Which Outpatient Wait-Time Measures Are Related to Patient Satisfaction?

Abstract

Long waits for appointments decrease patient satisfaction. Administrative wait-time measures are used by managers, but relationships between these measures and satisfaction have not been studied. Data from the Veterans Health Administration are used to examine the relationship between wait times and satisfaction. Outcome measures include patient-reported satisfaction and timely appointment access. Capacity and retrospective and prospective time stamp measures are calculated separately for new and returning patients. The time stamp measures consist of the date when the appointment was created in the scheduling system (create date [CD]) or the date the appointment was desired as the start date for wait-time computation. Logistic regression models predict patient satisfaction using these measures. The new-patient capacity, new-patient time stamp measures using CD, and the returning-patient desired-date prospective measure were significantly associated with patient satisfaction. Standard practices can be improved by targeting wait-time measures to patient subpopulations.

Managing waiting times to predict no-shows and cancelations at a children’s hospital

“…In order to overcome these limitations on waiting time measuring, the first next available appointment (FNA) and the desired date (DD) have been used as two of the waiting time measures that can also predict patient satisfaction (Prentice, Davies & Pizer, 2013). On one hand, the first next available appointment measures the time between the day an appointment is created and the day the first available open appointment slot occurs.…”

Metrics That Matter

“…A common strategy to address this problem is to compute facility-level average metrics that exclude the individual patient whose outcome is being assessed. 8,[13][14][15][16][17][18] This is a similar approach to using an instrumental variable analysis that allows consistent estimation of relationships between explanatory variables and outcomes even when key explanatory variables are correlated with the error terms in the models.…”

“…These clinical outcomes are not likely to be related to metrics that fail to measure access and unmet health care needs. 8,[13][14][15][16][17][18] Two key lessons emerged from this validation work. First, different access metrics predicted satisfaction for different patient populations.…”

“…The relationship is seen across multiple outcomes selfreported in SHEP including patients' ability to obtain timely appointments, access to treatments and specialists, and overall satisfaction with VHA care. 8 Predictive validation relates a potential performance metric to a conceptually related outcome in historical data. 12 If the metric is associated with the outcome as expected, users can have greater confidence that the metric truly reflects the intended concept.…”

“…Survey sampling frames also raise concerns of selection bias. [8][9][10] For example, only veterans who have used VHA services are surveyed in SHEP, limiting information about access for non-VHA users. 7 The ability to find and measure the entire population of patients that suffer from unmet health care needs in a timely matter is an important example of gaps in the metric landscape that the IOM identified.…”