Nursing Homes: CMS's Special Focus Facility Methodology Should Better Target the Most Poorly Performing Homes, Which Tended to Be Chain Affiliated and For-Profit
Released: 2009-09-28T00:00:00

Letter

Background

Titles XVIII and XIX of the Social Security Act establish minimum requirements that all nursing homes must meet to participate in the Medicare and Medicaid programs, respectively. The Omnibus Budget Reconciliation Act of 1987 focused the requirements on the quality of care actually provided by a home. To help beneficiaries make informed decisions when selecting or evaluating nursing homes, CMS increased the amount of information publicly available on its Nursing Home Compare Web site in 2008 by rating the quality of each nursing home on a five-level scale.

Standard Surveys and Complaint Investigations

To assess whether nursing homes meet federal quality standards, state survey agencies conduct standard surveys, which occur roughly once per year, and complaint investigations. A standard survey involves a comprehensive assessment of quality standards. In contrast, complaint investigations generally focus on a specific allegation regarding resident care or safety made by a resident, family member, or nursing home staff.

Federal quality standards focus on the delivery of care, resident outcomes, and facility conditions. These quality standards, totaling approximately 200, are grouped into 15 categories, such as Resident Rights, Quality of Care, Quality of Life, and Resident Behavior and Facility Practices. Nursing homes that meet these quality standards can be certified to participate in Medicare, Medicaid, or both programs. Homes may occasionally change their participation type, or, according to CMS, states may require nursing homes to change their participation type. We refer to this type of change that results in a new provider identification number as a “technical status change.” Such a change may affect the source of payment—Medicare or Medicaid—that the nursing home is eligible to receive. When a technical status change occurs, CMS’s SFF methodology as applied does not incorporate the nursing home’s complete survey history.

Survey Deficiencies

States classify deficiencies identified during either standard surveys or complaint investigations in 1 of 12 categories according to their scope (i.e., the number of residents potentially or actually affected) and severity (i.e., the potential for or occurrence of harm to residents). (See table 1.)

An A-level deficiency is the least serious and is isolated in scope, while an L-level deficiency is the most serious and is widespread throughout the nursing home. Nursing homes with deficiencies at the A, B, or C levels are considered to be in substantial compliance with quality standards, whereas nursing homes with D-level through L-level deficiencies are considered noncompliant. For most deficiencies, a home is required to prepare a plan of correction, and depending on the severity of the deficiency, surveyors may conduct a revisit to ensure that the nursing home has implemented its plan and corrected the deficiency. Revisits are not required for most deficiencies below the actual harm level—A through F.

As we reported in May 2008, there can be considerable variation among states in the proportion of nursing homes cited for deficiencies at the G through L levels. We concluded that this interstate variation suggests that surveyors in some states are missing some serious deficiencies or understating their scope and severity. We provided examples of such understatement in our May 2008 report. Specifically, we reported that during fiscal years 2002 through 2007, about 15 percent of federal comparative surveys nationwide found that the state surveys had failed to cite at least one deficiency at the most serious levels of noncompliance (G through L levels) and about 70 percent of them found that the state surveys had failed to cite at least one deficiency at the potential for more than minimal harm level (D through F levels).

Enforcement Actions

When deficiencies are cited, federal enforcement actions known as sanctions can be imposed to encourage homes to make corrections. Sanctions are generally reserved for serious deficiencies—those at the G through L levels—that constitute actual harm and immediate jeopardy to residents. Sanctions include fines known as civil money penalties, denial of payment for new Medicare or Medicaid admissions, and termination from the Medicare and Medicaid programs. Such sanctions can affect a home’s revenues and therefore provide financial incentives to return to and maintain compliance. CMS requires states to refer for immediate sanction homes that receive at least one G- through L-level deficiency on successive standard surveys or intervening complaint investigations. In addition, a nursing home with one or more deficiencies at the F through L level—but not G level—in Quality of Care, Quality of Life, or Resident Behavior and Facility Practices must be cited for substandard quality of care (SQC), which generally results in the home’s losing its approval to hold in-house or facility-sponsored nurse aide training.

CMS Efforts That Identify Poorly Performing Nursing Homes

Two of CMS’s efforts that identify poorly performing nursing homes are the SFF Program and the Five-Star System. CMS’s Nursing Home Compare Web site identifies nursing homes that are in the SFF Program, provides a rating of from one to five stars, and also includes data on deficiencies cited during standard surveys and complaint investigations, selected quality of care measures, and nurse staffing hours. Both the SFF Program and the Five-Star System score nursing homes by assigning points to deficiencies and the number of revisits, but the points assigned to certain deficiencies differ.

