Nursing Home Compare Research Articles

Accuracy of Pressure Ulcer Events in US Nursing Home Ratings.

Background:The US government relies on nursing home-reported data to create quality of care measures and star ratings for Nursing Home Compare (NHC). These data are not systematically validated, and some evidence indicates NHC’s patient safety measures may not be reliable.Objective:The objective of this study was to assess the accuracy of NHC’s pressure ulcer measures, which are chief indicators of nursing home patient safety.Research Design:For Medicare fee-for-service beneficiaries who were nursing home residents between 2011 and 2017, we identified hospital admissions for pressure ulcers and linked these to the nursing home-reported data at the patient level. We then calculated the percentages of pressure ulcers that were appropriately reported by stage, long-stay versus short-stay status, and race. After developing an alternative claims-based measure of pressure ulcer events, we estimated the correlation between this indicator and NHC-reported ratings.Subjects:Medicare nursing home residents with hospitalizations for pressure ulcers.Measures:Pressure ulcer reporting rates; nursing home–level claims-based measure of pressure ulcer events.Results:Reporting rates were low for both short-stay (70.2% of 173,043 stage 2–4 pressure ulcer hospitalizations) and long-stay (59.7% of 137,315 stage 2–4 pressure ulcer hospitalizations) residents. Black residents experienced more severe pressure ulcers than White residents, however, this translated into having slightly higher reporting rates because higher staged pressure ulcers were more likely to be reported. Correlations between our claims-based measure and NHC ratings were poor.Conclusions:Pressure ulcers were substantially underreported in data used by NHC to measure patient safety. Alternative approaches are needed to improve surveillance of health care quality in nursing homes.

Quality of Nursing Homes Admitting Working-Age Adults With Serious Mental Illness.

This cross-sectional study examined the association between nursing home quality and admission of working-age persons (ages 22-64 years) with serious mental illness. The study used 2015 national Minimum Data Set 3.0 and Nursing Home Compare (NHC) data. A logistic mixed-effects model estimated the likelihood (adjusted odds ratios [AORs] and 95% confidence intervals [CIs]) of a working-age nursing home resident having serious mental illness, by NHC health inspection quality rating. The variance partition coefficient (VPC) was calculated to quantify the variation in serious mental illness attributable to nursing home characteristics. Measures included serious mental illness (i.e., schizophrenia, bipolar disorder, and other psychotic disorders), health inspection quality rating (ranging from one star, below average, to five stars, above average), and other sociodemographic and clinical covariates. Of the 343,783 working-age adults newly admitted to a nursing home in 2015 (N=14,307 facilities), 15.5% had active serious mental illness. The odds of a working-age resident having serious mental illness was lowest among nursing homes of above-average quality, compared with nursing homes of below-average quality (five-star vs. one-star facility, AOR=0.78, 95% CI=0.73-0.84). The calculated VPC from the full model was 0.11. These findings indicate an association between below-average nursing homes and admission of working-age persons with serious mental illness, suggesting that persons with serious mental illness may experience inequitable access to nursing homes of above-average quality. Access to alternatives to care, integration of mental health services in the community, and improving mental health care in nursing homes may help address this disparity.

Admissions to High‐Quality Nursing Homes from Community: Racial Differences and Medicaid Policy Effects

