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données de santé, health data

Speaking the language of health data to improve its use

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The world of healthcare has extensive databases that are just waiting to be used. This is one of the issues Benjamin Dalmas, a data science researcher at Mines Saint-Étienne, is exploring in his work. His main objective is to understand the origin of this data to use it more effectively. As such, he is working with players from the public and private sectors for analysis and predictive purposes in order to improve management of health care institutions and our understanding of care pathways.

Research has made great strides in processing methods using machine learning. But what do we really know about the information that such methods use? Benjamin Dalmas is a health data science researcher at Mines Saint-Étienne. The central focus of his work is understanding health data, from its creation to its storage. What does this data include? Information such as the time of a patient’s arrival and discharge, exams carried out, practitioners consulted etc. This data is typically used for administrative and financial purposes.

Benjamin Dalmas’s research involves identifying and finding a straightforward way to present relevant information to respond to the concrete needs of public and private healthcare stakeholders. How can the number of beds in a hospital ward be optimized? Is it possible to predict the flow of arrivals in an emergency room? The responses to these problems rely on the same information: the medical administrative data produced every day by hospitals to monitor their patient pathways.

However, depending on the way in which it is considered, the same data can provide different information. It is the key witness to several investigations. So it must be approached in the right way to get answers.

Understanding data in order to prevent bias

Since it is primarily generated by humans, health data may be incorrect or biased. By focusing on its creation, researchers seek to identify the earliest potential bias. Benjamin Dalmas is working with Saint-Étienne University Hospital Center to study the codes assigned by the hospital upon a patient’s discharge. These codes summarize the reason for which the individual came to the hospital and received care. Doctors who specialize in this coding generate up to 16,000 different codes, a tedious task, for which the hospital wishes to seek assistance from a decision support tool to limit errors. “That means we must understand how humans code. By analyzing large quantities of data, we identify recurring errors and where they come from, and we can solve them,” explains Benjamin Dalmas. Greater accuracy means direct economic benefits for the institution.

However, this mass-produced data is increasingly used for other purposes than reimbursing hospitals. For the researcher, it is important to keep in mind that the data was not created for these new analyses. For example, he has noticed that such a straightforward notion as time may hide a number of different realities. When a consultation time is specified, it may mean one of three things: the actual time of consultation, the time at which the information was integrated in the file, or a time assigned by default. Since the primary objective of this information is administrative, the consultation time does not have a lot of importance. “If we don’t take the time to study this information, we run the risk of making biased recommendations that are not valid. Good tools cannot be created without understanding the data that fuels them,” says the researcher. Without this information, for example, a study focusing on whether or not social inequalities exist and taking into account how long a patient must wait before receiving care, could draw incorrect conclusions.

From reactive to proactive

So researchers must understand the data, but for what purpose? To predict, in order to anticipate, rather than just react. The development of predictive tools is the focus of a collaboration between Mines Saint-Étienne researchers and the company Move in Med. The goal is to anticipate the coordination of care pathways for breast cancer patients. In the case of chronic diseases such as cancer, the patient pathway is not limited to the hospital but also depends on a patient’s family, associations etc. To this end, the researchers are cross-referencing medical data with other social information (age, marital status, socio-economic background, place of residence etc.). Their aim is to identify unexpected factors, in the same way in which the weather, air quality and the even the occurrence of cultural events impact periods of peak arrival in emergency rooms. Predicting the complexity of a care pathway allows the company to allocate the appropriate resources and therefore ensure better care.

At the same time, the Auvergne Rhône-Alpes Regional Health Agency has been working with the researchers since May 2020 to predict hospital capacity strain levels for Covid arrivals. By reporting visual data based on systems of colors and arrows, the researchers provide information about changing dynamics and levels of hospital capacity strain in the region (Covid patient arrivals, positive PCR tests in the region, number of available beds etc.) In this work, researchers are tackling monitoring trends. How are these parameters evolving over time? At what threshold values do they alert the authorities that the situation is getting worse? To answer these questions, the research team provides maps and projections that the health agency can use to anticipate saturation and therefore prevent institutions from becoming overwhelmed, arrange for patients to be transferred etc.

Finding the right balance between volume and representativeness

The study of data raises questions about volume and representativeness, which depend on the user’s request. Proving without equipping oneself requires more data in order to fuel machine learning algorithms. “However, recovering public health data is quite an ordeal. We have to follow protocols that are highly regulated by the CNIL (the French Data Protection Authority) and ethics committees to justify the volume of data requested,” explains Benjamin Dalmas. On the other hand, a request for operational tools must be able to adapt to the on-the-ground realities faced by practitioners. That means working with limited amounts of information. It is a matter of finding the right balance.  

The Mines Saint-Étienne researchers are working with the Saint-Étienne-based company MJ INNOV on these aspects. The company offers an interactive facilitation tool to improve quality of life for individuals with cognitive impairments. Based on videos and sounds recorded during the stages of play, this research seeks to identify the impact of the practice on various subjects (nursing home residents, persons with Alzheimer’s disease etc.). In addition to using the information contained in residents’ files, this involves collecting a limited quantity of new information. “In an ideal world, we would have 360° images and perfect sound coverage. But in practice, to avoid disturbing the game, we have to plan on placing microphones under the table the patients are playing on, or fitting the camera directly within the inside of the table. Working with these constraints makes our analysis even more interesting,” says Benjamin Dalmas.