The Special Focus Facility Program

CMS compiles a list of SFF candidates for each state generally on a quarterly basis by using the numeric score generated by its SFF methodology. The SFF candidates are those nursing homes with the 15 highest total scores in each state. From the candidate list, state officials select, with CMS concurrence, nursing homes they think should participate in the program based on their knowledge of the candidates’ circumstances. With the exception of Alaska, each state has between one and six SFFs, depending on the number of nursing homes in the state. CMS requires states to survey SFFs twice as frequently as other nursing homes to help motivate SFFs to improve. If an SFF meets CMS’s criteria for improved performance, CMS removes the SFF designation and the nursing home “graduates” from the program. According to CMS guidance to states, SFFs that fail to significantly improve after three standard surveys, or about 18 months, may be involuntarily terminated from Medicare and Medicaid. Nursing homes may also choose to terminate from Medicare and Medicaid voluntarily. (See fig. 1.)

The SFF methodology assigns points to deficiencies on standard surveys and complaint investigations, and to revisits associated with deficiencies cited on standard surveys, as follows: Deficiencies. More points are assigned to deficiencies that are higher in scope and severity. Additional points are assigned to deficiencies classified as SQC. For example, a nursing home with one J-level deficiency in the Quality of Care category would be assigned 75 points (50 points plus an additional 25 points because the deficiency was SQC). See table 2 for a comparison of the deficiency points assigned by the SFF methodology and the Five-Star System.

Revisits. Multiple revisits are an indicator of more serious problems in achieving or sustaining compliance. The points for revisits are as follows: 0 for the first revisit, 50 for the second revisit, an additional 75 (total 125) for the third revisit, and an additional 100 (total 225) for the fourth revisit.

MS has changed its scope and From 1999 to 2004, each state had two SFFs at any one time, which they selected from a list of four candidates, and the SFF methodology assign a different numb eficiency data. der of points to deficiencies using only about 1 year of In 2005, CMS expanded the program’s scope by changing the number of SFFs from 1 to 6 per state (excluding Alaska), for a total of 136, and altered the SFF methodology by changing the points assigned to deficiencies and using about 3 years of deficiency data, weighted equally.

In 2007, CMS began requiring states to notify a nursing home and its other accountable parties (i.e., the nursing home’s administrator, owners, operators, and governing body) when the nursing home was designated as an SFF.

In 2008, CMS began designating SFFs on the Nursing Home Compare Web site and also changed the scoring methodology to assign weights to each year, such that the most recent year’s standard and complaint surveys are given the greatest weight.

The Five-Star Quality Rating System

During the course of our work, CMS implemented its Five-Star System for nursing homes. Every nursing home in the United States is rated from one (much below average) to five (much above average) stars.

The Five-Star System provides an overall quality rating based on individual ratings for three separate components: (1) assessment of federal quality standards from standard surveys and complaint investigations, which CMS refers to in the Five-Star System as health inspections; (2) ratings on nursing home staffing levels; and (3) ratings on quality of care measures. In December 2008, CMS’s Nursing Home Compare Web site began reporting the star ratings that nursing homes receive for each component of the Five-Star System as well as an overall quality rating. According to CMS officials, as of March 2009 the rating for the health inspections component was based on CMS’s SFF methodology, with one variation: the Five-Star System assigns more points to D- through I-level deficiencies than does the SFF methodology. (See table 2.) CMS explained that it changed some of the points assigned to deficiencies in the Five-Star System because the purpose is different from that of the SFF Program. The SFF Program focuses on facilities in each state whose performance is consistently extremely poor, and so it assigns many points to immediate jeopardy deficiencies relative to other, lower-level deficiencies. In contrast, the purpose of the Five-Star System is to distinguish performance across all nursing homes, rather than focus on the poorest performers, and so CMS modified the points to provide more emphasis on deficiencies at the potential for more than minimal harm and actual harm levels. The rating for the second component, staffing data, is based on two elements— total nursing hours per resident day and registered nurse hours per resident day. The rating for the third component of the Five-Star System is based on nursing home performance on 10 quality of care measures, such as percentage of high-risk residents who have pressure sores.

