Healthcare Professions And Jobs Going Digital

Aida Ponce Del Castillo

With the advent of new technologies, the healthcare sector is confronted with an unprecedented wave of change. What will the sector look like in a few years time? How can healthcare workers navigate an environment that is increasingly digital, data-dependent and interconnected?

For the general public, access to medical “knowledge” has skyrocketed these past years. In 2017, 325,000 health apps were available worldwide, allowing patients and medical professionals to infuse a more preventive pattern of behaviour and patients to share their data with others, data companies or healthcare systems. According to the 2017 European Commission’s communication ‘A Connected Digital Single Market’, patients will increasingly be able to transfer their medical data history via an Electronic Health Record, but also have the possibility to interact with and provide feedback to healthcare providers. General practitioners are literally becoming more digital every day. In minutes, patients can consult and meet with a ‘digital doctor’ via their mobile phone, using for example the KRY app or Doctors on Demand.

Other tech tools are available to doctors, surgeons, nurses and technicians, for both diagnosis and medical treatment purposes. Healthcare companies are having to transform their business models to be able to collect and manage data. All of this is affecting professions and redefining jobs. We need to think about how the workforce is involved in re-defining their job, and how their data privacy and rights are protected. Here we outline key issues about the transformation of healthcare professions, which also impacts the wider public. Let us look at three concrete examples:

Workers wearing exoskeletons. Exoskeletons such as the ‘power assist suit’ can be worn by nurses and physiotherapists to lift and carry patients mechanically and with less effort. This may look ideal, but the question is whether the task will be more efficient by wearing these systems overall. Such systems tend to focus on one precise objective but forget about working conditions. The worker’s perspective must be taken into account, as researchers point out that such exoskeletons may cause discomfort in certain parts of the body and result in possible injuries. Exoskeletons can also shift the centre of balance, thereby limiting the worker’s ability to move quickly in case of emergency. Researchers suggest looking into some key design aspects: transition into using this type of robots/equipment, physical-user-interface, biomechanics, anatomy of female and male bodies. Equally important, safety standards in using exoskeletons need to be implemented at the workplace (Zingman and colleagues and Looze et al 2016).

Human-robot interaction. A second example is about nurses and pharmacists supporting a robot that interfaces with hospital pharmacy systems and prepares medications in a precise dosage. In this case, the machine replaces nurses by robotizing the preparation of oncology drugs, a process which may be hazardous for nurses exposed to them. Researchers report that nurses and pharmacists have played a key role in the development of the so-called APOTECA chemo robot, by helping to identify and correct system errors, thus ensuring that the robot would work accurately (Palma, et al 2012). Further work is needed to analyse the new tasks and jobs available to nurses and pharmacists after the implementation of the robotic system and whether workers still interact with and influence the robot software, by either inputting, collecting, analysing or correcting data and whether their information is being kept anonymous.

Data workers or information managers. According to IBM, medical imaging makes up more than 90% of all medical data. Since 2013, deep learning is being used as a methodology for automated reading, classification, detection and image segmentation of organs, bones and tissues, in order to make predictions (Lijtgens et al 2017). How will humans deal with and manage such data (raw or analysed)? And how will they possibly do so together with a machine?

The use of digital imaging in cardiology, oncology, ophthalmology, pathology, nuclear medicine is increasing. The 2018 European Congress of Radiology heard that the profession is changing and uses increasingly sophisticated decision support tools. New skills are being incorporated, related to deep learning, biomedical imaging, mathematical modelling, informatics and engineering. Radiologists are becoming more and more data communicators and facing a re-definition of their roles within automated systems, and they still play a meaningful role as they make sense of the data sets and train systems, contrary to the prediction made by professor of computer science at the University of Toronto Geoffrey Hinton.

What is more worrying is the fact that professionals could become subjected to surveillance through the digital technologies they will be using. Workers who interact with robotic and AI systems need more understanding about data collection and use, in particular personal data. To illustrate this, in Liguria, Italy nurses have complained against the microchip inserted on their hospital clothes to control cloth washing. Nurses have argued this was an illegal way to control them and an invasion of their privacy. In short, they felt they were being spied on. To deal with all this, a better understanding of the General Data Protection Regulation (GDPR) rules on responsibility and accountability is needed. Ensuring workers’ rights under GDPR requires a transformation of how they work and of the work organisation.

Here are ways in which the transition to a more digitised work could happen. In particular, it should include the following:

As business models in healthcare organisations (hospitals) are changing and are producing and processing more and more data, the workforce should be supported by re-skilling, re-training, providing new skills and clarifying tasks, as well as working methods and possible risks. As more AI products are used in healthcare organisations, it will be necessary to identify opportunities for new jobs and tasks for the workforce involved. Likewise, the workers’ experience will need to be “injected” in the system development process.

Secondly, there is a need to adapt the working environment and organisational structure to accommodate robots, AI or deep learning systems. The usual roles and responsibilities of healthcare professionals will change and organisations will have to answer key questions, such as how to deal with situations where workers and robots inter-act and a mistake is made. Who will take decisions and how? What matters is what workers can do with the knowledge and understanding they acquire of these elements. This can involves identifying who is responsible for a given decision (accountability), receiving an explanation about the decision-making process (transparency), to correct it for the future, or even possibly legally contesting a given decision.

Finally, ‘deep awareness’ of data protection and the new sets of rights under GDPR needs to reach each individual worker. Workers need to know when their data is compromised, whether there could be issues for discrimination or surveillance at the workplace, misuse of their information or invasion of worker’s privacy.

Some of the ideas described here were presented in a panel discussion on ‘Privacy, healthcare and human-robot interaction’ at the 11^th International Conference ‘Computers, privacy and data protection 2018: The internet of bodies’, in Brussels, Belgium, on January 26 2018.