What do health problems have to do with engineering and logistics?

For a few years now, a degree course in Engineering and Management for Health has been created at the University of Bergamo, where Albachiara and I work. As part of the Master’s degree, from September 2020, I have been an adjunct lecturer on the Healthcare Logistics Management course.

Many friends and relatives ask me what logistics and engineering, in general, have to do with health. Yet, as a good logistics nerd, it happens to me to find myself in some medical-hospital situations and experience the consequences of a lack of good management. This is because when we find ourselves standing in never ending queues in waiting rooms for a specialistic visit or a blood sample (or worse, in an emergency room), when we are put off an operation that has been scheduled for months at the last minute, or when we book an appointment for a check-up that is set for the following year, it is not necessarily a medical problem. It is true that in Italy, the number of doctors is underestimated with respect to the population. It is also true that one can always run into the unexpected. Still, in most cases, the cause of the problems listed above is an organisational problem. In particular, in the cases mentioned, these are problems related to queue management and staff scheduling and operations, which are pretty common at a company level.

The management of anti-covid vaccines strongly demonstrated the need to address public health problems from this perspective. Estimating stocks, differentiating suppliers, choosing suppliers, organising distribution (and the consequent need for dedicated infrastructure and personnel) are issues whose complexity has probably been underestimated.

In reality, all these are typical problems of logistics and have been studied for years. So what makes these problems, particularly when it comes to healthcare? When I started to study these subjects, I found two main characteristics that make healthcare logistics problems particularly complex: the presence of many stakeholders and more restrictive constraints.

The first element was already present in the subject I did my PhD, namely urban logistics. Whenever we have so many stakeholders in the field, the problems become more complicated, especially if there is an important role played by the public sector because this usually means dealing not only with different interests and opinions of stakeholders but also with laws, procedures and regulations that are not always easy to interpret. Although I always tell my students that 70% of the logistical problems I have faced in my almost ten-year career are because people do not talk to each other. Healthcare is no exception. Just as the different sectors of a company don’t talk to each other, neither do the hospital’s various departments. And often it is not only people who do not speak to each other but also information systems. As shown in Figure 1, the system is complex; first of all, because, although health care is in the National Government’s hands, the regions are the actors that play the decisive public role. And while this autonomy, on the one hand, favours health management territoriality, it can also become a problem. In Italy, both in emergency and vaccines management, each region has organised itself differently, leading to significant disparities at a national level, delays, and a great deal of confusion.

However, the management of different stakeholders’ interests can be significantly improved through appropriate management strategies and generally improving communication between stakeholders. Improving communication does not always mean increasing the amount of information but making its exchange more efficient. For example, as far as Italy is concerned, standardising the information systems used in clinics and hospitals at a regional level could be an excellent first step.

Figure 1 – Stakeholders and flows involved in the Italian national health system.
Source: https://www.researchgate.net/publication/285194832_Italy_Health_system_review

The second element of complexity is more subtle to understand. In the mathematical models most commonly used in logistics, we are used to have a certain number of constraints that are usually linked to the field’s resources and consequently to costs. In the trade-offs generated between costs and service levels, there is a tendency to seek a good balance between the two, favouring the former (efficiency) over the latter. This is rarely the case in models dedicated to healthcare logistics. This is because the service level plays a fundamental role and neglecting to consider that it costs money and often lives.

A typical example of this is choosing an emergency room’s optimal location or a departure hub for ambulances and helicopters for a helicopter rescue. The choice must be such that a high level of service and 100% patient coverage can be guaranteed. For example, I may decide to locate ambulance departure points in a province so that any citizen can be reached by a vehicle in less than 10 minutes. This will result in a lot of hubs and therefore high costs. Still, suppose I decide to locate fewer hubs and save money. In that case, I am decreeing that some citizens will be reached in longer time, which can make a difference, especially for some diseases such as heart attacks or strokes.

So you have to choose the constraints correctly. You have to do careful analyses of all the elements that go into a model to precisely understand the variables and factors and the relationships between them. And performing this activity, which is basically whenever a mathematical model of a problem is to be created, can become particularly critical if there is little data available. Quite often data is not integrated, it comes from different sources, it is in multiple formats, and the stakeholders are not cooperative. Data collection is, therefore, an essential step in building effective and comprehensive models.

In future articles here on Young SCholars, we will try to explore some of the main problems addressed by health logistics, the models adopted, and the main criticisms.

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