ArticleJournal of medical systems2019
Post Ambulatory Discharge Follow-up Using Automated Text Messaging.
Article in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
Who cites it
8 citing papers in PubMed.
- Closing the Equity Gap in Hospital-to-Home Care Transitions with Automated Post-Discharge Calls, Text Messages, and Focused Nursing Outreach.Journal of general internal medicine · 2026Article
- Automated Discharge Instructions in Medical and Surgical Care: A Systematic Review of Patient Engagement and Clinical Outcomes.Healthcare (Basel, Switzerland) · 2026Review
- Beyond the Operating Room: A Narrative Review of Enhanced Recovery Strategies in Colorectal Surgery.Cureus · 2024Review
- Observational
- Evaluation of an Automated Text Message-Based Program to Reduce Use of Acute Health Care Resources After Hospital Discharge.JAMA network open · 2022Article
- Association of Automated Text Messaging With Patient Response Rate After Same-Day Surgery.JAMA network open · 2021Article
- Lack of recall after sedation for cataract surgery and its effect on the validity of measuring patient satisfaction.Korean journal of anesthesiology · 2020Article
- To Text or Not to Text? That is the Question.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ambulatory surgery has grown to be the most common procedure in developed countries. Efficient quality of care and safety often require calling patient at day one after outpatient surgery to check patient's recovery and search for complications. This increasing flow in same day surgery centres motivates the use of automatic systems to contact patients. The overall objective of this study was to evaluate automated software sending text messages (TM) to patients at day 1 after ambulatory surgery compared to classical phone calls. This prospective study took place in Rennes Teaching Hospital, France, from June 1st, 2015 to December 15th, 2016. All patients owning a mobile phone were included, adults and children by means of their parents. The primary end point was the rate of successfully contacted patients, compared to usual phone calls in 2014. In cases of no response or an abnormal response, an automatic alert was sent to the ambulatory unit. Within the 7246 patients included, response rate to TM was significantly higher than response to phone calls in 2014 (87% vs 57%, respectively p < 0.0001). Most patients (85%) responded in less than 60 min. The TM algorithm detected 36% alerts (12% for lack of response to TM and 24% for TM's content). The total of reached patients' rate with TM and then phone call after an alert was 90%. Post ambulatory discharge follow-up using automated TM was successfully and easily experienced as more patients were contacted.
Indexed as
Identifiers
31172292What OpenQuestion holds
Registered trials
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.