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Hospitals put to the test by shocks

Benoît Demil, I-site Université Lille Nord Europe (ULNE) and Geoffrey Leuridan, IMT Atlantique – Institut Mines-Télécom

The Covid-19 crisis has put lasting strain on the health care system, in France and around the world. Hospital staff have had to deal with increasing numbers of patients, often in challenging conditions in terms of equipment and space: a shortage of masks and protective equipment initially, then a lack of respirators and anesthetics, and more recently, overloaded intensive care units.

Adding to these difficulties, logistical problems have exacerbated shortage problems. Under these extreme conditions, and despite all the difficulties, the hospital system has withstood and absorbed the shock of the crisis. “The hospital system did not crack under pressure,” as stated by Étienne Minvielle and Hervé Dumez, co-authors of a report on the French hospital management system during the Covid-19 crisis.

While it is unclear how long such a feat can be maintained, and at what price, we may also ask questions about the resilience and reliability of the health care system. In other words, how can care capacity be maintained at a constant quality when the organization is under extreme pressure?

We sought to understand this in a study conducted over 14 months during a non-Covid period, with the staff of a critical care unit of a university hospital center.

High reliability organizations

The concepts of resilience and reliability, which have become buzzwords in the current crisis, have been studied extensively for over 30 years in organizational science research  – more particularly those focusing on High Reliability Organizations (HRO).

This research has offered insights into the mechanisms and factors that enable complex sociotechnical systems to maintain safety and a constant quality of service, although the risk of failure remains possible, with serious consequences.

The typical example of an HRO is an aircraft carrier. We know that deference to expertise and skills within a working group, permanent learning routines and training explain how it can ensure its primary mission over time. But much less is known about how the parties involved manage the resources required for their work, and how this management affects resilience and reliability.

Two kinds of situations

In a critical care unit, activity is continuous but irregular, both quantitatively and qualitatively. Some days are uneventful, with a low number of patients, common disorders and diseases, and care that does not present any particular difficulties. The risks of the patients’ health deteriorating are of course still present, but remain under control. This is the most frequently-observed context: 80 of the 92 intervention situations recorded and analyzed in our research relate to such a context.

At times, however, activity is significantly disrupted by a sudden influx of patients (for example, following a serious automobile accident), or by a rapid and sudden change in a patient’s condition. The tension becomes palpable within the unit, movements are quicker and more precise, conversations between health care workers are brief and focused on what is happening.

In both cases, observations show differentiated management of resources, whether human, technical or relating to space. To understand these differences, we must draw on a concept that has long existed in organizational theory: organizational slack, which was brought to light in 1963 by Richard Cyert and James March.

Slack for shocks

This important concept in the study of organizations refers to excess resources in relation to optimal operations. Organizations or their members accumulate this slack to handle multiple demands, which may be competing at times.

The life of organizations offers a multitude of opportunities for producing and using slack. Examples include the financial reserves a company keeps on hand “just in case”, the safety stock a production manager builds up, the redundancy of certain functions or suppliers, the few extra days allowed for a project, oversized budgets negotiated by a manager to meet his year-end targets etc. All of these practices, which are quite common in organizations, contribute to resilience in two ways.

First, they make it possible to avoid unpredictable shocks, such as the default of a subcontractor, an employee being out on sick leave,  an unforeseen event that affects a project or a machine breaking down. Moreover, in risk situations, they prevent the disruption of the sociotechnical system by maintaining it in a non-degraded environment.

Second, these practices absorb the adverse effects of shocks when they arise unexpectedly – whether due to a strike or the sudden arrival of patients in an emergency unit.

How do hospitals create slack?

Let us first note that in a critical care unit, the staff produces and uses slack all the time. It comes from negotiations that the head of the department has with the hospital administration to obtain and defend the spaces and staff required for the unit to operate as effectively as possible. These negotiations are far from the everyday care activity, but are crucial for the organization to run effectively.

At the operational level, health care workers also free up resources quickly, in particular in terms of available beds, to accommodate new patients who arrive unexpectedly.  The system for managing the order of priority for patients and their transfer is a method commonly used to ensure that there is always an excess of available resources.

In most cases, these practices of negotiation and rapid rotation of resources make it possible for the unit to handle situations that arise during its activity. At times, however, due to the very nature of the activity, such practices may not suffice. How do health care workers manage in such situations?

