Utilizing online reviews for analyzing digital healthcare consultation services: Examining perspectives of both healthcare customers and healthcare professionals

INTRODUCTION

Digital healthcare consultation services, also known as telemedicine, have seen a surge in their usage, especially after the COVID-19 pandemic. The purpose of this study is to investigate the satisfaction determinants of healthcare customers (patients) and healthcare professionals (doctors), providing digital healthcare consultation services.

METHODS

The analysis involved scraping online reviews of 11 telemedicine apps meant for patients and 7 telemedicine apps meant for doctors, yielding a total of 44,440 patient reviews and 4748 doctor reviews. A structural topic modeling analysis followed by regression, dominance, correspondence, and emotion analysis was conducted to derive insights.

RESULTS

The study identified ten determinants of satisfaction from patients' and eight from doctors' perspectives. For patients, 'service variety and quality' (β = 0.5527) was the top positive determinant, while 'payment disputes' (β = -0.1173) and 'in-app membership' (β = -0.031) negatively impacted satisfaction. For doctors, 'patient consultation management' (β = 0.2009) was the leading positive determinant, with 'profile management' (β = -0.1843), 'subscription' (β = -0.183), and 'customer care support' (β = -0.0908) being the negative ones. The most influential negative emotion for patients, anger, was closely associated with 'customer care service' and 'in-app memberships,' while joy was tied to 'service variety and quality' and 'offers and discounts.' For doctors, anger was associated with 'cost-effectiveness,' and joy with 'app responsiveness.'

CONCLUSION

This study offers new insights by examining patient and doctor determinants at a granular level which can be used by telemedicine app developers and managers to build customer-centric services.

A Systematic Review of Online Medical Consultation Research

Online medical consultation is a form of medical service that facilitates interactions between patients and doctors online, offering significant utility and value. This review aims to retrieve, screen, and analyze articles related to online medical consultations, formulating a theoretical framework and proposing future research directions. According to PRISMA guidelines, a systematic search was conducted in Web of Science, EBSCO, ScienceDirect, PubMed, and Scopus, retrieving a total of 4072 English records on 16 December 2023. After rigorous screening, 75 articles were included in this review. Among these, 8 articles focused on patients utilizing online medical consultation platforms, 5 on doctors participating in online medical platforms, 18 on patients' choice of doctors, 12 on doctors providing services, 7 on online reviews of patients, 14 on service quality for patients, 8 on rewards to doctors, and 11 on the spillover effect between online and offline services. These themes comprise the theoretical framework of the starting point, process, and outcomes of the online medical consultation system, providing a comprehensive understanding of the field and a foundation for future research.

Alternative cancer clinics' use of Google listings and reviews to mislead potential patients

Background

Alternative cancer clinics, who provide treatment associated with earlier time to death, actively seek to create favorable views of their services online. An unexplored means where alternative cancer clinics can shape their appeal is their Google search results.

Methods

We retrieved the Google listing and Google reviews of 47 prominent alternative cancer clinics on August 22, 2022. We then conducted a content analysis to assess the information cancer patients are faced with online.

Results

Google listings of alternative treatment providers rarely declared the clinic was an alternative clinic versus a conventional primary cancer treatment provider (12.8% declared; 83.0% undeclared). The clinics were highly rated (median, 4.5 stars of 5). Reasons for positive reviews included treatment quality (n = 519), care (n = 420), and outcomes (n = 316). 288 reviews presented the clinics to cure or improve cancer. Negative reviews presented alternative clinics to financially exploit patients with ineffective treatment (n = 98), worsen patients' condition (n = 72), provide poor care (n = 41), and misrepresent outcomes (n = 23).

Conclusions

The favorable Google listing and reviews of alternative clinics contribute to harmful online ecosystems. Reviews provide compelling narratives but are an ineffective indicator of treatment outcomes. Google lacks safeguards for truthful reviews and should not be used for medical decision-making.