Five Hundred Eighty Nursing Homes Could Be Considered the Most Poorly Performing—Fewer Than CMS’s SFF Program Candidates but More Than the Number of SFFs

We estimated that almost 4 percent—or 580—of the nation’s roughly 16,000 nursing homes could be considered the most poorly performing. These 580 homes overlap somewhat with the 755 SFF Program candidates and the 136 nursing homes actually selected as SFFs. For example, our estimate of 580 most poorly performing nursing homes includes (1) 302, or 40 percent, of the 755 SFF Program candidates as of December 2008 (see fig. 2) and (2) 65 nursing homes that 31 states selected as SFFs from among the SFF Program candidates, or about half of the active SFFs as of February 2009. In addition, our estimate resulted in some states having fewer or more poorly performing homes than CMS currently allocates to states under the SFF Program. For example, 10 states each had over 20 of the most poorly performing nursing homes. Indiana had the greatest number, with 52 such nursing homes, or almost 9 percent of the total of 580 homes. Eight states had no such nursing homes. (See fig. 3.)

CMS’s Application of the SFF Methodology Misses Many of the Nation’s Most Poorly Performing Nursing Homes

CMS has structured the SFF Program so that every state (except Alaska) has at least one SFF, and therefore the agency applies the SFF methodology to identify the 15 worst performing nursing homes in each state, which are not necessarily the worst performing homes in the nation. We developed an estimate that identified homes with worse compliance histories—more deficiencies at the potential for more than minimal harm level or higher and more revisits—than SFF Program candidates by applying CMS’s SFF methodology on a nationwide basis and using statistical scoring thresholds. These two changes had the greatest impact on the composition of the list of homes we identified as the most poorly performing compared to CMS’s approach. Our estimate also incorporated several refinements to the SFF methodology that moderately improve its ability to identify the most poorly performing nursing homes.

Compliance history. Our estimate of 580 nursing homes identified homes with more deficiencies at the potential for more than minimal harm level or higher and more revisits, on average, compared to the 755 SFF Program candidates. For example, the most poorly performing nursing homes averaged 46.5 percent more actual harm–level deficiencies and 19.5 percent more immediate jeopardy–level deficiencies, compared to the 755 SFF Program candidates. (See table 3.)

Nationwide estimate. We developed a nationwide estimate because the worst performing nursing homes in some states had high total scores from a combination of numerous deficiencies, serious deficiencies, and revisits, while the worst performing nursing homes in other states did not (see fig. 4). For example, in the preceding three cycles, we found that the worst performing nursing home in South Dakota had a score of about 68. The score was composed of 32 deficiencies at the D level or higher, 2 of which were at the actual harm level (where the highest scope and severity level was H) and none of which were at the immediate jeopardy level. In contrast, during the same three cycles, the worst performing nursing home in Tennessee had a score of about 1,512, with 63 deficiencies at the D level or higher. Of these deficiencies, 3 were at the actual harm level and 22 at the immediate jeopardy level (where the highest scope and severity level was L). Even the 15th highest-scoring home in Tennessee, with a score of about 253, had notably more deficiencies at the D level or higher and more severe deficiencies than the highest-scoring home in South Dakota: specifically, the Tennessee home had 63 deficiencies at the D level or higher, 2 of which were at the actual harm level and 8 of which were at the immediate jeopardy level. If CMS applied its SFF methodology to identify the worst 755 homes in the nation rather than the worst 15 in each state, the home ranked 755 would have a score of about 127; however, 48 percent of the SFF Program candidates had scores below this threshold. As a result, the SFF Program is missing some of the worst performing nursing homes in the nation.

Statistical scoring thresholds. Absent a fixed number of homes per state, we developed statistical scoring thresholds because there was no natural break point delineating the most poorly performing nursing homes from all other homes. The two statistical scoring thresholds we used were conservative, because they focused on chronic poor performance and nonchronic, very poor performance. About 87 percent of the 580 nursing homes that we identified as the most poorly performing exhibited chronic poor performance; that is, they had high scores in at least two of the three cycles measured, as well as a high total score. The remaining roughly 13 percent of nursing homes had nonchronic but very poor performance; that is, they had very serious poor performance in one cycle only, which resulted in a very high total score.

Homes that met our chronic poor performance threshold had total scores above the 93rd percentile for all nursing homes, or total scores ranging from approximately 168 to approximately 1,017. All of the nonchronic but very poor performing homes had total scores at or above the 99th percentile for all nursing homes, or total scores ranging from approximately 330 to approximately 1,577. Table 4 summarizes the compliance history of two of the most poorly performing homes identified by our estimate, and appendix II provides a detailed compliance history for these two homes.

