How AI can streamline patient care at Riverside Health

When I first started training Epic years ago, there were whispers of AI, but nothing to this degree... Technology is rapidly changing and I think the way we train Epic is about to completely change as well. Is this going to replace chart review altogether? Should residents and students become dependent on a tool like this? Is the AI 100% accurate? These are the questions I asked myself when my organization went live with these AI Insight Summary features… What questions and concerns do you have? #epic #ai #healthcareIT #epictraining

The Next 18–24 Months Will Define AI Leadership in Healthcare Healthcare is undergoing its fastest technological disruption in decades. AI is moving from experimental pilots to real-world deployment, and organizations that act now will define the next generation of patient care. Key Areas Driving AI Transformation 1. Clinical Decision Support AI-assisted diagnosis and treatment planning Early detection of risks using imaging and EMR patterns Real-time clinical insights at the point of care 2. Predictive Analytics Identifying high-risk patients and preventing complications Predicting OPD/IPD loads for better staffing & resource planning Reducing readmissions and length of stay 3. Patient Engagement & Experience AI chatbots for triage, follow-ups, appointment scheduling Virtual nursing assistants for chronic disease management Personalized patient communication at scale Why Early Adoption Matters ✔ Improved Clinical Outcomes AI supports clinicians with faster, more accurate decisions and personalized care pathways. ✔ Operational Efficiency Automation reduces administrative overload — billing, coding, discharge planning, bed management — saving significant costs. ✔ Competitive Advantage Hospitals integrating AI today will become: The preferred centers of excellence More efficient and profitable Magnet institutions for clinicians and patients alike The Call to Action AI in healthcare is no longer optional — it is the new standard. What organizations choose to implement in the coming months will determine their position for the next decade. The future of healthcare is being built now. Those who move early will lead.

Here are 3 real world solutions you may know about, but if not, take note and deploy in your org to help in preventing harm. Patient safety is everyone’s responsibility, but AI is becoming a powerful ally. Here’s how AI is stepping up in real clinical settings: 1. Fall Risk Prediction: AI models analyze patient data (age, meds, vitals) to flag those at high risk of falls in real-time, allowing nurses to intervene before an incident occurs. 2. Sepsis Early Detection: AI-driven systems monitor subtle changes in vital signs, labs, and notes to predict sepsis hours earlier than traditional methods, prompting immediate intervention and saving lives. 3. Medication Error Prevention: AI tools double-check prescriptions against patient data and flag potential errors or dangerous drug interactions, reducing adverse events and improving outcomes. My Perspective: As a physician and payer exec, I’ve seen how these AI systems transform care from reactive to proactive. When AI catches a fall risk or flags early sepsis, it’s not just technology, it’s a lifeline for patients and a safeguard for overworked clinical teams. 👍 So here’s the takeaway for those who just want the bottom line: AI isn’t replacing us, it’s EMPOWERING us. Share this with your patient safety teams, and let’s make AI a standard part of our safety toolkit. Less reaction and more pro-action! #PatientSafety #HealthcareAI #AIinHospitals MAYOCLINICAL QHSC, LLC ,EpicSystems Integrated IT Solutions , Cerner Corporation , Wolterskluwer N V

Hickam's Dictum - the one thing you need to know if you are at HLTH and looking at tech that affects clinical descion making Does this AI tool help me with Type 1 or Type 2 thinking? Type 1: quick, fast, simplest explanation - think attending rapid fire q’s on round - patient vignette during boards - snap judgement AKA Occam’s Razor Type 2: “as many diseases as he pleases” - systems based thinking - broad differential based on the constellation of symptoms - slower, intentional AKA Hickam's Dictum As we think toward implementation, we need to think about how the tech helps or hurts our clinical judgement. Same goes for tech that helps guide treatment. I recently saw a company posting about how their tech doesn’t only give deterioration score but “actionable treatment recommendations at the click of a button” Which - don’t get me wrong - actionable recs are awesome, I’m over sterile deterioration scores without helpful insight But does the “treatment at the click of a button” augment my clinical prowess? Or make me a glorified button pusher? These are important thoughts as we evaluate AI driven tech for inpatient use

