The Impact of AI on Healthcare Supply Chains: Phani Barla’s Perspectives

While modern society faces many challenges, healthcare remains the most critical one, as people’s lives depend on it. More than a dozen leading disease experts told Reuters that while monitoring the spread of bird flu, they noticed that it had recently affected 129 dairy herds in 12 US states, raising concerns that the virus could become human-transmissible. The COVID-19 pandemic revealed how vulnerable the healthcare supply chain (HSC) can be in turbulent times. However, with the help of AI, the quality of medical supplies can be improved, and these innovations could save thousands of lives worldwide by ensuring that the right equipment gets to doctors on time. Phani Chandra Barla, Principal Quality Engineer at medical device company Senseonics Inc., is a top expert in the field. In his recent article, “Enhancing Quality Control in Medical Devices Supply Chain Using Artificial Intelligence and Machine Learning,” published in ASRJETS-Journal, he shares his in-depth knowledge of implementing AI to improve HSC quality and reliability. With over ten years of industry experience and global recognition as a winner of the International Business Award Cases & Faces for Achievement in Engineering and a two-time jury member for the Globee Awards, Phani Chandra Barla shares unique and valuable insights on how AI can help humanity fight diseases.

Phani, in the research you discuss the potential of AI in improving demand forecasting and inventory management in medical device supply chains. Given your experience with various analytical tools and techniques such as FMEA and GAP analysis, how do you see the role of these techniques combined with AI to achieve even more accuracy and efficiency?

In my experience, combining AI with traditional techniques like FMEA and GAP analysis significantly enhances accuracy and efficiency in medical device supply chains. I’ve seen AI’s predictive capabilities identify failure modes and performance discrepancies that human analysis might miss. This synergy greatly improves our demand forecasting and inventory management.

What excites me is AI’s ability to learn continuously from real-time data, enabling dynamic adjustments to inventory and forecasts. By integrating AI-driven insights with established tools, we create a more proactive approach to supply chain management. I’ve observed how this allows us to anticipate issues preemptively, optimize inventory precisely, and respond swiftly to market changes.

In my view, this integration promises to reduce costs, minimize waste, and ultimately improve patient outcomes in the medical device industry.

You are experienced in developing standard operating procedures (SOPs), policies, and work instructions to comply with FDA regulations. How do you see AI being integrated into these procedures to ensure continued compliance with high quality and safety standards?

AI can be game-changing in automating the process of creating and reviewing documents, which will significantly improve our quality and safety standards. It’s amazing how AI can use natural language processing to create initial SOPs and ensure they are compliant while reducing errors and saving time.

AI could also be implemented for real-time compliance monitoring. It can quickly alert us to regulatory updates that may impact our procedures, allowing the team to make adjustments promptly. As for quality control, AI’s predictive analytics can identify potential issues before they occur, helping us make proactive changes to our SOPs.

Additionally, AI can personalize training programs and streamline compliance reporting, ensuring we maintain compliance while improving efficiency. With all of these innovations, our teams can focus on more complex tasks that require human expertise. It’s a powerful tool for maintaining high quality and safety standards in our industry!

Phani, you are currently serving as a Principal Quality Engineer at Senseonics Inc.,  a pioneering medical technology company dedicated to transforming diabetes management through innovative continuous glucose monitoring (CGM) systems. You directly impact product quality, regulatory compliance, risk management, leadership and strategic planning. How do your experience and expertise help you see the potential of using AI to improve these processes?

In this position, I have identified several areas where I see the potential of AI for our industry. I believe that through predictive analytics and automated inspections, AI can allow us to identify problems at an early stage, helping to improve product quality and consistency. In terms of regulatory compliance, AI’s ability to monitor regulatory changes can make our specialists’ work much easier by ensuring that they are always up-to-date and audit-ready. Speaking of risk management, AI can enhance our Failure Modes and Effects Analysis (FMEA) processes and provide real-time monitoring, allowing us to implement more proactive risk mitigation strategies. Another potential for AI is its ability to transform strategic planning by providing data and optimizing resource allocation. I am optimistic about implementing AI through carefully selected pilot projects, effective employee training, and continuous refinement of our approach to maximize benefits.

With your expertise in the medical device industry and a strong grasp of statistical tools like Minitab, Lean Manufacturing, DMAIC, and a Six Sigma Green Belt, how do you think AI and IO can help analyze and predict manufacturing defects? How can these innovations benefit the company?

Drawing from this experience, I’m really excited about integrating AI and Industrial Optimization (IO) for defect analysis and prediction in medical device manufacturing, particularly at Senseonics Inc. AI can transform our data collection and preprocessing by seamlessly integrating with existing systems and automating data cleaning. This enables powerful predictive analytics to identify defect patterns and detect real-time anomalies. For root cause analysis, AI can enhance our FMEA processes and perform advanced correlation analysis, providing deeper insights into defect causes.

In optimization and control, AI and IO can fine-tune manufacturing parameters and implement real-time monitoring to minimize defects. Benefits such as improved early defect detection, proactive maintenance, data-driven decision-making, reduced rework and scrap, enhanced compliance documentation, and support for continuous improvement—all perfectly align with Lean and Six Sigma principles, elevating efficiency and quality to new heights. I believe these advancements will benefit the company and solidify its reputation as a trusted medical device manufacturer.

Your research and teaching experience at institutions such as the Technical University of Dublin  and Chaitanya Bharathi Institute of Technology allows you to share your expertise with the next generation of engineers. What skills and knowledge do you think future professionals will need to successfully combine with new technologies?

I believe the key skills they’ll need to combine with new technologies are:

Firstly, providing strong technical skills in data analysis, machine learning, and AI. This includes proficiency in statistical tools like Minitab and programming languages like Python.

Secondly, give a solid understanding of medical device engineering, including regulatory standards like ISO 13485 and FDA regulations.

Thirdly, explaining how soft skills are crucial. Focusing on how to improve Critical thinking, adaptability, and effective communication for the students which are essential in today’s rapidly changing technological landscape.

Lastly, giving a deep understanding of quality management systems and regulatory compliance is vital, especially in the medical device industry.

I emphasize  practical experience through internship opportunities and industry projects for the students and stress the importance of lifelong learning. By focusing on these areas, I have prepared students to effectively integrate new technologies with traditional practices, equipping them for future challenges.

You have been a judge at the international Globee Awards and are a member of the International Association of Engineers – as part of a community of experts and innovators, what are the latest trends and developments in AI and machine learning that you think will have an impact on healthcare?

I was honored to be a jury member at the Globee Awards in various categories in 2023 and 2024. The rigorous assessment and scoring process of global nominations were exhilarating but also deeply gratifying, underscoring the profound impact of visionary minds and revolutionary advancements that are unequivocally redefining the course of our future. I loved the experience of reviewing the technology innovations firsthand for the solutions developed from different corners of the globe.

Being part of these innovative communities has given me insight into some remarkable AI and machine learning developments in healthcare. AI is revolutionizing personalized medicine by analyzing genomic data and predicting treatment outcomes. In medical imaging, it’s making diagnostics faster and more accurate, sometimes outperforming human radiologists. I’m particularly excited about AI in drug discovery and development. It’s accelerating the process by predicting drug efficacy and optimizing clinical trials. AI is also improving hospital operations through better resource allocation and workflow automation. Another trend I’m watching closely is AI in population health management, especially for predicting and managing public health crises. Of course, as we advance, it’s crucial to address ethical considerations like eliminating bias and ensuring data privacy. These innovations promise to significantly improve patient care, operational efficiency, and public health outcomes.