Lawton, Sarah Ann (2021) Automated Check-in Data Collection (AC DC): an investigation of utility, acceptability and effectiveness in general practice. Masters thesis, Keele University.

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Background: There are a range of data collection methodologies employed for collecting patient health related research data. Within primary care settings, and particularly within general practice, time and resources are limited. The utility of an automated check-in screen to collect brief research data from patients is a new option, that requires investigation.

Methods: A systematic literature search was conducted to identify articles describing the use of data collection methodologies that patients currently use and interact with independently, within primary care settings. A pilot feasibility descriptive cross-sectional study was then undertaken to investigate the utility of check-in screens and to collect brief research data from patients, whilst they self-completed an automated check-in screen, prior to their appointment.

Results: Limited evidence exists in health literature relating to the collection of research data using automated devices, within primary care settings. 9,274 participants were recruited to the Automated Check-in Data Collection (AC DC) Study from 9 general practices over a 3-week recruitment period. Almost 90% of all patients presented with the opportunity, participated in the study. 96.2% of participants answered the ‘clinical’ research question, reporting a degree of bodily pain experienced during the past 4 weeks. The severities of pain reported were comparable with results identified elsewhere. 89.3% of participants answered the ‘non-clinical’ research question, on happiness to be contacted about future research studies.

Conclusion: Choosing which data collection method to use when conducting research, remains a predicament for researchers. Using automated check-in facilities, to integrate research into routine general practice is an efficient and effective way to collect brief research data from patients, with no variation by age of patient. With the COVID-19 pandemic initiating an extensive digital transformation in society, now is an ideal time to investigate other ways in which electronic research data can be captured quickly and efficiently.

Item Type: Thesis (Masters)
Subjects: R Medicine > R Medicine (General)
Divisions: Faculty of Medicine and Health Sciences > School of Medicine
Contributors: Helliwell, T (Thesis advisor)
Mallen, C (Thesis advisor)
Depositing User: Lisa Bailey
Date Deposited: 18 Oct 2021 13:11
Last Modified: 18 Oct 2021 13:11

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