Association of Online Consumer Reviews of Skilled Nursing Facilities With Patient Rehospitalization Rates

Association Between Online Reviews of Substance Use Disorder Treatment Facilities and Drug-Induced Mortality Rates: Cross-Sectional Analysis

BACKGROUND

Drug-induced mortality across the United States has continued to rise. To date, there are limited measures to evaluate patient preferences and priorities regarding substance use disorder (SUD) treatment, and many patients do not have access to evidence-based treatment options. Patients and their families seeking SUD treatment may begin their search for an SUD treatment facility online, where they can find information about individual facilities, as well as a summary of patient-generated web-based reviews via popular platforms such as Google or Yelp. Web-based reviews of health care facilities may reflect information about factors associated with positive or negative patient satisfaction. The association between patient satisfaction with SUD treatment and drug-induced mortality is not well understood.

OBJECTIVE

The objective of this study was to examine the association between online review content of SUD treatment facilities and drug-induced state mortality.

METHODS

A cross-sectional analysis of online reviews and ratings of Substance Abuse and Mental Health Services Administration (SAMHSA)-designated SUD treatment facilities listed between September 2005 and October 2021 was conducted. The primary outcomes were (1) mean online rating of SUD treatment facilities from 1 star (worst) to 5 stars (best) and (2) average drug-induced mortality rates from the Centers for Disease Control and Prevention (CDC) WONDER Database (2006-2019). Clusters of words with differential frequencies within reviews were identified. A 3-level linear model was used to estimate the association between online review ratings and drug-induced mortality.

RESULTS

A total of 589 SAMHSA-designated facilities (n=9597 reviews) were included in this study. Drug-induced mortality was compared with the average. Approximately half (24/47, 51%) of states had below average ("low") mortality rates (mean 13.40, SD 2.45 deaths per 100,000 people), and half (23/47, 49%) had above average ("high") drug-induced mortality rates (mean 21.92, SD 3.69 deaths per 100,000 people). The top 5 themes associated with low drug-induced mortality included detoxification and addiction rehabilitation services (r=0.26), gratitude for recovery (r=-0.25), thankful for treatment (r=-0.32), caring staff and amazing experience (r=-0.23), and individualized recovery programs (r=-0.20). The top 5 themes associated with high mortality were care from doctors or providers (r=0.24), rude and insensitive care (r=0.23), medication and prescriptions (r=0.22), front desk and reception experience (r=0.22), and dissatisfaction with communication (r=0.21). In the multilevel linear model, a state with a 10 deaths per 100,000 people increase in mortality was associated with a 0.30 lower average Yelp rating (P=.005).

CONCLUSIONS

Lower online ratings of SUD treatment facilities were associated with higher drug-induced mortality at the state level. Elements of patient experience may be associated with state-level mortality. Identified themes from online, organically derived patient content can inform efforts to improve high-quality and patient-centered SUD care.

Prediction of rehospitalization and mortality risks for skilled nursing facilities using a dimension reduction approach

BACKGROUND

Hospitals are incentivized to reduce rehospitalization rates, creating an emphasis on skilled nursing facilities (SNFs) for post-hospital discharge. How rehospitalization rates vary depending on patient and SNF characteristics is not well understood, in part because these characteristics are high-dimensional. We sought to estimate rehospitalization and mortality risks by patient and skilled nursing facility (SNF) leveraging high-dimensional characteristics.

METHODS

Using 1,060,337 discharges from 13,708 SNFs of Medicare patients residing or visiting a provider in Wisconsin, Iowa, and Illinois, factor analysis was performed to reduce the number of patient and SNF characteristics. K-means clustering was applied to SNF factors to categorize SNFs into groups. Rehospitalization and mortality risks within 60 days of discharge was estimated by SNF group for various values of patient factors.

RESULTS

Patient and SNF characteristics (616 in total) were reduced to 12 patient factors and 4 SNF groups. Patient factors reflected broad conditions. SNF groups differed in beds and staff capacity, off-site services, and physical and occupational therapy capacity; and in mortality and rehospitalization rates for some patients. Patients with cardiac, orthopedic, and neuropsychiatric conditions are associated with better outcomes when assigned to SNFs with greater on-site capacity (i.e. beds, staff, physical and occupational therapy), whereas patients with conditions related to cancer or chronic renal failure are associated with better outcomes when assigned to SNFs with less on-site capacity.

