Benefits of Ambient AI for Healthcare Professionals

While taking my daily AI vitamin (💊 The AI Daily Brief podcast), I started exploring the functionality of 𝗮𝗺𝗯𝗶𝗲𝗻𝘁 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 and what they could mean for healthcare. Chatting back and forth with an LLM can feel forced: I know what I want, but I must steer the model toward it. Agentic coding tools (e.g., Anthropic's Claude Code, Replit, OpenAI's Codex) flipped that script. I describe an outcome, then watch multiple agents write, test, and publish code while I step in only when needed. Imagine that same fluidity inside an EHR. 𝗔 𝗱𝗮𝘆 𝗶𝗻 𝗰𝗹𝗶𝗻𝗶𝗰 𝘄𝗶𝘁𝗵 𝗮𝗻 𝗮𝗺𝗯𝗶𝗲𝗻𝘁 𝗮𝗴𝗲𝗻𝘁 𝘀𝘁𝗮𝗰𝗸 𝟭𝟮 𝗣𝗠 – 𝗻𝗶𝗴𝗵𝘁 𝗯𝗲𝗳𝗼𝗿𝗲 • 🩺 𝘗𝘢𝘵𝘪𝘦𝘯𝘵 𝘴𝘯𝘢𝘱𝘴𝘩𝘰𝘵 𝘢𝘨𝘦𝘯𝘵: review notes, labs, etc., build a one-page brief. ✅ Epic Outpatient Insights, Synopsis, and ambient dictation vendors are developing pre charting workflows. • 📋 𝘎𝘶𝘪𝘥𝘦𝘭𝘪𝘯𝘦 & 𝘊𝘋𝘚 𝘴𝘸𝘦𝘦𝘱 𝘢𝘨𝘦𝘯𝘵: flag gaps (vaccines, overdue labs), cross check meds vs. alerts. ✅ Most EHRs expose CDS hooks today.  𝟲𝟬 𝗺𝗶𝗻 𝗽𝗿𝗲 𝘃𝗶𝘀𝗶𝘁 • 🗂️ 𝘏𝘰𝘷𝘦𝘳 𝘤𝘩𝘦𝘤𝘬𝘭𝘪𝘴𝘵 𝘢𝘨𝘦𝘯𝘵: a persistent hover card that ranks open issues (🔴 critical → 🟡 routine). 🔭 Doesn’t exist yet, but the building blocks (APIs) are there. 𝗗𝘂𝗿𝗶𝗻𝗴 𝘃𝗶𝘀𝗶𝘁 • 🎙️ 𝘈𝘮𝘣𝘪𝘦𝘯𝘵 𝘥𝘪𝘤𝘵𝘢𝘵𝘪𝘰𝘯 + 𝘤𝘩𝘦𝘤𝘬𝘭𝘪𝘴𝘵 𝘴𝘺𝘯𝘤 𝘢𝘨𝘦𝘯𝘵: live transcription while a checklist auto-ticks off or adds items. ✅ Several AI vendors handle transcription (Abridge, Microsoft, Ambience Healthcare; auto checklist is a great opportunity (🔭). • 📝 𝘖𝘳𝘥𝘦𝘳 & 𝘤𝘰𝘥𝘦 𝘴𝘶𝘨𝘨𝘦𝘴𝘵𝘪𝘰𝘯𝘴 𝘢𝘨𝘦𝘯𝘵: real time drafts of orders + CPT/ICD codes. ✅ Rolling out with current ambient dictation vendors. 𝗔𝗳𝘁𝗲𝗿 𝘃𝗶𝘀𝗶𝘁 • 💌 𝘗𝘢𝘵𝘪𝘦𝘯𝘵 𝘧𝘰𝘭𝘭𝘰𝘸 𝘶𝘱𝘴 𝘢𝘨𝘦𝘯𝘵𝘴: patient-friendly instructions + scheduling assistance. ✅ Emerging in ambient platforms; For example, Hyro already handles smart scheduling. 𝗕𝗲𝘁𝘄𝗲𝗲𝗻 𝘃𝗶𝘀𝗶𝘁𝘀 • 🔔 𝘉𝘢𝘤𝘬𝘨𝘳𝘰𝘶𝘯𝘥 𝘮𝘰𝘯𝘪𝘵𝘰𝘳𝘪𝘯𝘨 𝘢𝘨𝘦𝘯𝘵: scan inbox, new labs, RPM feeds; bundle outstanding items into a daily digest. 🔭 Pieces exist (message rules, pop health dashboards), but true cross-system agent orchestration is lacking. This could scale to including additional agents that do things such as scan for recent literature on a patient's conditions (OpenEvidence), pre-piloting prior auth forms with patient data and info scoured from payer portals... None of this is impossible. • Coding agents already show that LLMs can plan, iterate, and act on entire workflows autonomously. • EHR native AI plugins are landing every quarter, proving the plumbing is ready. What’s missing is the connective, ambient layer that stitches today’s point solutions into one continuous, event-driven loop, with clinicians setting the course instead of steering everything.