Additional homes might have been identified as the most poorly performing had we used different thresholds. For example, one nursing home with a total score of about 324 did not meet our definition for chronic poor performance and was below the threshold of 330 for nonchronic, very poor performance. During the three-cycle period, this nursing home had 41 D- through F-level deficiencies, 5 immediate jeopardy deficiencies, and a second revisit that contributed to the score, but most of the deficiencies and the revisit occurred in one cycle.

Refinements made to CMS’s SFF methodology. Our three refinements to CMS’s SFF methodology had a moderate effect on the composition of the list of homes we identified as the most poorly performing.

Deficiency points. We believe that the deficiency points used in the Five- Star System are more appropriate for identifying the most poorly performing nursing homes nationwide than those used in the SFF methodology because they compensate somewhat for understatement and the interstate variation in the citation of serious deficiencies. First, given the significant disparity between immediate jeopardy (50 to 150 points) compared to lower-level deficiencies (2 to 6 points for D- through F-level deficiencies), our use of SFF deficiency points to identify the most poorly performing nursing homes nationwide might have missed poorly performing nursing homes in states with significant understatement. Second, there is considerable interstate variation in the citation of serious deficiencies, including immediate jeopardy–level deficiencies. For example, in 2008, about 11.3 percent of deficiencies were at the immediate jeopardy level in one state, but less than 1.0 percent of deficiencies were cited at that level in 26 states. The Five-Star System, on average, doubles the points assigned to deficiencies below the immediate jeopardy level, giving a D-level deficiency 4 points and a G-level deficiency 20 points, compared to 2 and 10 points, respectively, using the SFF deficiency methodology. As a result, using the Five-Star System deficiency points, homes with numerous D- through I-level deficiencies are more likely to be identified as the most poorly performing. CMS officials told us that they planned to evaluate the effect of using the Five-Star System deficiency points on identifying SFF candidates; our analysis showed that it changed the composition of SFF Program candidates by an average of about 2.5 candidates per state.

Substandard quality of care. In comparison with the SFF methodology and the Five-Star System, we assigned 5 more points to G-level deficiencies that occurred in any of the three categories of standards that CMS considers to be SQC. As noted earlier, CMS does not classify any G-level deficiencies as SQC. Without this modification, an F-level deficiency in an SQC area is assigned the same number of points as a G- level deficiency even if the G-level deficiency is in an SQC standard—10 and 20 points, respectively, under the SFF or Five-Star System methodologies. (See table 2.) This adjustment was important because approximately 45 percent of all nursing homes had one or more G-level deficiencies in an SQC category during the three cycles used for calculating SFF scores. Therefore, assigning SQC points to G-level deficiencies had an effect on total scores for the nursing homes, which we used to determine the most poorly performing homes nationwide, and would have an effect on the composition of CMS’s SFF candidate list. For example, about 4 percent of the SFF Program candidates—or less than one candidate per state on average—would change if CMS assigned SQC points to G-level deficiencies.

Technical status changes. While the SFF methodology does not consider all deficiencies and revisits identified within the three-cycle period that occurred before the nursing home’s technical status change, we incorporated the full histories of nursing homes that underwent a technical status change. At the time of a technical status change, a new provider identification number is assigned, and the nursing home’s complete history under the old number is not combined with that of the new provider number. For example, a nursing home with a status change on January 1, 2008, might have a compliance history for only 1 year at the time we did our work instead of the three cycles called for in the SFF methodology. The SFF scores for nursing homes that have undergone technical status changes within the last three cycles are almost always lower than would be the case if three cycles of deficiency history were included and, therefore, more favorable than would be justified by the complete history. We found that almost 1 percent of all nursing homes (148), including 11 of the 580 we identified as the most poorly performing, had a technical status change during the last three cycles that affected their SFF scores. For most states, this change would not have affected their SFF candidate lists.

Key Characteristics, such as Chain Affiliation and For- Profit Status Differentiated the Most Poorly Performing Nursing Homes

Compared to all other nursing homes, the most poorly performing nursing homes in the nation averaged notably more deficiencies at the D level or higher, more serious deficiencies, and more revisits. They were also more likely to be for-profit and part of a chain and have more beds and residents. In addition, they had an average of almost 24 percent fewer registered nurse hours per resident per day.

Actual Harm and Immediate Jeopardy Deficiencies Occurred Significantly More Often for the Most Poorly Performing Nursing Homes

Compared to all other nursing homes, deficiencies over the last three cycles at the actual harm (G through I) level occurred over 5 times as often, and deficiencies at the immediate jeopardy (J through L) level occurred 15 times as often for the most poorly performing homes. (See table 5.) Furthermore, we found that revisits were made to the most poorly performing nursing homes 6 times as often as to all other nursing homes.

