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Modeling No-Show Behavior in a Midwestern VA Medical Center

Modeling No-Show Behavior in a Midwestern VA Medical Center. Joanne Daggy, MS Laura Sands, PhD Mark Lawley, PhD Deanna Willis, MD, MBA Purdue University. No-shows in a primary care have been reported to range from 5 to 55%.

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Modeling No-Show Behavior in a Midwestern VA Medical Center

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  1. Modeling No-Show Behavior in a Midwestern VA Medical Center Joanne Daggy, MS Laura Sands, PhD Mark Lawley, PhD Deanna Willis, MD, MBA Purdue University

  2. No-shows in a primary care have been reported to range from 5 to 55%. No-shows cause a loss of revenue, disrupts patient flow and continuity of care. No Shows to Clinic Appointments are Common

  3. Project Objective To determine risk for no-show and develop scheduling strategies to reduce the number of daily no-shows and the variance in daily no-shows.

  4. Our Data – Demographics • 32,166 visits obtained for 5,218 non-new patients from a Midwestern VA hospital. • 76% are older than 50. • 98% are male • 54% have been in the system for more than 5 years. • 14.7% of scheduled visits from non-new patients result in a no show.

  5. Variables explored • Age, Marital Status, Education. • Diagnoses. • Distance to VA, AM appt.,day of week, season, rain, snow • Days since last appt, Days since appt was scheduled (lead time), Level of costs to VA, Private insurance. • Prior cumulative no show rate, Prior number of scheduled visits.

  6. Table 1: Results from Logistic Regression (N = 3,464)

  7. Methods for Model Validation • Defined risk groups based on predicted probability from validation data. • Simulations – 5000 samples of size 30 were drawn without replacement from validation data under different scenarios. • Demonstrate how no-show rate is affected by scheduling with varying numbers of patients from low, medium, and higher-risk groups.

  8. Summary • Developing a model to describe risk for no-show and using the information to develop scheduling schemes which can reduce: • Loss of revenue • Disruptions in patient flow • Disruptions in patient care

  9. Future Plans for Project • Obtaining scheduling data from primary care clinics of Indiana University Medical Group which offers a richer dataset. • reason for visit, diagnosis, information on physician characteristics, costs. • Use richer data to more accurately simulate cost-effectiveness of various scheduling schemes and effect on patient outcomes.

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