ELLKAY’s Post

Child Healthcare and AI: Transforming Pediatric Care Child healthcare is transforming with the integration of Artificial Intelligence (AI), promising better health outcomes for children. AI's potential spans early diagnosis, personalized treatment plans, remote monitoring, and administrative efficiency. AI's ability to quickly analyze vast data enables early diagnosis, allowing for earlier interventions and significantly improving prognosis. It also assists in creating personalized treatment plans by analyzing a child's unique medical history and genetic makeup, ensuring effective and tailored therapies. Enhanced Telehealth and Remote Monitoring The rise of telehealth, accelerated by the COVID-19 pandemic, has made remote healthcare more prevalent, with AI enhancing real-time monitoring and analysis. Wearable devices track vital signs, detect anomalies, and alert healthcare providers to potential issues, offering continuous monitoring beneficial for managing chronic conditions and post-operative care. AI also streamlines administrative tasks, reducing the burden on healthcare providers and allowing them to focus more on patient care. Ethical Considerations and Future Prospects However, the integration of AI in child healthcare is not without challenges. Ethical considerations around data privacy, algorithmic biases, and the need for robust regulatory frameworks are critical. Ensuring AI systems are transparent, accountable, and designed with input from diverse stakeholders is essential for building trust and ensuring equitable access. As technology evolves, we can expect even more sophisticated AI applications in pediatric care. From virtual health assistants providing 24/7 support to advanced diagnostic tools revolutionizing early detection, AI is set to play a pivotal role in shaping the future of child healthcare. Embracing these advancements while addressing ethical and regulatory challenges will be key to realizing the full potential of AI in this vital field.

After hours of research for an upcoming pitch, I found these innovative healthcare companies. Excited to share my research notes with you all! Healthcare is evolving. The focus has shifted to better care delivery through cutting-edge tech. Be it telemedicine, AI diagnosis, or EHR, there's a pool of transformative solutions. And I found these 10 companies not only unique in their approach but also creating real differences in healthcare- Artera (Guillaume de Zwirek): They offer a SAAS platform for unified patient communication. ArteraAI (Andre Esteva, PhD): It provides an AI-powered test for personalized treatment decision-making in prostate cancer. Bend Health (Kurt Roots): They offer tech-enabled pediatric mental healthcare services for kids, teens, and families. (Love their UI!) Qventus, Inc (Mudit Garg): It offers a unique solution for optimizing healthcare operations using GenAI and expert services. Rad AI (Doktor Gurson): AI radiology solutions company that streamlines healthcare workflows and enhances patient care. Laudio (Russ Richmond): It's an AI-powered platform to streamline frontline leader workflows and healthcare operational efficiency. Populate (Chance Rodriguez): They develop advanced EMRs to reduce clinician burnout and improve patient care. Pearl Health (Michael Kopko): They provide tech and services to help primary care providers succeed in value-based care. Embodied Labs® (Carrie Shaw): It offers immersive training for caregiver education and professional staff development. Podimetrics (Jon Bloom, M.D.): They provide unique solutions for diabetic foot complications with their SmartMat and care management services. Which other companies are advancing healthcare innovation? Share your thoughts and recommendations. I'm all ears! #HealthcareInnovation #HealthTech #Telemedicine #AI #EHR #PatientCare #HealthcareOperations #ProstateCancer #MentalHealth #Pediatrics #Radiology #FrontlineLeaders #EMR #ValueBasedCare #CaregiverEducation #DiabetesCare #HealthcareTech #InnovativeHealthcare #PitchPrep #ResearchNotes