The most poorly performing nursing homes were more frequently cited for deficiencies in important care areas and specific standards related to the delivery of care compared to all other nursing homes. Seven of the 10 most frequently cited deficiencies at the immediate jeopardy level involved standards in the categories of care that CMS considers to be SQC and four of the 10 are related to abuse or neglect. For example, about 42 percent of the most poorly performing nursing homes had at least one immediate jeopardy deficiency related to being free of accident hazards in the last three cycles, compared with about 5 percent for all other nursing homes. (See table 6.) A larger proportion of the most poorly performing nursing homes were cited for actual harm in each of the three SQC areas—about 90 percent in Quality of Care, about 31 percent in Resident Behavior and Facility Practices, and about 17 percent in Quality of Life. In comparison, a smaller proportion of all other nursing homes were cited for actual harm in those same categories of care—about 42 percent in Quality of Care, about 6 percent in Resident Behavior and Facility Practices, and about 2 percent in Quality of Life. (See app. III.)

We also found that from fiscal years 2006 through 2008 the most poorly performing nursing homes were much more likely to have had deficiencies that could have resulted in the imposition of at least one immediate sanction compared to all other nursing homes. For example, in fiscal year 2008, about 33 percent of the most poorly performing homes may have been at risk of having at least one immediate sanction imposed, compared to about 4 percent for all other nursing homes. Nursing homes that receive at least one G- through L-level deficiency on successive standard surveys or complaint investigations must be referred for immediate sanctions, and about 15 percent of the deficiencies for the most poorly performing nursing homes, on average over the last three cycles, were at the actual harm or immediate jeopardy level.

The Most Poorly Performing Nursing Homes Differed from All Others in Several Ways, Including Chain Affiliation and For-Profit Status

We found that the most poorly performing nursing homes differed from all other nursing homes in terms of the proportion of each group that was chain affiliated, for-profit, or both. They also differed in size and nurse staffing.

Type of organization. We found that the most poorly performing nursing homes were less likely to be hospital based compared to all other nursing homes. Additionally, compared to all other nursing homes, we found that the most poorly performing nursing homes were more likely to be part of for-profit organizations, more likely to be affiliated with a chain organization, and more likely to be both for-profit and affiliated with a chain organization. About 55 percent of the most poorly performing nursing homes were for-profit and chain affiliated, compared to about 41 percent of all other homes. (See table 7.)

Participation in Medicare and Medicaid. We found that a higher percentage of the most poorly performing homes participated in both Medicare and Medicaid, and a smaller percentage of such homes participated only in Medicare or only in Medicaid, compared to all other nursing homes. (See table 8.)

Beds and residents. We found that a larger percentage of the most poorly performing homes had more than 100 beds, compared to all other nursing homes. On average, the most poorly performing nursing homes had about 23 percent more beds than all other nursing homes. Additionally, our analysis found that on average, the most poorly performing homes had almost 14 percent more residents, a lower occupancy rate, and a greater share of Medicaid patients. (See table 9.)

Nurse staffing levels. Compared to all other nursing homes, the most poorly performing homes had almost 24 percent fewer registered nurse hours per resident per day on average. One effect of this difference is that the most poorly performing nursing homes averaged fewer registered nurse hours per resident per day as a share of total nursing hours. Specifically, registered nurse hours made up about 8 percent of total nurse staffing hours in the most poorly performing nursing homes, compared to about 10 percent in all other nursing homes. (See table 10.)

Conclusions

Our estimate of the most poorly performing nursing homes nationwide is more than four times greater than the 136 homes that receive enhanced scrutiny under CMS’s SFF Program. We believe that our estimate is conservative, because we focused only on those nursing homes with chronic poor performance over time or with very poor performance in one survey cycle. Because of resource constraints, CMS limits the size of the SFF Program, requiring every state except Alaska to select from 1 to 6 homes—an allocation based on the number of nursing homes in each state—from a list of 15 candidates. The homes selected are not necessarily the most poorly performing homes in the nation but rather are among the poorest performers in each state. In contrast, the 580 homes we identified have more deficiencies at the potential for more than minimal harm level or higher and more revisits on average than the 755 homes identified as potential SFF candidates using CMS’s SFF methodology. Our estimate also revealed that the state-by-state distribution of the most poorly performing homes nationwide is uneven, calling into question the approach CMS uses to allocate SFFs across states.

