İnaç Rabia Çevik, Ekmekçi İsmail
Department of Industrial Engineering (Ph.D. Program), Institute of Pure and Applied Science, Istanbul Commerce University, Kucukyali, Istanbul 34445, Turkey.
Department of Industrial Engineering, Istanbul Commerce University, Kucukyali, Istanbul 34445, Turkey.
Healthcare (Basel). 2023 Jan 20;11(3):319. doi: 10.3390/healthcare11030319.
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 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 R 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.
家庭医疗服务是一种公共或私人服务，旨在为社会弱势群体、病人、贫困者、残疾人和老年人提供居家医疗服务。本研究旨在通过分析影响家庭医疗实践的因素来提高其质量。在伊斯坦布尔这个大城市，通过考虑影响家庭医疗实践的14个不同输入变量，使用了来自1707名患者的数据。在家庭医疗实践中发挥积极作用的患者和医护人员的人口统计学、地理和生活条件构成了本研究输入参数的核心主题。回归方法被用于研究影响患者需要家庭医疗服务时长的因素，这是该研究的输出变量。本文通过展示如何使用为家庭健康服务开发的方法避免手工规划患者医疗申请，为家庭医疗实践提供了短规划时间和灵活的解决方案。此外，在本研究中，利用具有已知输入参数的患者和人员数据开发了机器学习算法中的AB、RF、GB和NN算法，以使家庭医疗申请规划正确。计算了这些算法的准确性和误差范围，并比较了算法的结果。对于预测数据，AB模型表现最佳，该算法的R值计算为0.903。该算法的均方根误差（RMSE）、均方误差（MSE）和平均绝对误差（MAE）的误差范围分别为0.136、0.018和0.043。本文通过避免使用在家庭健康服务背景下开发的方法进行手工患者医疗申请规划，为家庭医疗实践提供了短规划时间和灵活的解决方案。