I have been using an ambient AI scribe in clinic now for a few months, and have learned a ton. My first time- I walked in straight, talked more with my patients but had to redo and edit notes for 2-3 hours at the end of the day. The notes lacked completeness mainly in assessment and plans and in the exam. It glossed over details of prior evaluations and recent labs that were integral to describing the assessments. After three days- I pre-prepped/ pre-rounded for clinic, shaved 3-4 min off each visit but still was editing notes for an hour a day. After a month- I spend half the time I used to pre-round, used up my entire visit time with patients, but my notes were completed by the end of visits. Single issue urgent care visits were easy to walk in, allow AI to scribe, and do a quick edit before the next visit staying on time. Quick tips and lessons learned: 💠 Pre-rounding for clinic is needed for patients with complex needs including those with multiple specialty visits or hospitalizations between my visits. But AI allows my quick bullet reminders to remain at a high level and efficient. 💠 Pre-rounding for single issue urgent care is often not needed 💠 In the visit, training yourself to fully vocalize your exam, details of labs you want in your assessment reasoning documentation, your assessment and plan problems list, and each step of the plan in review at the end make the note much more accurate. The assessments and plans often have missing details without it. 💠 Try to save 3min at the end of the visit to download your scribe note and edit it in real time. It is much easier doing it when everything is fresh in your mind than doing 10 notes at once later in the day. Ultimately, it takes a few months to retrain yourself into a new practice, and it's an investment worth taking. Compared to my non-AI process, my pre-rounding time is cut in half, my in-clinic time is nearly the same but I feel like now I can be more complete spending time with my patients, and I'm back to ending my visits with minimal time needed to finish documentation. It allows me the time to pre-plan visits focused at a higher level and less focused on note prep, spend more time counseling and examining patients, but there is still a need to keep a close eye on the completeness of assessments and plans that get documented through current ambient AI models. How has your experience been going? What other tips do you have? #AIscribe #primarycare #burnout

I don’t need AI to be my replacement. I need it to give me back my presence. This month, three new tools made headlines in healthcare AI: 🟣Ambience Healthcare – a real-time ambient scribe that listens in the room, writes the note, and codes the visit—automatically. 🟢 OpenEvidence – a GPT-powered search engine that reads 35,000 papers in seconds and answers clinical questions with speed and citations. 🔵 Doximity Scribe – a mobile ambient AI that records the visit and generates a SOAP note you can review before leaving the room. Each one isn’t trying to replace clinicians. They’re trying to give us back what paperwork stole. Less typing → more listening. Less Googling → more curiosity. Less documenting → more connecting. I don’t want to spend my patient’s most vulnerable moment staring at a screen. I want to look them in the eye and help them navigate the problem. That’s the future these tools are hinting at—not an AI that does the job for us, but one that gets out of the way. But we have to build it right. If we optimize for volume over value, clicks over connection, we’ll miss the moment. #HealthcareonLinkedin #AI #Healthcare #Digitalhealth

🚫🖥️ No Keyboard. No Mouse. Just Care. 🏥✨ Imagine walking into a patient room where the technology disappears—and the human connection takes center stage. Welcome to the keyboard less patient room: a space where clinicians speak, gesture, and move freely, while AI and ambient tech do the heavy lifting in the background. ✅ No more typing while talking. ✅ No more looking at the back of your clinician’s head ✅ No more delays in documentation. Instead, we get: 💡 Faster visits – Ambient voice tech captures notes in real time. 💡 Deeper trust – Eye contact and empathy are no longer sacrificed for screens. 💡 Better outcomes – Our clinicians focus on healing, not hardware. This isn’t the future. It’s happening now—and it’s transforming the way we deliver care. The question isn’t if we will get rid of keyboard and mice....it’s how fast we can get there. #HealthcareInnovation #DigitalHealth #AmbientTech #PatientExperience #FutureOfCare #CIOPerspective

In the rush to integrate AI, it's easy to focus on what it can automate. But in healthcare, AI's most profound impact might be its ability to support human connection, not replace it. Imagine AI as a tool that: • Creates smoother transitions between care teams, so patients feel consistently supported • Preserves time for face-to-face interactions, even in tech-driven workflows • Amplifies trust-building moments that truly impact patient outcomes Five ways AI can strengthen human connection: 1. Protect Conversation Time • Automate documentation in the background • Handle routine coordination invisibly • Free mental space for active listening • Enable eye contact instead of screen focus 2. Support Team Relationships • Share insights across care teams naturally • Enable smoother handoffs • Facilitate timely collaboration • Build trust through better information flow 3. Create Space for Empathy • Handle routine tasks quietly • Allow for longer patient interactions • Support emotional awareness • Enable presence over process 4. Enable Better Transitions • Keep everyone informed appropriately • Reduce communication gaps • Support continuous care relationships • Maintain connection through changes 5. Amplify Human Insight • Surface patterns that need human attention • Support clinical judgment, don't replace it • Enable deeper patient understanding • Strengthen team collaboration By approaching AI with a relationship-centered lens, we can design technology that strengthens the interactions and collaborations that make healthcare effective—and deeply human.

