Sandeep Rath never set out to revolutionize healthcare systems. Trained as an engineer, his career began in industrial operations across India and oilfields abroad. But a blend of curiosity, global exposure, and a knack for structured problem-solving led him from factory floors to hospital corridors. Now a leading voice in healthcare operations, Sandeep is using data, empathy, and real-world insight to help hospitals run better, cut costs, and care smarter.

What initially drew you to operations management, and how did your journey lead you to specialize in healthcare systems?

My first job out of college was as part of a management development program at ITC Ltd., a large Indian conglomerate. Most of us in the program, especially the engineers, were placed in operations-related roles, so in a way, I stumbled into operations by chance. But I quickly realized I liked it. Operations felt tangible, and in both that role at ITC and my second job at Schlumberger Ltd., an oilfield services firm, I saw firsthand how good operations were at the core of an organization’s success.

At Schlumberger, I got the opportunity to attend training programs where I interacted with business school faculty from U.S. universities who specialized in operations. I really liked the structured way they approached problems around efficiency and resource allocation. That exposure is what ultimately motivated me to pursue a PhD in operations management.

When I started my PhD at UCLA Anderson, my advisor encouraged me to explore real-world problems, and with great support from management at UCLA Health,  I ended up spending time observing how anesthesiologists were staffed and scheduled in the operating rooms at UCLA Ronald Reagan Hospital. This was around 2012, just as U.S. hospitals, under the Affordable Care Act, were transitioning to Electronic Health Record (EHR) systems. I could see the challenges of managing operations in a healthcare setting, but also the huge opportunity. For the first time, hospitals had access to incredibly granular data on resource usage. I was struck by both the similarities and differences in how manufacturing and healthcare organizations made decisions around resource allocation. The idea of using this newly available data to help make healthcare delivery more efficient seemed both interesting and impactful. I’ve been working in healthcare operations ever since.

If I were to give a one line answer to this question, it would be that a mix of chance and curiosity led me to this career.

You’ve worked across different continents, from India to the U.S. to Europe. How have these diverse experiences shaped your approach to research and problem-solving?

 As a part of the management training program at ITC Ltd., I got to see a variety of businesses and locations across India,  from paper manufacturing to consumer goods to hotels, in many different regions of India. Later, while working and training at Schlumberger, I traveled to the UAE, France, the UK, and the US, and saw very different types of operations. I spent time at oil rigs, product development centers, and manufacturing facilities. I also visited sites of vendors and customers of these firms, which gave me a good sense of how different pieces of the supply chain fit together.

If I were to appropriate Leo Tolstoy’s quote about all happy families being alike, I noticed that all well-run operations had some common traits. They were very visual and transparent in their actions, there was very little fire-fighting, most of the work was planned, employees had the autonomy and tools they needed to do their jobs well, and decisions were as data-driven as possible. These patterns showed up again and again, no matter the country, industry, or culture.

At the same time, I also learned that while these fundamentals stay the same, the way they are put into practice has to fit the local context. The best teams and leaders were the ones who could adapt to local conditions while still sticking to the core principles of good operations.

All of this has shaped how I think about research and problem-solving. I try to look for those core patterns and ideas that hold across settings, but I also ask how they might need to be adjusted depending on the situation. And I’ve learned the value of going out and observing real operations, because seeing how things work on the ground often gives you insights you wouldn’t get from data alone.

Your research helps hospitals save millions through better resource planning. Can you share an example of a project that made a real difference in patient care or hospital efficiency?

One project I worked on that was particularly exciting was around optimizing surgical trays for operating rooms. Surgical trays are sets of instruments such as scalpels and forceps that are prepped for surgeries. But over time, these trays tend to get bloated with instruments that are rarely, if ever, used. The problem is that hospitals still have to clean, sterilize, and handle all those instruments before every surgery, which adds up to a huge cost. A medium-sized hospital can easily spend several million dollars a year just on cleaning and reprocessing instruments that often never leave the tray.

I worked with other faculty at UNC Kenan-Flagler, along with UNC Health and a healthcare software startup called Opflow, which was founded by KFBS alumni. Together, we developed a data-driven algorithm to optimize which instruments should go on these trays. Opflow has since implemented these optimized trays in multiple hospital systems across the country, helping them cut costs and reduce waste.

