Can competition improve hospital quality of care? A difference-in-differences approach to evaluate the effect of increasing quality transparency on hospital quality

Public reporting on the quality of care is intended to guide patients to the provider with the highest quality and to stimulate a fair competition on quality. We apply a difference-in-differences design to test whether hospital quality has improved more in markets that are more competitive after the first public release of performance data in Germany in 2008. Panel data from 947 hospitals from 2006 to 2010 are used. Due to the high complexity of the treatment of stroke patients, we approximate general hospital quality by the 30-day risk-adjusted mortality rate for stroke treatment. Market structure is measured (comparatively) by the Herfindahl–Hirschman index (HHI) and by the number of hospitals in the relevant market. Predicted market shares based on exogenous variables only are used to compute the HHI to allow a causal interpretation of the reform effect. A homogenous positive effect of competition on quality of care is found. This effect is mainly driven by the response of non-profit hospitals that have a narrow range of services and private for-profit hospitals with a medium range of services. The results highlight the relevance of outcome transparency to enhance hospital quality competition.

The Application of Salutogenesis in Hospitals

Hospitals, in developed countries the center of curative health care in practice, research, and education, still have a dominantly pathogenic orientation. Therefore, salutogenic principles definitely have to offer quality improvement of cure and care in hospitals. But salutogenesis also is a considerable challenge to be implemented in hospitals, and hospitals are challenging for health and salutogenesis promoters. In this chapter, the authors first demonstrate how salutogenesis, if understood as a specific dimension of hospital quality, could considerably contribute to better health gain for patients and hospital staff. Second, drawing on a comprehensive literature search, it is highlighted which aspects of salutogenesis in relation to hospitals already are covered in descriptive and intervention research focusing on patients (and family members), staff, and the hospital as an organization.

Hospital Facebook Reviews Analysis Using a Machine Learning Sentiment Analyzer and Quality Classifier

While experts have recognised the significance and necessity of social media integration in healthcare, no systematic method has been devised in Malaysia or Southeast Asia to include social media input into the hospital quality improvement process. The goal of this work is to explain how to develop a machine learning system for classifying Facebook reviews of public hospitals in Malaysia by using service quality (SERVQUAL) dimensions and sentiment analysis. We developed a Machine Learning Quality Classifier (MLQC) based on the SERVQUAL model and a Machine Learning Sentiment Analyzer (MLSA) by manually annotated multiple batches of randomly chosen reviews. Logistic regression (LR), naive Bayes (NB), support vector machine (SVM), and other methods were used to train the classifiers. The performance of each classifier was tested using 5-fold cross validation. For topic classification, the average F1-score was between 0.687 and 0.757 for all models. In a 5-fold cross validation of each SERVQUAL dimension and in sentiment analysis, SVM consistently outperformed other methods. The study demonstrates how to use supervised learning to automatically identify SERVQUAL domains and sentiments from patient experiences on a hospital’s Facebook page. Malaysian healthcare providers can gather and assess data on patient care via the use of these content analysis technology to improve hospital quality of care.

Wisdom of the Experts versus Wisdom of the Crowd in Hospital Quality Ratings: Cross-Sectional Analyses of Google Ratings (Preprint)

BACKGROUND Popular online portals provide free and convenient access to user-generated quality reviews. Centers for Medicare and Medicaid Services (CMS) also provide patients with Hospital Compare Star Ratings (HCSR), a single public measure of hospital quality aggregating multiple quality dimensions. Consumers often use crowdsourced hospital ratings on platforms such as Google to select hospitals, but it is unknown if these ratings reflect a comprehensive measure of clinical quality. OBJECTIVE We analyze if Google online quality ratings, which reflect the wisdom of the crowd, are associated with HCSR, which reflect the wisdom of the experts. CMS revised the methodology of assigning star ratings to hospitals. Therefore, we analyze these associations before and after the 2021 revisions of the CMS rating system. METHODS We extracted Google ratings using Application Programming Interface (API) in June 2020. The HCSR data of April 2020 (before the revision of HCSR methodology) and April 2021 (after the revision of HCSR methodology) were obtained from CMS’ Hospital Compare (HC) website. We also extracted scores for the individual components of hospital quality for each of the hospitals in our sample using the code provided by HC. Fractional Response Model (FRM) was used to estimate the association between Google Ratings and HCSR and individual components of quality. RESULTS Results indicate that Google ratings are statistically associated with HCSR (P<.001) after controlling for hospital level effects. A one star improvement in CMS ratings before the change in methodology (after the change in methodology) is expected to increase the Google ratings by 0.145 (0.135) on average (95% CI 0.127- 0.163; P<.001, 95% CI 0.116-0.153; P<.001). The analyses with individual components of hospital quality reveal that Google ratings are not associated with components of HCSR that require medical expertise such as ‘Safety of care’ or ‘Readmissions’. The revised CMS rating system ameliorates previous partial inconsistencies in association between Google ratings and component scores of HCSR. CONCLUSIONS Overall, crowd sourced Google hospital ratings are informative about expert CMS hospital quality ratings and several individual quality components that are easier for patients to evaluate. Therefore, hospitals should not expect improvements in quality metrics that require expertise to assess such as safety of care and readmission to result in improved Google star ratings. Hospitals can benefit from using crowd-sourced ratings as timely, easily available, and dynamic indicators of their quality performance.

Assessing Patient-Perceived Hospital Service Quality and Sentiment in Malaysian Public Hospitals Using Machine Learning and Facebook Reviews

Social media is emerging as a new avenue for hospitals and patients to solicit input on the quality of care. However, social media data is unstructured and enormous in volume. Moreover, no empirical research on the use of social media data and perceived hospital quality of care based on patient online reviews has been performed in Malaysia. The purpose of this study was to investigate the determinants of positive sentiment expressed in hospital Facebook reviews in Malaysia, as well as the association between hospital accreditation and sentiments expressed in Facebook reviews. From 2017 to 2019, we retrieved comments from 48 official public hospitals’ Facebook pages. We used machine learning to build a sentiment analyzer and service quality (SERVQUAL) classifier that automatically classifies the sentiment and SERVQUAL dimensions. We utilized logistic regression analysis to determine our goals. We evaluated a total of 1852 reviews and our machine learning sentiment analyzer detected 72.1% of positive reviews and 27.9% of negative reviews. We classified 240 reviews as tangible, 1257 reviews as trustworthy, 125 reviews as responsive, 356 reviews as assurance, and 1174 reviews as empathy using our machine learning SERVQUAL classifier. After adjusting for hospital characteristics, all SERVQUAL dimensions except Tangible were associated with positive sentiment. However, no significant relationship between hospital accreditation and online sentiment was discovered. Facebook reviews powered by machine learning algorithms provide valuable, real-time data that may be missed by traditional hospital quality assessments. Additionally, online patient reviews offer a hitherto untapped indication of quality that may benefit all healthcare stakeholders. Our results confirm prior studies and support the use of Facebook reviews as an adjunct method for assessing the quality of hospital services in Malaysia.