Intelligent health in the IS area: A literature review and research agenda

As the global demand for healthcare services continues to grow, improving the efficiency and effectiveness of the healthcare ecosystem has become a pressing concern. Information systems are transforming the healthcare delivery process, shifting the focus of healthcare services from passive disease treatment to proactive health prevention and the healthcare management model from hospital-centric to patient-centric. This study focuses on reviewing research in IS journals on the topic of e-health and is dedicated to constructing a theoretical model of intelligent health to provide a research basis for future discussions in this field. In addition, as the innovation of intelligent healthcare services has led to changes in its elements (e.g., an increase in the number of stakeholders), there is an urgent need to sort out and analyze the existing research.

A text analytics approach for mining public discussions in online cancer forum: Analysis of multi-intent lung cancer treatment dataset

BACKGROUND

Online cancer forums (OCF) are increasingly popular platforms for patients and caregivers to discuss, seek information on, and share opinions about diseases and treatments. This interaction generates a substantial amount of unstructured text data, necessitating deeper exploration. Using time series data, our study exploits topic modeling in the novel domain of online cancer forums (OCFs) to identify meaningful topics and changing dynamics of online discussion across different lung cancer treatment intent groups.

METHODS

For this purpose, a dataset comprising 27,998 forum posts about lung cancer was collected from three OCFs: lungcancer.net, lungevity.org, and reddit.com, spanning the years 2016 to 2018.

RESULTS

The analysis reflects the public discussion on multi-intent lung cancer treatment over time, taking into account seasonal variations. Discussions on cancer symptoms and prevention garnered the most attention, dominating both curative and palliative care discussions. There were distinct seasonal peaks: curative care topics surged from winter to late spring, while palliative care topics peaked from late summer to mid-autumn. Keyword analysis highlighted that lung cancer diagnosis and treatment were primary topics, whereas cancer prevention and treatment outcomes were predominant across multi-care contexts. For the study period, curative care discussions predominantly revolved around informational support and disease syndromes. In contrast, social support and cancer prevention prevailed in the palliative care context. Notably, topics such as cancer screening and cancer treatment exhibit pronounced seasonal variations in curative care, peaking in frequency during the summers (May to August) of the study period. Meanwhile, the topic of tumor control within palliative care showed significant seasonal influence during the winters and summers of 2017 and 2018.

CONCLUSION

Our text analysis approach using OCF data shows potential for computational methods in this novel domain to gain insights into trends in public cancer communication and seasonal variations for a better understanding of improving personalized care, decision support, treatment outcomes, and quality of life.

Potential Pitfalls With Automatic Sentiment Analysis: The Example of Queerphobic Bias

Automated sentiment analysis can help efficiently detect trends in patients' moods, consumer preferences, political attitudes and more. Unfortunately, like many natural language processing techniques, sentiment analysis can show bias against marginalised groups. We illustrate this point by showing how six popular sentiment analysis tools respond to sentences about queer identities, expanding on existing work on gender, ethnicity and disability. We find evidence of bias against several marginalised queer identities, including in the two models from Google and Amazon that seem to have been subject to superficial debiasing. We conclude with guidance on selecting a sentiment analysis tool to minimise the risk of model bias skewing results.

Quantitative risk analysis of treatment plans for patients with tumor by mining historical similar patients from electronic health records using federated learning

