Download to read offline
More Related Content
Similar to From Diagnosis to Delivery: How AI is Revolutionizing the Patient Experience
From Diagnosis to Delivery: How AI is Revolutionizing the Patient Experience
- 5. What we’ll covertoday: 5 Breakthroughs in Healthcare Value Proposition and Digital Patient Journey Use Case 1 • Enhance Patient Communication 📲 Explore how AI facilitates more personalized, timely, and efficient patient interactions, improving overall engagement and satisfaction Use Case 1I • Leverage AI for Improved Patient Care & Predictive Medicine 🧬 Learn how AI helps healthcare providers offer more precise, preventive care by predicting patient outcomes and optimizing treatment plans Use Case III • Streamline Healthcare Workflows Discover how AI-driven analytics and automation are increasing 📊 efficiency in clinical settings Challenges and risks with AI and ways to overcome them Future of AI – where are we headed? AI is not just transforming healthcare; it's revolutionizing the patient experience, from the initial diagnosis to the final delivery of care.
- 6. AUDIENCE POLLS -I 6 How familiar are you with AI applications in healthcare? Options: • Very familiar • Somewhat familiar • Not very familiar • Not at all familiar Do you believe AI will significantly improve patient outcomes in the next 5 years? Options: • Definitely • Probably • Possibly • Probably not • Definitely not I currently use AI at my workplace to help with my role. Options: • Yes • Maybe • No
- 7. AUDIENCE POLLS -II 7 Do you trust AI-powered medical devices to make accurate diagnoses? Options: • Yes, completely • Yes, to some extent • Neutral • No, not really • No, not at all What is your biggest concern about the use of AI in healthcare? Options: • Data privacy and security • Job displacement • Ethical implications of AI decision-making • Lack of transparency and accountability • Other What do you think is the most promising area for AI in healthcare? Options: • Drug discovery and development • Medical imaging analysis • Personalized medicine • Administrative tasks and workflow optimization • Other
- 8. Breakthroughs in HealthcareLeveraging AI: 8 • Medical Imaging: AI can identify tumors in mammograms or detect early signs of diseases like Alzheimer's in brain scans. • Drug Discovery: AI is accelerating drug discovery by analyzing vast amounts of biological data to identify potential drug targets and predict the effectiveness of drug candidates. • Personalized Medicine: AI can analyze a patient's genetic information, medical history, and lifestyle factors to develop personalized treatment plans. • Robotic Surgery: AI-powered robotic systems assist surgeons in performing complex procedures with greater precision and accuracy. • Patient Monitoring: AI can analyze real-time patient data from wearable devices and hospital sensors to detect early signs of deterioration and alert healthcare providers.
- 9. VALUE PROPOSITION 9 Operational Efficiency AI Enhanced DecisionMaking Improved Customer Experience Process automation, resource optimization, increased productivity Data analysis, predictive analytics, decision automation Personalization, 24/7 customer service, predictive behavior Efficient operations = better customer service AI can streamline operations by improving quality of decisions Decision making with AI drives customer satisfaction
- 10.
- 11. Use Case I:Enhancing Patient Communication 📲 11 • The Communication Gap: Highlight the challenges of traditional communication methods in healthcare (delayed responses, generic interactions, lack of personalization). • AI-Driven Solutions: Discuss technologies like chatbots, voice assistants, and natural language processing (NLP) for appointment scheduling, symptom tracking, and patient education. • Interactive Patient Portals: Showcase how AI powers self-service tools for patients to access lab results, reminders, and post-visit instructions. Case Study: • Example: Mayo Clinic’s implementation of AI chatbots to answer patient queries, resulting in a 40% reduction in call center inquiries. • Outcome: Improved patient engagement and satisfaction through timely, personalized communication. KeyTakeaway: AI enhances communication by providing personalized, efficient, and accessible patient interactions, boosting engagement and trust.
- 12. Case Study: 12 Use Case: Teledocempowers people to live their healthiest lives by transforming the healthcare experience through telemedicine and virtual care. Provide 24/7 virtual care, chronic condition management, mental health support, and AI-driven insights to patients globally. Focuses on diseases like diabetes, hypertension, weight management, mental health and virtual primary care. Impact: Diabetes Management with Livongo: Teladoc's program provided smart glucose meters,AI-driven coaching, and 24/7 support. Outcome: • 58% improvement in blood sugar levels among participants. • Significant cost savings for the employer through reduced healthcare claims.
- 13. Use Case II:Improved Patient Care & Predictive Medicine 🧬 13 • AI in Diagnosis: Discuss how AI supports clinicians by analyzing medical imaging, lab results, and genetic data. • Predictive Medicine: Highlight how machine learning algorithms forecast patient outcomes, helping identify high-risk patients early. • Case Study: • Example: Google DeepMind’s work with Moorfields Eye Hospital to detect over 50 eye diseases from retinal scans. • Outcome: Earlier detection, leading to timely interventions and better patient outcomes. • PersonalizedTreatment Plans: Explain how AI tailors care by analyzing patient history, lifestyle, and genomic data. • Example:Tempus AI platform in oncology, enabling precision treatment recommendations for cancer patients. • Preventive Care:AI applications in wearable devices that monitor vitals and alert users to anomalies before symptoms arise. KeyTakeaway: AI empowers providers to deliver precise, preventive, and personalized care by predicting outcomes and optimizing treatments.
