Quality Information Disclosure and Patient Reallocation in the Healthcare Industry: Evidence from Cardiac Surgery Report Cards

Yoon, Tae Jung

doi:10.1287/mksc.2018.1146

Cited by 19 publications

(18 citation statements)

References 33 publications

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“…These arguments are further supported by Wu [ 21 ], who stated that information quality is highly a critical factor that influences the patients’ online behavior. Moreover, Yoon [ 31 ] stated that patients tend to adopt that information, which is more factual, relevant, and useful. Hence, quality information about a doctor enables patients to obtain further information on healthcare providers and hospitals.…”

Section: Theoretical Background and Research Hypothesismentioning

confidence: 99%

Exploring the Impact of Linguistic Signals Transmission on Patients’ Health Consultation Choice: Web Mining of Online Reviews

Shah

Ali

Qayyum

et al. 2021

IJERPH

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Background: Patients face difficulties identifying appropriate physicians owing to the sizeable quantity and uneven quality of information in physician rating websites. Therefore, an increasing dependence of consumers on online platforms as a source of information for decision-making has given rise to the need for further research into the quality of information in the form of online physician reviews (OPRs). Methods: Drawing on the signaling theory, this study develops a theoretical model to examine how linguistic signals (affective signals and informative signals) in physician rating websites affect consumers’ decision making. The hypotheses are tested using 5521 physicians’ six-month data drawn from two leading health rating platforms in the U.S (i.e., Healthgrades.com and Vitals.com) during the COVID-19 pandemic. A sentic computing-based sentiment analysis framework is used to implicitly analyze patients’ opinions regarding their treatment choice. Results: The results indicate that negative sentiment, review readability, review depth, review spelling, and information helpfulness play a significant role in inducing patients’ decision-making. The influence of negative sentiment, review depth on patients’ treatment choice was indirectly mediated by information helpfulness. Conclusions: This paper is a first step toward the understanding of the linguistic characteristics of information relating to the patient experience, particularly the emerging field of online health behavior and signaling theory. It is also the first effort to our knowledge that employs sentic computing-based sentiment analysis in this context and provides implications for practice.

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Section: Theoretical Background and Research Hypothesismentioning

confidence: 99%

Exploring the Impact of Linguistic Signals Transmission on Patients’ Health Consultation Choice: Web Mining of Online Reviews

Shah

Ali

Qayyum

et al. 2021

IJERPH

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“…Most of the literature employed "argument quality or information quality" to measure the information quality as a predictor of users' behavior in the healthcare domain. In this literature review, we found 17 (32.69%) articles that indicated the impact of linguistic signals on patients' behavior [52,[67][68][69][70][71][72][73][74][75][76][77][78][79][80][81][82].…”

Section: Linguistic Signalingmentioning

confidence: 99%

Examining Different Factors in Web-Based Patients’ Decision-Making Process: Systematic Review on Digital Platforms for Clinical Decision Support System

Shah

Muhammad

Lee

2021

IJERPH

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(1) Background: The appearance of physician rating websites (PRWs) has raised researchers’ interest in the online healthcare field, particularly how users consume information available on PRWs in terms of online physician reviews and providers’ information in their decision-making process. The aim of this study is to consistently review the early scientific literature related to digital healthcare platforms, summarize key findings and study features, identify literature deficiencies, and suggest digital solutions for future research. (2) Methods: A systematic literature review using key databases was conducted to search published articles between 2010 and 2020 and identified 52 papers that focused on PRWs, different signals in the form of PRWs’ features, the findings of these studies, and peer-reviewed articles. The research features and main findings are reported in tables and figures. (3) Results: The review of 52 papers identified 22 articles for online reputation, 15 for service popularity, 16 for linguistic features, 15 for doctor–patient concordance, 7 for offline reputation, and 11 for trustworthiness signals. Out of 52 studies, 75% used quantitative techniques, 12% employed qualitative techniques, and 13% were mixed-methods investigations. The majority of studies retrieved larger datasets using machine learning techniques (44/52). These studies were mostly conducted in China (38), the United States (9), and Europe (3). The majority of signals were positively related to the clinical outcomes. Few studies used conventional surveys of patient treatment experience (5, 9.61%), and few used panel data (9, 17%). These studies found a high degree of correlation between these signals with clinical outcomes. (4) Conclusions: PRWs contain valuable signals that provide insights into the service quality and patient treatment choice, yet it has not been extensively used for evaluating the quality of care. This study offers implications for researchers to consider digital solutions such as advanced machine learning and data mining techniques to test hypotheses regarding a variety of signals on PRWs for clinical decision-making.