Research ObjectiveTo examine racial differences in admissions to high‐quality nursing homes (NHs) among residents with Alzheimer's disease and related dementias (ADRD), and whether such racial differences can be influenced by dementia‐related state Medicaid add‐on policies.Study DesignMultiple CY2011‐2017 national data were linked: The Minimum Data Set (MDS) 3.0, Medicare Beneficiary Summary File (MBSF), Medicare Provider Analysis and Review (MedPAR), and nursing home compare (NHC). We also collected information on state dementia‐related Medicaid add‐on policies and the 2015 national Area Deprivation Index (ADI).For each identified resident, we constructed a choice set of NHs, defined as a set of NHs within a certain driving distance radius from home (15 miles in urban areas and 20 miles in rural areas), based on the 5‐digit zip‐code of a NH and individuals' community residence. We excluded individuals who did not choose a NH within their choice set and individuals with only one NH within their choice set.The dependent variable was whether a NH is “chosen” or not (binary). Key independent variables included the interaction of NH quality (measured by 5‐star rating) and race (i.e. black versus white), and interaction of NH quality, race, and state Medicaid dementia‐related add‐on policies. Individual‐ and facility‐level covariates (e.g. distance from residence to NHs) were identified. A set of McFadden's choice (conditional logit) models were estimated.Population StudiedWe identified Medicare enrolled NH residents with ADRD who were newly admitted from community (i.e. no NH episodes or hospitalizations in the prior 30 days) between 01/01/2011 and 12/31/2017. In total, 874,526 individuals in 15,755 NHs were identified.Principal FindingsIn the study sample, 89% were whites and 11% were blacks. Overall, 50% of whites and 35% of blacks were admitted to high‐quality NHs (4‐ or 5‐star). Blacks were less likely to have aggressive behaviors (14.7% vs. 16.6%, P < 0.01) but more likely to have severe cognitive impairment (9.6% vs. 6.8%, P < 0.01) than whites. Blacks were more likely to live in social‐economically disadvantaged neighborhoods and be Medicare‐Medicaid dually eligible. Results from the McFadden's model suggested that blacks were less likely to be admitted to a high‐quality NH than whites (OR = 0.7, P < 0.01). Although residents with low socio‐economic status, cognitive impairment and behavioral problems were less likely to enter high‐quality NHs, these characteristics did not fully explain the observed racial differences in entering high‐quality NHs. However, the racial difference was reduced in states with dementia‐related add‐on policies, compared to states without these policies (OR = 1.2, P < 0.01).ConclusionsBlacks with ADRD were less likely to be admitted to high‐quality NHs than whites. Individuals' health conditions and social‐economic status could not fully explain this racial difference. State Medicaid policies that provided additional subsidies to ADRD residents may reduce this racial difference.Implications for Policy or PracticeIt is important to deliver high quality NH care for individuals with ADRD. Racial minorities still face barriers in accessing high‐quality NHs. Medicaid add‐on policies appear to reduce the racial difference in entering high‐quality NHs.Primary Funding SourceNational Institutes of Health.

Racial disparity in end-of-life hospitalizations among nursing home residents with dementia.

Explore within and across nursing home (NH) racial disparities in end-of-life (EOL) hospitalizations for residents with Alzheimer's disease or related dementia (ADRD), and examine whether severe cognitive impairment influences these relationships. Observational study merging, at the individual level, C2014-2017 national-level Minimum Data Set (MDS), Medicare Beneficiary Summary Files (MBSF), and Medicare Provider Analysis and Review (MedPAR). Nursing Home Compare (NHC) was also used. Long-stay residents who died in a NH or a hospital within 8 days of discharge. Analytical sample included 665,033 decedent residents with ADRD in 14,595 facilities. The outcome was hospitalization within 30 days of death. Key independent variables were race, severe cognitive impairment, and NH-level proportion of black residents. Other covariates included socio-demographics, dual eligibility, hospice enrollment, and chronic conditions. Facility-level characteristics were also included (e.g. profit status, staffing hours, etc.). We fit linear probability models with robust standard errors, fixed and random effects. Compared to whites, black decedents had a significantly (p < 0.01) higher risk of EOL hospitalizations (7.88%). Among those with severe cognitive impairment, whites showed a lower risk of hospitalizations (6.04%). But EOL hospitalization risk among blacks with severe cognitive impairment was still significantly elevated (β=0.0494; p < 0.01). A comparison of the base model with the fixed and random-effects models showed statistically significant hospitalization risk by decedent's race both within and across facilities. We found disparities between black and white residents with ADRD both within and across facilities. The within-facility disparities may be due to residents' preferences and/or NH practices that contribute to differential treatment. The across facility differences point to the overall quality of care disparities in homes with a higher prevalence of black residents. Persistence of such systemic disparities among the most vulnerable individuals is extremely troubling.