🔵 Code Blue: The Critical State of DEI in Healthcare A recent paper in Nature Medicine by Bajaj et al. sounds the alarm on the challenges that DEI efforts face in medicine [1]. Despite decades of initiatives, diversity in healthcare remains on life support. Let's examine some vital signs: 🩻 The Ripple Effect: Studies show that diversity in healthcare teams leads to better patient outcomes. A 2020 study in PNAS found that Black newborns cared for by Black physicians had significantly lower mortality rates [2]. This isn't just about representation—it's about saving lives. 🩺 The Pipeline Problem: While this infographic highlights current physician demographics, the issue starts earlier. In 2019, only 7.1% of medical school matriculants were Black and 6.2% were Hispanic, despite these groups making up 13% and 18% of the U.S. population, respectively [3]. 💼 Beyond Clinical Roles: DEI isn't just about doctors. A 2021 report showed that only 8% of healthcare organization CEOs and 9% of C-suite executives were from racial or ethnic minority backgrounds [4]. How can we ensure diversity at all levels of healthcare? 🌍 Global Perspectives: The U.S. isn't alone in this challenge. The UK's NHS reports that while 22% of their workforce is from ethnic minority backgrounds, these groups are underrepresented in senior positions [5]. 🤖 The Tech Factor: As AI and machine learning become more prevalent in healthcare, ensuring diversity in tech teams is crucial. Biased algorithms could exacerbate health disparities if not carefully developed and monitored [6]. 🔬 Research Gaps: A 2020 study found that only 2% of NIH principal investigators were Black [7]. Underrepresentation could perpetuate harmful race-based medicine and lead to gaps in research for conditions that disproportionately affect minority populations. What potential consequences concern you the most? How can we continue this crucial dialogue and invite more stakeholders to the table? 📍REFERENCES: [1] Bajaj, S. S. et al. (2024). Medicine's DEI backlash offers an opportunity to refocus on evidence-based approaches. Nature Medicine. [2] Greenwood, B. N. et al. (2020). Physician–patient racial concordance and disparities in birthing mortality for newborns. PNAS, 117(35), 21194-21200. [3] Association of American Medical Colleges. (2019). Diversity in Medicine: Facts and Figures 2019. [4] Wharton, T., & Oyenubi, O. (2021). The State of Diversity in Healthcare Leadership: 2021 Trends and Takeaways. The Conference Board. [5] NHS Workforce Race Equality Standard. (2020). 2020 Data Analysis Report for NHS Trusts and Clinical Commissioning Groups. [6] Gichoya, J. W. et al. (2022). AI recognition of patient race in medical imaging: a modelling study. The Lancet Digital Health, 4(6), e406-e414. [7] Stevens, K. R. et al. (2021). Fund Black scientists. Cell, 184(3), 561-565.

Six days in intensive care changed my view on AI in medicine - but not in the way you might think. (And yes, I know everyone says their hospital stay was eye-opening, but bear with me...) Close to a week in the ICU really does reshape your perspective on healthcare. Between the endless beeping and what felt like my 100th blood draw, I found myself thinking less about the "future of AI" and more about what actually helps doctors and nurses do their jobs. I couldn't rest much, so I ended up reading this fascinating study (https://lnkd.in/ewjXea3z) - funny how a hospital stay makes you extra interested in medical research, right? It was by Ethan Goh, Robert Gallo, and teams from Stanford University, Beth Israel Deaconess Medical Center and UVA Health, Harvard University and even Microsoft! The numbers surprised me: • AI working solo: 92% accurate at diagnosis (show-off...) • Doctors with AI help: 76.3% accurate • Doctors with regular tools: 73.7% • AI helpers made doctors slightly faster (519 vs 565 seconds per case, but who's counting?) Here's the thing that hit home though - lying there watching my care team work, you realize medicine is way more than checking diagnostic boxes. It's the night nurse who noticed I was uncomfortable before I said anything. The resident who stopped to explain everything twice because I was too groggy the first time (thanks, docs!). The attending who somehow kept track of every little detail. That's why I'm really interested in what the ARiSE network (https://lnkd.in/eVQmdsKB) is doing. They get it - it's not about replacing anyone, it's about making these tools actually useful in the real world. Look, I'm no expert - just a patient who had way too much time to think about this stuff while waiting for my next vitals check. But I keep wondering: how do we bridge this gap? How do we take AI's clear diagnostic abilities and make them truly helpful in the daily chaos of healthcare? Healthcare folks - I'd love your thoughts. What would actually help you in your day-to-day? (And please don't say "better hospital coffee" - though that probably wouldn't hurt either...) oh and stay tuned, I’ve got a few ideas. Gonna take a village. #Healthcare #Medicine #PatientCare #MedicalResearch #Innovation [Edited from my iPhone, so apologies for any weird formatting - still a bit shaky with these hospital hands!] Illustration made prompting Ideogram