I really enjoyed this project because it tackled a very relevant problem and one that’s symptomatic of a lot of inefficiency in healthcare. These legacy processes build up over time and end up wasting resources, and what they really need is a structured, data-driven approach to rethink and improve them. It was also a great experience getting to collaborate with healthcare systems, academics, and entrepreneurs all working toward the same goal.

One of your studies looks at physicians’ workload and electronic health records (EHRs). What are some key lessons from this research that could apply beyond healthcare, perhaps in education or the corporate world?

Physicians spend a large part of their day typing notes and entering information into EHR systems. In that study, we looked at detailed timestamp data to understand when physicians do this work — whether before, during, or after patient appointments — and how they balance it with their face-to-face time with patients. What we found was interesting: spending just a small amount of time before an appointment reviewing the patient’s records and preparing could significantly reduce the overall EHR workload for the physician.

I think this has lessons beyond healthcare. In most knowledge work, whether in education or the corporate world, we face a similar challenge. We have meetings or classes, and then there’s the prep work or follow-up tasks that come with them, which we need to fit around those scheduled blocks. What this research suggests is that carving out a bit of time before meetings or classes to review notes, think through the agenda, or get organized can make a big difference in how efficiently the rest of the day goes. The exact benefit will vary depending on the type of work, but in our study, the improvement for physicians was large enough that it made us think differently about how to structure a physician’s schedule.

The pandemic exposed major inefficiencies in healthcare. From your perspective, what long-term changes do hospitals need to implement to improve patient care and operations?

The pandemic really exposed a lot of operational inefficiencies in our healthcare system. I think it’s important to separate structural issues from the efforts of healthcare professionals themselves. From what I saw through family, friends, and colleagues in healthcare, the people on the front lines went above and beyond to deliver care. But they were often held back by system-level shortcomings.

One of the biggest challenges was the lack of flexibility in the system. We struggled to move capacity, whether ICU beds, ventilators, or staff, across locations as the pandemic shifted. There was also a lack of timely, accurate data on how the pandemic was evolving, which made it harder to allocate resources where they were needed most. These are classic operations problems: mismatched capacity, poor information flows, and rigid processes that couldn’t adapt to rapidly changing demand. All of this highlights that operational resilience isn’t just about having more resources, it’s about being able to quickly reconfigure and deploy them.

The long-term impacts are still with us. Healthcare utilization patterns have shifted, with ER visits and other services not yet back to pre-pandemic levels. We’ve also seen higher levels of burnout and attrition in the healthcare workforce. Some hospital closures have left permanent capacity gaps. 

I think there are two big operational takeaways. First, we need to design healthcare systems that are more flexible and better informed. That means real-time data sharing, more agile supply chains, and the ability to move capacity across locations quickly. Second, we need to focus on making healthcare professionals’ workflows more efficient. Reducing unnecessary administrative work, improving scheduling, and streamlining processes not only make the system more efficient but also help with retention and well-being. In the end, good operations design is about making it easier for people to do their jobs well, especially in times of crisis.

You teach Operations and Healthcare Management at UNC. What’s the biggest misconception students have about operations, and what excites you most about teaching this field?

I think there are two main misconceptions. First, some students feel that operations management is ‘uncool’ and not very engaging. Second, they think there aren’t many exciting career opportunities in the field of operations.

I (obviously) disagree with both. Good operations are absolutely essential to an organization’s success. Many top CEOs, like Tim Cook at Apple, come from an operations background. What really sets companies like Apple and Amazon apart isn’t just their products or marketing,  it’s how well they run their operations. And in services, managing operations also means understanding people, customer behavior, and employee dynamics, which makes it all the more interesting.

The second misconception is around career opportunities. A lot of roles that don’t have “operations” in the title are, in reality, operations roles. Most consulting jobs, and a big chunk of data science and analytics roles, are about making operational improvements, driving efficiency, and helping organizations make better decisions. All of that requires a solid foundation in operations management.

What I really enjoy about teaching operations is seeing that lightbulb moment when students realize how relevant and impactful this field can be. It’s especially rewarding when students come up to me after class and say, “I didn’t think operations could be this interesting,” and some even tell me they’re considering a career in operations because of what they learned. Even for those who don’t pursue it as a career, I hope my class helps them see how much operations touch everything around us and how important it is in whatever field they end up choosing.

Many students—especially those from cultures that emphasize “safe careers”—hesitate to pursue research-heavy fields. What advice would you give to students who are passionate about research but feel uncertain about its career prospects?