CONCLUSIONS

Risks of rehospitalization and mortality appear to vary significantly by patient and SNF, with certain SNFs being better suited for some patient conditions over others.

Online Customer Reviews of Assisted Living Communities: Association with Community, County, and State Factors

OBJECTIVES

Online reviews provided by users of assisted living communities may offer a unique source of heretofore unexamined data. We explored online reviews as a possible source of information about these communities and examined the association between the reviews and aspects of state regulations, while controlling for assisted living, county, and state market-level factors.

DESIGN

Cross-sectional, observational study.

SETTING AND PARTICIPANTS

Sample included 149,265 reviews for 8828 communities.

METHODS

Primary (eg, state regulations) and secondary (eg, Medicare Beneficiary Summary Files) data were used. County-level factors were derived from the Area Health Resource Files, and state-level factors from the integrated Public Use Microdata series. Information on state regulations was obtained from a previously compiled regulatory dataset. Average assisted living rating score, calculated as the mean of posted online reviews, was the outcome of interest, with a higher score indicating a more positive review. We used word cloud to visualize how often words appeared in 1-star and 5-star reviews. Logistic regression models were used to determine the association between online rating and a set of community, county, and state variables. Models were weighted by the number of reviews per assisted living bed.

RESULTS

Overall, 76% of communities had online reviews. We found lower odds of positive reviews in communities with greater proportions of Medicare/Medicaid residents [odds ratio (OR) = 0.986; P < .001], whereas communities located in micropolitan areas (compared with urban), and those in states with more direct care worker hours (per week per bed) had greater odds of high rating (OR = 1.722; P < .001 and OR = 1.018, P < .05, respectively).

CONCLUSIONS AND IMPLICATIONS

Online reviews are increasingly common, including in long-term care. These reviews are a promising source of information about important aspects of satisfaction, particularly in care settings that lack a public reporting infrastructure. We found several significant associations between online ratings and community-level factors, suggesting these reviews may be a valuable source of information to consumers and policy makers.

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.

Understanding the expression of loneliness on Twitter across age groups and genders

Some individuals seek support around loneliness on social media forums. In this work, we aim to determine differences in the use of language by users—in different age groups and genders (female, male), who publish posts on Twitter expressing loneliness. We hypothesize that these differences in the use of language will reflect how these users express themselves and some of their support needs. Interventions may vary depending on the age and gender of an individual, hence, in order to identify high-risk individuals who express loneliness on Twitter and provide appropriate interventions for these users, it is important to understand the variations in language use by users who belong to different age groups and genders and post about loneliness on Twitter. We discuss the findings from this work and how they can help guide the design of online loneliness interventions.

VA nursing home compare metrics as an indicator of skilled nursing facility quality for veterans

BACKGROUND

The Veterans Administration (VA) provides several post-acute care (PAC) options for Veterans, including VA-owned nursing homes (called Community Living Centers, CLCs). In 2016, the VA released CLC Compare star ratings to support decision-making. However, the relationship between CLC Compare star ratings and Veterans CLC post-acute outcomes is unknown.

METHODS

Retrospective observational study using national VA and Medicare data for Veterans discharged to a CLC for PAC. We used a multivariate regression model with hospital random effects to examine the association between CLC Compare overall star ratings and PAC outcomes while controlling for patient, facility, and hospital factors. Our sample included Veteran enrollees age 65+ who were community-dwelling, experienced a hospitalization, and were discharged to a CLC in 2016-2017. PAC outcomes included 30-day unplanned hospital readmission, 30-day mortality, 100-day successful community discharge, and a secondary composite outcome of unplanned readmission or death within 30-days of the hospital discharge.

RESULTS

Of the 25,107 CLC admissions, 4088 (16.3%) experienced an unplanned readmission, 4069 (16.2%) died within 30-days of hospital discharge, and 12,093 (48.2%) had a successful 100-day community discharge. Admission to a lower-quality (1-star) facility was associated with lower odds of successful community discharge (OR 0.78; 95% CI 0.66, 0.91) and higher odds of a combined endpoint of 30-day mortality and readmission (OR 1.27; 95% CI 1.09, 1.49), compared to 5-star facilities. However, outcomes were not consistently different between 5-star and 2, 3, or 4-star facilities. Star ratings were not associated with individual readmission or mortality outcomes when considered separately.

