Analysis of health appointment no-shows

A community health centre in the United States was becoming increasingly frustrated at the high prevalence of no-show appointments: nearly one-third of all patients failed to keep scheduled visits. In addition to potential health ramifications, this scenario represented a major source of lost revenue, inefficient use of clinical resources, and difficulty recruiting physicians and qualified personnel. Serving a diverse, underprivileged population, significant no-show rates jeopardise a clinic’s ability to appropriately treat vulnerable patients. This includes inappropriate monitoring of chronic conditions such as diabetes (Karter et al 2004), and a significantly higher risk of hospital readmission (Nelson et al 2000). The financial costs associated with noshow appointments can total nearly 15 per cent of a clinic’s annual income (Moore et al 2001). No-show rates range from 3 per cent to 80 per cent, depending on clinic specialty and appointment type (Rust et al 1995); missed community clinic appointments vary from approximately 25 per cent to 42 per cent (Macharia et al 1992, Maxwell et al 2001). Demographics significantly influence the likelihood of no-shows, including being male, from a minority ethnic group, younger, from lower socioeconomic levels, or unmarried (Neal et al 2001, Waller and Hodgkin 2000). Other factors are distance from the clinic, language or other cultural barriers, and a history of no-shows (Waller and Hodgkin 2000). Medicaid and managed care patients are 2.4 times more likely to fail scheduled appointments (Neal et al 2001, Feinberg and Hicks 2003, Lilja 1994), the latter possibly due to lower individual provider allegiances. Private pay patients also miss more appointments than those with insurance (O’Brien et al 1999). Clinic characteristics also play a significant role. For example, smaller clinics or those with lower patient-provider ratios experience fewer problems (Macharia et al 1992), while physicians encounter twice the no-shows as nurse practitioners or other providers (Mantyjarvi 1994). Even the time gap between when appointments are scheduled and the visit date can dramatically affect no-show rates (Festinger et al 2002). The Health Belief Model (Becker and Maiman 1975) is frequently used to explore treatment adherence, as a patient’s perceived susceptibility to illness, its severity, and the subsequent treatment benefits affect decisions to seek care. The model also considers potential barriers, including emotional resistance or inconvenience (for example discomfort or invasive procedures such as colonoscopy) (Turner et al 2004), financial costs, transportation or other access issues. Linking theory to appointment behaviour, routine or preventive services may be viewed as lower priorities relative to other needs, with a greater no-show risk (Peeters and Bayer 1999). Follow-up appointments are missed more frequently than acute illness visits (Andrews et al 1990), as are gynaecological, pre-natal or child prevention services (Campbell et al 2000). Similarly, appointments for chronic conditions and maintenance treatment are missed more often than acute illness care. For example, appointment attendance for life-threatening conditions such as HIV (Israelski et al 2001) or haemodialysis (Rocco and Burkart 1993) is far more consistent than for diabetes, psychiatric conditions and other chronic illnesses (Reda and Makhoul 2001, Vikander et al 1986). Fortunately, patient education may be effective in addressing the importance of preventive care, contributing to the likelihood that treatment will be pursued. Despite the information on risk factors, clinics continue to struggle to create effective policies