872. Burden of Influenza Outbreaks in Long-Term Care Facilities in Philadelphia, 2012-2020

BackgroundIn the United States, influenza and other acute respiratory viruses contribute to a high burden of disease in long-term care facility (LTCF) residents. We aim to characterize the disease patterns and inter-seasonal variation of influenza virus outbreaks in LTCFs and identify institutional, environmental, and public health interventions associated with differences in outbreak outcomes and characteristics.MethodsWe conducted a retrospective, longitudinal study of influenza outbreaks in LTCFs reported to the Philadelphia Department of Public Health (PDPH) over eight consecutive seasons (November 2012 through March 2020). Characteristics of individual outbreaks, facilities, and infection control measures were reported in the PDPH Influenza Outbreak database, while quality measures and other facility-level data were extracted from the CMS Nursing Home Compare (NHC) database. Cases of influenza-like illness (ILI) in residents and staff were reported.Results131 influenza outbreaks were reported among 56 facilities, leading to 1196 cases of ILI, 227 influenza-related hospitalizations and 20 deaths. The median number of facility residents affected per outbreak was 4 (range, 0-52), and the resident attack rate was 3.0% (IQR, 1.6-7.4). Facility staff were affected in 56 (43%) of outbreaks. A greater number of facility beds was associated with sites reporting 3 or more outbreaks compared to those that did not (180 vs 133 beds, p=0.019). Public health measures were associated with lower total attack rates for surveillance (1.9% vs 4.3%, p=0.0015), education (1.9% vs 5.1%, p=0.0004), visitation restrictions (1.8% vs 3.1%, p=0.049), and vaccination policies of staff and/or residents (1.6% vs 2.7%, p=0.047). Smaller outbreaks were associated with the implementation of droplet precautions (3 vs 6, p=0.0071), antiviral treatment and prophylaxis (3 vs 6, p=0.024), and admission restrictions (4 vs 7, p=0.015).ConclusionLarger facility size was associated with an increased frequency of outbreaks. Public health measures may reduce the size and severity of influenza outbreaks in LTCFs. These results emphasize the importance of ensuring consistent utilization of recommended infection prevention strategies.Disclosures All Authors: No reported disclosures

Inside the Black Box of Improving on Nursing Home Quality Measures.

Nursing Home Compare (NHC) reports quality measures (QMs) for nursing homes (NHs) as part of its 5-star rating system. Most of the QMs are based on facility self-reported data, prompting questions about their validity. To better understand how NHs interact with the QMs, we used qualitative methods, including semistructured interviews with NH personnel (n = 110), NH provider association representatives (n = 23), and observations of organizational processes in 12 NHs in three states. We found that most NHs are working to improve the quality of care they provide, not merely their QM scores. However, our interviews and observations revealed limitations with the QMs, suggesting that the QMs-on their own-may not accurately reflect the quality of care NHs provide. Our findings suggest several changes to improve NHC, including adding information related to resident and family experience, providing greater risk adjustment, and providing incentives for NHs that serve socially and medically complex residents.

Quality of Nursing Homes and Admission of Residents With Alzheimer's Disease and Related Dementias: The Potential Influence of Market Factors and State Policies

ObjectivesThis study aimed to examine the associations between nursing home (NH) quality and prevalence of newly admitted NH residents with Alzheimer's disease and related dementias (ADRD), and to assess the extent to which market-level wages for certified nursing assistants (CNAs) and state Medicaid behavioral and mental health add-on policy may influence such associations. DesignRetrospective cohort study. Setting and ParticipantsThe analytical sample included 2777 NHs with either high or low quality, located in urban areas of 41 states from 2011 to 2014. MethodsThe outcome variable was the prevalence of ADRD among newly admitted NH residents. NH quality was defined as dichotomous, based on the Nursing Home Compare (NHC) star rating system. We considered an NH with 5-star rating as having high quality and with 1-star rating as having low quality. Information on county-level CNA wages and state Medicaid behavioral and mental health add-on policies was included. Linear regression models with NH random effects and robust standard errors were estimated. A set of sensitivity analyses were performed. ResultsAfter accounting for NH-level aggregated resident characteristics and market/state-level factors, the prevalence of ADRD among newly admitted residents was 3% lower in high-quality NHs compared with low-quality NHs (P < .01). A 1-dollar increase in CNA hourly wage was associated with a 0.9–percentage point decrease in the prevalence of ADRD among newly admitted residents (P < .01). State Medicaid behavioral and mental health add-on policy was associated with a 2.5–percentage point increase in the prevalence of ADRD in high-quality NHs (P < .05), but not in low-quality NHs. Conclusions and ImplicationsOur findings suggest that high-quality NHs are less likely to admit residents with ADRD. The effect size of this relationship is modest and may be influenced by state Medicaid behavioral and mental health add-on policies. Future studies are needed to better understand reasons leading to these associations so that effective interventions can be developed to incentivize high-quality NHs to more readily serve residents with ADRD.

