Qualitative and quantitative approach to assess of the potential for automating administrative tasks in general practice.

Abstract

To identify the extent to which administrative tasks carried out by primary care staff in general practice could be automated. A mixed-method design including ethnographic case studies, focus groups, interviews and an online survey of automation experts. Three urban and three rural general practice health centres in England selected for differences in list size and organisational characteristics. Observation and interviews with 65 primary care staff in the following job roles: administrator, manager, general practitioner, healthcare assistant, nurse practitioner, pharmacy technician, phlebotomist, practice nurse, pharmacist, prescription clerk, receptionist, scanning clerk, secretary and medical summariser; together with a survey of 156 experts in automation technologies. 330 hours of ethnographic observation and documentation of administrative tasks carried out by staff in each of the above job roles, followed by coding and classification; semistructured interviews with 10 general practitioners and 6 staff focus groups. The online survey of machine learning, artificial intelligence and robotics experts was analysed using an ordinal Gaussian process prediction model to estimate the automatability of the observed tasks. The model predicted that roughly 44% of administrative tasks carried out by staff in general practice are 'mostly' or 'completely' automatable using currently available technology. Discussions with practice staff underlined the need for a cautious approach to implementation. There is considerable potential to extend the use of automation in primary care, but this will require careful implementation and ongoing evaluation.