公众初级卫生保健满意度的价值维度分析:前瞻性观察研究

S. D. Mazunina, S. B. Petrov, K. Melkonian, D. V. Veselova
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引用次数: 0

摘要

背景。人工神经网络模型可以用于分析和预测门诊主要流程的价值维度内的结构成分,作为患者满意度的指标。目的-形成和测试方法,用于分析和预测门诊诊所主要流程价值维度内的结构成分,作为患者对医疗服务可用性和质量的满意度指标。方法。对525名患者进行问卷调查,分析其对全科医生预约的满意度。选择由径向基网络和多层感知器组成的网络集合作为神经网络模型的基础。模型测试涉及基洛夫的五个门诊诊所。调查对象共计217名患者。统计处理包括数据描述和分析。定性属性用相对值表示(P, %)。定性资料差异的统计学意义采用卡方检验。通过非参数Spearman相关分析评估观测数据与预测数据之间的相关性。以p <0.05为显著性水平(p),统计学数据处理采用Statistica 13.0.Results软件。对满意度价值维度的分析显示,“预约前”阶段占主导地位:挂号员的工作(对接受医疗服务有85.29%的重要性)、等待医生预约的时间(66.76%的受访者指出其重要性)、直接在办公室等待的时间(69.11%的受访者指出其重要性)。“预约”阶段按照全科医生预约的一般流程(面谈、检查、推荐)形成,从患者的价值角度进行评估。优先的组成部分包括预约时间的充分性(在88.27%的病例中显著),检查满意度(在85.14%的病例中显著)以及咨询的完整性和信息性(在89.9%的病例中显著)。观测数据与预测数据之间有很强的直接相关性(ρxy = 0.9;P < 0.05)。在所有医疗机构中,观察到的总体患者满意度水平与预测的总体患者满意度水平之间没有统计学上的显著差异。提出的神经网络模型可作为建立信息管理系统的基础,以监测医疗机构新模式的有效性标准;以及为组织最佳患者管理相关的行政决策提供必要的支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of Value Dimensions in Public Satisfaction with Primary Health Care: Prospective Observational Study
Background. Artificial neural network models can be used to analyze and predict structural components within the value dimension of the main processes in an outpatient clinic as indicators of patient satisfaction.Objective — to form and test the methodology for analyzing and predicting structural components within the value dimension of the main processes in an outpatient clinic, as indicators of patient satisfaction with availability and quality of medical care, using artificial intelligence.Methods. The results of questionnaires administered to 525 patients were used to analyze their satisfaction with GP appointments. A network ensemble consisting of radial basis network and multilayer perceptron was chosen as the basis for a neural network model. The model testing involved five outpatient clinics in Kirov. The total number of respondents comprised 217 patients. Statistical processing included data description and analysis. Qualitative attributes were represented by relative values (P, %). The statistical significance of differences in qualitative data was assessed using the Chi-square test. The correlation between the observed and predicted data was assessed by means of nonparametric Spearman correlation analysis. The value of p <0.05 was chosen as the significance level ( p). Statistical data processing was performed using Statistica 13.0.Results. Analysis of the value dimensions of satisfaction showed a predominance of “pre-appointment” stage: work of a registrar (85.29% significance in the receiving medical services), waiting time for an appointment with a doctor (66.76% respondents noted its significance), duration of waiting directly at the office (important for 69.11% of respondents). “Appointment” stage was formed according to the common procedure of a GP appointment (interview, examination, recommendations) and was assessed from the value perspective of the patient. The priority components included sufficiency of appointment duration (significant in 88.27% of cases), satisfaction with examination (significant in 85.14% of cases), as well as completeness and informativeness of consultation (significant in 89.9% of cases). A strong direct correlation between the observed and predicted data (ρxy = 0.9; p < 0.05) was found out. Statistically significant differences between the observed and predicted levels of general patient satisfaction were not revealed in all medical organizations.Conclusion. The suggested neural network models can be used as the basis when creating information management systems that monitor meeting the effectiveness criteria for a new model of a medical organization; as well as an essential support for administrative decisions related to organizing the optimal patient management.
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