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

There are areas of skilled nursing facility (SNF) experience of importance to the public that are not currently included in public reporting initiatives on SNF quality. Whether patients, hospitals, and payers can leverage the information available from unsolicited online reviews to reduce avoidable rehospitalizations from SNFs is unknown. To assess the association between rehospitalization rates and online ratings of SNFs; to compare the association of rehospitalization with ratings from a review website vs Medicare Nursing Home Compare (NHC) ratings; and to identify specific topics consistently reported in reviews of SNFs with the highest vs lowest rehospitalization rates using natural language processing. A retrospective cross-sectional study of 1536 SNFs with online reviews on Yelp (a website that allows consumers to rate and review businesses and services, scored on a 1- to 5-star rating scale, with 1 star indicating the lowest rating and 5 stars indicating the highest rating) posted between January 1, 2014, and December 31, 2018. The combined data set included 1536 SNFs with 8548 online reviews, NHC ratings, and readmission rates. A mean rating from the review website was calculated through the end of each year. Risk-standardized rehospitalization rates were obtained from NHC. Linear regression was used to measure the association between the rehospitalization rate of a SNF and the online ratings. Natural language processing was used to identify topics associated with reviews of SNFs in the top and bottom quintiles of rehospitalization rates. The 1536 SNFs in the sample had a median of 6 reviews (interquartile range, 3-13 reviews), with a mean (SD) review website rating of 2.7 (1.1). The SNFs with the highest rating on both the review website and NHC had 2.0% lower rehospitalization rates compared with the SNFs with the lowest rating on both websites (21.3%; 95% CI, 20.7%-21.8%; vs 23.3%; 95% CI, 22.7%-24.0%; P = .04). Compared with the NHC ratings alone, review website ratings were associated with an additional 0.4% of the variation in rehospitalization rates across SNFs (adjusted R2 = 0.009 vs adjusted R2 = 0.013; P = .003). Thematic analysis of qualitative comments on the review website for SNFs with high vs low rehospitalization rates identified several areas of importance to the reviewers, such as the quality of physical infrastructure and equipment, staff attitudes and communication with caregivers. Skilled nursing facilities with the best rating on both a review website and NHC had slightly lower rehospitalization rates than SNFs with the best rating on NHC alone. However, there was marked variation in the volume of reviews, and many SNF characteristics were underrepresented. Further refinement of the review process is warranted.

Assessment of nursing home reporting of major injury falls for quality measurement on nursing home compare.

ObjectiveTo assess the accuracy of nursing home self‐report of major injury falls on the Minimum Data Set (MDS).Data SourcesMDS assessments and Medicare claims, 2011‐2015.Study Design/MethodsWe linked inpatient claims for major injury falls with MDS assessments. The proportion of claims‐identified falls reported for each fall‐related MDS item was calculated. Using multilevel modeling, we assessed patient and nursing home characteristics that may be predictive of poor reporting. We created a claims‐based major injury fall rate for each nursing home and estimated its correlation with Nursing Home Compare (NHC) measures.Principal FindingsWe identified 150,828 major injury falls in claims that occurred during nursing home residency. For the MDS item used by NHC, only 57.5 percent were reported. Reporting was higher for long‐stay (62.9 percent) than short‐stay (47.2 percent), and for white (59.0 percent) than nonwhite residents (46.4 percent). Adjusting for facility‐level race differences, reporting was lower for nonwhite people than white people; holding constant patient race, having larger proportions of nonwhite people in a nursing home was associated with lower reporting. The correlation between fall rates based on claims vs the MDS was 0.22.ConclusionsThe nursing home‐reported data used for the NHC falls measure may be highly inaccurate.