Constant juggling

Our observations show that other practices offset the temporary lack of resources.

Examples include calling in the unit’s day staff as well as night staff, or others from outside the unit to “lend a hand”, reconfiguring the space to create an additional bed with the necessary technical equipment or negotiating a rapid transfer of patients to other departments.  

This constant juggling allows health care workers to handle emergency situations that may otherwise overwhelm them and put patients lives in danger. For them, the goal is to make the best use of the resources available, but also to produce them locally and temporarily when required by emergency situations.

Are all costs allowed?

The existence of slack poses a fundamental problem for organizations – in particular those whose activity requires them to be resilient to ensure a high degree of reliability. Keeping unutilized resources on hand “just in case” goes against a managerial approach that seeks to optimize the use of resources, whether human, financial or equipment  – as called for by New Public Management since the 1980s, in an effort to lower the costs of public services.

This approach has had a clear impact on the health care system, and in particular on the French hospital system over the past two decades, as the recent example of problems with strategic stocks of masks at the beginning of the Covid pandemic unfortunately illustrated .

Beyond the hospital, military experts have recently made the same observation, noting that “economic concerns in terms of defense, meaning efficiency, are a very recent idea,” which “conflicts with the military notions of ‘reserve,’ ‘redundancy’ and ‘escalation of force,’ which are essential to operational effectiveness and to what is now referred to as resilience.”

Of course, this quest for optimization does not only apply to public organizations. But it often goes hand in hand with greater vulnerability of the sociotechnical systems involved. In any case, this was observed during the health crisis, in light of the optimization implemented at the global level to reduce costs in companies’ supply chains. 

To understand this, one only needs to look at the recent stranding of the Ever Given. Blocked for a week in the Suez Canal, this giant container paralyzed 10% of global trade for a week. What lessons can be learned  from this?

A phenomenon made invisible in emergencies

First of all, it is important for organizations aiming for high reliability to keep in mind that maintaining slack has a cost, and that that they must therefore identify the systems or sub-systems for which resilience must absolutely be ensured.  The difference between slack that means wasting resources and slack that allows for resilience is a very fine line.

Bearing this cost calls for education efforts, since it must not only be fully agreed to by all of the stakeholders, but also justified and defended.

Lastly, the study we conducted in a critical care unit showed that while slack is produced in part during action, it disappears once a situation has stabilized. 

This phenomenon is therefore largely invisible to managers of hospital facilities. While these micro-practices may not be measured by traditional performance indicators, they nevertheless contribute significantly: this might not be a new lesson, but it is worth repeating to ensure that it is not forgotten.

Benoît Demil, professor of strategic management, I-site Université Lille Nord Europe (ULNE) and Geoffrey Leuridan, research professor, IMT Atlantique – Institut Mines-Télécom

This article has been republished from The Conversation under a Creative Commons license. Read the  original article (in French).

Digital innovations in health

Innovation in health: towards responsibility

Digital innovations are paving the way for more accurate predictive medicine and a more resilient healthcare system. In order to establish themselves on the market and reduce their potential negative effects, these technologies must be responsible. Christine Balagué, a researcher in digital ethics at Institut Mines-Télécom Business School, presents the risks associated with innovations in the health sector and ways to avoid them.

Until now, the company has approached technology development without looking at the environmental and social impacts of the digital innovations produced. The time has come to do something about this, especially when it comes to human lives in the health sector”, says Christine Balagué, a researcher at Institut Mines-Telecom Business School and co-holder of the Good in Tech Chair [1]. From databases and artificial intelligence for detecting and treating rare diseases, to connected objects for monitoring patients; the rapid emergence of tools for prediction, diagnosis and also business organization is making major changes in the healthcare sector. Similarly, the goal of a smarter hospital of the future is set to radically change the healthcare systems we know today. The focus is on building on medical knowledge, advancing medical research, and improving care.

However, for Christine Balagué, a distinction must be made between the notion of “tech for good” – which consists of developing systems for the benefit of society – and “good in tech”. She says “an innovation, however benevolent it may be, is not necessarily devoid of bias and negative effects. It’s important not to stop at the positive impacts but to also measure the potential negative effects in order to eliminate them.” The time has come for responsible innovation. In this sense, the Good in Tech chair, dedicated to responsibility and ethics in digital innovations and artificial intelligence, aims to measure the still underestimated environmental and societal impacts of technologies on various sectors, including health.

