Poorly Performing Nursing Homes: Special Focus Facilities Are Often Improving, but CMS's Program Could Be Strengthened
Released: 2010-04-19T00:00:00

Letter

Background

Titles XVIII and XIX of the Social Security Act establish minimum standards that all nursing homes must meet to participate in the Medicare and Medicaid programs, respectively. Provisions added by the Omnibus Budget Reconciliation Act of 1987 focused the standards on the quality of care actually provided by a home. To encourage improvement at nursing homes that demonstrate chronic noncompliance with these quality of care standards, CMS initiated the SFF Program in 1998 and subsequently expanded and strengthened the program in December 2004.

Ensuring Compliance with Federal Quality of Care Standards

To assess whether nursing homes meet federal quality of care standards, state survey agencies conduct standard surveys, which occur on average once a year, and complaint investigations as needed. A standard survey involves a comprehensive assessment of federal 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 member. Deficiencies identified during either standard surveys or complaint investigations are classified in 1 of 12 categories labeled A through L 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; homes with D-level or higher 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.

Federal Enforcement

Nursing homes that fail to meet federal quality standards may be subject to federal enforcement actions known as sanctions. Sanctions can affect a home’s revenues and therefore provide financial incentives to return to and maintain compliance. Enforcement of nursing home quality of care standards is a shared federal-state responsibility. In general, sanctions are (1) initially proposed by the state based on a cited deficiency, (2) reviewed and imposed by CMS regional offices, and (3) implemented—that is, put into effect—by the same CMS regional office. Homes may have an opportunity to correct deficiencies after the imposition of a sanction, which can result in the sanction being rescinded, that is, not implemented. At state and regional office discretion, sanctions may be imposed immediately without giving homes an opportunity to correct deficiencies after a required notice period. 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 (CMP), denial of payment for new Medicare or Medicaid admissions (DPNA), and termination from the Medicare and Medicaid programs. Overall, two sanctions—CMPs and DPNAs—accounted for about 75 percent of federal sanctions from 2005 through 2008, while terminations were less than 1 percent. By statute, DPNA and termination are mandatory sanctions in certain circumstances, but discretionary DPNA and termination may also be imposed.

CMP. Unlike most other sanctions, CMPs require no notice period; in addition, they can be applied retroactively to the first date of noncompliance. CMPs may be either per day or per instance. CMS regulations specify a per day CMP range from $50 to $10,000 for each day a home is noncompliant—from $50 to $3,000 for nonimmediate jeopardy and $3,050 to $10,000 for immediate jeopardy. Per instance CMPs range from $1,000 to $10,000 per episode of noncompliance. In 2007, CMS issued guidance to states and regional offices to encourage consistency in CMP amounts.

DPNA. A DPNA denies a home payments for new admissions until deficiencies are corrected. A DPNA is required by statute within 3 months of the end of a survey if, for example, a home fails to correct deficiencies and return to compliance. In contrast, discretionary DNPAs can go into effect much sooner—after a 15-day notice period, which is shortened to 2 days in the case of immediate jeopardy. Unlike CMPs, DPNAs cannot be imposed retroactively.

Termination. CMS can terminate a nursing home by implementing either a mandatory or a discretionary termination. Mandatory termination is required by regulation if within 23 days of the end of a survey a home fails to correct immediate jeopardy deficiencies or within 6 months if it fails to correct nonimmediate jeopardy deficiencies. CMS may also impose discretionary terminations in situations other than those specified above, which require the same notice period applicable to discretionary DPNAs.

In addition, nursing homes can and do terminate voluntarily, which may be related to the fact that the home is at risk of involuntary termination.

The SFF Program

The objective of the SFF Program is to decrease the number of persistently poorly performing nursing homes by focusing more attention on a small number of nursing homes in each state with a record of poor quality performance. Since the program’s inception, CMS has changed the SFF Program’s scope and methodology to enhance the agency’s goal of improving nursing home performance.