Socioeconomic and Geographic Disparities in Accessing Nursing Homes With High Star Ratings

ObjectivesThe Centers for Medicare & Medicaid Services Nursing Home Compare (NHC) serves as the most comprehensive repository of Medicaid- and/or Medicare-certified nursing homes providing services to approximately 1.4 million US residents. A gap in the literature exists in understanding on the national level whether residents from socioeconomically disadvantaged counties experience disparities in the access to nursing homes with higher NHC star ratings. The study aimed to examine nursing home quality variations with regard to county-level socioeconomic, geographic, and metropolitan status, while adjusting for nursing home facility-level characteristics. DesignCross-sectional ecological study. Setting and participants15,090 Medicaid/Medicare-certified nursing homes with nonmissing star ratings. MeasuresStudy outcomes were NHC overall, health inspection survey, nurse staffing, and quality measure star ratings. County-level measures included SES index, geographic regions, and metropolitan status. Facility-level characteristics included ownership, chain affiliation, type and length of Medicaid/Medicare certification, hospital affiliation, continuing care retirement community status, number of certified beds, and occupancy. ResultsCounties with average adjusted overall, nurse staffing, and quality measure star ratings below 3 stars appeared to be clustered in the South. Nursing homes located in counties with lower SES were associated with lower overall star ratings [adjusted mean stars: 3.66 to 3.84, 95% confidence interval (CI): (3.54, 3.79) to (3.73, 3.95)]. A similar pattern was observed in staffing [adjusted mean stars: 3.75 to 4.23, 95% CI: (3.54, 3.97) to (4.10, 4.35)] and quality ratings [adjusted mean stars: 3.29 to 3.52, 95% CI: (3.12, 3.47) to (3.35, 3.69)]. ConclusionsResidents in socioeconomically disadvantaged counties experience disparities in accessing nursing homes with higher star ratings. These areas may lack sufficient resources to adequately staff the facility and deliver care that meets industry quality standards. These issues are likely to persist and possibly even worsen for the lower- and middle-class geriatric population given the current uncertainty around healthcare reform.

Examination of the New Short-Stay Nursing Home Quality Measures: Rehospitalizations, Emergency Department Visits, and Successful Returns to the Community.

In 2016, the Centers for Medicare & Medicaid Services (CMS) introduced 3 new quality measures (QMs) to its report card, Nursing Home Compare (NHC). These measures—rehospitalizations, emergency department visits, and successful discharges to the community—focus on short-stay residents. We offer a first analysis of nursing homes’ performance in terms of these new measures. We examined their properties and distribution across nursing homes using descriptive statistics and regression models. We found that, similar to other QMs, performance varies across the country, and that there is very minimal correlation between these 3 new QMs as well as between these QMs and other NHC QMs. Regression models reveal that better performance on these QMs tends to be associated with fewer deficiencies, higher staffing and more skilled staffing, nonprofit ownership, and lower proportion of Medicaid residents. Other characteristics are associated with better performance for some but not all 3 QMs. We also found improvement in all 3 QMs in the second year of publication. This study contributes to the validity of these measures by demonstrating their relationship to these structural QMs. It also suggests that these QMs are important by demonstrating their large variation across the country, suggesting substantial room for improvement, and finding that nursing homes are already responding to the incentives created by publication of these QMs.

Nursing Home Response to Nursing Home Compare: The Provider Perspective.

Nursing Home Compare (NHC) publishes composite quality ratings of nursing homes based on a five-star rating system, a system that has been subject to controversy about its validity. Using in-depth interviews, we assess the views of nursing home administrators and staff on NHC and unearth strategies used to improve ratings. Respondents revealed conflicting goals and strategies. Although nursing home managers monitor the ratings and expend effort to improve scores, competing goals of revenue maximization and avoidance of litigation often overshadow desire to score well on NHC. Some of the improvement strategies simply involve coding changes that have no effect on resident outcomes. Many respondents doubted the validity of the self-reported staffing data and stated that lack of risk adjustment biases ratings. Policy makers should consider nursing home incentives when refining the system, aiming to improve the validity of the self-reported domains to provide incentives for broader quality improvement.

