Sri Kanth Kasala’s Post

Artificial intelligence (AI) in healthcare has significantly evolved from theoretical concepts to practical implementations, profoundly influencing patient care and operational processes. One major use is in documentation automation, where AI aids in digitizing and categorizing medical notes and fax documents, leading to improved operational efficiencies and cost reductions. This automation facilitates better clinical decision-making by seamlessly integrating critical patient data, such as lab records and medication details, into Electronic Health Records (EHR) systems. #Telehealth #DigitalHealth #ArtificialIntelligence https://lnkd.in/enr-6XeP

Artificial intelligence is on everyone’s minds, but is it truly “intelligent”? Patient Safety managing editor, Caitlyn Allen, sat with Dr. Avishek Choudhury, AI healthcare researcher, to discuss the promise and limitations of this increasingly prevalent technology. Read this and more in our highlights issue of PATIENT SAFETY: https://lnkd.in/gEpYaEfE

Artificial intelligence (AI) in healthcare has significantly evolved from theoretical concepts to practical implementations, profoundly influencing patient care and operational processes. One major use is in documentation automation, where AI aids in digitizing and categorizing medical notes and fax documents, leading to improved operational efficiencies and cost reductions. This automation facilitates better clinical decision-making by seamlessly integrating critical patient data, such as lab records and medication details, into Electronic Health Records (EHR) systems. #Telehealth #DigitalHealth #ArtificialIntelligence https://lnkd.in/dd43HTjD

Artificial intelligence (AI) in healthcare has significantly evolved from theoretical concepts to practical implementations, profoundly influencing patient care and operational processes. One major use is in documentation automation, where AI aids in digitizing and categorizing medical notes and fax documents, leading to improved operational efficiencies and cost reductions. This automation facilitates better clinical decision-making by seamlessly integrating critical patient data, such as lab records and medication details, into Electronic Health Records (EHR) systems. #Telehealth #DigitalHealth #ArtificialIntelligence https://lnkd.in/g9E42Mrs

Artificial intelligence (AI) in healthcare has significantly evolved from theoretical concepts to practical implementations, profoundly influencing patient care and operational processes. One major use is in documentation automation, where AI aids in digitizing and categorizing medical notes and fax documents, leading to improved operational efficiencies and cost reductions. This automation facilitates better clinical decision-making by seamlessly integrating critical patient data, such as lab records and medication details, into Electronic Health Records (EHR) systems. #Telehealth #DigitalHealth #ArtificialIntelligence https://lnkd.in/e3-MjVFZ

Tune in and hear industry experts talk about how AI can help with operational transformation. Proud to call these two my colleagues! PINC AI™ #aiapplications #transformation

Artificial intelligence (AI) in healthcare has significantly evolved from theoretical concepts to practical implementations, profoundly influencing patient care and operational processes. One major use is in documentation automation, where AI aids in digitizing and categorizing medical notes and fax documents, leading to improved operational efficiencies and cost reductions. This automation facilitates better clinical decision-making by seamlessly integrating critical patient data, such as lab records and medication details, into Electronic Health Records (EHR) systems. #Telehealth #DigitalHealth #ArtificialIntelligence https://lnkd.in/eGP993XT

Integrating artificial intelligence into federal health care agencies could revolutionize patient care, according to Steven Sousa, Senior Vice President of Health and Social Services at CGI's federal arm. Harnessing the power of big data can enhance decision-making, streamline administrative processes, and drive innovation in diagnostics. Check out the article to learn more about the potential of AI integration in federal healthcare agencies. #AI #healthcare #bigdata #innovation

Another exciting day at the HLTH event, and the buzz around AI continues to grow. But I must admit, I'm a bit surprised by the superficial approach many speakers have toward AI. 🤔 Sure, AI has the potential to revolutionize healthcare. But we must not overlook the power of deterministic algorithms. The deterministic algorithms are relying on signal processing and time series analysis. These algorithms deliver faster, more predictable, and accurate results with less data. In healthcare, predictability is crucial to mitigate risks for patients. Despite this, I firmly believe AI can significantly advance healthcare. The biggest issue is that most patient data are still text-based. Additionally, different physicians used varied terminologies for the same conditions, observations, or medications. This inconsistency makes it challenging to aggregate data properly. Today, we can harness the data from a patient’s lifetime to improve health delivery quality. Yet the volume of this data is daunting. A Large Language Model (LLM) could digest 3000+ pages of patient history into useful, digestible insights for physicians. This is where our efforts should be focused. This is the key topic in AI for healthcare. Of course, this brings up the crucial question: Who owns the data? The healthcare system remains highly siloed. The data is scattered across various systems, hospitals, tech companies, and even on the laptops of general practitioners. There’s a lot the government can do to ensure big actors don’t dictate our health future. Ultimately, patients should own their data. I’m confident that most would agree to share their data anonymously to advance science. There’s a lot of work ahead, but with the right focus and collaboration, the future of healthcare looks promising. Let’s keep pushing forward! 💪

The HTI-1 Final Rule's transparency requirements for AI and predictive algorithms mark a pivotal moment for healthcare, emphasizing fairness, validity, and safety. While Large Language Models offer immense potential, they also pose challenges for compliance and understanding. Collaboration among stakeholders is crucial to navigate this new era, ensuring patient safety and fostering innovation in healthcare. https://lnkd.in/deJJUj9j