Digital innovations: what are the risks for healthcare systems?

In healthcare, it is clear: an algorithm that cannot be explained is unlikely to be commercialized, even if it is efficient. Indeed, the potential risks are too critical when human lives are at stake. However, a study published in 2019 in the journal Science on the use of commercial algorithms in the U.S. health care system demonstrated the presence of racial bias in the results of these tools. This discrimination between patients, or between different geographical areas, therefore gives rise to an initial risk of unequal access to care. “The more automated data processing becomes, the more inequalities are created,” says Christine Balagué. However, machine learning is increasingly being used in the solutions offered to healthcare professionals.

For example, French start-ups such as Aiintense, incubated at IMT Starter, and BrainTale use it for diagnostic purposes. Aiintense is developing decision support tools for all pathologies encountered in intensive care units. BrainTale is looking at the quantification of brain lesions. These two examples raise the question of possible discrimination by algorithms. “These cases are interesting because they are based on work carried out by researchers and have been recognized internationally by the scientific peer community, but they use deep learning models whose results are not entirely explainable. This therefore hinders their application by intensive care units, which need to understand how these algorithms work before making major decisions about patients,” says the researcher.

Furthermore, genome sequencing algorithms raise questions about the relationship between doctors and their patients. Indeed, the limitations of the algorithm, the presence of false positives or false negatives are rarely presented to patients. In some cases, this may lead to the implementation of unsuitable treatments or operations. It is also possible that an algorithm may be biased by the opinion of its designer. Finally, unconscious biases associated with the processing of data by humans can also lead to inequalities. Artificial intelligence in particular thus raises many ethical questions about its use in the healthcare setting.

What do we mean by a “responsible innovation”? It is not just a question of complying with data processing laws and improving the health care professional’s way of working. “We must go further. This is why we want to measure two criteria in new technologies: their environmental impact and their societal impact, distinguishing between the potential positive and negative effects for each. Innovations should then be developed according to predefined criteria aimed at limiting their negative effects,” says Christine Balagué.

Changing the way innovations are designed

Liability is not simply a layer of processing that can be added to an existing technology. Thinking about responsible innovation implies, on the contrary, changing the very manner in which innovations are designed. So how do we ensure they are responsible? Scientists are looking for precise indicators that could result in a “to do list” of criteria to be verified. This starts with the analysis of the data used for learning, but also by studying the interface developed for the users, through the architecture of the neural network that can potentially generate bias. On the other hand, existing environmental criteria must be refined by taking into account the design chain of a connected object and the energy consumption of the algorithms. “The criteria identified could be integrated into corporate social responsibility in order to measure changes over time,” says Christine Balagué.

In the framework of the Good In Tech chair, several research projects, including a thesis, are being carried out on our capacity to explain algorithms. Among them, Christine Balagué and Nesma Houmani (a researcher at Télécom SudParis) are interested in algorithms for electroencephalography (EEG) analysis. Their objective is to ensure that the tools use interfaces that can be explained to health care professionals, the future users of the system. “Our interviews show that explaining how an algorithm works to users is often something that designers aren’t interested in, and that making it explicit would be a source of change in the decision-making process,” says the researcher. The ability to explain and interpret results are therefore two key words guiding responsible innovation.

Ultimately, the researchers have identified four principles that an innovation in healthcare must follow. The first is anticipation in order to measure the potential benefits and risks upstream of the development phase. Then, a reflexive approach allows the designer to limit the negative effects and to integrate into the system itself an interface to explain how the technological innovation works to physicians. It must also be inclusive, i.e. reaching all patients throughout the territory. Finally, responsive innovation facilitates rapid adaptation to the changing context of healthcare systems. Christine Balagué concludes: “Our work shows that taking into account ethical criteria does not reduce the performance of algorithms. On the contrary, taking into account issues of responsibility helps to promote the acceptance of an innovation on the market”.

[1] The Chair is supported by the Institut Mines-Télécom Business School, the School of Management and Innovation at Sciences Po, and the Fondation du Risque, in partnership with Télécom Paris and Télécom SudParis.

Anaïs Culot

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