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Excerpts from podcast with Dr. Vinaytosh Mishra
Dr. Vinaytosh Mishra is the Director of Thumbay Institute for AI in Healthcare and Associate Professor for Healthcare Management (Management, Digital Health, and Heath Analytics) at the College of Healthcare Management and Economics, Gulf Medical University, Ajman (UAE).
Dr. Mishra has done his engineering and Ph.D. from the prestigious India Institute of Technology (BHU), Varanasi. He has more than 17 years of experience in industries like Healthcare, EdTech, Finance, and Information Technology. He is also a mentor for leading business incubators in India and UAE and a domain expert for AI implementation with Al Hathboor Bikal.ai, UAE.
 

Excerpts from the Interview

FAQs

  • Healthcare systems globally, including the US, are facing a value crisis with rising costs and stagnant quality. Despite spending a significant portion of its GDP on healthcare, the US needs to rank higher in quality.
  • AI is seen as a solution to reduce costs and address inefficiencies in healthcare systems. Standard processes in healthcare can be defined and automated using AI, particularly in achieving efficiency.
  • In the post-GPT era, there is an expectation of increased robotic process automation in AI, focusing on achieving efficiency as the low-hanging fruit. AI’s role extends to developing prediction systems for diseases and risk assessment, focusing on efficiency and risk prediction.
  • While efficiency and risk prediction are immediate priorities, the future may see advancements in digital therapeutics and other treatment modalities.
  • Significant progress is observed with many FDA approvals coming in AI, particularly in efficiency and risk prediction systems.
  • Technology investment is not exclusive to large organizations; there has been democratization in the AI landscape. Open-source and cloud-based systems are widely available, making AI accessible to organizations of various sizes.
  • Medium-scale organizations or hospitals with up to 50-bit capacity can utilize cloud-based ERP systems. Present record monitoring can be automated using Natural Language Processing (NLP) to convert unstructured data into structured data.
  • AI implementation can result in cost savings, particularly in personnel costs for roles like physician assistants, nurses, or clerks.
  • Small organizations can afford AI, and various models are available, including free and premium ones.
  • The challenge lies in organizations’ need for more leadership and intent to adopt AI, especially in healthcare, where doctors may initially be technology-averse.
  • The adoption of AI in healthcare is described as a “game of intent,” emphasizing the need for commitment and willingness to embrace technology, even among technology-averse professionals like doctors.
  • The discussion emphasizes the significance of adopting technology from a bottom-line perspective, highlighting its positive effects on overall business profitability.
  • AI, under the concept of DIGITAL LEAN, frees up resources, particularly crucial for businesses with limited capital and a hunger for resources to achieve the next level.
  • It can be substantiated by an example of an entrepreneur with a hospital automating tasks, reducing the need for multiple receptionists, and reallocating resources for home-based care.
  • Small hospitals can optimize human resources by reallocating staff, such as using nurses for home-based care and streamlining reception tasks.
  • Highlights efficiency gains in ERP (Enterprise Resource Planning) implementation, suggesting the potential for further advancements with AI integration. The role of AI in supporting decision-making, reducing costs, and minimizing manual errors.
  • AI’s advantage lies in real-time data analysis, eliminating reliance on traditional methods like EOQ (Economic Order Quantity) and thumb rules.
  • AI enables vendor-managed inventory with automatic triggers, reducing manual touchpoints and minimizing stockouts, which is especially crucial in healthcare.
  • AI can identify patterns in data that may not have been considered before, enhancing the depth of analysis. Emphasizes the critical role of AI in inventory management, particularly in healthcare settings where stockouts can have fatal consequences.
  • Implementing AI infrastructure leads to automation and digitalization, mitigating the bullwhip effect – the distortion of information in demand, which can lead to excess inventory.
  • AI’s role in digitalizing processes helps prevent the bullwhip effect, avoiding the accumulation of excess inventory due to distorted demand information.
  • Small and medium businesses are differentiated based on revenue, and small businesses may not afford customized AI solutions.
  • Technology partners offer solutions for small and medium businesses, encouraging them to match their needs with available solutions in the market.
  • AI emphasizes the potential for significant cost savings for small businesses, highlighting that the only barrier is a mental block about the scale of operation.
  • Small-scale organizations can leverage open source and premium models, even engaging companies like Techment for customized solutions.
  • The discussion encourages small-scale organizations to establish a strategic relationship with technology implementation partners to overcome resource ownership challenges.
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