INTRODUCTION: Missed clinic appointments ("no-shows") waste health system resources, decrease physician availability, and may worsen patient outcomes. Appointment reminders reduce no-shows, though evidence on the optimal number of reminders is limited and sending multiple reminders for every visit is costly. Risk prediction models can be used to target reminders for visits that are likely to be missed. METHODS: We conducted a randomized quality improvement project at Kaiser Permanente Washington among patients with primary care and mental health visits with a high no-show risk comparing the effect of one text message reminder (sent 2 business days prior to the appointment) with 2 text message reminders (sent 2 and 3 days prior) on no-shows and same-day cancellations. We estimated the relative risk (RR) of an additional reminder using G-computation with logistic regression adjusted for no-show risk. RESULTS: Between February 27, 2019 and September 23, 2019, a total of 125,076 primary care visits and 33,593 mental health visits were randomized to either 1 or 2 text message reminders. For primary care visits, an additional text message reduced the chance of no-show by 7% (RR = 0.93, 95% CI: 0.89-0.96) and same-day cancellations by 6% (RR = 0.94, 95% CI: 0.90-0.98). In mental health visits, an additional text message reduced the chance of no-show by 11% (RR = 0.89, 95% CI: 0.86-0.93) but did not impact same-day cancellations (RR = 1.02, 95% CI: 0.96-1.11). We did not find effect modification among subgroups defined by visit or patient characteristics. CONCLUSION: Study findings indicate that using a prediction model to target reminders may reduce no-shows and spend health care resources more efficiently.
INTRODUCTION: Missed clinic appointments ("no-shows") waste health system resources, decrease physician availability, and may worsen patient outcomes. Appointment reminders reduce no-shows, though evidence on the optimal number of reminders is limited and sending multiple reminders for every visit is costly. Risk prediction models can be used to target reminders for visits that are likely to be missed. METHODS: We conducted a randomized quality improvement project at Kaiser Permanente Washington among patients with primary care and mental health visits with a high no-show risk comparing the effect of one text message reminder (sent 2 business days prior to the appointment) with 2 text message reminders (sent 2 and 3 days prior) on no-shows and same-day cancellations. We estimated the relative risk (RR) of an additional reminder using G-computation with logistic regression adjusted for no-show risk. RESULTS: Between February 27, 2019 and September 23, 2019, a total of 125,076 primary care visits and 33,593 mental health visits were randomized to either 1 or 2 text message reminders. For primary care visits, an additional text message reduced the chance of no-show by 7% (RR = 0.93, 95% CI: 0.89-0.96) and same-day cancellations by 6% (RR = 0.94, 95% CI: 0.90-0.98). In mental health visits, an additional text message reduced the chance of no-show by 11% (RR = 0.89, 95% CI: 0.86-0.93) but did not impact same-day cancellations (RR = 1.02, 95% CI: 0.96-1.11). We did not find effect modification among subgroups defined by visit or patient characteristics. CONCLUSION: Study findings indicate that using a prediction model to target reminders may reduce no-shows and spend health care resources more efficiently.
Authors: Krisda H Chaiyachati; Rebecca A Hubbard; Alyssa Yeager; Brian Mugo; Stephanie Lopez; Elizabeth Asch; Catherine Shi; Judy A Shea; Roy Rosin; David Grande Journal: JAMA Intern Med Date: 2018-03-01 Impact factor: 21.873