Evidence map›Paper›PMID 30311107›Full record

ArticleHealth care management science2019

Patient scheduling based on a service-time prediction model: a data-driven study for a radiotherapy center.

Dina Bentayeb, Nadia Lahrichi, Louis-Martin Rousseau

Abstract read
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In one paragraph

Article in Health care management science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

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  13. A Probabilistic Patient Scheduling Model with Time Variable Slots.Computational and mathematical methods in medicine · 2020
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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

3 authors.

Dina BentayebEcole Polytechnique de Montréal, CP 6079 Succ. Centre-ville, Montréal, H3C3A7, Canada.
Nadia LahrichiEcole Polytechnique de Montréal, CP 6079 Succ. Centre-ville, Montréal, H3C3A7, Canada. nadia.lahrichi@polymtl.ca.ORCID http://orcid.org/0000-0002-9185-9077
Louis-Martin RousseauEcole Polytechnique de Montréal, CP 6079 Succ. Centre-ville, Montréal, H3C3A7, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the growth of the population, access to medical care is in high demand, and queues are becoming longer. The situation is more critical when it concerns serious diseases such as cancer. The primary problem is inefficient management of patients rather than a lack of resources. In this work, we collaborate with the Centre Intégré de Cancérologie de Laval (CICL). We present a data-driven study based on a nonblock approach to patient appointment scheduling. We use data mining and regression methods to develop a prediction model for radiotherapy treatment duration. The best model is constructed by a classification and regression tree; its accuracy is 84%. Based on the predicted duration, we design new workday divisions, which are evaluated with various patient sequencing rules. The results show that with our approach, 40 additional patients are treated daily in the cancer center, and a considerable improvement is noticed in patient waiting times and technologist overtime.

Indexed as

Appointments and SchedulesCancer Care FacilitiesClinical Decision RulesEfficiency, OrganizationalWaiting ListsData MiningHealth Services AccessibilityHumansNeoplasmsPersonnel Staffing and SchedulingProgram EvaluationQuebecRadiotherapyRegression AnalysisTime FactorsData-driven approachGrid designNonblock schedulingPatient schedulingPrediction modelsSequencing rules

Identifiers

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.