CONCLUSION

These findings suggest comparisons of 1-star and 5-star CLCs may provide meaningful information for Veterans making decisions about post-acute care. Identifying ways to alter the star ratings so they are differentially associated with outcomes meaningful to Veterans at each level is essential. We found that 1-star facilities had higher rates of 30-day unplanned hospital readmission/death, and lower rates of 100-day successful community discharges compared to 5-star facilities. Yet, like past work on CMS Nursing Home Compare ratings, these relationships were found to be inconsistent or not meaningful across all star levels. CLC Compare may provide useful information for discharge and organizational planning, with some limitations.

Reporting of Discrimination by Health Care Consumers Through Online Consumer Reviews

IMPORTANCE

Little is known about how discrimination in health care relates to inequities in hospital-based care because of limitations in the ability to measure discrimination. Consumer reviews offer a novel source of data to capture experiences of discrimination in health care settings.

OBJECTIVE

To examine how health care consumers perceive and report discrimination through public consumer reviews.

DESIGN, SETTING, AND PARTICIPANTS

This qualitative study assessed Yelp online reviews from January 1, 2011, to December 31, 2020, of 100 randomly selected acute care hospitals in the US. Word filtering was used to identify reviews potentially related to discrimination by using keywords abstracted from the Everyday Discrimination Scale, a commonly used questionnaire to measure discrimination. A codebook was developed through a modified grounded theory and qualitative content analysis approach to categorize recurrent themes of discrimination, which was then applied to the hospital reviews.

EXPOSURES

Reported experiences of discrimination within a health care setting.

MAIN OUTCOMES AND MEASURES

Perceptions of how discrimination in health care is experienced and reported by consumers.

RESULTS

A total of 10 535 reviews were collected. Reviews were filtered by words commonly associated with discriminatory experiences, which identified 2986 reviews potentially related to discrimination. Using the codebook, the team manually identified 182 reviews that described at least 1 instance of discrimination. Acts of discrimination were categorized by actors of discrimination (individual vs institution), setting (clinical vs nonclinical), and directionality (whether consumers expressed discriminatory beliefs toward health care staff). A total of 53 reviews (29.1%) were coded as examples of institutional racism; 89 reviews (48.9%) mentioned acts of discrimination that occurred in clinical spaces as consumers were waiting for or actively receiving care; 25 reviews (13.7%) mentioned acts of discrimination that occurred in nonclinical spaces, such as lobbies; and 66 reviews (36.3%) documented discrimination by the consumer directed at the health care workforce. Acts of discrimination are described through 6 recurrent themes, including acts of commission, omission, unprofessionalism, disrespect, stereotyping, and dehumanizing.

CONCLUSIONS AND RELEVANCE

In this qualitative study, consumer reviews were found to highlight recurrent patterns of discrimination within health care settings. Applying quality improvement tools, such as the Plan-Do-Study-Act cycle, to this source of data and this study's findings may help inform assessments and initiatives directed at reducing discrimination within the health care setting.

Association Between Crowdsourced Health Care Facility Ratings and Mortality in US Counties

IMPORTANCE

Mortality across US counties varies considerably, from 252 to 1847 deaths per 100 000 people in 2018. Although patient satisfaction with health care is associated with patient- and facility-level health outcomes, the association between health care satisfaction and community-level health outcomes is not known.

OBJECTIVE

To examine the association between online ratings of health care facilities and mortality across US counties and to identify language specific to 1-star (lowest rating) and 5-star (highest rating) reviews in counties with high vs low mortality.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective population-based cross-sectional study examined reviews and ratings of 95 120 essential health care facilities across 1301 US counties. Counties that had at least 1 essential health care facility with reviews available on Yelp, an online review platform, were included. Essential health care was defined according to the 10 essential health benefits covered by Affordable Care Act insurance plans.