Furthermore, we believe that CMS’s SFF Program and the Five-Star System could be strengthened by incorporating the three enhancements we made to identify the most poorly performing homes nationwide: First, we adopted the deficiency points that CMS developed for its Five- Star System because they compensate somewhat for understatement and the interstate variation in the citation of serious deficiencies, an important consideration for our nationwide estimate of the most poorly performing nursing homes. Currently, CMS uses a different set of deficiency points for the SFF methodology, but agency officials told us that they planned to study the effect of using a common set of numeric points—the Five-Star System deficiency points—for both methodologies.

Second, we added points for G-level deficiencies in the three standard areas that CMS considers to be an indication of SQC. We found that about 4 percent of the SFF candidates—or less than 1 candidate per state, on average—would change if CMS assigned SQC points to G-level deficiencies when they were cited in an SQC area. Without such an adjustment, an F-level deficiency in an SQC area would receive the same number of deficiency points as a G-level deficiency in the same standard area. Approximately 45 percent of all nursing homes had one or more G-level deficiencies in an SQC category during the three cycles used for calculating SFF scores.

Third, we incorporated the full compliance history of homes that underwent technical status changes. For example, a nursing home with a technical status change on January 1, 2008, might have a compliance history for only 1 year at the time we did our work instead of the three cycles called for by the SFF methodology. The SFF scores of homes that have undergone technical status changes within the last three cycles are almost always lower than if all three cycles were considered. We also found that the Five-Star System does not accurately take into consideration technical status changes because it imputes a total score to account for one missing standard survey rather than using actual survey results.

Recommendations for Executive Action

To improve the targeting of scarce survey resources, the Administrator of CMS should consider an alternative approach for allocating the 136 SFFs across states, by placing more emphasis on the relative performance of homes nationally rather than on a state-by-state basis, which could result in some states having only one or not any SFFs and other states having more than they are currently allocated.

To improve the SFF methodology’s ability to identify the most poorly performing nursing homes, the Administrator of CMS should make the following three modifications: 1. Consider using a common set of numeric points for identifying poorly performing nursing homes by determining the effect of adopting those associated with the Five-Star System for the SFF methodology. 2. Assign points to G-level deficiencies in SQC areas equivalent to those additional points assigned to H- and I-level deficiencies in SQC areas. 3. Account for a nursing home’s full compliance history regardless of technical status changes.

To ensure consistency with the SFF methodology, CMS should also consider making two of these modifications—the SQC and full compliance history changes—to its Five-Star System.

Agency Comments and Our Evaluation

We obtained written comments on our draft report from CMS, which are reprinted in appendix IV. CMS noted that our report adds value regarding the methods that CMS and the nursing home industry should use to address the issue of homes that consistently demonstrate quality of care problems and indicated that the agency would seriously consider all of our recommendations. CMS generally agreed in principle with our recommendations.

In response to our first recommendation, CMS noted that it would evaluate a “hybrid” approach that would assign some SFFs using homes’ performance in each state and other SFFs on their relative national ranking. If implemented, CMS’s proposed hybrid approach would address our recommendation that it consider placing more emphasis on the relative performance of homes nationally, which might result in some states having fewer SFFs and others having more than their current allocation. We did not recommend that CMS allocate SFFs solely on the basis of the relative performance of homes nationally, an approach CMS would disagree with according to its comments.

CMS agreed in principle with our remaining recommendations—intended to improve the SFF methodology’s ability to identify the most poorly performing nursing homes and ensure its consistency with the agency’s Five-Star System—and noted that it would evaluate the effects of adopting them. The agency explained that there might be technical barriers to fully implementing our recommendation that it account for a nursing home’s full compliance history regardless of technical status changes, but noted that it would implement the recommended adjustment to the maximum extent practicable. CMS agreed that although this change would affect a small number of providers, it would improve the accuracy of ratings for those providers.

CMS also provided technical comments, which we incorporated as appropriate.

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Appendix I: Scope and Methodology

This appendix provides a more detailed description of our scope and methodology.