Measuring the impact of healthcare decision support tools

In the best-case scenario, researchers successfully create a decision support tool that is accessible online. But is the tool always adopted by the interested parties? “There are very few studies on the ergonomics of tools delivered to users and therefore on their impact and actual use,” says Benjamin Dalmas. Yet, this is a crucial question in his opinion, if we seek to improve data science research in such a concrete area of application as healthcare.  

To this end, an appropriate solution often means simplicity. First of all, by being easy-to-read: color schemes, shapes, arrows etc. Visualization and interpretation of data must be intuitive. Second, by promoting explainability of results. One of the drawbacks of machine learning is that the information provided seems to come from a black box. “Research efforts must now focus on the presentation of results, by enhancing communication between researchers and users,” concludes Benjamin Dalmas.

By Anaïs Culot

Read more on I’MTech: When AI helps predict a patient’s care pathway

personnel hospitalier

Do hospital staff feel prepared?

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Marie Bossard, a specialist in the social psychology of health, has been studying the feeling of preparedness among hospital staff in the face of exceptional health situations in her PhD since 2018. She explores the factors that may influence this feeling to better understand the dynamics of preparation in health systems.

The Covid-19 crisis is a case in point: our care system must sometimes confront exceptional health situations. Hospital staff are trained to respond to such situations, but there is little scientific literature on the way in which those concerned perceive their preparation. So how do caregivers, medical doctors, administrative staff and medical center directors feel in the face of these exceptional situations? This is the subject of Marie Bossard’s PhD at IMT Mines Alès and the University of Nîmes.

When she began her work in 2018, the Covid-19 crisis and pandemics were not yet a major concern. Exceptional health situations include anything that goes beyond the usual functioning of healthcare services. “We originally had in mind the emergency services being overwhelmed after an attack”, explains Gilles Dusserre, a researcher in risk sciences at IMT Mines Alès and joint supervisor of Marie Bossard with Karine Weiss at the University of Nîmes. Whatever the cause, this research fits into a global reflection on the current problems in emergency medicine. This is what the researchers want to understand better in order to provide operational responses to special users or hospital staff.

The feeling of “preparedness“

“The idea is to start with the individual and study how each person perceives his or her level of preparedness, and then develop these reflections on a collective scale,” says Marie Bossard. The aim is to measure the feeling of “preparedness” and identify the factors that influence it, as well as to apply psychosocial models to the level of preparedness of hospital staff. The PhD student is exploring the social representations of hospital staff through interviews with medical doctors, paramedics, health executives and administrative employees in different French university hospitals.

“We can differentiate the feeling of preparedness, the perception of our preparation, and the reported preparation”, explains Marie Bossard. If hospital staff consider that exceptional health situations are only linked to an attack, for example, they might never be prepared for a fire,” she continues.

And, although the preparation received has an influence on the feeling of preparedness, she insists that “there are many other aspects to take into account. The feeling of self-efficacy is important, in particular.” This psycho-social concept represents, in a way, the power to act: the individual perception of having sufficient skills to manage a situation and knowing how to apply them. The perception of preparation, whether positive or negative, also affects the feeling of preparedness. The role of the collective is also undeniable. “A common response is that, individually, the person doesn’t feel ready, but they still have confidence in the collective, she adds. There’s a certain resignation”, says the joint PhD supervisor. “Hospital systems are already going through a difficult time and are coping, so collectively they feel capable of facing one more challenge.”

In a second phase, the aim is to propose hypotheses on the structure and content of these social representations. For example, health executives do not give the same type of spontaneous responses as paramedics when asked to list words in connection with exceptional health situations. The former generally talk about the practice of preparation (logistics, influx), while the second generally mention everyday examples or emotion (danger, serious, disaster).

The context of the Covid crisis

Given that the development of an exceptional health situation was completely unforeseeable, it initially seemed impossible to carry out a field study. However, the pandemic caused by the new coronavirus in early 2020 provided a characteristic field of study for the researchers. Marie Bossard and her joint supervisors reorganized their methodology and two new studies were prepared. The first before the arrival of the virus in France, which studied the preparedness of more than 400 participants among personnel and collectives. The second after the first peak of the epidemic and before a potential second wave, which was still an uncertainty at the time. The questionnaires from the study carried out among 534 participants provide a comparison between the feeling of readiness before and after Covid-19.

The post-Covid study confirmed that the feeling of preparedness depends on psycho-social variables and not just the level of preparation. Age and years of professional experience also influence this feeling, as do the profession and any previous experience of managing an exceptional health situation. These are individual variables, but the role of the collective was also confirmed. “The more ready and prepared others are, the higher the perception of personal preparedness, says Marie Bossard. Similarly, perceiving the hospital as ready, with sufficient human and material resources, has a great influence.” The PhD student is currently studying the results of the latest study conducted in September.

The situation, although difficult, provides “a context for the answers given during the first interviews,” says the PhD student. For example, it confirms that all hospital staff are involved, not just those considered on the front line. Indeed, the mobilization affects every hospital department. She admits that “the Covid-19 health crisis has given us a new perspective on this PhD subject, which is now topical and concretely demonstrates the need for a better understanding in this field“. It is also an opportunity to explore the effect of this exceptional health situation on the feeling of preparedness among those first concerned and the factors that influence this feeling with a concrete application of the subject.

“We haven’t found any previous studies that have explored this subject from the same angle, says Marie Bossard. We’re starting from scratch. The aim is to remain as open-minded as possible to identify initial indicators, and then dig deeper into more specific questions,” she concludes. It could lead to new studies, for example to understand why the feeling of auto-efficacy plays such an important role in the feeling of preparedness.

 Tiphaine Claveau