Helping Organizations Make Better Decisions

In a time where private sector efficiency is often synonymous with profit, the public sector faces a different challenge: maximizing access to services with limited resources. How can "the science of decision-making" be used to save time and resources, and better society as a whole? 

In this interview, Vedat Verter, professor and Stephen J.R. Smith Chair of Management Analytics, explores the differences between public and private sector objectives and explains the role operations research plays in bridging that gap. He argues for the necessity of moving from transactional relationships to strategic collaborations between government and industry.

Drawing on research, he explains his passion for applying operations research to societal challenges. For example, he highlights how it can optimize hospital footprints for nursing efficiency, as well as support strategic data use to increase participation in upstream health interventions such as cancer screening programs.

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Vedat Verter

00:06 How does operations research help organizations make better decisions?

Operations research can be defined as the science of making decisions. It is a family of quantitative methods that enable us to abstract problems from reality, and then work on those problems in mathematical form, and to identify the most appropriate solution for the problem at hand. I believe in having a passion when you pursue your research challenges, and it's just I'm more passionate working on societal problems rather than maximizing somebody else's revenue.

00:45 How has your research contributed to reducing the burden on nursing staff? 

Our research on the use of real-time location systems in a hospital was based on the fact that we wanted to follow care providers and collect data in a hospital setting, particularly in inpatient wards. Now, the important issue is that in the emergency department things happen in the order of minutes, but that nothing will happen for a couple of days. So, the classical method of putting an observer there to collect data would not work. This is why we used the latest technology systems in tracking people indoors that gave us data about the nurses’ activities in terms of how much time they spend with the patients, for example. 

The issue was that the surgical services department of the hospital was moving to a new pavilion with a four times larger footprint. Therefore, the assignment of patients to nurses became an important issue because if you don't do that carefully, there's a possibility that the nurses spend more time walking than providing care to patients. We have run some analysis in terms of the improvement, and there was a 40 per cent increase in the direct care time the nurses could allocate under the improved nurse patient assignment.

02:15 What’s a real-world example of data-driven decision-making? 

The problem that we looked at was a collaboration with the Ministry of Health in Quebec, and that involved the subsidy program that they were establishing for preventive breast cancer screening. At the time, there were already 40 imaging s in Montreal, most of which were private. There was an issue of recruiting these centres to the program, and they already knew that there wasn't sufficient demand to recruit all 40 of them to the program, so the Ministry wanted to know which centres they should be recruiting. Their objective was to maximize the number of women who would participate in the breast cancer screening every other year, women ages of 50 to 69, so that they can catch early breast cancer episodes.

We approached the problem using operational research. We worked with them to identify 20 out of 40 centres who would be recruited for their program, and they actually approached them and recruited 18 of the 20 that we recommended and captured a 50% per cent participation rate. I think that's one of the better studies that we have done in terms of having a real-life impact. 

03:38 What’s different about using operations research in the public vs. private sector?

The public sector and private sector have a number of similarities and a number of important differences. One of the important differences is the objectives that you're pursuing. In private sector, it's often maximizing the profit, maximizing revenue or shareholder value. But in the public sector, you're not providing services, typically, to make revenue. You would like to actually maximize the access to the services that you're providing. 

It is important to recognize that the businesses and the public sector actually do not necessarily have aligned objectives. So the key to these two sectors actually collaborating is No. 1, to go from a transactional relationship to a more strategic partnership; in other words, not pursuing a government subsidy on the basis of a single project. And secondly, to the extent that you could do that, then you can actually bring together the strengths of both sectors. 

Each sector has its own different types of data that they keep, and so therefore, bringing together data infrastructure would be very, very helpful in making more informed decisions. But I think the key issue is to identify a set of common objectives that they can align their interests in and also agree on certain measurements as to how those objectives are being measured. If that is not done, then the way we operate is that the government makes regulations and industry tries to find loopholes in those regulations to operate the way they want to operate, and that is not a very sustainable approach.