<![CDATA[Between Care and Code]]>A podcast by Spencer and Luis, the co-founders of Serelora, where medicine meets technology and everything in between.

]]>www.serelora.comRiverside.fm (https://riverside.com)Mon, 02 Nov 2026 04:37:49 GMTSat, 24 Oct 2026 04:44:37 GMT60Serelora<p>A podcast by Spencer and Luis, the co-founders of <a href="https://serelora.com" rel="noopener noreferrer nofollow">Serelora</a>, where medicine meets technology and everything in between.</p>episodicSereloraspencer@serelora.comno<![CDATA[AI Agents Are About to Change Healthcare]]>Can AI agents finally break down the barriers that have kept healthcare data fragmented for decades?

In this episode of Between Care and Code, Serelora co-founders Luis Cisneros and Spencer Wozniak explore how artificial intelligence is challenging traditional electronic health records (EHRs), healthcare interoperability, and the administrative systems that shape modern medicine.

From patient trust in clinical AI to expensive EHR integrations, insurance claims, medical record access, and autonomous AI agents, we examine what happens when technology begins to expose the inefficiencies built into healthcare.

We also discuss human oversight in clinical decision-making, open-source AI models, quantitative risk stratification, and why the next generation of healthcare technology should focus on improving the practice of medicine rather than simply automating existing bureaucracy.

Topics include
- AI agents and the future of electronic health records
- Healthcare interoperability, FHIR APIs, and patient data access
- Human-in-the-loop AI and clinical decision-making
- Automating insurance claims and administrative workflows
- Open-source AI versus proprietary models
- Clinical intelligence, risk stratification, and longitudinal patient data
- How Serelora is rethinking the EHR as a system of record and intelligence

Between Care and Code explores the intersection of medicine, artificial intelligence, healthcare technology, and the systems shaping the future of patient care.

Learn more at https://serelora.com

]]>https://serelora.comdf5e4d9b-5274-4cb1-ab2d-6f86510547adSat, 31 Oct 2026 05:37:15 GMT<p>Can AI agents finally break down the barriers that have kept healthcare data fragmented for decades?<br /><br />In this episode of <b><strong>Between Care and Code</strong></b>, Serelora co-founders Luis Cisneros and Spencer Wozniak explore how artificial intelligence is challenging traditional electronic health records (EHRs), healthcare interoperability, and the administrative systems that shape modern medicine.<br /><br />From patient trust in clinical AI to expensive EHR integrations, insurance claims, medical record access, and autonomous AI agents, we examine what happens when technology begins to expose the inefficiencies built into healthcare.<br /><br />We also discuss human oversight in clinical decision-making, open-source AI models, quantitative risk stratification, and why the next generation of healthcare technology should focus on improving the practice of medicine rather than simply automating existing bureaucracy.<br /><br /><b><strong>Topics include</strong></b><br />- AI agents and the future of electronic health records<br />- Healthcare interoperability, FHIR APIs, and patient data access<br />- Human-in-the-loop AI and clinical decision-making<br />- Automating insurance claims and administrative workflows<br />- Open-source AI versus proprietary models<br />- Clinical intelligence, risk stratification, and longitudinal patient data<br />- How Serelora is rethinking the EHR as a system of record and intelligence<br /><br /><b><strong>Between Care and Code</strong></b> explores the intersection of medicine, artificial intelligence, healthcare technology, and the systems shaping the future of patient care.<br /><br />Learn more at <a href="https://serelora.com" rel="noopener noreferrer nofollow">https://serelora.com</a></p>no00:46:4612AI Agents Are About to Change Healthcarefull<![CDATA[Clinical AI, Physician Liability, and the Problem With Black-Box AI]]>Serelora co-founders Spencer Wozniak and Luis Cisneros discuss what trustworthy clinical AI should look like, prompted by a dispute involving OpenEvidence and Doximity. Drawing on clinical experience and product-building lessons, they argue for transparent reasoning, traceable sources, physician oversight, and AI that augments rather than replaces medical judgment.