- 14. Case Study: 14 Use Case: FoundationMedicine applies AI to analyze genomic data, enabling personalized cancer treatment plans based on individual tumor profiles. It develops companion diagnostics to match cancer patients with targeted therapies for personalized therapy. Impact: Patients are matched with targeted therapies, immunotherapies or clinical trials based on the genes driving to their cancer resulting in higher survival rates and monitoring of cancer therapies. Provides actionable insights into managing cancer patients.
- 15. Case Study: 15 Use Case: Anumanadevelops AI algorithms to detect cardiac conditions like arrhythmias from ECG data, providing early and accurate diagnoses. These algorithms extract rich, previously inaccessible diagnostic insights from the electrical activity of the heart. Impact: It enables early detection of conditions that are asymptomatic or invisible in traditional ECG interpretation, enabling treatment before symptoms arise. Transforms the humble ECG—a low-cost and ubiquitous tool—into an advanced diagnostic device without requiring additional hardware. It enables early detection of rare heart conditions through accessible technology.
- 16. Use Case III:Streamlining HealthcareWorkflows 📊 16 • Administrative Efficiency: Discuss how AI automates repetitive tasks like billing, coding, and appointment scheduling. • Example: Robotic Process Automation (RPA) used in hospitals, reducing administrative workloads by up to 60%. • Clinical Decision Support (CDS): Explore how AI aids decision-making by providing real-time insights from vast datasets. • Example: Cleveland Clinic’s use of AI to predict patient deterioration in the ICU, allowing rapid intervention. • Supply Chain Optimization: Explain how AI predicts resource demands, reducing waste and improving readiness for emergencies. • Example: Predictive analytics in managing PPE supply during the COVID-19 pandemic. KeyTakeaway: AI-driven analytics and automation improve efficiency and allow providers to focus more on patient care.
- 17. Case Study: 17 Use Case: Biofourmisleverages AI-driven wearable devices and predictive analytics to monitor acute and chronic conditions and alert providers before critical events occur.They enable predictive and personalized treatment including: • Remote patient monitoring • Hospital at home Impact: Reduced hospital readmissions and personalized care management for patients. • 70% reductions in readmissions for patients receiving acute care at home • 38% avg cost reduction for acute care patients receiving hospital-level care at home
- 18. Case Study: 18 Use Case: Viz.aiemploys a mix of artificial intelligence, machine learning, and advanced imaging technology to analyze and triage medical imaging in real time. Key features include AI-powered imaging analysis for CT scans, integrating within hospital workflows, care coordination for patients. Impact: Identifies LVOs in CT scans within minutes, sends alerts to specialists, and expedites decisions on interventions like thrombectomy. • 30% reduction in door-to-treatment time. • Increased access to specialized stroke care, even in remote locations.
- 19. CHALLENGES AND RISKSWITH AI AND OVERCOMINGTHEM: 19 Ethical Concerns: - Bias and Discrimination mitigate bias in training data, use diverse and inclusive datasets for training - Black Box models use interpretable AI models and integrate explainability - Ethical Frameworks Establish clear guidelines for AI development, continuously refine and monitor to address emerging disparities Security and Privacy Concerns: - Data Privacy measures for data protection and ownership - Responsible AI Practices accountability and transparency, compliance with HIPAA and GDPR - Regulation and Oversight governance on AI, ensure strict adherence to regulatory frameworks Integration with Clinical Workflows: - EHR integration adequate training and support for end users - One of platforms find ways to integrate within existing workflows and systems Regulatory and Legal Challenges: - Complex approval work closely with FDA, EMA and other regulatory bodies on the approval - Process for updates define clear compliance standards and process for updating and refining the models
- 20. WHERE ARE WEHEADED WITH AI? 20 GenAI, is a type of artificial intelligence that can create new content, like text, images, music, and even code. It works by learning patterns from existing data and then generating new content that fits those patterns.Think of it as an AI that can "imagine" and create things based on what it has learned! Deep learning – learning by trials AI intelligence timeline 2010 Human intelligence powering the computation, i.e., human solving the problem first and then writing the code for the computer to solve in a very quick, accurate, massive scale. Computers find their own intelligence; we only give data. Code has become drastically reduced. Rise of LLMs. 2020 ChatGPT launched Multimodal models can generate various types of data such as text, images, audio, video. IQ higher than that of Albert Einstein. 2023 SINGULARITY
- 21. HOWTOTHRIVE INTHE AGEOF GENAI? 21 1. Constant Change and Adaptability 1. Invest in reskilling and upskilling 2. Foster a culture of growth mindset 3. Integrate AI tools into your workflow 1. Content creation, data analysis, customer service etc. 2. Focus on Authenticity and Creativity 1. Linguistic intelligence is integrated within AI but not emotional intelligence 2. Embracing your true self that cannot be copied 3. Social Connectedness 1. Loneliness pandemic – rise of humanoids, we will crave true connections 2. Invest in communities that help you grow “The key of succeeding in the age of AI is Emotional Intelligence and Connection” says Mo Gawdat, former CBO at Google X
