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Predictors of failed attendances in a multi-specialty outpatient centre using electronic databases
Vernon J Lee1, Arul Earnest, Mark I Chen
1Department of Clinical Epidemiology, Tan Tock Seng Hospital, Singapore. vernon_lee_jm@ttsh.com.sg
BMC Health Services Research
|August 9, 2005
Summary
Predicting patient no-shows is possible using electronic health records. Key factors like age, race, appointment scheduling, and contact information help identify patients likely to miss outpatient appointments, improving clinic efficiency.
Area of Science:
- Healthcare Management
- Health Informatics
- Predictive Analytics
Background:
- Missed outpatient appointments lead to significant healthcare inefficiencies and costs.
- Identifying factors contributing to appointment failures is crucial for optimizing clinic operations.
Purpose of the Study:
- To identify factors within electronic databases associated with failed outpatient appointments.
- To develop a predictive probability model for missed appointments to enhance intervention effectiveness.
Main Methods:
- A retrospective study analyzed 22,864 randomly sampled outpatient attendances from Tan Tock Seng Hospital (2000-2004).
- Logistic regression analysis was used to determine factors associated with failed appointments, focusing on the patient's latest appointment.
Main Results:
- Appointment failures occurred in 21% of all appointments and 39% of latest appointments.
- Significant predictors included age, race, longer scheduling-to-appointment times, prior failed appointments, providing a cell phone number, and distance from the hospital.
- The predictive model demonstrated over 80% diagnostic accuracy in identifying appointment failures.
Conclusions:
- Key variables from electronic databases effectively predict failed outpatient appointments.
- These predictive factors can inform the development of technological solutions to reduce missed appointments in outpatient clinics.