Determining the Number and Comparing the Performance of the Most Poorly Performing Nursing Homes

To determine the number of most poorly performing nursing homes in the nation and compare their performance to that of homes identified using the Centers for Medicare & Medicaid Services’ (CMS) approach, we began by interviewing agency officials about the Special Focus Facility (SFF) Program and methodology and by reviewing documentation related to the methodology. In addition, we interviewed officials in all 10 CMS regional offices and 14 state survey agencies regarding their impressions of the SFF methodology and also asked the state survey agencies what they consider to be indicators of poor performance. To ensure that we calculated the scores for each nursing home consistent with CMS’s SFF methodology, we obtained a copy of the computer programming that CMS used to score and rank nursing homes, verified that our use of CMS’s program generated results that were consistent with output on scores that CMS provided to states, and used the program as the basis for our estimate of the most poorly performing nursing homes in the United States. The SFF methodology creates a total score for each nursing home over three cycles by assigning points to the following data, which we obtained from CMS’s On-Line Survey, Certification, and Reporting system (OSCAR) database: (1) deficiencies cited on the three most recent standard surveys, (2) deficiencies cited on the last 3 years of complaint investigations, and (3) revisits associated with the three most recent standard surveys. Additional points are assigned to deficiencies classified as substandard quality of care (SQC). Each cycle consists of one standard survey, revisits associated with the standard survey, and 12 months of complaint investigations. We extracted these data from OSCAR in December 2008.

To learn about methods used to rate nursing home performance, we interviewed officials of two nursing home associations—the American Health Care Association and the American Association of Homes and Services for the Aging, interviewed experts in long-term care research, attended meetings that CMS held to seek input from long-term care researchers on the development of the agency’s Five-Star Quality Rating System (Five-Star System), analyzed information available at CMS’s Providing Data Quickly Web site, reviewed prior GAO reports, and interviewed officials from some states with nursing home rating systems. We also reviewed documentation describing additional approaches to rating nursing home performance. Specifically, we reviewed eight nursing home rating systems, which considered a variety of rating factors.

Examination of the SFF Methodology

To determine the adequacy of the SFF methodology, we compared the methodology to other compliance-based measures of poor performance and tested the sensitivity of the methodology to variations, such as weighting.

We compared the SFF methodology to two other compliance-based measures of poor performance—SQC and immediate sanctions. We found that those nursing homes with the worst total scores in the nation were much more likely to have met the criteria for SQC in the last 1, 2, and 3 years compared to all other nursing homes. Similarly, we found that the same nursing homes were much more likely to have had deficiencies that could have resulted in the imposition of at least one immediate sanction in the last 1, 2, and 3 years compared to all other nursing homes. In addition, we tested the sensitivity of CMS’s SFF methodology to several variations, some of which led us to consider making modifications to the methodology that affected facility scores. For example, in one variation, we modified the SFF methodology so that the cycle scores were no longer weighted—CMS began weighting the cycle scores in June 2008. We concluded from this test that the SFF methodology was sensitive to weighting, which influenced our decision to impose a requirement in our scoring thresholds such that the most poorly performing nursing homes have high scores in at least two of three cycles or a very high score overall.

Based on our examinations of the SFF methodology, document review, and interviews, we concluded that the SFF methodology is reasonable and comprehensive because it uses multiple years of data, includes all deficiencies as opposed to a subset of deficiencies, includes deficiencies from standard surveys and complaint investigations, and accounts for the scope and severity of deficiencies and the number of revisits. Furthermore, CMS has refined the SFF methodology over time.

Development of the Nationwide Statistical Scoring Thresholds

Although we concluded that estimating the number of the most poorly performing nursing homes on a state-by-state rather than on a national basis would yield inconsistent results, we determined that there was no natural break point that differentiated the most poorly performing nursing homes from all other homes. As a result, we investigated several statistical approaches and determined that Tukey’s method was appropriate because the distribution of nursing homes’ total scores is highly skewed. Tukey’s method is meant to identify the extreme ends of the distribution. It labels an observation as a potential outlier if its value is greater than the threshold identified by the following equation: Potential Outlier Threshold = Q3 + 1.5 * (Q3 – Q1) Where: Q3 = 75th percentile and Q1 = 25th percentile The range identified by (Q3 – Q1), called the interquartile range, covers 50 percent of the observations in the center of the distribution. We applied this method by identifying nursing homes that had scores that were above the potential outlier threshold.

We then explored several options to identify the most poorly performing nursing homes using Tukey’s method as a basis. For each option, we analyzed the group of resulting nursing homes identified as poor performers and those missed by the thresholds. As the result of our examination of the SFF methodology and prior work in which we classified nursing homes as low, moderately, or high performing, we knew that nursing homes that have serious deficiencies in one year may not demonstrate consistent poor performance—what we term chronic poor performance in this report. Thus, another option we considered was to identify as a poor performer any nursing home that had a total score that (1) was above the potential outlier threshold and (2) was also above the potential outlier threshold for at least two of its three cycle scores. This option identified 507 nursing homes. Because these nursing homes had poor performance in at least two of three cycles as well as high total scores, we concluded that this threshold identified chronic poor performance. However, we found that when we limited the most poorly performing nursing homes to this group of chronic poor performers we missed some nursing homes with very poor performance that was not chronic. Therefore, we established a second threshold to identify those very poor performers—those nursing homes that were at or above the 99th percentile—or approximately 330—of total score. This threshold added another 73 nursing homes.