CMS’s SFF Methodology and Selection of SFFs

CMS uses its SFF methodology to identify a list of the 15 worst performing nursing homes in each state. The SFF methodology assigns points to deficiencies on standard surveys and complaint investigations, and to revisits associated with deficiencies cited on standard surveys.19, 20 More points are assigned to deficiencies that are higher in scope and severity, and additional points are assigned to deficiencies classified as substandard quality of care (SQC). (See table 2.) For every nursing home, CMS sums the points associated with the deficiencies (including SQC) and the revisits to create a cycle score for each of the last three cycles. CMS then creates the total score by weighting the more recent cycle scores more heavily. Nursing homes with the highest number of points are the worst performing homes.

To avoid potential double-counting, deficiencies that appear on complaint surveys conducted within 15 days of a standard survey (either prior to or after the standard survey) are counted only once. If the scope or severity differs on the two surveys, the highest scope and severity combination is used.

From 1999 to 2004, the SFF methodology assigned a different number of points to deficiencies using only about 1 year of deficiency data. In 2005, CMS altered the number of points assigned to deficiencies and based a nursing home’s numeric score on about 3 years of deficiency data, weighted equally. In 2007, CMS removed fire safety deficiencies from the SFF methodology, to provide an increased focus on quality of care deficiencies.

CMS refers to the resulting list of the 15-worst performing homes in each state as the SFF candidate list. The list is generally distributed quarterly to CMS’s regional offices, which then distribute it to states as necessary. Except for Alaska, each state and the District of Columbia have between one and six SFFs at any time, depending on the total number of nursing homes in the state. (See fig. 1.) If there is an opening in the state’s program because an SFF has graduated from the program or been terminated from Medicare and Medicaid, the state selects a new SFF from its candidate list. CMS allows states discretion in determining which of the candidates to choose for the program, and the CMS regional office concurs with the states’ selections.

In addition to specifying that states select SFFs from a list of candidate nursing homes, figure 2 shows that CMS’s guidance to states on the operation of the SFF Program includes two other key requirements: States are instructed to increase scrutiny of SFFs in the following ways: Survey frequency. States provide additional scrutiny of SFFs by conducting two surveys per fiscal year for each SFF, which is twice as frequent as at other nursing homes. CMS measures each state’s compliance with this requirement annually through state performance reviews. The state performance reviews emphasize three aspects of the survey program: survey timeliness, survey quality, and the enforcement and remedy of problems found during surveys. If a regional office determines that a state has not conducted two surveys for every SFF allotted to the state per fiscal year, the state must develop a corrective action plan.

Enforcement. States are required to impose sanctions that increase in severity and are more immediate when SFFs do not improve. For example, a state should propose a CMP or a DPNA with the minimum notice period.

After three standard surveys (approximately 18 months) in the SFF Program, one of the following three outcomes are expected: Graduation. States determine if the SFF is eligible to “graduate” from the program by meeting CMS’s criteria for improved performance: two consecutive standard surveys and any intervening complaint investigations must have no deficiencies at the F level or higher. In addition, an SFF cannot have a deficiency higher than the F level on the fire safety portion of its most recent standard survey.

Retention. States keep the SFF in the program if it does not meet the graduation criteria but is showing improvement.

Termination. States recommend that the SFF be terminated from participation in Medicare and Medicaid if it fails to make significant progress.

CMS made several key changes to the SFF Program in 2007 and 2008 to increase both provider and public awareness of the program. In 2007, CMS began requiring states to notify the governing bodies, owners, operators, and administrators that a facility had been designated an SFF. CMS also issued a press release in 2007 listing those SFFs that had failed to significantly improve their care; previously, the identity of SFFs was not provided to the public. In an effort to increase transparency, CMS added the identity of all SFFs to its Nursing Home Compare Web site during 2008. That same year, information on the SFF methodology and on SFFs, such as those that failed to significantly improve their care after about one survey, was added to CMS’s Web site.

In 2008, CMS also added ratings from its Five-Star Quality Rating System to its Nursing Home Compare Web site to further assist consumers in judging nursing home quality. Every home in the United States is rated from one star (much below average) to five stars (much above average).