Keywords

clinical AI, healthcare AI, medical AI transparency, explainable AI, clinical decision support, AI scribes, physician liability, evidence-based medicine

Key Topics

  • Spencer Wozniak and Luis Cisneros introduce Serelora and explain their clinical backgrounds and motivation for building healthcare AI.
  • The OpenEvidence-Doximity dispute raises questions about data privacy, competitive intelligence, and whether clinical AI systems should protect their reasoning.
  • Luis argues that clinical AI should expose its reasoning and the evidence behind recommendations so physicians can assess risk drivers and trust outputs.
  • They discuss how clinical differences across specialties, institutions, and populations make a single centralized reasoning approach inadequate.
  • Source links, patient documentation, benchmarks, and physician feedback can help reveal hallucinations and improve clinical systems.
  • They distinguish qualitative clinical reasoning from quantitative analysis, arguing that calculations should use validated tools rather than rely on language models over unstructured records.
  • AI scribes should preserve physician judgment: reviewable notes, addenda, edits, transcripts, and audit trails can support learning and legal traceability.
  • A physician's experience, intuition, and ability to notice what a patient does not say remain essential; AI is best treated as an augmenting tool.
  • A failure during an 80-minute scribe session exposed a server memory limit; processing transcripts in chunks fixed the issue.
  • Patient skepticism about outreach messages prompted the founders to consider testing clearer communications and alternative channels.

Takeaways

  • “We need to glass box it.”
  • “The AI is an extension of you.”
  • “The doctor is gonna be able to capture what the patient didn't say.”
  • “They are tools to augment our capacity.”
  • "AI is nothing more than a machine to translate your ideas.”
  • “You still need to review it.”
  • “The only way to get better is to be able to have a system that provides you that.”
  • “There’s not one school of thought that exists in medicine.”
  • “The real question is, how do you make it useful”
  • “We want to build and learn.”
  • "There's an art to it too."
]]>https://serelora.com508af821-eb0e-4a0b-812e-99f4ccc37c8eSat, 24 Oct 2026 05:08:51 GMT<p><a href="https://serelora.com" rel="noopener noreferrer nofollow">Serelora</a> co-founders Spencer Wozniak and Luis Cisneros discuss what trustworthy clinical AI should look like, prompted by a dispute involving OpenEvidence and Doximity. Drawing on clinical experience and product-building lessons, they argue for transparent reasoning, traceable sources, physician oversight, and AI that augments rather than replaces medical judgment.</p><p>Keywords</p><p>clinical AI, healthcare AI, medical AI transparency, explainable AI, clinical decision support, AI scribes, physician liability, evidence-based medicine</p><p>Key Topics</p><ul><li>Spencer Wozniak and Luis Cisneros introduce Serelora and explain their clinical backgrounds and motivation for building healthcare AI. </li><li>The OpenEvidence-Doximity dispute raises questions about data privacy, competitive intelligence, and whether clinical AI systems should protect their reasoning.</li><li>Luis argues that clinical AI should expose its reasoning and the evidence behind recommendations so physicians can assess risk drivers and trust outputs.</li><li>They discuss how clinical differences across specialties, institutions, and populations make a single centralized reasoning approach inadequate.</li><li>Source links, patient documentation, benchmarks, and physician feedback can help reveal hallucinations and improve clinical systems.</li><li>They distinguish qualitative clinical reasoning from quantitative analysis, arguing that calculations should use validated tools rather than rely on language models over unstructured records.</li><li>AI scribes should preserve physician judgment: reviewable notes, addenda, edits, transcripts, and audit trails can support learning and legal traceability.</li><li>A physician's experience, intuition, and ability to notice what a patient does not say remain essential; AI is best treated as an augmenting tool.</li><li>A failure during an 80-minute scribe session exposed a server memory limit; processing transcripts in chunks fixed the issue.</li><li>Patient skepticism about outreach messages prompted the founders to consider testing clearer communications and alternative channels.</li></ul><p>Takeaways</p><ul><li>“We need to glass box it.”</li><li>“The AI is an extension of you.”</li><li>“The doctor is gonna be able to capture what the patient didn't say.”</li><li>“They are tools to augment our capacity.”</li><li>"AI is nothing more than a machine to translate your ideas.”</li><li>“You still need to review it.”</li><li>“The only way to get better is to be able to have a system that provides you that.”</li><li>“There’s not one school of thought that exists in medicine.”</li><li>“The real question is, how do you make it useful”</li><li>“We want to build and learn.”</li><li>"There's an art to it too."</li></ul>no00:48:2411Clinical AI, Physician Liability, and the Problem With Black-Box AIfull