Generative AI (Gen AI) is a powerful ally in supporting patients and providers. At CenterWell, we’re not just adopting technology for technology’s sake; we’re thinking about how to leverage AI to address real-world challenges in health care, such as physician burnout. Our ambient dictation tool is one use case that has improved satisfaction and engagement at appointments from patients and providers, which is why we’ve continued to scale the program across our primary care centers. Jason Couch, DNP, APRN, FNP-C, one of our providers testing ambient dictation, had this to say about the tool: “A positive outcome is the patient summary. I’ve had patients request I use this technology in follow-up visits, because of the patient summary I’m able to provide to them. The entire note is broken down, so they understand what we did during the appointment, what was the problem, and what are the changes I want them to make. It’s also had a positive impact on my documenting. I’m able to give patients the face-to-face visit they truly need. Patients feel relieved and comforted, and I’ve had no out-of-office documenting time.” Ambient dictation is: · Improving the patient experience (feeling like they’re having a conversation with their provider) · Enhancing the provider experience and effectiveness during appointments · Elevating quality of care (shifting the focus to be more on the patient) Patients like the summary the ambient dictation tool provides, and this could lead to improved health literacy and health outcomes for our seniors. Our team will continue to apply AI in new ways to improve experiences and outcomes. What ways are you leveraging AI to shape the future of senior care?   #AIinHealthcare #SeniorCare #ValueBasedCare

Here is a paradox: AI can make us more human. You know why? Staff shortages everywhere are causing burnout, stress and mental health issues. Healthcare professionals suffer from the burden of administrative tasks which divert them from what they love doing: caring for patients. AI-enabled automation can help to take away these menial tasks that suck the joy of out of medicine. In this Financial Times article, you will find some inspiring examples of how AI can help healthcare workers to do what they love most. 👉 Therapeutic radiographer Naman Julka-Anderson uses auto segmentation on prostate cancer patients to speed up the process of reading patient scans: “Instead of a human having to go through 100-plus slices of a patient scan to contour each aspect of the anatomy, it can be done very quickly with AI machine learning – within an hour or so, or even quicker.” 👉 The NHS is piloting a smart appointment-scheduling system that takes into account individual patient characteristics. The aim is to reduce missed appointments and minimize the administrative hassle of rescheduling. This system holds significant potential for saving time. If we work backwards from real needs, AI will not replace humans, but make us more human. 👇

Early evidence shows AI scribes reduce clinician burnout and improve patient experience. But there's little proof they boost efficiency or productivity, and there's a risk they could drive up the total cost of care. Those are the key takeaways from a thoughtful new report by the Peterson Health Technology Institute (PHTI). It’s no surprise that ambient scribes are sweeping American healthcare. They address a massive pain point (clinical documentation), may increase productivity (if clinicians see more patients), and can boost revenue (through higher volume and coding). Plus, the risk is relatively low: while scribe-generated notes aren't 100% accurate, neither are clinician-generated ones — and since clinicians are "in the room where it happens," they can catch errors. Measuring the impact is tricky across multiple dimensions: Burnout, cognitive load, documentation time, clinical efficiency, patient experience, safety, and quality. So far, scribes appear to reduce burnout and cognitive load while boosting patient experiences. We shouldn’t expect too much, too soon. Not all clinicians want scribes — and few will use them for every visit. For example, only one-third of Permanente Medical Group (TPMG) clinicians used an AI scribe 100+ times over a year [DOI: 10.1056/CAT.25.0040]. Additionally, conversations account for only ~1/3 of a typical note; the rest comes from copied-forward or linked text [doi:10.1001/jamanetworkopen.2021.15334]. What’s next? Expect AI scribes to expand into inpatient and emergency settings, reach new users like nurses and therapists, and move into related activities such as pre-charting, summarization, clinical decision support, order entry, and coding. (As I noted in Forbes, only select vendors will achieve the deeper EHR integration required.) Bottom line: the overall impact of AI scribes is still uncertain. There are clear benefits yet — like all technologies — one size will not fit all, and tradeoffs will exist. More work ahead! https://lnkd.in/gc-wvnpp