ObjectiveTo identify the extent to which administrative tasks carried out by primary care staff in general practice could be automated.DesignA mixed-method design including ethnographic case studies, focus groups, interviews and an online survey of automation experts.SettingThree urban and three rural general practice health centres in England selected for differences in list size and organisational characteristics.ParticipantsObservation 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.Methods330 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.ResultsThe 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.ConclusionsThere is considerable potential to extend the use of automation in primary care, but this will require careful implementation and ongoing evaluation.
In the Introduction to this special issue on the Social Informatics of Knowledge, the editors of the issue reflect on the history of the term “social informatics” and how the articles in this issue both reflect and depart from the original concept. We examine how social informatics researchers have studied knowledge, computerization, and the workplace, and how all of those have evolved over time. We describe the process by which articles were included, how they help us understand the field of social informatics scholarship today, and reflect briefly on what the future of the field holds.
The use of intelligent machines—digital technologies that feature data‐driven forms of customization, learning, and autonomous action—is rapidly growing and will continue to impact many industries and domains. This is consequential for communities of researchers, educators, and practitioners concerned with studying, supporting, and educating information professionals. In the face of new developments in artificial intelligence (AI), the research community faces 3 questions: (a) How is AI becoming part of the world of work? (b) How is the world of work becoming part of AI? and (c) How can the information community help address this topic of Work in the Age of Intelligent Machines (WAIM)? This opinion piece considers these 3 questions by drawing on discussion from an engaging 2019 iConference workshop organized by the NSF supported WAIM research coordination network (note: https://waim.network).
Substance use and mental health problems are often underdiagnosed and undertreated in primary care. Veterans affairs facilities are using the Behavioral Telehealth Center (BTC) to provide evidence-based assessments for primary care patients via telephone. Whether participation in BTC services is associated with (1) increases in healthcare utilization and (2) decreases in symptoms based on behavioral health screening instruments, post-BTC services compared with pre-BTC services were investigated. Retrospective data were extracted for 1,820 patients who were referred to the BTC. Differences in utilization rates and symptom scores pre- and post-BTC services were tested using repeated measures analysis of covariance while controlling for relevant sociodemographic variables. Participants (1) utilized significantly more substance use and mental health treatment services and (2) had significantly lower alcohol and depression screening scores post-BTC services compared with pre-BTC services. This initial evaluation provides support that BTC services are associated with increased healthcare utilization and decreased alcohol and depressive symptoms.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.