Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Background: A key feature of a good general practice consultation is that it is patient-centred. A number of verbal and non-verbal behaviours have been identified as important to establish a good relationship with the patient. However, the use of the computer detracts the doctor's attention away from the patient, compromising these essential elements of the consultation. Current methods to assess the consultation and the influence of the computer on them are time consuming and subjective. If it were possible to measure these quantitatively, it could provide the basis for the first truly objective way of studying the influence of the computer on the consultation. The aim was to assess whether pattern recognition software could be used to measure the influence and pattern of computer use in the consultation. If this proved possible it would provide, for the first time, an objective quantitative measure of computer use and a measure of the attention and responsiveness of the general practitioner towards the patient. Methods: A feasibility study using pattern recognition software to analyse a consultation was conducted. A web camera, linked to a data-gathering node was used to film a simulated consultation in a standard office. Members of the research team enacted the role of the doctor and the patient, using pattern recognition software to try and capture patient-centred, non-verbal behaviour. As this was a feasibility study detailed results of the analysis are not presented. Results: It was revealed that pattern recognition software could be used to analyse certain aspects of a simulated consultation. For example, trigger lines enabled the number of times the clinician's hand covered the keyboard to be counted and wrapping recorded the number of times the clinician nodded his head. It was also possible to measure time sequences and whether the movement was brief or lingering. Conclusion: Pattern recognition software enables movements associated with patient-centredness to be recorded. Pattern recognition software has the potential to provide an objective, quantitative measure of the influence of the computer on the consultation.

Original publication




Journal article


BMC Medical Informatics and Decision Making

Publication Date





1 - 10