Determining the Characteristics of the Most Poorly Performing Nursing Homes

To determine the characteristics of the most poorly performing nursing homes that distinguish them from all other nursing homes, we analyzed deficiencies and revisits from the three most recent cycles—that is, the three most recent standard surveys as of the date of our data extract (December 17, 2008) and any associated revisits, as well as deficiencies cited on complaint investigations conducted 3 years before our data extract. We also analyzed other data that describe the characteristics of nursing homes: a December 17, 2008, extract of other OSCAR variables; case-mix-adjusted nurse staffing hours available from CMS’s Five-Star System, which were dated November 2008; and a list developed by CMS of nursing homes whose deficiency histories could have subjected them to immediate sanctions, which we obtained from CMS in October 2008.

Following are highlights of how we analyzed certain characteristics: We calculated the number of nursing homes in each fiscal year that had deficiencies that could have resulted in the imposition of at least one immediate sanction.

Nursing homes self-report their ownership type. We created the ownership type of for-profit by combining three categories of for-profit nursing homes designated in CMS’s data (individual, partnership, and corporation) and the category of limited liability corporation. Similarly, we created the ownership type of nonprofit by combining three categories of nonprofit nursing homes (corporation, church related, and other), and the ownership type of government from the six designations made in CMS data (state, county, city, city/county, hospital district, and federal).

CMS maintains a variable in its data called multi–nursing home (chain) ownership, which is self-reported by nursing homes and which we refer to as chain affiliation. According to CMS, multi–nursing home chains have two or more homes under one ownership or operation. We determined the percentage of nursing homes that were for-profit and chain affiliated, nonprofit and chain affiliated, or government owned and chain affiliated by combining the ownership type described above with CMS’s designation of multi–nursing home (chain) ownership.

We used the number of beds certified for payment for Medicare, Medicaid, or both to calculate the following: the average number of beds per nursing home and the percentage of nursing homes by bed size category (0 to 49, 50 to 99, 100 to 199, and more than 199 beds).

We calculated the percentage share of residents by resident type (Medicare, Medicaid, or other) by dividing the number of Medicare, Medicaid, and other patients by the number of total residents.

We calculated the occupancy rate by dividing the total number of residents by the number of certified beds. We used certified beds to calculate the occupancy rate instead of total beds because CMS officials told us that certified beds provided more reliable information.

We analyzed the following nurse staffing hours, which were case-mix adjusted by CMS for use in its Five-Star System: registered nurse hours per resident per day, licensed practical nurse and vocational nurse hours per resident per day, nurse aide hours per resident per day, and total staffing hours per resident per day. We calculated resident nurse hours as a share of the total. Unadjusted nurse staffing hours data are collected by CMS, self-reported by nursing homes, and represent staffing levels for a 2-week period before the state inspection. CMS case-mix adjusted the staffing data using the average minutes of nursing care used to care for residents in a given resource utilization group category as reflected in the Medicare skilled nursing facility prospective payment system. CMS acknowledges that the staff hours collected from nursing homes have certain limitations. In order to increase the accuracy and comprehensiveness of the staffing data, CMS has been investigating whether it can use nursing home payroll data to report staffing levels on the Nursing Home Compare Web site.

Appendix II: Detailed Compliance History for Two of the Most Poorly Performing Nursing Homes, as of December 2008

The following table provides the detailed compliance history over three cycles for two of the most poorly performing homes in the nation.

Appendix III: Performance by Standard Area for the Most Poorly Performing and All Other Nursing Homes, as of December 2008

The following table provides the percentages of the most poorly performing and all other nursing homes that were cited for actual harm or immediate jeopardy by standards area over three cycles.

Appendix IV: Comments from the Centers for Medicare & Medicaid Services

Appendix V: GAO Contact and Staff Acknowledgments

John E. Dicken, (202) 512-7114 or dickenj@gao.gov.

Acknowledgments

In addition to the contact named above, Walter Ochinko, Assistant Director; Ramsey Asaly; Daniel Lee; Shannon Slawter Legeer; Jessica Morris; Jessica Nysenbaum; Dae Park; Roseanne Price; Jennifer Rellick; Kathryn Richter; and Jessica Smith made key contributions to this report.

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