Healthcare 2023, 11, 319. https://doi.org/10.3390/healthcare11030319 www.mdpi.com/journal/healthcare Article Analysis of Home Healthcare Practice to Improve Service Quality: Case Study of Megacity Istanbul Rabia Çevik İnaç 1, * and İsmail Ekmekçi 2 1 Department of Industrial Engineering (Ph.D. Program), Institute of Pure and Applied Science, Istanbul Commerce University, Kucukyali, Istanbul 34445, Turkey 2 Department of Industrial Engineering, Istanbul Commerce University, Kucukyali, Istanbul 34445, Turkey * Correspondence: rcevik@gmail.com Abstract: Home 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 algo- rithms, which are among the machine learning algorithms, were developed using patient and per- sonnel 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 R 2 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 plan- ning 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. Keywords: home healthcare services; regression model; machine learning algorithms; estimation; performance measurements; service quality; Istanbul 1. Introduction Home healthcare services are provided to individuals who need them due to several diseases, including social and mental counselling services in their homes and families [1]. Home care services try to lessen the effects of illness and disability by making it easier for people to live their daily lives, finding the best way to treat them, and improving their quality of life [2]. This service is also suitable for those who prefer to stay at home and whose treatment and care continue, but it is necessary for those who cannot be cared for by their close family and friends. Home health services include various health services offered in their own homes upon the requests of patients who cannot access health institutions. Home health services are attractive because of their advantages when the treatment is carried out in a hospital or home environment [3]. Patients, healthcare workers, and health institutions are the three most important factors in the home health services process [4]. The problem be- comes quite complex when the specific constraints of all stakeholders are brought to- gether. Controls in health institutions include how long it takes to plan, how often Citation: Çevik İnaç, R.; Ekmekçi, İ. Analysis of Home Healthcare Practice to Improve Service Quality: Case Study of Megacity Istanbul. Healthcare 2023, 11, 319. https://doi.org/10.3390/ healthcare11030319 Academic Editor: Daniele Giansanti Received: 1 December 2022 Revised: 16 January 2023 Accepted: 18 January 2023 Published: 20 January 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/license s/by/4.0/).