The determination of a treatment plan for a target patient with tumor is a difficult problem due to the existence of heterogeneity in patients' responses, incomplete information about tumor states, and asymmetric knowledge between doctors and patients, and so on. In this paper, a method for quantitative risk analysis of treatment plans for patients with tumor is proposed. To reduce the impacts of the heterogeneity in patients' responses on analysis results, the method conducts risk analysis by mining historical similar patients from Electronic Health Records (EHRs) in multiple hospitals using federated learning (FL). For this, the Recursive Feature Elimination based on the Support Vector Machine (SVM) and Deep Learning Important FeaTures (DeepLIFT) are extended into the FL framework to select key features and determine key feature weights for identifying historical similar patients. Then, in the database of each collaborative hospital, the similarities between the target patient and all historical patients are calculated, and the historical similar patients are determined. According to the statistics of tumor states and treatment outcomes of historical similar patients in all collaborative hospitals, the related data (including the probabilities of different tumor states and possible outcomes of different treatment plans) for risk analysis of the alternative treatment plans can be obtained, which can eliminate the asymmetric knowledge between doctors and patients. The related data are valuable for the doctor and patient to make their decisions. Experimental studies have been conducted to verify the feasibility and effectiveness of the proposed method.

Are online reviews of assisted living communities associated with patient-centered outcomes?

BACKGROUND

Existing literature on online reviews of healthcare providers generally portrays online reviews as a useful way to disseminate information on quality. However, it remains unknown whether online reviews for assisted living (AL) communities reflect AL care quality. This study examined the association between AL online review ratings and residents' home time, a patient-centered outcome.

METHODS

Medicare beneficiaries who entered AL communities in 2018 were identified. The main outcome is resident home time in the year following AL admission, calculated as the percentage of time spent at home (i.e., not in institutional care setting) per day being alive. Additional outcomes are the percentage of time spent in emergency room, inpatient hospital, nursing home, and inpatient hospice. AL online Google reviews for 2013-2017 were linked to 2018-2019 Medicare data. AL average rating score (ranging 1-5) and rating status (no-rating, low-rating, and high-rating) were generated using Google reviews. Linear regression models and propensity score weighting were used to examine the association between online reviews and outcomes. The study sample included 59,831 residents in 12,143 ALs.

RESULTS

Residents were predominately older (average 81.2 years), non-Hispanic White (90.4%), and female (62.9%), with 17% being dually eligible for Medicare and Medicaid. From 2013 to 2017, ALs received an average rating of 4.1 on Google, with a standard deviation of 1.1. Each one-unit increase in the AL's average online rating was associated with an increase in residents' risk-adjusted home time by 0.33 percentage points (p < 0.001). Compared with residents in ALs without ratings, residents in high-rated ALs (average rating ≥4.4) had a 0.64 pp (p < 0.001) increase in home time.

CONCLUSIONS

Higher online rating scores were positively associated with residents' home time, while the absence of ratings was associated with reduced home time. Our results suggest that online reviews may be a quality signal with respect to home time.

The Credibility of Physician Rating Websites: A Systematic Literature Review

OBJECTIVES

Increasingly, the credibility of online reviews is drawing critical attention due to the lack of control mechanisms, the constant debate about fake reviews and, last but not least, current developments in the field of artificial intelligence. For this reason, the aim of this study was to examine the extent to which assessments recorded on physician rating websites (PRWs) are credible, based on a comparison to other evaluation criteria.

METHODS

Referring to the PRISMA guidelines, a comprehensive literature search was conducted across different scientific databases. Data were synthesized by comparing individual statistical outcomes, objectives and conclusions.

RESULTS

The chosen search strategy led to a database of 36,755 studies of which 28 were ultimately included in the systematic review. The literature review yielded mixed results regarding the credibility of PRWs. While seven publications supported the credibility of PRWs, six publications found no correlation between PRWs and alternative datasets. 15 studies reported mixed results.

CONCLUSIONS

This study has shown that ratings on PRWs seem to be credible when relying primarily on patients' perception. However, these portals seem inadequate to represent alternative comparative values such as the medical quality of physicians. For health policy makers our results show that decisions based on patients' perceptions may be well supported by data from PRWs. For all other decisions, however, PRWs do not seem to contain sufficiently useful data.

