Analysis of home healthcare practice to improve service quality: Case study of megacity İstanbul

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dc.contributor.authorİnanç, Rabia Çevik
dc.contributor.authorEkmekçi, İsmail
dc.date.accessioned2023-03-17T10:37:05Z
dc.date.available2023-03-17T10:37:05Z
dc.date.issued2023en_US
dc.departmentEnstitüler, Fen Bilimleri Enstitüsü, Endüstri Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractHome healthcare services are public or private service that aims to provide health services at home to socially disadvantaged, sick, needy, disabled, and elderly individuals. This study aims to increase the quality of home healthcare practice by analyzing the factors affecting it. In Megacity Istanbul, data from 1707 patients were used by considering 14 different input variables affecting home healthcare practice. The demographic, geographic, and living conditions of patients and healthcare professionals who take an active role in home healthcare practice constituted the central theme of the input parameters of this study. The regression method was used to look at the factors that affect the length of time a patient needs home healthcare, which is the study's output variable. This article provides short planning times and flexible solutions for home healthcare practice by showing how to avoid planning patient healthcare applications by hand using methods that were developed for home health services. In addition, in this research, the AB, RF, GB, and NN algorithms, which are among the machine learning algorithms, were developed using patient and personnel data with known input parameters to make home healthcare application planning correct. These algorithms' accuracy and error margins were calculated, and the algorithms' results were compared. For the prediction data, the AB model showed the best performance, and the R2 value of this algorithm was computed as 0.903. The margins of error for this algorithm were found to be 0.136, 0.018, and 0.043 for the RMSE, MSE, and MAE, respectively. This article provides short planning times and flexible solutions in home healthcare practice by avoiding manual patient healthcare application planning with the methods developed in the context of home health services.en_US
dc.identifier.doi10.3390/healthcare11030319en_US
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85147710588en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/6431
dc.identifier.urihttps://doi.org/10.3390/healthcare11030319
dc.identifier.volume11en_US
dc.identifier.wosWOS:000930294900001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.ispartofHealthcare (Switzerland)en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectIstanbul; estimation; home healthcare services; machine learning algorithms; performance measurements; regression model; service quality.en_US
dc.titleAnalysis of home healthcare practice to improve service quality: Case study of megacity İstanbulen_US
dc.typeArticleen_US

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