Relational Coordination in Emergency Medical Response: The Influence of a Machine-learning Technology Improving Cardiac Arrest Identification

Abstract

It is challenging to coordinate professionals in healthcare, especially given the nature of uncertainty and time-sensitivity in emergency medical response. The fragmentation among different groups and complexity of understanding working towards the same goal makes coordination more difficult. The objective of our research was to gain more insight into how professionals in Copenhagen’s Emergency Medical Services (EMS) anticipate the oncoming of a new technology to help improve their ability to identify cardiac arrest episodes among patients who are the subjects of calls to the emergency line. Further, the research aims to gather their perspectives on how the technology may be used to address challenges related to communication or relationships in the emergency response unit. For the research of our paper, we used Relational Coordination theory developed by Jody Hoffer Gittell, which focuses analyzing dimensions of relationships and communication in the workplace. We conducted qualitative interviews with nurse responders in the 112 phone line within the Copenhagen EMS, and examined our findings with regards to relational coordination between the nurses and other groups with which they coordinate. We further discuss the implications of implementing the assistive cardiac arrest identification technology in the 112 response unit of the EMS and the potential effect on relational coordination among colleagues in the unit. Analysis of the data collected suggests how the respondents may be able to improve the outcomes of their roles as emergency phone line responders who accurately identifying patient symptoms and order ambulances accordingly. This research will not change the dominating institutional forces that shape the EMS unit, but it will help to increase our understanding about the implementation of the technology and provide insights into how to improve job performance by using Relational Coordination in such an organization. It raises ethical concerns regarding the accountability using the machine learning technology to identify the cardiac arrest episode. The aim of thesis is to make an analytical and empirical contribution to the existing Relational Coordination theory by conducting qualitative research. Through the investigation of the employees at the EMS, we aim to figure out how nurses and other employee teams coordinating in response to emergency situations on a regular basis are able to make decisions faster and accurate. As this is a relatively unexplored area, the goal is to be able to make an empirical contribution concerning the relevant topic. Finally, it may also help to explore the feasibility in identifying other medical incidents in the future, in Denmark and possibly worldwide.