Evidence map›Paper›PMID 36426240›Full record

ArticlePeerJ. Computer science2022

Predicting no-shows for dental appointments.

Yazeed Alabdulkarim, Mohammed Almukaynizi, Abdulmajeed Alameer, Bassil Makanati, Riyadh Althumairy, Abdulaziz Almaslukh

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ. Computer science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Article
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.

Yazeed AlabdulkarimInformation Systems Department, King Saud University, Riyadh, Saudi Arabia.
Mohammed AlmukayniziInformation Systems Department, King Saud University, Riyadh, Saudi Arabia.
Abdulmajeed AlameerComputer Science Department, King Saud University, Riyadh, Saudi Arabia.
Bassil MakanatiInformation Systems Department, King Saud University, Riyadh, Saudi Arabia.
Riyadh AlthumairyDepartment of Restorative Dental Sciences, King Saud University, Riyadh, Saudi Arabia.
Abdulaziz AlmaslukhInformation Systems Department, King Saud University, Riyadh, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Patient no-shows is a significant problem in healthcare, reaching up to 80% of booked appointments and costing billions of dollars. Predicting no-shows for individual patients empowers clinics to implement better mitigation strategies. Patients' no-show behavior varies across health clinics and the types of appointments, calling for fine-grained studies to uncover these variations in no-show patterns. This article focuses on dental appointments because they are notably longer than regular medical appointments due to the complexity of dental procedures. We leverage machine learning techniques to develop predictive models for dental no-shows, with the best model achieving an Area Under the Curve (AUC) of 0.718 and an F1 score of 66.5%. Additionally, we propose and evaluate a novel method to represent no-show history as a binary sequence of events, enabling the predictive models to learn the associated future no-show behavior with these patterns. We discuss the utility of no-show predictions to improve the scheduling of dental appointments, such as reallocating appointments and reducing their duration.

Indexed as

Dental appointmentsDental no-showsMachine learningPatient no-show

Identifiers

PMID36426240
PMCPMC9680883

What OpenQuestion holds

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LicenceCC BY
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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.