MAIN OUTCOMES AND MEASURES

The mean rating of essential health care facilities was calculated by county from January 1, 2015, to December 31, 2019. Ratings were on a scale of 1 to 5 stars, with 1 being the worst rating and 5 the best. County-level composite measures of health behaviors, clinical care, social and economic factors, and physical environment were obtained from the University of Wisconsin School of Medicine and Public Health County Health Rankings database. The 2018 age-adjusted mortality by county was obtained from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiological Research database. Multiple linear regression analysis was used to estimate the association between mean facility rating and mortality, adjusting for county health ranking variables. Words with frequencies of use that were significantly different across 1-star and 5-star reviews in counties with high vs low mortality were identified.

RESULTS

The 95 120 facilities meeting inclusion criteria were distributed across 1301 of 3142 US counties (41.4%). At the county level, a 1-point increase in mean rating was associated with a mean (SE) age-adjusted decrease of 18.05 (3.68) deaths per 100 000 people (P < .001). Words specific to 1-star reviews in high-mortality counties included told, rude, and wait, and words specific to 5-star reviews in low-mortality counties included Dr, pain, and professional.

CONCLUSIONS AND RELEVANCE

This study found that, at the county level, higher online ratings of essential health care facilities were associated with lower mortality. Equivalent online ratings did not necessarily reflect equivalent experiences of care across counties with different mortality levels, as evidenced by variations in the frequency of use of key words in reviews. These findings suggest that online ratings and reviews may provide insight into unequal experiences of essential health care.

Understanding user communication around loneliness on online forums

Increasingly, individuals experiencing loneliness are seeking support on online forums-some of which focus specifically on discussions around loneliness (loneliness forums); loneliness may influence how these individuals communicate in other online forums not focused on loneliness (non-loneliness forums). In order to provide effective and appropriate online interventions around loneliness, it is important to understand how users who publish posts in a loneliness forum communicate in the loneliness forum and non-loneliness forums they belong to. In this paper, using language features, the following analyses are conducted: (1) Posts published on an online loneliness forum on Reddit, /r/Lonely are compared to posts (published by the same users and around the same time period) on two Reddit online forums i.e. an advice seeking forum, /r/AskReddit and a forum focused on discussions around depression (depression forum), /r/depression. (2) Interventions related to loneliness may vary depending on if an individual is lonely and depressed or lonely but not depressed; language use differences in posts published in /r/Lonely by the following set of users are identified: (a) users who post in both /r/Lonely and a depression forum and (b) users who post in /r/Lonely but not in the depression forum. The findings from this work gain new insights, for example: (i) /r/Lonely users tend to seek advice/ask questions related to relationships in the advice seeking forum, /r/AskReddit and (ii) users who are members of the loneliness forum but not the depression forum tend to publish posts (on the loneliness forum) on topic themes related to work/job, however, those who are members of the loneliness and depression forums tend to use more words associated with anger, negation, death, and post on topic themes related to affection relative to relationships in their loneliness forum posts. Some of the findings from this work also align with prior work e.g. users who express loneliness in online forums tend to make more reference to self. These findings aid in gaining insights into how users communicate on these forums and their support needs, thereby informing loneliness interventions.

Experiences With Hearing Health Care Services: What Can We Learn From Online Consumer Reviews?

Objective The aim of this study was to examine experiences of hearing health care services as described in online consumer reviews. Design This study used a cross-sectional design. Online consumer reviews about hearing health care services generated from Google.com to an open-ended question "Share details of your own experience at this place" and perceived overall experience (indicated on a 5-point rating scale: "very good" to "very poor") were extracted from 40 different cities across the United States. The open text contributed a text corpus of 9,622 unique consumer reviews. These responses were analyzed with the cluster analysis approach using an open-source automated text analysis software program, IRaMuTeQ, to identify key themes. Association between clusters and consumer experience ratings as well as consumer metadata (percentage of older adults in the city, region) were examined using the chi-square analysis. Results The majority of consumers appeared satisfied with their hearing health care services, with nearly 95% of consumers reporting "very good" and "good" on the global experience scale. The analysis of text responses resulted in seven clusters within two domains. Domain 1 (Clinical Processes) included the three clusters: administration processes, perceived benefits, and device acquisition. Domain 2 (Staff and Service Interactions) included the four clusters: clinician communications, staff professionalism, customer service, and provider satisfaction. Content relating to administration processes was associated with overall rating regarding the hearing health care service experience. Consumer's reviews relating to administration processes mostly described negative experiences, and these participants were more inclined to provide poorer overall experience ratings. In addition, city characteristics (i.e., percentage of older adults, region) had bearing toward what elements of hearing health care services are highlighted more in the consumer reviews. Conclusions Consumers comment on a variety of elements when describing their experiences with hearing health care services. Experiences reported in most clusters were generally positive, although some concerns in the "clinical process" are associated with lower satisfaction. Employing patient-centered strategies and ensuring patients have good experiences in the areas of concern may help improve both patient experience and their satisfaction. Supplemental Material https://doi.org/10.23641/asha.16455924.

