Evidence map›Paper›PMID 35609163›Full record

Trial reportThe Permanente journal2022

Pragmatic Randomized Study of Targeted Text Message Reminders to Reduce Missed Clinic Visits.

Ernesto Ulloa-Pérez, Paula R Blasi, Emily O Westbrook, Paula Lozano, Katie F Coleman, R Yates Coley

Abstract readRandomized Controlled TrialPragmatic Clinical Trial
In one paragraph

Trial report in The Permanente journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Predictive model-based interventions to reduce outpatient no-shows: a rapid systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Ernesto Ulloa-PérezDepartment of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA.
Paula R BlasiKaiser Permanente Washington Health Research Institute, Seattle, WA, USA.
Emily O WestbrookKaiser Permanente Washington Health Research Institute, Seattle, WA, USA.
Paula LozanoKaiser Permanente Washington Health Research Institute, Seattle, WA, USA.
Katie F ColemanKaiser Permanente Washington Health Research Institute, Seattle, WA, USA.
R Yates ColeyDepartment of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA.

Funding

AHRQ HHS K12 HS026369
6 · The paper itself

Abstract

introductionMissed 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.

methodsWe 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.

resultsBetween 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.

conclusionStudy findings indicate that using a prediction model to target reminders may reduce no-shows and spend health care resources more efficiently.

Indexed as

Text MessagingAmbulatory CareAmbulatory Care FacilitiesAppointments and SchedulesHumansReminder Systems

Identifiers

PMID35609163
PMCPMC9126539

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.