Has COVID-19 changed carer's views of health and care integration in care homes? A sentiment difference-in-difference analysis of on-line service reviews

Closer integration of health and social care is a policy priority in many countries. The COVID-19 pandemic has reinforced the necessity of joining up health and social care systems, especially in care home settings. However, the meaning and perceived importance of integration for residents' and carers' experience is unclear and we do not know whether it has changed during the pandemic. Using unique data from on-line care home service reviews, we combined multiple methods. We used Natural Language Processing with supervised machine learning to construct a measure of sentiment for care home residents' and their relatives' (measured by AFINN score). Difference-in-difference analysis was used to examine whether experiencing integrated care altered these sentiments by comparing changes in sentiment in reviews related to integration (containing specific terms) to those which were not. Finally, we used network analysis on post-estimation results to assess which specific attributes stakeholders focus on most when detailing their most/least positive experiences of health and care integration in care homes, and whether these attributes changed over the pandemic. Reviews containing integration words were more positive than reviews unrelated to integration in the pre-pandemic period (about 2.3 points on the AFINN score) and remained so during the first year of the pandemic. Overall positive sentiment increased during the COVID-19 period (average by +1.1 points), mainly in reviews mentioning integration terms at the beginning of the first (+2.17, p-value 0.175) and second waves (+3.678, p-value 0.027). The role of care home staff was pivotal in both positive and negative reviews, with a shift from aspects related to care in pre-pandemic to information services during the pandemic, signalling their importance in translating integrated needs-based paradigms into policy and practice.

Physician Gender, Patient Risk, and Web-Based Reviews: Longitudinal Study of the Relationship Between Physicians' Gender and Their Web-Based Reviews

Background

Web-based reviews of physicians have become exceedingly popular among health care consumers since the early 2010s. A factor that can potentially influence these reviews is the gender of the physician, because the physician’s gender has been found to influence patient-physician communication. Our study is among the first to conduct a rigorous longitudinal analysis to study the effects of the gender of physicians on their reviews, after accounting for several important clinical factors, including patient risk, physician specialty, and temporal factors, using time fixed effects. In addition, this study is among the first to study the possible gender bias in web-based reviews using statewide data from Alabama, a predominantly rural state with high Medicaid and Medicare use.

Objective

This study conducts a longitudinal empirical investigation of the relationship between physician gender and their web-based reviews using data across the state of Alabama, after accounting for patient risk and temporal effects.

Methods

We created a unique data set by combining data from web-based physician reviews from the popular physician review website, RateMDs, and clinical data from the Center for Medicare and Medicaid Services for the state of Alabama. We used longitudinal econometric specifications to conduct an econometric analysis, while controlling for several important clinical and review characteristics across four rating dimensions (helpfulness, knowledge, staff, and punctuality). The overall rating and these four rating dimensions from RateMDs were used as the dependent variables, and physician gender was the key explanatory variable in our panel regression models.

Results

The panel used to conduct the main econometric analysis included 1093 physicians. After controlling for several clinical and review factors, the physician random effects specifications showed that male physicians receive better web-based ratings than female physicians. Coefficients and corresponding SEs and P values of the binary variable GenderFemale (1 for female physicians and 0 otherwise) with different rating variables as outcomes were as follows: OverallRating (coefficient –0.194, SE 0.060; P=.001), HelpfulnessRating (coefficient –0.221, SE 0.069; P=.001), KnowledgeRating (coefficient –0.230, SE 0.065; P<.001), StaffRating (coefficient –0.123, SE 0.062; P=.049), and PunctualityRating (coefficient –0.200, SE 0.067; P=.003). The negative coefficients indicate a bias toward male physicians versus female physicians for aforementioned rating variables.

Conclusions

This study found that female physicians receive lower web-based ratings than male physicians even after accounting for several clinical characteristics associated with the physicians and temporal effects. Although the magnitude of the coefficients of GenderFemale was relatively small, they were statistically significant. This study provides support to the findings on gender bias in the existing health care literature. We contribute to the existing literature by conducting a study using data across the state of Alabama and using a longitudinal econometric analysis, along with incorporating important clinical and review controls associated with the physicians.