Assessing Quality of Care and Elder Abuse in Nursing Homes via Google Reviews.

BackgroundIt is challenging to assess the quality of care and detect elder abuse in nursing homes, since patients may be incapable of reporting quality issues or abuse themselves, and resources for sending inspectors are limited.ObjectiveThis study correlates Google reviews of nursing homes with Centers for Medicare and Medicaid Services (CMS) inspection results in the Nursing Home Compare (NHC) data set, to quantify the extent to which the reviews reflect the quality of care and the presence of elder abuse.MethodsA total of 16,160 reviews were collected, spanning 7,170 nursing homes. Two approaches were tested: using the average rating as an overall estimate of the quality of care at a nursing home, and using the average scores from a maximum entropy classifier trained to recognize indications of elder abuse.ResultsThe classifier achieved an F-measure of 0.81, with precision 0.74 and recall 0.89. The correlation for the classifier is weak but statistically significant: = 0.13, P < .001, and 95% confidence interval (0.10, 0.16). The correlation for the ratings exhibits a slightly higher correlation: = 0.15, P < .001. Both the classifier and rating correlations approach approximately 0.65 when the effective average number of reviews per provider is increased by aggregating similar providers.ConclusionsThese results indicate that an analysis of Google reviews of nursing homes can be used to detect indications of elder abuse with high precision and to assess the quality of care, but only when a sufficient number of reviews are available.

Prototype End-of-Life Quality Measures Based on MDS 3 Data.

The Nursing Home Compare (NHC) report card does not include end-of-life (EOL) quality measures (QMs). To develop and examine the properties of EOL QMs. A total of 39,590 nursing home decedents in 626 facilities in New York State in fiscal year 2012. Statistical analyses of Minimum Data Set 3 data, including multivariable regression analyses and descriptive statistics. Death in the hospital, number of hospitalizations, pain, and depression during the last 90 days before death. Overall, 32% of residents died in the hospital. They averaged 0.49 hospitalizations in the last 90 days before death, 10% reported moderate to severe pain, and 17% had depressive symptoms. The EOL QMs exhibited variation across facilities similar to that observed for other QMs. They showed low or moderate correlations. The pain and depression QMs were significantly better among nursing homes ranked by NHC as 4 and 5 stars compared with those ranked as 1 and 2 stars for most dimensions. The hospitalizations QMs were significantly better among nursing homes ranked by NHC as 4 and 5 stars compared with those ranked as 1 and 2 stars only when compared on the staffing dimension. The Minimum Data Set 3 includes much information that can be used to assess quality of EOL care in nursing homes. The prototype measures we developed could be improved if information about advance directives and the nonclinical aspects of care, such as comfort and emotional support for both the resident and the family and respect for resident and family preferences, were collected.

Nursing home report card and performance gap.

With the aging of our society, concerns about the quality of nursing homes have been increasing. The Nursing Home Compare (NHC) report card is believed to reduce information asymmetry between the facility and potential residents and, consequently, improve nursing home quality. However, there is limited evidence about how nursing homes use this publicly disseminated performance information. The aim of this study was to analyze the performance gap between nursing home administrators' self-assessment and the NHC report card performance ratings and explore the factors contributing to the gap. All 515 Medicare- and Medicaid-certified nursing homes in Indiana were surveyed between August and December 2013. The response rate was approximately 45%. The survey data were compared with the NHC data to identify the gap. Ordered logistic regression analysis was conducted. Despite voluntary participation, a reasonably high response rate, and anonymity, this study found that 62% of respondents either under- or overrated their facilities' performance, with the majority overrating their performance. The following factors were associated with a smaller performance gap: nonprofit or government-owned status, higher staffing levels, a higher percentage of hours worked by registered nurses, and a higher market concentration. Longer work experience and greater county occupancy rates were associated with a larger performance gap. The significant gap implies that administrators either do not regularly review the NHC report card or do not concur with the NHC ratings. To reduce the gap and thereby improve performance, it is necessary to stabilize the administrators' position so that administrators know how they stand in the market and are consequently better equipped to successfully respond to the market. Facilities may also need to provide regular training on current technologies and facilitate use of the NHC report card, targeting administrators who are old and have many years of work experience.