I can only speak from my own experience, and I will not try to generalize too much. After college, I was on what I thought was a safe path: an engineering role in a large organization after completing my undergraduate and master’s degrees in engineering. But the 2009 financial crisis hit in the early days of my career. While I was fortunate not to be laid off, many around me were, and jobs suddenly became scarce. It made me realize that I had probably underestimated the risks in what I thought was a stable career.

I think that’s true for career decisions in general. We often underestimate the risks in traditional “safe” paths and overestimate the risks in alternative ones. My suggestion to students who are passionate about research, or really any path that feels less conventional,  is to focus on building strong skills and knowledge. Don’t get too locked into one idea of what your career has to look like. Be open and agile enough to pivot toward opportunities that excite you and where your skills can add value, even if they don’t fit into the typical mold.

Of course, financial realities are important, and I’m not saying to ignore them. But even with those constraints, I believe there’s room to explore paths that are less traditional but still fulfilling and sustainable. Often, the best careers come from finding that intersection of what you’re good at, what excites you, and where you can make an impact, rather than simply following what seems safe from the outside.

You’ve worked with top institutions like UCLA, UNC, and Veterans Health Administration. What’s one skill or mindset shift you think students need to succeed in today’s fast-changing job market?

This is something I have slowly come to realize myself. Every few years, a new trend seems to take over the job market. When I was doing my PhD, it was Big Data. A few years later, it was blockchain. Then came machine learning, and now it is generative AI. It is easy to get caught up in the excitement around the latest tool or technology. And while these technologies have definitely changed how we work, and it is important to stay informed, we have to remember that they are just tools.

The real focus should be on solving meaningful problems. Making healthcare more affordable, improving access to financial services, and helping organizations become more sustainable are the kinds of problems that bring real value to people and society. The tools we use to solve those problems will keep changing, but the problems themselves stay relevant.

My advice to students is to keep up with the latest technologies but stay focused on the problem you are trying to solve. The problem should always come first, and the tools you use come second. If you work with that mindset, you will not feel like you are being pushed around by every new trend. Instead, you will build a strong foundation that stays relevant no matter what the next big thing is.

 With AI and data-driven decision-making becoming more dominant, how do you see technology shaping the future of operations management—especially in industries like healthcare and education?

As computing power and tools like AI become more accessible, organizations have moved toward making their decision-making more data-driven. The role of the “data scientist” has really evolved in the past 10 to 15 years. Now, every major organization has people working on helping them use data better in their day-to-day decisions. But I still think we are just scratching the surface. As data infrastructure and data literacy continue to improve, we will see even more gains in efficiency and productivity.

A lot of research in operations management over the past few decades was held back from practical use because organizations either did not have the right data infrastructure or the ability to interpret and use the data. I believe we are going to see much more of this research being put into practice in the coming years.

In healthcare, we now have much better access to detailed data on how care is delivered. This opens up possibilities for more personalized care and more cost-efficient use of resources, whether it is medication management or workforce and capacity planning. As more healthcare decisions become data-driven, I believe we can improve access to care and reduce costs, which is an urgent need globally. A lot of progress has already been made, but there is still so much more to do. I am excited to see how these improvements can reach more people across the world.

In education, I think AI offers a unique opportunity to provide personalized learning at scale. We have heard this promise before with online education, but generative AI feels like an important missing piece. To be honest, I think most educational institutions are still figuring out how to integrate these systems in a reliable way that truly delivers on that promise. But I am hopeful that we will start to see meaningful progress soon.

If you could go back and give your younger self one piece of career advice, what would it be?

I think I would tell myself a quote I came across in Sheryl Sandberg’s book Lean In, where she quotes journalist Pattie Sellers: “Careers are a jungle gym, not a ladder.” Coming out of school, it is easy to think of your career as a straight path where you move from one step to the next, climbing higher. But the reality is often different, and that is not a bad thing. Lateral moves, unexpected shifts, and trying new things that may not fit into a neat, linear plan are all part of building a well-rounded career.

My advice would be to focus on building skills and experiences, and not to be afraid of making those lateral moves when they feel like a better fit or when you believe you can make a bigger impact. Over time, those choices help you build a career that is more closely aligned with who you are and what excites you. They also make you unique and more interesting, both to yourself and to others.

And maybe most importantly, I would tell my younger self not to be too hard on myself. Careers are long, and not every step will feel perfect or certain. It is okay to take detours, make mistakes, and change directions. What matters is that you keep learning, stay curious, and stay open to opportunities that come your way.