The Five-Star System provides an overall quality rating based on individual ratings for three separate components: (1) health inspections— deficiencies cited on standard surveys and any associated revisits and complaint investigations; (2) nursing home staffing levels; and (3) quality of care measures, which are computed using data submitted to CMS by nursing homes on their residents’ health, physical functioning, mental status, and general well-being. SFFs can have high ratings on any one of the three components, but their overall rating is capped at three stars. However, as of February 2009, no SFFs had a five star rating in the health inspection component of the Five-Star System.

States Considered Factors Other Than Rank When Selecting SFFs, Which Were More Likely to Be Chain Affiliated and For-Profit Than Other Nursing Homes

When selecting SFFs from the candidate list, state officials considered factors other than rank, such as their own knowledge of each candidate’s circumstances. Such discretion is key not only because of states’ familiarity with each candidate’s circumstances but also because the list has limitations. The characteristics of SFFs differed from those of other nursing homes in terms of the homes’ organization type and the number of beds and residents. For example, SFFs were more likely than other homes to be chain affiliated and for-profit and to have more beds and more total residents.

States Exercise Discretion When Selecting SFFs by Considering Factors Such as Surveyor Workload or Nursing Home Ownership

Although rank and score play an important role in the selection of SFFs, the states we interviewed consider additional factors when making their selections, including the candidate’s compliance and enforcement history, state surveyor workload, nursing home ownership, and financial concerns. We found that a majority of states selected SFFs from among their five worst performers. While the first-ranked candidate (the worst performing home) was selected about 17 percent of the time from January 2006 through February 2009, about 57 percent of SFFs selected during that period were among the five worst-ranked candidates.

Compliance and enforcement history. Most states we interviewed specified that they review the details of a nursing home’s overall compliance with federal quality standards. For example, a state might review the scope and severity level, type, and repetitiveness of deficiencies for candidates it considers. When reviewing a nursing home’s history, a few states indicated that they are likely to select candidates with more recent poor performance, which is also reflected in the SFF score beginning in 2008. In addition, some states we interviewed review and take into consideration a nursing home’s enforcement history, such as any CMPs or DPNAs imposed for noncompliance with federal standards.

State surveyor workload. Some states we interviewed avoid concentrating SFFs in the same district in their state due to the additional workload associated with the SFF Program. For example, California tries to distribute SFFs geographically so that no state district office has more than one SFF at a time. To help accomplish this, the California state survey agency meets with the district offices to review the candidate list.

Nursing home ownership. Some states we interviewed consider the nursing home’s ownership, including any recent changes or knowledge of other homes owned by the same chain. State officials explained that a recent change of ownership could influence their SFF selection. For example, if state officials are aware of a history of problems with the new owner, they may select the home for the SFF Program in order to scrutinize it more closely; on the other hand, they may not select a home if they perceive that the new owner has a good reputation, which would give the new owner adequate time to correct quality problems.

Financial concerns. Officials from three of the states we interviewed said they might consider any known financial concerns when making an SFF selection. In addition, regional offices told us that they may learn about nursing homes’ financial difficulties on an ad-hoc basis, such as through resident and staff complaints about inadequate services or the home’s inability to meet its payroll or through a home’s request to delay or reduce payments from CMPs due to financial hardship.

Limitations in the Candidate List Highlight the Necessity for States’ Discretion

The majority of state officials we interviewed told us that CMS’s candidate list accurately identifies poorly performing nursing homes in each state, such as those homes that habitually cycle in and out of compliance with federal nursing home standards; however, we found that certain limitations in the list highlight the importance of states’ discretion in selecting SFFs. First, because of lag times between when surveys are conducted and when the results are reflected on the SFF candidate list, some states and regions found that the candidate list may not reflect a home’s most recent survey. Consequently, some candidates could appear to have a better or worse score than reflected in the most recent survey, or homes may be included or excluded inappropriately from the list. Second, some states told us that the candidate list does not provide them with a sufficient number of nursing homes from which to select new SFFs. Specifically, the list includes current SFFs if they are still ranked among the worst 15 homes in the state, which narrows the state’s selection possibilities. Officials from Indiana, Illinois, and California, which are allocated 4, 5, and 6 SFFs, respectively, explained that having existing SFFs on their list left them with fewer than 15 nursing homes to consider for the program.