The crisis in primary care is undeniable: physician shortages and widespread burnout are stretching our healthcare system to its breaking point. Traditional solutions aren't keeping pace, but what if AI could be the key to not just improving efficiency, but fundamentally extending our capacity to care? As Thomas Kluz highlights, AI offers a powerful path to help existing clinicians practice at the top of their license and reclaim valuable time. Here's how AI is stepping up as a primary care extender: * **Automated Medical Scribes:** Early rollouts show these tools free up doctor's attention and save time, significantly improving patient rapport by allowing clinicians to focus on interaction rather than documentation. * **AI-Assisted Chatbots & Triage:** Platforms like Cedars-Sinai’s CS Connect are already supporting thousands of patients, reducing administrative burden and speeding up care navigation. * **Enhanced Decision Support:** AI can critically analyze patient data to provide "optimal" recommendations, helping physicians adapt treatment plans more effectively. * **Shift to Continuous Care:** Moving beyond episodic visits, AI-powered remote patient monitoring and predictive algorithms enable ongoing oversight, detecting anomalies early and allowing for proactive, data-driven interventions. Crucially, this isn't about replacing doctors or depersonalizing care. When designed thoughtfully, AI can strip away administrative drudgery, giving clinicians more time for the human connection—listening, explaining, and engaging—that builds trust and is at the heart of medicine. The promise of AI in healthcare will only be realized if these tools integrate seamlessly into workflows, prioritize privacy and transparency, and are developed in close collaboration with clinicians and patients. The choice is clear: either continue down a path where clinicians are thinly stretched, or embrace AI to extend human capacity. How can we ensure AI truly empowers healthcare professionals and preserves the art of medicine? #AIinHealthcare #PrimaryCare #PhysicianShortage #HealthcareInnovation #MedicalTechnology

https://lnkd.in/eazzms_Q Excellent report. Procurement cycles are compressing dramatically for health systems and especially outpatient providers- AI tools becoming the system of work: ambient scribes, billing/coding, prior authorization. Though stickiness of scribes is already a concern. According to this report, in relative terms, payers and biopharma are lagging. The incumbents’ advantage should not be underestimated. Healthcare’s AI moment is here.

Transforming Patient Intake: How AI is Augmenting Clinical Workflows (Safely and Securely) The future of healthcare focuses on empowering doctors with intelligent tools to streamline the initial patient journey. Our latest architecture highlights a secure, multi-modal AI agent serving as a robust initial clinical assistant. Here’s a simplified breakdown of the journey: 1️⃣ Informed & Secure Start: Patient consent initiates the process, ensuring encrypted data from the first interaction. 2️⃣ Multi-Modal Input: Patients share information through speech, text, or medical images (DICOM). 3️⃣ Intelligent Data Processing: Raw data is processed, encoded, and structured, including speech-to-text conversion and image analysis. 4️⃣ The Orchestration Engine: Structured data is directed to the core Diagnostic Agent Engine. 5️⃣ Context-Aware Analysis: Real-time queries to a knowledge base aid in analysis by referencing clinical cases, hospital protocols, and medical guidelines. 6️⃣ Secure EHR Integration: Connection to Electronic Health Record systems ensures a comprehensive patient history. 7️⃣ Augmented, Not Autonomous Output: AI provides preliminary analysis, filtered for safety and accuracy. 8️⃣ The Essential Human-in-the-Loop: Medical professionals review and validate AI-generated outputs before final diagnosis. This seamless Human-AI collaboration enhances efficiency, reduces administrative burden, and upholds patient safety and care quality standards. What are your thoughts on the role of AI agents in clinical settings

