Reducing VA No-Shows: Evaluation of Predictive Overbooking Applied to Colonoscopy
| Status: | Completed |
|---|---|
| Conditions: | Colorectal Cancer, Cancer |
| Therapuetic Areas: | Oncology |
| Healthy: | No |
| Age Range: | 18 - Any |
| Updated: | 4/17/2018 |
| Start Date: | July 8, 2013 |
| End Date: | June 30, 2016 |
In this research study, investigators use colonoscopy as a case example to evaluate a
predictive overbooking model derived using patient-level predictors of absenteeism. The
no-show overbooking intervention employs a logistic regression model that uses patient data
to predict the odds of no-showing with 80% accuracy. These projected no-show appointments
will be overbooked by clerks for patients who agree to join a "fast track" short-call line.
By rapidly processing endoscopy patients and moving them out of traditional slots,
investigators predict more scheduling slots would become available for patients awaiting
colonoscopy.
predictive overbooking model derived using patient-level predictors of absenteeism. The
no-show overbooking intervention employs a logistic regression model that uses patient data
to predict the odds of no-showing with 80% accuracy. These projected no-show appointments
will be overbooked by clerks for patients who agree to join a "fast track" short-call line.
By rapidly processing endoscopy patients and moving them out of traditional slots,
investigators predict more scheduling slots would become available for patients awaiting
colonoscopy.
Patient "no-shows" are especially common in VA gastrointestinal (GI) endoscopy units, where
both open-access endoscopy scheduling and patient dislike of procedures contribute to high
absenteeism. In this proposal, investigators use endoscopy as a case example to evaluate a
predictive overbooking model derived using patient-level predictors of absenteeism. The
no-show overbooking intervention employs a logistic regression model that uses patient data
to predict the odds of no-showing with 80% accuracy. These projected no-show appointments
will be overbooked by clerks for patients who agree to join a "fast track" short-call line.
However, patients scheduled for upper endoscopies in the "fast track" assume a small risk of
service denial on the day of their overbooking in case of inaccurate predictions. If this
occurs, the patient is guaranteed service in the next available position and is assured of
having a shorter wait time. Patients scheduled for colonoscopies will never be turned down
but may experience delays in the waiting room the day of their "fast track" appointment. By
rapidly processing endoscopy patients and moving them out of traditional slots, investigators
predict more scheduling slots would become available for patients awaiting colonoscopy.
Investigators propose to conduct a prospective, 24-month, interrupted time series (ITS) trial
in the WLAVA (West Los Angeles Veterans Administration) GI clinic endoscopy unit. During
intervention periods, investigators will activate the no-show predictive overbooking strategy
described above. Investigators will compare outcomes between scheduling strategies, including
differences in percent utilization of capacity (primary outcome), number of Veterans served,
mean patient lag time between scheduling and procedure, number of unexpected service denials
("bumps") from no-show predictive overbooking, and direct costs of care. Investigators will
analyze differences using both traditional univariate and multivariate approaches, and using
autoregressive integrated moving average (ARIMA) analyses to adjust for auto-correlations in
ITS data.
both open-access endoscopy scheduling and patient dislike of procedures contribute to high
absenteeism. In this proposal, investigators use endoscopy as a case example to evaluate a
predictive overbooking model derived using patient-level predictors of absenteeism. The
no-show overbooking intervention employs a logistic regression model that uses patient data
to predict the odds of no-showing with 80% accuracy. These projected no-show appointments
will be overbooked by clerks for patients who agree to join a "fast track" short-call line.
However, patients scheduled for upper endoscopies in the "fast track" assume a small risk of
service denial on the day of their overbooking in case of inaccurate predictions. If this
occurs, the patient is guaranteed service in the next available position and is assured of
having a shorter wait time. Patients scheduled for colonoscopies will never be turned down
but may experience delays in the waiting room the day of their "fast track" appointment. By
rapidly processing endoscopy patients and moving them out of traditional slots, investigators
predict more scheduling slots would become available for patients awaiting colonoscopy.
Investigators propose to conduct a prospective, 24-month, interrupted time series (ITS) trial
in the WLAVA (West Los Angeles Veterans Administration) GI clinic endoscopy unit. During
intervention periods, investigators will activate the no-show predictive overbooking strategy
described above. Investigators will compare outcomes between scheduling strategies, including
differences in percent utilization of capacity (primary outcome), number of Veterans served,
mean patient lag time between scheduling and procedure, number of unexpected service denials
("bumps") from no-show predictive overbooking, and direct costs of care. Investigators will
analyze differences using both traditional univariate and multivariate approaches, and using
autoregressive integrated moving average (ARIMA) analyses to adjust for auto-correlations in
ITS data.
Inclusion Criteria:
- Patients who are scheduled for upper endoscopy and agree to the terms of "fast track"
offer.
Exclusion Criteria:
- If a patient expresses concern about service denial, confusion about the bargain, or
refuses to participate, the investigators will schedule these patients routinely.
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