Moreover, we found additional limitations to the list, including considerable variation in SFF candidate scores, that further highlight the importance of states’ discretion. As noted in our August 2009 report, the candidate list identifies poorly performing nursing homes in each state but does not necessarily identify the most poorly performing nursing homes in the nation. This limitation becomes apparent when comparing SFF scores for nursing homes on the candidate list both across and within states. As a result of the variation across states, some candidates may have the worst score in their state and yet not score among the worst homes in the nation. In addition, wide variation in candidates’ scores is also found within individual states. For example, on the January 2009 candidate list, the score of the worst home in Tennessee was about 1,512. However, all SFF candidates in each of 22 states had lower scores than the 15th worst candidate in Tennessee, which had a score of about 253. (See fig. 3.)

SFFs Were More Likely to Be Chain Affiliated and For-Profit Than Other Nursing Homes

SFFs differed from other nursing homes not only in terms of their compliance history but also in terms of other key characteristics. We compared deficiencies from the 59 SFFs that (1) were added to the SFF Program in 2008 and (2) had a standard survey that occurred prior to their entry into the program to deficiencies from other homes that had a standard survey conducted in 2008. We included in this analysis deficiencies from complaint investigations that occurred in the year prior to the 2008 standard survey and revisits associated with the standard survey. Consistent with the SFF methodology, we found that, on average, these 59 SFFs had both a greater number of D- through L-level deficiencies, including more at each scope and severity level, and a greater number of deficiencies cited on standard surveys and complaint investigations than other nursing homes. For example, SFFs had, on average, 5.5 times more actual harm deficiencies and 19 times more immediate jeopardy deficiencies. In addition, these SFFs had, on average, a greater number of revisits. (See table 3.)

Additional key characteristics of SFFs also differentiated them from other nursing homes. Comparing the characteristics of 133 homes in the SFF Program as of February 2009 to those of other homes, we found that they differed in terms of type of organization and participation in Medicare and Medicaid, the number of beds and residents, nurse staffing levels, and ratings on CMS’s Five-Star System. (See app. III.)

Type of organization and participation in Medicare and Medicaid. A higher percentage of SFFs were part of a chain organization, for-profit organization, or both. For example, about 55 percent of SFFs were for- profit and chain affiliated, compared to about 42 percent of other nursing homes. Furthermore, fewer SFFs participated in Medicaid only compared to other nursing homes—about 2 percent and about 5 percent, respectively.

Beds and residents. On average, SFFs had more beds and total residents, but a lower occupancy rate, than other nursing homes. Specifically, SFFs averaged approximately 131 beds and 104 residents—about a 79 percent occupancy rate; other nursing homes had approximately 106 beds and 90 residents—about an 85 percent occupancy rate. SFFs also had a greater share of Medicaid patients on average than other nursing homes— 71 percent compared to 60 percent.

Nurse staffing. SFFs had fewer registered nurse hours per resident-day as a share of total hours per resident-day—about 9.0 percent compared to about 9.9 percent for other nursing homes.

CMS’s Five-Star System. SFFs were much more likely than other nursing homes to be ranked lower on the overall quality rating and health inspection component rating of the Five-Star System. Specifically, about 74 percent of SFFs received one star on the overall quality rating, and about 93 percent received one star on the health inspection component of the rating. In comparison, about 22 percent of other nursing homes received one star on the overall quality rating, and about 19 percent received one star on the health inspection component.

State and Regional Office Adherence to SFF Program Guidance Was Uneven, but Most SFFs Improved Their Performance While in the Program

We found that some states did not consistently follow CMS’s basic SFF Program requirements, such as surveying SFFs twice a year. We also found that CMS’s enforcement guidance is vague and results in inconsistent interpretations. Most SFFs did eventually graduate, not all met CMS’s graduation criteria, and some remained in the program well beyond CMS’s 18-month time frame for improvement. However, most SFFs improved their performance while in the program, although many graduates failed to sustain their performance.

