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The American Journal of Managed Care August 2018
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Optimizing Number and Timing of Appointment Reminders: A Randomized Trial
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Optimizing Number and Timing of Appointment Reminders: A Randomized Trial

John F. Steiner, MD, MPH; Michael R. Shainline, MS, MBA; Jennifer Z. Dahlgren, MS; Alan Kroll, MSPT, MBA; and Stan Xu, PhD
Two text message or phone reminders were more effective in reducing missed primary care appointments than a single reminder, particularly in patients at high risk of missing appointments.
Study Measures: Electronic Health Records and Visit Satisfaction Survey

We used appointment records to determine the primary outcome: whether the appointment was missed, cancelled, or completed. We selected patient characteristics as covariates and potential predictors of missed appointments based on the findings of prior studies.8,12-16 These variables included age, sex, race/ethnicity, marital status, employment, day and time of the appointment, number of comorbid conditions in the 2 years prior to the visit,17 health insurance type, duration of enrollment in KPCO (a proxy for the level of familiarity with the system), lead time to appointment (defined as the number of days between scheduling the appointment and the date of that appointment), the sum of the number of emergency department (ED) visits and hospitalizations within KPCO or other institutions reimbursed by KPCO health insurance within 6 months of the index appointment, and the number of missed primary care appointments within 6 months prior to the index appointment.

KPCO developed and conducted ongoing surveys to assess member satisfaction after completed primary care visits. These surveys were distinct from standard surveys of patient experience, such as the Hospital Consumer Assessment of Healthcare Providers and Systems survey.18 Members who had missed or cancelled their appointments were not surveyed. The 58-item survey included a global item on visit satisfaction: “Thinking just about your visit on [date] with [clinician name], and everything that happened from the time you scheduled the appointment until the time you left the medical office, how would you rate it?” Response options were excellent, good, adequate, and fair to poor. We added 4 items to surveys administered between October 1, 2016, and December 31, 2016. These items (with response options in parentheses) were: (1) Did you receive a reminder message before your visit? (yes/no/don’t remember); (2) Approximately how many reminder messages did you receive (1/2/3/4 or more/don’t remember); (3) How satisfied were you with the timing of reminders we sent you before your visit? (extremely satisfied/very satisfied/somewhat satisfied/not very satisfied/not at all satisfied/don’t know/not applicable); and (4) How satisfied were you with the number of reminders we sent you before your visit? (same response options as for question 3).

The survey vendor attempted to complete 50 surveys from each primary care department (internal medicine, family medicine, or pediatrics) at each site. Surveys were initially administered by email. If the member did not respond, they received a reminder email. Patients without email addresses received a phone survey with up to 5 call attempts. Surveys were typically completed within a week of the visit, with a historical response rate of 35%.

Sample Size Estimation

We defined a 1.0% absolute reduction in the rate of missed appointments (eg, from 6.0% to 5.0%) as an operationally significant effect. We calculated that 100,000 primary care visits, equally apportioned among study arms, would detect this difference, and projected that 2 months would enable us to reach this sample size. To estimate the power to detect differences in visit satisfaction, we used pretrial data that 84% of patients rated their visit as excellent or good to determine that 726 surveys per study arm would detect a 5% change in satisfaction.

Statistical Methods

To compare patient demographics, clinical characteristics, and intervention outcomes among the 3 intervention groups, we used Wilcoxon rank sum tests for nonnormally distributed continuous variables and χ2 tests for discrete variables. We conducted a prespecified subgroup analysis to compare the effectiveness of the reminder interventions in the quartile of patients at highest risk of missing appointments with that in the 3 quartiles at lower risk. We developed a predictive model for the risk of missing an appointment based on previously described variables from the electronic health record. We excluded cancelled visits from this model. The clinical department (designated as an integrated primary care department, family medicine, internal medicine, or pediatrics) and the 25 clinic sites were entered into the model as fixed effects to address clustering of patients within departments and departments within clinic site. Linear and quadratic terms for continuous predictor variables were assessed, and both terms were included in the final model if statistically significant. Missing values for each predictor were included in the model as a separate category. Backward selection with Wald χ2 tests guided the selection of predictors. The final prediction model included an indicator variable for treatment group and all covariates with P values <.05. The discrimination of the model was assessed with the C statistic, defined as the probability that in any 2 individuals randomly drawn from the sample, the predicted risk of a missed appointment is higher among the individual who actually missed the appointment.19

This project was approved by the KPCO Institutional Review Board with a waiver of individual informed consent. Because the project was conducted to address an operational issue, it was not registered on

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