Most healthcare executives have the perspective that they are going to use AI to revolutionize patient intake. But the reality is that we find chatbots asking "Rate your pain 1-10" and basic symptom checkers. Because of the buzz surrounding AI, there is a misconception that it can improve intake procedures. It cannot! -First, we should map our current intake process. -Second, identify where clinical decision-making actually happens. -Third, deploy AI to augment clinician judgment. Leading health systems are actually utilizing AI in patient intake in the following five ways: 1. Pre-Visit Clinical Intelligence AI compares patient responses to their EHR chart and displays the following information in place of nurses spending fifteen minutes obtaining a basic history: → Relevant clinical context from previous visits → Medication reconciliation flags → Risk factors requiring immediate attention 2. Protocol-Driven Question Routing AI is more reliable than humans at adhering to therapeutic protocols, guaranteeing: → Condition-specific screening questions are never missed → Appropriate triage based on symptom severity → Standardized data collection across all providers 3. Chart-Informed Follow-Up Suggestions AI suggests particular inquiries that medical professionals should make based on patient history analysis: → "Patient mentioned knee pain 3 months ago - has this worsened?" → "Last visit showed elevated BP - current medications working?" → "Due for preventive screenings based on age/risk factors" 4. Real-Time Clinical Decision Support During intake, AI identifies possible problems, such as: → Drug interaction alerts based on new symptoms → Red flag symptom combinations requiring urgent care → Care gap identification for chronic conditions 5. Seamless Workflow Integration Instead than taking the place of physicians, AI functions as a distinct workflow tab that offers: → Structured intake summaries → Recommended clinical pathways → Evidence-based next steps This results in doctors spending less time on information gathering and more time on actual patient care and clinical decision-making. P.S Our team at TechVariable build clinical intelligence solutions for health systems who want to augment their care teams. #healthteach #clinicalai #patientintake #healthcareinnovation #digitalhealth #teachvariable #artificialintelligence #populationhealth #caredelivery

Did you know AI can transform patient engagement and follow-up care—from missed appointments to better outcomes? Patients often leave the clinic with questions. They feel unsure about medications. Care teams are stretched thin. That gap is where AI adds real, human-centered value. AI-powered reminders and conversational agents deliver timely, personalized check-ins. They pick the best channel and time for each patient. They catch early warning signs through patterns in data. That means fewer no-shows, better medication adherence, and more timely interventions. More importantly, AI frees clinicians from routine follow-up tasks. Nurses and care coordinators can focus on complex cases that need human judgment. Patients feel heard and supported between visits. Families sleep better knowing someone is monitoring progress. Practical tips: start with a single use case (post-discharge follow-up or medication reconciliation). Integrate with workflows and EHRs. Design for empathy, privacy, and equity. Measure meaningful outcomes: readmissions, adherence, patient-reported experience, and clinician time saved. AI isn’t a replacement for clinicians. It’s a tool to extend reach, personalize care, and close the follow-up gap that costs lives and money. Have you seen AI-driven follow-up change outcomes in your organization? Let me know your take. #relencyai #AI #PatientEngagement #DigitalHealth #HealthTech

We sat down with Dr. T. Y. Alvin Liu, M.D. to focus on where AI is actually working, in the clinic and in the revenue cycle. What stood out: • Retina economics are unforgiving: brand‑name injections run about $2,500 each. Miss prior auth on even 1% and it hits the practice. • Workflows are still manual: staff pre‑check clinic lists (50–60 patients a day) to verify PA eligibility, one by one. • Hopkins piloted a GenAI PA co‑pilot: it reads payer‑portal rules and pulls medical‑necessity evidence from Epic—now scaled across service lines (ophthalmology pending centralized rollout). • Build vs. buy: buying often wins for speed, productization, UX, and customer success—even inside research‑rich health systems. • Where adoption is real today: ambient scribing; revenue cycle (front, mid, back); and voice AI as the digital patient front door. • Strategy matters: choose your provider segment (outpatient vs. integrated) and RCM focus (front/mid/back). Doing 1–2 segments exceptionally beats trying to do all three at once. • KPIs that resonate: time per PA; PAs per FTE; clean claim rate/charges dropped; % denials handled end‑to‑end by AI; dollars recovered. • For outpatient retina, the biggest lift is front‑end PA and back‑end denials; coding/CDI is less impactful given a small CPT set. We left optimistic about what’s next: specialty multimodal co‑pilots that augment clinicians, and oculomics turning retinal images into scalable risk screening. Listen to the full conversation and tell us which RCM segment your team is prioritizing this year. Link in comments; DM for a direct link.