Understanding User Experiences in Home Health Software: An Online Reviews Analytics
Abstract
The growing population has increased the demand for people to benefit from home health care (HHC) agencies, therefore, raising the need to use home health software (HHS) products to improve the quality of service and management of HHC companies. HHS products play a key role in managing and scheduling appointments, which leads to saving time and improving the productivity of HHC companies. Analyzing online reviews of HHS products helps the decision-makers in improving their products which leads to more satisfied customers (i.e. HHC agencies) and increases the quality of the HHS applications. In this study, a total of 5338 online reviews were gathered from the SoftwareAdvise website using a customized crawler. After data preprocessing and imputation, machine learning (ML) techniques including the Latent Dirichlet Allocation (LDA) topic model were used to reveal the major topics that influenced the customers’ perception. Self-organized maps (SOM) were utilized to segment the customers according to their ratings behavior which eventually led to six groups. Finally, classification and regression trees (CART) were used to predict customer outcomes based on criteria ratings. The results of this study revealed that customers have similar rating behaviors in terms of ease of use and functionality criteria in HHS. In addition, users of HHS were interested mostly in ten features including scheduling, management, support, privacy, integration, ease of use, accessibility, controllability, flexibility, and efficiency. These outcomes help researchers define their features while delivering surveys for evaluating customers’ satisfaction with HHS products.
Keywords
References
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