Access to GP services supported by IT infrastructure readiness: Analysing publically available GP practice data
Lengu, D, Sapountzis, S, Smith, R, Kagioglou, M and Kobbacy, KAH 2013, Access to GP services supported by IT infrastructure readiness: Analysing publically available GP practice data , in: 5th European Conference on Intelligent Management Systems in Operations, 3-4 July, Salford.
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The overuse of hospital accident and emergency (A&E) departments has long been an issue ofconcern in most Western countries. Patients who attend A&E with non-urgent needs consume limitedA&E resources and they may impede access for other patients with urgent and emergency needs. Anumber of studies have found that patients often turn to A&E for care because they lack timely access to general practitioner (GP) services. Recent advances in technology may help GP improve patients‟access to GP services andallow them to be more responsive their patients‟ needs. Digital and online technology can ease interaction and information-sharing between patients and their GPs. In this study, exploratory data mining is carried out in order to better understand the reationship between A&E attendance and various GP practice characteristics. The data used in this exercise is GP practice data publically available from the NHS Information Centre website. This data covered 39 different practice attributes related to IT infrastructure, patient care experience, patient deprivationand disease prevalence rates. Cluster analysis is used to divide GP practices into meaningful clusters and the attribute s that define each cluster are identified. The differences between the five identified clusters suggest that the problem of non-urgent A&E attendances should be addressed in a moretargeted fashion. Our analysis also suggests that GP practices with poor patient satisfaction levels are adopting online technologies at a slower pace wh en compared with others that have higher patient satisfaction levels.
|Item Type:||Conference or Workshop Item (Paper)|
|Uncontrolled Keywords:||Primary care access Cluster analysis Data mining|
|Themes:||Built and Human Environment|
|Schools:||Schools > School of the Built Environment
Schools > School of the Built Environment > Centre for Built Environment Sustainability and Transformation (BEST)
|Funders:||Non funded research|
|Depositing User:||Stelios Sapountzis|
|Date Deposited:||28 Jun 2013 14:42|
|Last Modified:||29 Oct 2015 01:12|
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