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'Predictive no-show overbooking' intervention. Patients who volunteer to enroll in "fast-track" line, which gives them an opportunity to overbook their appointment for endoscopy earlier in a predictive no-show slots.

During intervention period, every Veteran scheduled for an endoscopy will be offered "fast-track" offer, which gives them a chance to get their endoscopy procedure done earlier than usual scheduling by overbooking their appointment in a predictive no-show slot.

Predictive no-show overbooking

Paul G. Shekelle, MD PhD MPH

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.

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.

Reducing VA No-Shows: Evaluation of Predictive Overbooking Applied to Colonoscopy

Investigators' primary objective will be to evaluate the impact of no-show predictive overbooking on percentage of the GI endoscopy clinic that are filled on a given day. Days where at least one Fast-tracked patient attended an appointment were compared to days where only Control patients attended appointments. Percentage of GI Clinic Capacity is calculated as the number of appointments completed divided by number of appointment spots available on a given day. This percentage was compared between Fast-tracked days and Control days, using data from 1672 patients.

The investigators will calculate the mean daily lag time for all colonoscopy and upper endoscopies performed per day

The investigators will compare the number of patients bumped per day between scheduling approaches

The investigators will compare daily advanced adenomatous polyp detection and daily cecal intubation rates between groups.

Length of Workday in hours (comparing days with Fast-Tracked Appointments to Control days without)

For cost comparisons, the investigators will aggregate total provider overtime costs for colonoscopies performed. Cost is reported per day.

VA Office of Research and Development

This study involved scheduling patients for appointments, so the odds of an adverse event are extremely low.

Patients who volunteer to enroll in "fast-track" line, which gives them an opportunity to overbook their appointment for endoscopy earlier in a predictive no-show slots.

Predictive no-show overbooking: During intervention period, every Veteran scheduled for an upper endoscopy will be offered "fast-track" offer, which gives them a chance to get their endoscopy procedure done earlier than usual scheduling by overbooking their appointment in a predictive no-show slot.

Fast-tracked

Control

Total

Age, Categorical

Age, Continuous

Sex: Female, Male

Ethnicity (NIH/OMB)

Race (NIH/OMB)

United States

Region of Enrollment

This study was impeded by our ability to schedule Veterans into Fast-Tracked appointments. Although the method is very promising, integration of predictive scheduling is vital for this program to benefit Veterans on a large scale.

Jennifer Talley Soares

We counted days containing at least one Fast-tracked participant as a "Fast-tracked" day for the sake of analysis at the clinic level. Our null hypothesis is that these days will not differ in terms of percentage of clinic capacity filled. We are powered to detect a 0.5 Standard Deviation (SD) difference in clinic capacity (Type I error rate = 5%; Power = 81%), with a equal ratio of Experimental and Control days.

These 1672 patients are a subset of all patients who were enrolled in this study. We dropped data on a first wave of participants (69 Fast-tracked and 3294 Controls) because of problems incorporating recruitment strategy into clinic. However, data from these individuals was used to build predictive model.

Percentage of GI Clinic Capacity Filled

We calculated this measure on the patient level. Our null hypothesis is that the lag time between scheduling and appointment, measured in days, will not differ between groups. We are powered to detect a 0.3 SD difference in lag time (Type I error rate = 5%; Power = 84%), accounting for the large sampling ratio (approximately 14:1).

Scheduling-to-procedure Lag Time

We did not explicitly power this study to detect differences in the number of service bumps, but we can compare them using Fisher's Exact Test

Daily Service Denials ("Bumps")

We calculated this measure on the patient level, but were hampered by a large sampling ratio (more than 50:1). Our null hypothesis is that the number of polyps detected will not differ between groups. We are powered to detect a 0.25 SD difference in polyp count per patient (Type I error rate = 5%; Power = 80%).

We only collected data on polyp detection for the first half of our Fast-tracked participants and all Controls seen over the same time period (4897 in total).

Advanced Adenoma Detection/Cecal Intubation Rates

We counted days containing at least one Fast-tracked participant as a "Fast-tracked" day for the sake of analysis at the clinic level. Our null hypothesis is that these days will not differ in terms of length of workday. We are powered to detect approximately 0.5 SD difference in workday length in hours (Type I error rate = 5%; Power = 81%), with a equal ratio of Experimental and Control days.

Length of Workday

We counted days containing at least one Fast-tracked participant as a "Fast-tracked" day for the sake of analysis at the clinic level. Our null hypothesis is that these days will not differ in terms of cost per day. We are powered to detect a 0.5 SD difference in clinic capacity (Type I error rate = 5%; Power = 81%), with a equal ratio of Experimental and Control days.

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization	Link
Reducing VA No-Shows: Evaluation of Predictive Overbooking Applied to Colonoscopy	NCT01639443	Colon Cancer	Predictive no-s...	18 Years -	VA Office of Research and Development
Reducing VA No-Shows: Evaluation of Predictive Overbooking Applied to Colonoscopy	NCT01639443	Colon Cancer	Predictive no-s...	18 Years -	VA Office of Research and Development

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