Predictors of failed attendances in a multi-specialty outpatient centre using electronic databases.
BACKGROUND
Failure to keep outpatient medical appointments results in inefficiencies and costs. The objective of this study is to show the factors in an existing electronic database that affect failed appointments and to develop a predictive probability model to increase the effectiveness of interventions.


METHODS
A retrospective study was conducted on outpatient clinic attendances at Tan Tock Seng Hospital, Singapore from 2000 to 2004. 22864 patients were randomly sampled for analysis. The outcome measure was failed outpatient appointments according to each patient's latest appointment.


RESULTS
Failures comprised of 21% of all appointments and 39% when using the patients' latest appointment. Using odds ratios from the mutliple logistic regression analysis, age group (0.75 to 0.84 for groups above 40 years compared to below 20 years), race (1.48 for Malays, 1.61 for Indians compared to Chinese), days from scheduling to appointment (2.38 for more than 21 days compared to less than 7 days), previous failed appointments (1.79 for more than 60% failures and 4.38 for no previous appointments, compared with less than 20% failures), provision of cell phone number (0.10 for providing numbers compared to otherwise) and distance from hospital (1.14 for more than 14 km compared to less than 6 km) were significantly associated with failed appointments. The predicted probability model's diagnostic accuracy to predict failures is more than 80%.


CONCLUSIONS
A few key variables have shown to adequately account for and predict failed appointments using existing electronic databases. These can be used to develop integrative technological solutions in the outpatient clinic.
Lee
Vernon J
VJ
Department of Clinical Epidemiology, Tan Tock Seng Hospital, Singapore. vernon_lee_jm@ttsh.com.sg
Earnest
Arul
A
Chen
Mark I
MI
Krishnan
Bala
B
D000328:Adult; D000368:Aged; D001071:Appointments and Schedules; D019992:Databases as Topic; D005260:Female; D005544:Forecasting; D006297:Health Services Accessibility / Q000706:statistics & numerical data; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016017:Odds Ratio; D010044:Outpatient Clinics, Hospital / Q000706:statistics & numerical data*; D010352:Patient Dropouts / Q000706:statistics & numerical data*; D011336:Probability; D012846:Singapore; D016312:Treatment Refusal / Q000208:ethnology / Q000706:statistics & numerical data*