■目的是探索影响孟加拉国患者决策过程的因素及其对印度医疗旅游的满意度。
■该研究采用了定量研究方法,并进行了横断面调查。数据是从患者或其亲属(N=388)那里收集的,他们决定前往吉大港印度签证中心(IVAC)进行医疗和治疗。数据是使用结构化的,预先测试,和主持人管理的问卷,主要包括社会人口特征,健康状况,医疗旅游信息和医疗旅游指数。进行分层回归分析,以探讨影响他们对印度医疗旅游满意度的因素。
■超过四分之三的参与者曾访问印度进行自我治疗。在参与者中,14%是心脏病患者,13%患有癌症。超过四分之一的受访者的亲属是有关医疗旅游的主要信息来源。印度有经验丰富的医生,高标准的医院/医疗设施,训练有素的医生,信誉良好的医生,优质治疗和医疗材料是排名第一的项目。回归结果表明,设施和服务表现为最强的因素(β=0.24,t=4.71,p<0.001),其次是旅游目的地因素(β=0.16,t=3.11,p=0.002),医疗旅游成本因子(β=0.16,t=3.24,p=0.001)和国家环境因素(β=0.15,t=2.69,p=0.007)。
■我们发现与设施和服务相关的因素是我们模型中最强的预测因素之一。因此,母国必须加强医疗保健提供者的高级专业培训,包括服务态度。此外,重要的是要减少语言障碍,减少医疗游客的机票,并使患者的治疗费用更实惠。
The aims are to explore the factors influencing Bangladeshi patients\' decision-making process and their satisfaction level toward medical tourism in India.
The study used a quantitative research approach with a cross-sectional survey. Data were collected from the patients or their relatives (N = 388) who would have decided to travel to India for medical and treatment purposes at the Chittagong Indian visa center (IVAC). Data were collected using a structured, pre-tested, and facilitator-administered questionnaire, which mainly included the social demographic characteristics, health status, medical tourism information and medical tourism index. Hierarchical regression analysis was performed to explore the factors influencing their satisfaction level toward medical tourism in India.
More than three-fourths of the participants had visited India for self-treatment. Of the participants, 14% were cardiology patients, and 13% suffered from cancer. The relatives were the key source of information regarding medical tourism for more than one-fourth of the respondents. India\'s availability of well-experienced doctors, hospital/medical facilities with high standards, well-trained doctors, reputable doctors, and quality treatments and medical materials were top-ranked items. Regression results depict that
facility and services appeared as the strongest factor (β = 0.24, t = 4.71, p < 0.001) followed by tourism destination factor (β = 0.16, t = 3.11, p = 0.002), medical tourism costs factor (β = 0.16, t = 3.24, p = 0.001) and country environment factor (β = 0.15, t = 2.69, p = 0.007).
We found that the factor related to
facility and services is one of the strongest predictors in our models. Therefore, home countries must strengthen the health care providers\' advanced professional training, including service attitudes. Moreover, it is important to lessen the language barrier, reduce the airfare for medical tourists, and make the treatment cost more affordable for patients.