d-8d3c-9cc4540d7a7a</guid><pubDate>Sat, 11 Jul 2026 00:12:28 +0000</pubDate><enclosure url="https://dts.podtrac.com/redirect.mp3/api.spreaker.com/download/episode/72922827/audio.mp3" length="5276569" type="audio/mpeg"/><podcast:transcript url="https://transcription.spreaker.com/starship/58c4282d-91fc-41fc-add3-d604c968c856/58c4282d-91fc-41fc-add3-d604c968c856.srt" type="application/x-subrip" language="en"/><podcast:transcript url="https://transcription.spreaker.com/starship/58c4282d-91fc-41fc-add3-d604c968c856/58c4282d-91fc-41fc-add3-d604c968c856.txt" type="text/plain" language="en"/><podcast:transcript url="https://transcription.spreaker.com/starship/58c4282d-91fc-41fc-add3-d604c968c856/58c4282d-91fc-41fc-add3-d604c968c856.vtt" type="text/vtt" language="en"/><itunes:author>Jude</itunes:author><itunes:subtitle>In this episode, we cut through the hype to examine what actually trims minutes from a nurse’s shift versus what piles on extra review work. From flowsheets that demand repeated clicks and double-checks to handoffs stretched by bloated summaries and...</itunes:subtitle><itunes:summary><![CDATA[In this episode, we cut through the hype to examine what actually trims minutes from a nurse’s shift versus what piles on extra review work. From flowsheets that demand repeated clicks and double-checks to handoffs stretched by bloated summaries and alert fatigue burying real priorities, the focus stays on bedside-tested realities rather than polished vendor demos. We explore how AI suggestions in flowsheets can speed documentation only when they pull directly from monitors and learn unit patterns, while poorly sourced pulls create new errors to catch. Handoff tools shine when they generate short, change-focused summaries under one page, but longer repeats often cost more time than a quick verbal exchange. Alert filtering helps by grouping urgency and removing duplicates from the same trend, yet risks hiding items nurses still need to see.<br /><br /> Key takeaways:<br /> - Time one flowsheet template with AI suggestions on versus your usual method to measure real savings after a few uses.<br /> - Request handoff drafts limited to new orders and abnormal values only, then edit as the outgoing nurse.<br /> - Track alerts for one shift to spot which ones need action and test context-based grouping.<br /><br /> 📩 Have questions or want to share your experience? Reach out at ai@senseofthisshit.com.<br /> 💛 Join Our Supporters Club ($3 a month) 💛 Ad-free listening + early episodes — help keep independent media alive. Click Here: <a href="https://www.spreaker.com/podcast/ai-for-nurses-bedside-reality--7167789/support" target="_blank" rel="noreferrer noopener">https://www.spreaker.com/podca...</a>]]></itunes:summary><itunes:duration>330</itunes:duration><itunes:keywords>aifornurses,aiinhealthcare,ainursingtools,alertfatigue,alertmanagement,bedsidenursing,clinicalalerts,flowsheetoptimization,flowsheets,handoffs,nurseai,nurseefficiency,nurseworkflow,nursingdocumentation,nursinghandoffs,practicalainurses,reducingnurseburden,reviewburden,shiftefficiency,shifttime</itunes:keywords><itunes:explicit>false</itunes:explicit><itunes:image href="https://d3wo5wojvuv7l.cloudfront.net/t_rss_itunes_square_1400/images.spreaker.com/original/e7660c0bf358610e5dc9e4efcdfb24bc.jpg"/><itunes:season>1</itunes:season><itunes:episode>1</itunes:episode><itunes:episodeType>full</itunes:episodeType></item></channel></rss>
