Dynamic Price Application to Prevent Financial Losses to Hospitals Based on Machine Learning Algorithms

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dc.contributor.authorAtalan, Abdulkadir
dc.contributor.authorDonmez, Cem Cagri
dc.date.accessioned2025-01-27T20:34:55Z
dc.date.available2025-01-27T20:34:55Z
dc.date.issued2024
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractHospitals that are considered non-profit take into consideration not to make any losses other than seeking profit. A model that ensures that hospital price policies are variable due to hospital revenues depending on patients with appointments is presented in this study. A dynamic pricing approach is presented to prevent patients who have an appointment but do not show up to the hospital from causing financial loss to the hospital. The research leverages three distinct machine learning (ML) algorithms, namely Random Forest (RF), Gradient Boosting (GB), and AdaBoost (AB), to analyze the appointment status of 1073 patients across nine different departments in a hospital. A mathematical formula has been developed to apply the penalty fee to evaluate the reappointment situations of the same patients in the first 100 days and the gaps in the appointment system, considering the estimated patient appointment statuses. Average penalty cost rates were calculated based on the ML algorithms used to determine the penalty costs patients will face if they do not show up, such as 22.87% for RF, 19.47% for GB, and 14.28% for AB. As a result, this study provides essential criteria that can help hospital management better understand the potential financial impact of patients missing appointments and can be considered when choosing between these algorithms.
dc.identifier.doi10.3390/healthcare12131272
dc.identifier.issn2227-9032
dc.identifier.issue13
dc.identifier.pmid38998807
dc.identifier.scopus2-s2.0-85198335144
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/healthcare12131272
dc.identifier.urihttps://hdl.handle.net/20.500.12428/23511
dc.identifier.volume12
dc.identifier.wosWOS:001269839600001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofHealthcare
dc.relation.publicationcategoryinfo:eu-repo/semantics/openAccess
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20250125
dc.subjectshow-up
dc.subjectno-show
dc.subjectappointment system
dc.subjectmachine learning
dc.subjectdynamic price policy
dc.titleDynamic Price Application to Prevent Financial Losses to Hospitals Based on Machine Learning Algorithms
dc.typeArticle

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