UC Irvine is making big strides with nursing-focused AI models - Healthcare IT news #AIinHealthcare UC Irvine is leading the way in developing nursing-focused AI models to improve patient care and outcomes. #UCIrvine #AIinNursing #Collaboration The success of these AI models is attributed to collaboration between nurses and data scientists at UC Irvine. #NurseDataScientistCollaboration #PredictiveAnalytics These AI models utilize predictive analytics to anticipate patient needs and provide personalized care. #PredictiveCare #PatientOutcomes By leveraging AI technology, UC Irvine has seen significant improvements in patient outcomes and satisfaction. #ImprovedOutcomes #FutureImplications The development of nursing-focused AI models has promising implications for the future of healthcare delivery. #AIinHealthcareFuture ai.mediformatica.com #health #data #healthcare #research #nurses #cancer #learning #machinelearning #patientoutcomes #datasets #patientcare #nursing #digitalhealth #healthit #healthtech #healthcaretechnology @MediFormatica (https://buff.ly/3vx50ox)

Exciting technology collaboration with Microsoft to prototype generative AI tools aimed at improving pediatric care, focusing on AI-driven solutions for documentation, workflow efficiency, and personalized patient safety. #Hiring #AIinHealthcare #PediatricCare #GenerativeAI #HealthTech #Innovation #Microsoft #HealthcareAI

Representation matters. Diversity is crucial, from participants in clinical trials to healthcare practitioners. Without diversity, we all suffer. Nita summarized the current state quite well in her post👇🏻 #Healthcare #Diversity #Medicine

𝑪𝒖𝒓𝒓𝒆𝒏𝒕 𝑨𝒅𝒐𝒑𝒕𝒊𝒐𝒏 𝒂𝒏𝒅 𝑭𝒖𝒕𝒖𝒓𝒆 𝑶𝒖𝒕𝒍𝒐𝒐𝒌 𝒐𝒇 𝑬𝑴𝑹 𝑺𝒚𝒔𝒕𝒆𝒎𝒔 𝒊𝒏 𝑷𝒔𝒚𝒄𝒉𝒐𝒍𝒐𝒈𝒚 𝑪𝒍𝒊𝒏𝒊𝒄𝒔 Electronic Medical Records have become a cornerstone of healthcare, streamlining processes and improved patient care. While their adoption in traditional medical settings has been widespread, their penetration into psychology clinics has been slower. This article explores the current state of EMR adoption in psychology clinics and delves into the factors driving its growth and the potential benefits it offers 𝗖𝘂𝗿𝗿𝗲𝗻𝘁 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗥𝗮𝘁𝗲𝘀: While EMR adoption in psychology clinics has been gaining momentum, it still lags behind other healthcare sectors. Several factors contribute to this slower pace, including: Cost: The initial investment in EMR software and hardware can be significant for smaller clinics. Complexity: Psychology practices often involve unique documentation requirements and assessment tools that may not be fully integrated into standard EMR systems. Privacy Concerns: The sensitive nature of psychological records necessitates robust data security measures. Resistance to Change: Some clinicians may be reluctant to adopt new technology, particularly if they are comfortable with traditional paper-based methods. Despite these challenges, a growing number of psychology clinics are recognizing the benefits of EMRs and are making the transition. Key drivers include: Improved Efficiency: EMRs can streamline administrative tasks such as scheduling, billing, and insurance verification, allowing clinicians to spend more time with patients. Enhanced Patient Care: EMRs can facilitate better communication between providers and patients, improve medication management, and support evidence-based practices. Regulatory Compliance: EMRs can help clinics comply with electronic health record (EHR) incentive programs and other regulatory requirements.  Future Outlook The future of EMRs in psychology clinics is promising. As technology continues to advance and costs decrease, we can expect to see increased adoption rates. Here are some trends to watch: Cloud-Based Solutions: Cloud-based EMRs offer scalability, flexibility, and reduced infrastructure costs, making them attractive to psychology clinics. Integration with Telehealth: The growing popularity of telehealth necessitates EMR systems that can support virtual appointments and remote patient monitoring. Artificial Intelligence: AI-powered tools can assist with tasks such as clinical documentation, decision support, and patient engagement. In conclusion, while the adoption of EMRs in psychology clinics has been slower than in other healthcare sectors, it is steadily increasing. The benefits of improved efficiency, enhanced patient care, and regulatory compliance are driving this growth. As technology continues to evolve, we can expect to see EMRs becoming an indispensable tool for psychology practice #Doctor24By7 #EMR