- 22. Our Mission isto ‘Democratize Peak Potential’ Design the Future with an AI-Powered, Human Centric Platform 22
- 23. 23 Thank you, let’sstay in touch! www.tattva.world www.linkedin.com/in/kaur-sk88/
Editor's Notes
- #8 Medical Imaging: AI algorithms can analyze medical images like X-rays, CT scans, and MRIs to detect abnormalities and assist in diagnosis. For example, AI can identify tumors in mammograms or detect early signs of diseases like Alzheimer's in brain scans. Drug Discovery: AI is accelerating drug discovery by analyzing vast amounts of biological data to identify potential drug targets and predict the effectiveness of drug candidates. This can significantly reduce the time and cost of developing new medications. Personalized Medicine: AI can analyze a patient's genetic information, medical history, and lifestyle factors to develop personalized treatment plans. This allows for more precise and effective treatments tailored to each individual patient. Virtual Health Assistants: AI-powered virtual health assistants can provide patients with information, answer medical questions, and even offer basic diagnoses. This can improve patient engagement and access to healthcare, especially in remote areas. Robotic Surgery: AI-powered robotic systems assist surgeons in performing complex procedures with greater precision and accuracy. This can lead to faster recovery times and reduced risk of complications. Patient Monitoring: AI can analyze real-time patient data from wearable devices and hospital sensors to detect early signs of deterioration and alert healthcare providers. This enables timely interventions and improves patient outcomes.
- #9 Its become very hard to distinguish between what’s real and what’s fake, based on the dataset that the AI is trained on it can be subjective and biased. Not everything we are being told id true, media is biased. Be careful of what you feed your mind and what you believe.
- #10 Its become very hard to distinguish between what’s real and what’s fake, based on the dataset that the AI is trained on it can be subjective and biased. Not everything we are being told id true, media is biased. Be careful of what you feed your mind and what you believe.
- #11 Its become very hard to distinguish between what’s real and what’s fake, based on the dataset that the AI is trained on it can be subjective and biased. Not everything we are being told id true, media is biased. Be careful of what you feed your mind and what you believe.
- #12 Its become very hard to distinguish between what’s real and what’s fake, based on the dataset that the AI is trained on it can be subjective and biased. Not everything we are being told id true, media is biased. Be careful of what you feed your mind and what you believe.
- #13 Its become very hard to distinguish between what’s real and what’s fake, based on the dataset that the AI is trained on it can be subjective and biased. Not everything we are being told id true, media is biased. Be careful of what you feed your mind and what you believe.
- #14 Its become very hard to distinguish between what’s real and what’s fake, based on the dataset that the AI is trained on it can be subjective and biased. Not everything we are being told id true, media is biased. Be careful of what you feed your mind and what you believe.
- #15 Its become very hard to distinguish between what’s real and what’s fake, based on the dataset that the AI is trained on it can be subjective and biased. Not everything we are being told id true, media is biased. Be careful of what you feed your mind and what you believe.
- #16 Its become very hard to distinguish between what’s real and what’s fake, based on the dataset that the AI is trained on it can be subjective and biased. Not everything we are being told id true, media is biased. Be careful of what you feed your mind and what you believe.
- #17 Its become very hard to distinguish between what’s real and what’s fake, based on the dataset that the AI is trained on it can be subjective and biased. Not everything we are being told id true, media is biased. Be careful of what you feed your mind and what you believe.
- #18 Its become very hard to distinguish between what’s real and what’s fake, based on the dataset that the AI is trained on it can be subjective and biased. Not everything we are being told id true, media is biased. Be careful of what you feed your mind and what you believe.
- #19 Its become very hard to distinguish between what’s real and what’s fake, based on the dataset that the AI is trained on it can be subjective and biased. Not everything we are being told id true, media is biased. Be careful of what you feed your mind and what you believe.
- #20 ChatGPT came out and democratized AI, making it accessible to anyone with a smart phone. Linguistic intelligence exists, emotional intelligence is being integrated but its still further behind. We are reaching a point of singularity in the sense the rules of the same are chaging so quickly that we don’t know exactly how its going to play out. Gemini has an IQ greater than Einstein of 162 Intelligence has become a utility
- #21 Role of AI agents and our agency within it, graduate students who need to be trained 100 billion AI agents – think of them like interns ChatGPT learnt from how people used it, first we develop base models and then they need to get specialized Leader who can leverage this technology to multiply to create more value People who can capitalize on human connection and build the right networks will outperform those that don’t. There is a constant fear – flight or fight – its important to learn how to control your own inner narrative. Artificial intelligence agents, or AI agents, are software programs that can act independently on your behalf. Right now they have a liberal art degree and know a bit of everything