Classification of reviews of e-healthcare services to improve patient satisfaction: Insights from an emerging economy

Dhakate, Nikhil; Joshi, Rohit

doi:10.1016/j.jbusres.2023.114015

Cited by 7 publications

(6 citation statements)

References 78 publications

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“…More obvious and easy to research are online healthcare platforms such as webmd, practo, good doctors, etc. These platforms allow doctors to make the best use of utilizing their spare time and medical expertise to provide services to remote patients, enabling patients to experience appropriate services through online platforms [11]. People can access health information and services or manage their health conditions by consulting remote doctors in hospitals across the country [15].…”

Section: Related Researchmentioning

confidence: 99%

“…(doctor online score) = 0.037 + 0.040 (treatment e cacy satisfaction) + 0.037 (treatment modality access) + 0.038 (treatment cost$) + 0.054 (attitude satisfaction) + 0.052 * (treatment process expectation) + 0.046 (corresponding to the total number of doctor visits) + µ + ε (10) (Satisfaction with treatment e cacy) = 0.037 + 0.041 (doctor's online rating) + 0.038 (access to treatment modalities) + 0.038 (cost of treatment $) + 0.054 (attitudinal satisfaction) + 0.053 * (expectations of the treatment process) + 0.046 (corresponding to the total number of doctor's visits) + µ + ε (11) where µ denotes the capture of unobserved individual-speci c effects and ε it denotes the residual random error term. The coe cient of the interaction term is positive with R2 = 0.871, which is a good t, indicating the presence of moderating variables and high reliability of the model.…”

Section: Hypothesis Validation and Model T Assessmentmentioning

confidence: 99%

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Do people only believe what they want to believe? An empirical analysis of the Pygmalion effect in telemedicine platforms based on linear regression algorithms

Shen,

Yan,

Liu

2024

Preprint

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In addition to exploring how people's expectations and beliefs about healthcare information and services affect their actual health outcomes, this study aims to empirically analyze whether there is a Pygmalion effect in healthcare platforms using machine learning and natural language processing. Regression modeling will be used to provide treatment recommendations for more common diseases. We gathered a 10-month panel dataset from a major Chinese online medical portal, containing information from 10,243 physicians. We discovered a strong linear correlation between users' expectations for their final level of recovery and satisfaction and their access to doctors, medical information, treatment alternatives, and healthcare experiences. People's choice of therapy for more complicated illnesses, like heart valve lesions and breast cancer, should lean more away from conventional information sources. Patients' expectations and treatment adherence are strongly connected with the expectations of their doctors, and treatment outcomes are also significantly influenced by the beliefs and expectations of the patients themselves. Using sentiment analysis and multiple robustness polls of user ratings on healthcare platforms, we demonstrate that the treatment choices made by users are distributed linearly across various complexity levels of diseases. As a result, this study highlights the real influence of patient and physician expectations and beliefs on healthcare outcomes, proves the presence of the Pygmalion effect on healthcare platforms, and explores it for particular diseases. This has real-world implications for raising patient happiness, enhancing medical service quality, and strengthening the doctor-patient bond.

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Section: Related Researchmentioning

confidence: 99%

Section: Hypothesis Validation and Model T Assessmentmentioning

confidence: 99%

Do people only believe what they want to believe? An empirical analysis of the Pygmalion effect in telemedicine platforms based on linear regression algorithms

Shen,

Yan,

Liu

2024

Preprint

View full text Add to dashboard Cite

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“…Therefore, hospitals are required to provide quality services in accordance with established standards and can reach all levels of society (Martin et al, 2021). Quality health services are health services that can satisfy every service user in accordance with the average level of satisfaction of the population and its implementation in accordance with established professional standards and codes of ethics (Dhakate & Joshi, 2023).…”

Section: Introductionmentioning

confidence: 99%

Lean Management as an Innovation to Reduce Complaint Handling Time in Hospital

Munaa,

Faiz,

Vica

et al. 2023

noval

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Customer service is a service product from Hospital X. The better the service at a hospital, the number of complaints will decrease. In the customer service flow for Hospital X there is Complaint Handling and monitoring and evaluation. The purpose of this research is to provide lean management innovation as an effort to reduce the waiting time for complaint handling. In this study the method used was community service with the time period April-July 2023 at Hospital X. The column for patient complaints at the hospital was not clear and detailed. So the added innovation is to cut steps or steps that lack value by creating or adding a complaint menu on the Hospital website. The complaints menu contains information and complaints services

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“…Jiang et al (2022) [58], assessed the effectiveness of MS Azure sentiment analysis service in conjunction with other sentiment analysis tools with metaphoric testing. In another study [59], the MS Azure sentiment machine learning was used to calculate the sentiment and satisfaction expressed by patients regarding online doctor services.…”

mentioning

confidence: 99%

A Fuzzy Synthetic Evaluation Approach to Assess Usefulness of Reviews by considering Bias Inherited in Sentiments and Articulacy

Kardaras,

Troussas,

Barbounaki

et al. 2024

Preprint

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The reviews usefulness has been the aim of several research studies. However, results regarding the significance of usefulness determinants are often contradicting, thus decreasing the accuracy of reviews’ helpfulness estimation. Also, bias in user reviews attributed to differences e.g. in gender, nationality, etc., may result into misleading judgments thus diminishing reviews’ usefulness. Research is needed for sentiment analysis algorithms that incorporate bias embedded in reviews, thus improving their usefulness, readability, credibility, etc. This study utilizes fuzzy relations and fuzzy synthetic evaluation (FSE) in order to calculate reviews’ usefulness by incorporating users’ biases as expressed in terms of reviews’ articulacy and sentiment polarity. It selected and analysed 95.678 hotel user reviews from Tripadvisor, for five nationalities. The findings indicate that there are differences among nationalities. The British are most consistent in their judgments expressed in titles and review documents. The British and the Greek review titles suffice to convey any negative sentiments. The Dutch use fewer words in their reviews than the other nationalities. This study suggests that fuzzy logic captures subjectivity which is often found in reviews, and it can be used to quantify users’ behavioral differences, calculate reviews usefulness, and provide the means for developing more accurate voting systems.

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Classification of reviews of e-healthcare services to improve patient satisfaction: Insights from an emerging economy

Cited by 7 publications

References 78 publications

Do people only believe what they want to believe? An empirical analysis of the Pygmalion effect in telemedicine platforms based on linear regression algorithms

Do people only believe what they want to believe? An empirical analysis of the Pygmalion effect in telemedicine platforms based on linear regression algorithms

Lean Management as an Innovation to Reduce Complaint Handling Time in Hospital

A Fuzzy Synthetic Evaluation Approach to Assess Usefulness of Reviews by considering Bias Inherited in Sentiments and Articulacy

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