Evaluation of Online Consumer Reviews of Hospitals and Experiences of Racism Using Qualitative Methods

This qualitative study examines online consumer reviews for experiences of racism in US hospitals among patients.

Online Reviews of Mental Health Treatment Facilities: Narrative Themes Associated With Positive and Negative Ratings

OBJECTIVE

Previous studies indicate that patients' satisfaction with mental health care is correlated with both treatment outcomes and quality of life. The aims of this study were to describe online reviews of mental health treatment facilities, including key themes in review content, and to evaluate the correlation between narrative review themes, facility characteristics, and review ratings.

METHODS

United States National Mental Health Services Survey (N-MHSS) facilities were linked to corresponding Yelp pages, created between March 2007 and September 2019. Correlations between review ratings and both machine learning-generated latent Dirichlet allocation topics and N-MHSS-reported facility characteristics were measured by using Spearman's rank-order correlation coefficient. Significance was defined by a Bonferroni-adjusted p<0.001.

RESULTS

Of 10,191 unique mental health treatment facilities, 1,383 (13.6%) had relevant Yelp pages with 8,133 corresponding reviews. The number of newly reviewed facilities and the number of new reviews increased throughout the study period. Narrative topics positively correlated with review ratings included caring staff (Spearman's ρ=0.39) and nonpharmacologic treatment (ρ=0.16). Topics negatively correlated with review ratings included rude staff (ρ=-0.14) and safety and abuse (ρ=-0.14). Of 126 N-MHSS survey items, 11 were positively correlated with review rating, including "outpatient mental health facility" (ρ=0.13), and 33 were negatively correlated with review rating, including accepting Medicare (ρ=-0.21).

CONCLUSIONS

Narrative topics provide information beyond what is currently collected through the N-MHSS. Topics associated with positive and negative reviews, such as staff attitude toward patients, can guide improvement in patients' satisfaction and engagement with mental health care.

Predicting Cardiovascular Risk Using Social Media Data: Performance Evaluation of Machine-Learning Models

Background

Current atherosclerotic cardiovascular disease (ASCVD) predictive models have limitations; thus, efforts are underway to improve the discriminatory power of ASCVD models.

Objective

We sought to evaluate the discriminatory power of social media posts to predict the 10-year risk for ASCVD as compared to that of pooled cohort risk equations (PCEs).

Methods

We consented patients receiving care in an urban academic emergency department to share access to their Facebook posts and electronic medical records (EMRs). We retrieved Facebook status updates up to 5 years prior to study enrollment for all consenting patients. We identified patients (N=181) without a prior history of coronary heart disease, an ASCVD score in their EMR, and more than 200 words in their Facebook posts. Using Facebook posts from these patients, we applied a machine-learning model to predict 10-year ASCVD risk scores. Using a machine-learning model and a psycholinguistic dictionary, Linguistic Inquiry and Word Count, we evaluated if language from posts alone could predict differences in risk scores and the association of certain words with risk categories, respectively.

Results

The machine-learning model predicted the 10-year ASCVD risk scores for the categories <5%, 5%-7.4%, 7.5%-9.9%, and ≥10% with area under the curve (AUC) values of 0.78, 0.57, 0.72, and 0.61, respectively. The machine-learning model distinguished between low risk (<10%) and high risk (>10%) with an AUC of 0.69. Additionally, the machine-learning model predicted the ASCVD risk score with Pearson r=0.26. Using Linguistic Inquiry and Word Count, patients with higher ASCVD scores were more likely to use words associated with sadness (r=0.32).

Conclusions

Language used on social media can provide insights about an individual’s ASCVD risk and inform approaches to risk modification.