1. Develop a robust quantitative framework that will show the probability of automation of tasks within the healthcare sector;
2. Underpin this framework with rich empirical data that places this framework within the actual experiences of stakeholders in the health sector;
3. Report these findings in formats designed for public understanding and for the ability to influence policy in addition to more standard academic outputs.
Computerisation and automation are changing employment practices across all sectors. The rapid progress of the digital age has already seen new technologies being preferred to human labour for a diverse range of tasks, as evidenced by stagnant real median wages and a decreasing share of GDP paid for labour across the OECD. Looking ahead, continued progress in autonomous robots (able to replace truck drivers and hospital porters) and intelligent machine learning algorithms working upon big data (able to substitute for sales assistants and legal secretaries) will disrupt even hitherto protected sectors. In a report with Deloitte, co-I’s Frey and Osborne identified 35% of current UK employment as being at high risk of becoming automatable in the next two decades.
With respect to the NHS, the main concern is not about the loss of jobs due to automation, as might be the case in other industries. The main concern is about shortage of skilled workers and inability to meet future demands, so computerisation and automation are seen by policy makers as opportunities rather than threats. That should not be taken to imply that there isn’t considerable resistance to automation amongst segments of the workforce in the context of a somewhat inflexible system – there undoubtedly is, but it does mean that clarifying the areas where automation could increase productivity and the challenges that must be overcome will be really useful.
This project then turns the lens on the health sector, looking in detail at health provision in the UK and the working tasks that support the provision of healthcare within the NHS, particularly focusing on GP services. At the moment, our understanding of the level and impact of automation within the GP ecosystem is mostly anecdotal. While some health sector automation will undoubtedly involve routine tasks like scheduling and certain lab tasks, automation in the case of healthcare is also likely to involve technologies that are unlike in other sectors and as yet in their infancy: humanoid robots that support interpersonal interaction, algorithms that translate routine but complex tasks into workflows by means of machine learning, or reorganizing GPs’ administrative and diagnostic-support systems to include elements of artificial intelligence (AI). However, a variety of scenario-building and modelling techniques can be combined to anticipate potential challenges and opportunities.
Our central research question is “what is the probability of automation, using a robust quantitative framework, of tasks within the health care sector?” This question has two main components (as identified in the overall aims of the project above) to:
1. Develop a robust quantitative framework that will show the probability of automation of tasks within the healthcare sector.
2. Underpin this framework with rich empirical data that places this framework within the actual experiences of stakeholders in the health sector.
These questions, while looking at specific tasks and the probability of automation, are not narrowly limited to technical concerns but contribute to our understanding of broad and important social issues. As such, we are influenced by, and well aligned with, recent work from Susskind and Susskind that looks at the broad relevance of professions in the 21st century as well as thinking about the role of robots in society from scholars such as Lucy Suchman. Furthermore, it is important to note the extent to which algorithms can stand in for human judgement and inadvertently produce gaps in thinking (e.g. reliance on blackboxed tools can cause people using the tools to stop questioning what is happening inside the box). Our work, which will contribute to opening the blackbox of automation, will help counter this natural human tendency. These, and other works, think about the big picture questions of automation that drive our thinking, and to which debates focused studies such as the one proposed here can contribute.
This collaborative project will involve all team members throughout, but the two major work packages will be the primary responsibility of various team members identified below:
Work package 1: In-depth qualitative ethnographic research and quantitative survey research (Led by: Meyer, Coulter, Willis)
Before being able to perform algorithmic classification of tasks in primary care (WP2), we must first develop a much more comprehensive understanding of the various tasks performed by people and machines in Primary Care and related sectors. This work package aims to provide:
1. a rich description of the socio-technical interactions (between and among people and machines) in Primary Care based on in-depth ethnographic observation;
2. baseline data about the current levels of task automation in Primary Care, using a combination of time-based studies and survey methods informed by the ethnographic data collected above;
3. detailed task-level data that feeds into the tools developed in WP2.
Work package 2: Algorithmic classification and modelling (Led by: Osborne, Frey, Duckworth)
Determining the degree to which a task is automatable currently requires laborious expert assessment. This work package aims to develop algorithmic classification tools to automate this assessment, thereby providing:
1. a flexible methodology for rapid classification of tasks, even as new tasks emerge in the future;
2. a framework to incorporate richer data on tasks than has previously been used for the analysis of automation: specifically, the method will unpick the quantitative relations between task skill requirements, social acceptance of technology and eventual automation;
3. and a method sufficiently scalable to enable conclusions to be drawn about the workforce in primary healthcare as a whole.