Task Automation in Healthcare

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Loose Coupling as a Source of Improvement with Iris Beerepoot 

The complex and variable nature of healthcare work makes alignment of health information systems (HISs) with healthcare processes a major challenge. Because the HIS is not well-aligned with their work practices, caregivers often deviate from the prescribed procedures. These practices of loose coupling are the focus of our research, which includes observations of caregivers in multiple healthcare organisations in the Netherlands. In recent years, several authors have proposed to gather knowledge on loose coupling practices to expose issues and improve work practices. We build on this and propose a systematic approach to capture motivations and consequences of such practices and suggest actions that lead to direct improvement of the work system. Currently, we are looking specifically into two types of loose coupling: those caused by subjectivity and power dynamics.

A Task Framework to Predict the Effects of Automation on Jobs with Jelmer Koorn

The swift advancement of new technologies in the field of artificial intelligence changes the nature of work. Nowadays, even complex tasks can be automated and reliably performed by machines. This new wave of automation has led to an increased interest in predicting the effects of automation on job design. However, the predications diverge to a considerable degree. A fundamental issue underlying these predictions is the question of how to categorize tasks. Some authors simply divide tasks into routine and non-routine tasks, others also consider which kind of cognitive abilities are required. Since the predicted effect of automation directly relates to the categories considered, a sound task framework is essential for useful predictions. Recognizing that existing task models are limited in terms of granularity and time, we use a literature study, interviews, and an analysis of historical data to systemically develop a new task framework for predicting the effects of automation. We conduct an evaluation of our framework to demonstrate the generalizability of the framework and compare the framework with existing models.