Factors associated with attendance for first appointments at an alcohol clinic and the effects of telephone prompting

Abstract

Aims: To identify variables associated with response to first appointments at a specialist alcohol treatment clinic, and to determine the effect on patients' attendance of a brief telephone prompt prior to the appointment. Design, participants and intervention: Multivariate analysis of patient characteristics from referral letters and aspects of the quality of the referral process was carried out. Consecutive non-repliers (n~100), non-attenders (n~100) and attenders (n~100) were studied. A further 100 patients were matched and allocated to two groups, one of which received a telephone prompt prior to their appointment. Clinic attendance was noted. Findings: Results showed that patients' older age, shorter travelling distance to the clinic, shorter waiting time and administrative delay, faster response by the patient to the appointment invitation, and morning appointments were associated with positive responses. Patients not receiving a telephone prompt were less likely to attend. Conclusions: Reductions in waiting time for first appointment, locally based services and prompting patients by telephone would appear to hold promise as strategies for reducing non-attendance in alcohol dependency. Planning would need to take account of whether services could adequately cope with additional demand. Further research to assess the influence of patients' readiness to change prior to appointments is warranted.

Targeting the Use of Reminders and Notifications for Uptake by Populations (TURNUP): a systematic review and evidence synthesis

“…In contrast, a study of attendance at a specialist alcohol treatment centre in the UK 89 undertook multivariate analysis of patient characteristics of non-repliers (n = 100), non-attenders (n = 100) and attenders (n = 100) and found no statistically significant association between source of referral and attendance. A study in a paediatric dermatology clinic in Hong Kong 122 found that non-attendance was significantly higher among referrals from the emergency department than among referrals by private practitioners (p = 0.05).…”

“…In addition, limited and inconsistent evidence was yielded on the effect of source of appointment on attendance behaviour. Just two UK papers 89,121 reviewed included quantitative analyses addressing this hypothesised relationship and these related to contrasting settings and employed inconsistent categorisation of sources.…”

“…This significantly reduced the numbers of people failing to attend a mental health clinic appointment. Booth and Bennett 89 conducted a comparative study utilising telephone reminders incorporating information about appointments, directions to the clinics and other relevant information that reduced non-attendance at first appointments at a specialist alcohol treatment clinic. However, neither of these studies incorporated control groups receiving a simple reminder only and, therefore, it is not possible to determine from these studies the additive effect of orientation information.…”

Targeted Reminder Phone Calls to Patients at High Risk of No-Show for Primary Care Appointment: A Randomized Trial

“…[8][9][10][11][12][13][14][15][16] Studies in primary care and specialty clinics have demonstrated that staff phone calls are an effective intervention for reducing no-shows. [17][18][19][20][21][22][23][24][25][26][27][28][29][30] However, this convincing work is difficult to apply, because staff phone calls are timeconsuming and costly. Moreover, while this intervention is thought to be cost effective in clinics with high baseline noshow rates, the efficacy is less clear when baseline no-show rates are low.…”

“…18,19,30,31 Nevertheless, even in practices with low baseline no-show rates, the risk of no-show is heterogeneous-that is, some patients are more likely of noshows than others. 9,10,28,29,[32][33][34] Prior work has effectively demonstrated that no-shows are predictable; validated models can accurately predict the likelihood that a patient will fail to keep a scheduled appointment. 9,[35][36][37][38] Subspecialty clinics have made use of the predictability of no-shows by integrating predictive modeling to target interventions to those at high risk of no-show.…”