Market Conditions and Performance in the Nursing Home Compare Five-Star Rating

Previous studies have documented that market conditions affect nursing home performance; however, the evidence is inconsistent and conflicting. This study introduces three groups of county market conditions and a peer effect variable, and tests their impacts on the Nursing Home Compare (NHC) Five-Star overall rating. Indiana nursing home data and county characteristics are taken mainly from the NHC and Census Bureau websites. The result of the ordered logistic regression analysis indicates that nursing homes in excess demand markets, namely those that are highly concentrated and have fewer nursing homes, tend to perform better than their counterparts in both excess supply and balanced markets. In addition, a peer effect variable, measured as the average overall rating of the competitors, promotes performance improvement. These findings imply that small markets enable consumers to be well informed about a provider's reputation for quality, consequently enhancing performance. Furthermore, not only consumers but also providers seem to seek performance information on the report card to understand their relative position in the market, which thus affects their market strategies and subsequently performance.

Visibility and Findability of the Nursing Home Compare Website

Nursing Home Compare (NHC) is a federal government website providing information for selecting a nursing home. However, not many consumers were aware of or can locate the site. This study analyzed 50 official state and District of Columbia websites from September through December 2013. Using Google “inlink:” operator, this study evaluated the visibility and findability of NHC links in each state-level website. The results show that a link to NHC is available in all states except for Connecticut, Florida, and Michigan. Although it took only 4.7 clicks on average to the page with a NHC link, consumers may still have difficulty to find NHC from a state website. This article provides a snapshot of the visibility and findability of NHC and indicates a need for further investigation of promising website dissemination strategies not yet adequately evaluated.

End-of-Life Quality-of-Care Measures for Nursing Homes: Place of Death and Hospice

The Centers for Medicare and Medicaid Services (CMS) publishes a web-based quality report card for nursing homes. The quality measures (QMs) do not assess quality of end-of-life (EOL) care, which affects a large proportion of residents. This study developed prototype EOL QMs that can be calculated from data sources available for all nursing homes nationally. The study included approximately 1.5 million decedents residing in 16,000 nursing homes during 2003-2007, nationally. Minimum Data Set (MDS) data were linked to Medicare enrollment files, hospital claims, and hospice claims. Random effect logistic models were estimated to develop risk-adjustment models predicting two outcome measures (place of death [POD] and hospice enrollment), which were then used to construct two EOL QMs. The distributional properties of the QMs were investigated. The QMs exhibited moderate stability over time. They were more stable in identifying quality outliers among the larger nursing homes and in identifying poor-quality outliers than high-quality outliers. This study offers two QMs specialized to EOL care in nursing homes that can be calculated from data that are readily available and could be incorporated in the Nursing Home Compare (NHC) report card. Further work to validate the QMs is required.

Nursing Home Price and Quality Responses to Publicly Reported Quality Information

To assess whether the release of Nursing Home Compare (NHC) data affected self-pay per diem prices and quality of care. Primary data sources are the Annual Survey of Wisconsin Nursing Homes for 2001-2003, Online Survey and Certification Reporting System, NHC, and Area Resource File. We estimated fixed effects models with robust standard errors of per diem self-pay charge and quality before and after NHC. After NHC, low-quality nursing homes raised their prices by a small but significant amount and decreased their use of restraints but did not reduce pressure sores. Mid-level and high-quality nursing homes did not significantly increase self-pay prices after NHC nor consistently change quality. Our findings suggest that the release of quality information affected nursing home behavior, especially pricing and quality decisions among low-quality facilities. Policy makers should continue to monitor quality and prices for self-pay residents and scrutinize low-quality homes over time to see whether they are on a pathway to improve quality. In addition, policy makers should not expect public reporting to result in quick fixes to nursing home quality problems.

Nursing Home Quality Reaps Rewards

Nursing Home Quality Reaps Rewards