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“…The policy effects of hospital price and quality transparency tools have not been thoroughly investigated. A stream of literature has estimated and synthesized [2,3,[15][16][17][18][19][20][21][22][23][24][25][26][27] the impact of hospital price and quality transparency tools on provider's provision and quality improvement behaviors [28][29][30][31] and consumer's healthcare seeking behavior [32][33][34][35][36][37][38], while the relationship between hospital quality transparency and healthcare spending (i.e., the price of healthcare procedures and the payment of consumers) was overlooked to some extent. There were only 2 reviews synthesizing this relationship with a narrow scope (e.g., they omitted insurers' contract provision and pricing behaviors), limited space, and dated evidence [22,27].…”

Section: Introductionmentioning

confidence: 99%

The impact of hospital price and quality transparency tools on healthcare spending: a systematic review

Chen

Miraldo

2022

Health Econ Rev

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Background Global spending on health was continuing to rise over the past 20 years. To reduce the growth rates, alleviate information asymmetry, and improve the efficiency of healthcare markets, global health systems have initiated price and quality transparency tools in the hospital industry in the last two decades. Objective The objective of this review is to synthesize whether, to what extent, and how hospital price and quality transparency tools affected 1) the price of healthcare procedures and services, 2) the payments of consumers, and 3) the premium of health insurance plans bonding with hospital networks. Methods A literature search of EMBASE, Web of Science, Econlit, Scopus, Pubmed, CINAHL, and PsychINFO was conducted, from inception to Oct 31, 2021. Reference lists and tracked citations of retrieved articles were hand-searched. Study characteristics were extracted, and included studies were scored through a risk of bias assessment framework. This systematic review was reported according to the PRISMA guidelines and registered in PROSPERO with registration No. CRD42022319070. Results Of 2157 records identified, 18 studies met the inclusion criteria. Near 40 percent of studies focused on hospital quality transparency tools, and more than 90 percent of studies were from the US. Hospital price transparency reduced the price of laboratory and imaging tests except for office-visit services. Hospital quality transparency declined the level or growth rates of healthcare spending, while it adversely and significantly raised the price of healthcare services and consumers’ payment in higher-ranked or rated facilities, which was referred to as the reputation premium in the healthcare industry. Hospital quality transparency not only leveraged private insurers bonding with a higher-rated hospital network to increase premiums, but also induced their anticipated pricing behaviors. Conclusion Hospital price and quality transparency was not effective as expected. Future research should explore the understudied consequences of hospital quality transparency programs, such as the reputation/rating premium and its policy intervention.

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Quality Information Disclosure and Patient Reallocation in the Healthcare Industry: Evidence from Cardiac Surgery Report Cards

Abstract: This paper shows that quality information disclosure in the healthcare industry can have a negative impact on positive assortative matching between patients and healthcare providers.

Cited by 19 publications

References 33 publications

Exploring the Impact of Linguistic Signals Transmission on Patients’ Health Consultation Choice: Web Mining of Online Reviews

Exploring the Impact of Linguistic Signals Transmission on Patients’ Health Consultation Choice: Web Mining of Online Reviews

Examining Different Factors in Web-Based Patients’ Decision-Making Process: Systematic Review on Digital Platforms for Clinical Decision Support System

The impact of hospital price and quality transparency tools on healthcare spending: a systematic review

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