Evidence map›Paper›PMID 39517373›Full record

ArticleHealthcare (Basel, Switzerland)2024

A Solution to Reduce the Impact of Patients' No-Show Behavior on Hospital Operating Costs: Artificial Intelligence-Based Appointment System.

Kerem Toker, Kadir Ataş, Alpaslan Mayadağlı, Zeynep Görmezoğlu, Ibrahim Tuncay, Rümeyza Kazancıoğlu

Abstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

  1. Article
  2. Review
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.

Kerem TokerFaculty of Health Sciences, Health Management, Bezmiâlem Vakıf University, Istanbul 34065, Türkiye.ORCID 0000-0002-1904-1406
Kadir AtaşDepartment of Information Technologies, Bezmiâlem Vakıf University, Istanbul 34093, Türkiye.
Alpaslan MayadağlıFaculty of Medicine, Bezmiâlem Vakıf University, Istanbul 34093, Türkiye.
Zeynep GörmezoğluGeneral Secretariat, Bezmiâlem Vakıf University, Istanbul 34093, Türkiye.ORCID 0000-0002-0197-0653
Ibrahim TuncayFaculty of Medicine, Bezmiâlem Vakıf University, Istanbul 34093, Türkiye.
Rümeyza KazancıoğluFaculty of Medicine, Bezmiâlem Vakıf University, Istanbul 34093, Türkiye.ORCID 0000-0003-1217-588X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPatient no-show behavior is a critical factor complicating hospital resource optimization and causing waste. The inefficiency caused by patients' no-shows and the resulting increased operating costs negatively affect the hospitals' financial structure and service quality. For this reason, health managers must make accurate predictions about whether patients will attend an appointment and plan the appointment system within the framework of these predictions. This research aims to optimize the hospital appointment system by making accurate predictions regarding the no-show behavior of the patients, based on recorded data.

methodsAn artificial intelligence-based appointment system has been developed according to patients' demographics and past behavior patterns. The forecast results and realized performance results were compared. The artificial intelligence we have developed continuously improves appointment assignments by learning from past and current data.

resultsAccording to the findings, the artificial intelligence-based appointment system increased the rate of patients attending appointments by 10% per month. Likewise, the hospital capacity utilization rate increased by 6%.

conclusionsFindings from the study confirmed that no-show risks could be managed in the appointment process through artificial intelligence. This artificial intelligence-based design for appointment systems significantly decreases hospital costs and improves service quality performance.

Indexed as

artificial intelligencehospital appointment systemshospital costsno-show behavior

Identifiers

PMID39517373
PMCPMC11545362

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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.