Characteristics of children who do not attend their hospital appointments, and GPs' response: a mixed methods study in primary and secondary care.

BRITISH JOURNAL OF GENERAL PRACTICE(2017)

引用 16|浏览3
暂无评分
摘要
Background Children who do not attend (DNA) their hospital outpatient appointments are a concern because this potentially compromises the child's health and incurs financial cost. Little is known about children who DNA or the views of GPs to non-attendance. Aim To describe the characteristics of children who DNA hospital paediatric outpatient appointments, and explore how GPs view and respond to DNAs. Design and setting A mixed methods study of data from all new referrals to a children's hospital in the South West of England between 1 September and 31 October 2012. Method Data were extracted from patients' hospital and GP records, and Stata was used to analyse the data quantitatively. Analysis focused on describing the characteristics of children who DNA, and the process of care that followed. Practices that had either the highest or lowest number of DNAs were purposefully sampled for GPs who had referred children to secondary care at the study hospital within the previous year. Interviews were held between May 2014 and July 2015, and were analysed thematically. Results Children who DNA are more likely to be from an area of greater deprivation (adjusted odds ratio [AOR] 1.02, 95% confidence interval [CI] = 1.00 to 1.02, P = 0.04), and with a child protection alert in their hospital notes (AOR 2.72, 95% CI = 1.26 to 5.88, P = 0.01). Non-attendance is communicated poorly to GPs, rarely coded in patients' GP records, and few GP practices have a formal policy regarding paediatric DNAs. Conclusion Non-attendance at hospital outpatient appointments may indicate a child's welfare is at risk. Communication between primary and secondary care needs to be improved, and guidelines developed to encourage GPs to monitor children who DNA.
更多
查看译文
关键词
appointments and schedules,attitude of health personnel,child welfare,no-show patients,primary health care
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要