A whirlwind of activity last week at the America's Physician Groups conference. Lots of solid insights shared by health system leaders. Some notable thoughts: +++ Artificial intelligence is intelligent in different ways: By now, we all know ai has a diverse set of use cases, incorporated in different applications and tools. But there are fundamental differences in how the intelligence itself is brought to life. Examples include: - Predictive AI, used to forecast future outcomes by leveraging historical data. Shout out to UCLA’s CKD/ESRD early intervention protocol (Joycee Berin) and Diagnostic Robotics’ intelligence care post-dicharge journeys (Kira Radinsky) - Protocolized AI, used to automating a user’s administrative load by using a set of detailed, pre-defined rule sets. Thanks to ARC for sharing their experience using Elaborate 's automated outpatient lab summaries (Manish Naik) - Generative AI, used to create new content. Great overview of ambient documentation at MGB as well as Cedars’ Sinai talking through its medical ontology model powering acute triage (Caroline Goldzweig) Across system leaders and experts, there was clear agreement that the first step to implementing intelligence intelligently is defining the ‘jobs to be done’. That way, internal and external stakeholders are clear on what they're solving for when they architect the solution.  +++ Scaling AI takes time and rigorous observation: Market leaders describe a phased pilot approach that allows clinicians to build confidence and system leaders to measure ROI before deploying systemwide. Loved hearing Rebecca Mishuris outline MGB’s novel approach of piloting two solutions against one another, to pressure test implementation, workflow, and value creation. +++ Proving ROI continues to be challenging: Lots of vendors using lots of different metrics to prove value, but the majority of leaders agreed that estimating the ROI of these new tools today is part science, part art. Demonstrating the ROI of the “soft” metrics around provider burnout and retention seemed to be a big opportunity, especially for investment in more efficient primary care and PCP wellbeing. After all, PCPs drive referrals to specialty-level care (and higher reimbursements) for health systems. Appreciated Anurang Revri's overview of how working team setup can also impact ROI estimates. +++ Value-based care = tools + humanity: VBC will require not only implementing tools to support clinical documentation, risk capture, upcoding, etc, but also humanizing interactions to engage patients. Countless examples, but one that made me smile was a depression focused chat bot responding in the following interaction: - Context: post-discharge check in Bot: Hi Freida, how are you feeling today after your surgery?  Patient: I’m having a tough time, am in a lot of pain.  Bot: Well, it can’t be that bad, I’m sure it will get better with a positive mindset!    🤣 🤣 🤣 Can't wait for next go-around!

A fantastic and realistic overview of the state of this technology.