Some States Did Not Meet CMS’s Requirement to Survey SFFs Twice a Year

Some states did not meet CMS’s requirement to conduct standard surveys of SFFs twice a year. While states improved their compliance with this requirement in fiscal year 2008—8 states did not conduct two surveys for SFFs, compared to 26 states in the previous fiscal year—some SFFs were still not receiving increased scrutiny through additional standard surveys, a fundamental component of the program. In addition, 18 states had at least one SFF that had more than 10 months between surveys from calendar year 2005 through 2008. Further, between January 2005 and February 2009, 15 states did not survey at least one of their SFFs within the first 7 months that the home was in the program.

A CMS official told us the agency was aware that states were not always conducting two surveys per fiscal year for SFFs. In fiscal year 2007, CMS began measuring states’ adherence to this requirement as part of its annual state performance reviews. CMS required seven states that did not meet this standard in fiscal year 2008 to complete corrective action plans.

CMS’s Enforcement Guidance Is Vague and Results in Inconsistent Interpretations

CMS’s SFF Program guidance on enforcement, under which states and regions must impose more robust enforcement on SFFs that do not demonstrate significant improvement, is vague and open to interpretations. According to CMS, “more robust” is intended to mean that homes with continued noncompliance should receive sanctions that both increase in severity and are immediate; that is, homes should not be provided with an opportunity to correct deficiencies before the sanction goes into effect. CMS guidance also allows states discretion in determining significant improvement, and therefore which homes receive more robust enforcement and which do not. In our interviews with CMS’s central office, an official stated that CMS’s SFF enforcement guidance could be more specific and noted that CMS plans to release descriptive guidance explaining exactly how states and regions should apply more robust enforcement. In addition, we found that officials in the regional offices and states we interviewed had inconsistent interpretations of the guidance. For example, some officials told us that when determining sanctions, they treat SFFs no differently than they would any poorly performing nursing home. For other officials, there was little consensus on the most appropriate sanctions for SFFs—CMPs, or other sanctions, such as discretionary DPNAs or discretionary termination.

Despite the existence of SFF Program enforcement guidance, we found that SFFs were not necessarily more likely to be subject to CMPs and DPNAs—the most frequently cited sanctions—than SFF candidates, which are also poorly performing nursing homes. For example, about 28 percent of SFFs had at least one CMP implemented in 2008, compared to about 34 percent of candidates in the same year. In addition, the rates of implemented CMPs and DPNAs decreased for SFFs relative to candidates from 2006 to 2008, while the rates of other nursing homes remained low. (See fig. 4.) We found similar trends in the average CMP values; for example, SFF candidates received the highest average per day CMPs in 2008—approximately $4,262 per day—compared to approximately $3,969 for SFFs and $1,990 for other nursing homes. However, it is unclear if improvement in SFFs’ performance could explain the higher rates and average values we observed for SFF candidates compared to SFFs.

Our review of the detailed enforcement histories of six SFFs with consecutive noncompliance cycles at the G level or higher found that the sanctions imposed by regions and states on the homes ranged in terms of their immediacy and severity. For example, in three out of seven noncompliance cycles with G-level or higher deficiencies, one SFF was allowed an opportunity to correct its deficiencies before sanctions could go into effect. Still, this same SFF had sanctions of increasing severity imposed with each successive recurrence of noncompliance at the G level—the state and regional office increased CMPs from $300 per day of noncompliance to $600 per day of noncompliance. Conversely, anot SFF was assessed no CMPs, even though it had a similar history of consecutive noncompliance cycles that could have resulted in CMPs ranging from $300 to $825 per day of noncompliance, suggesting that CMS’s monitoring of enforcement is insufficient. However, both of these SFFs had immediate sanctions imposed; that is, the state and regional offices imposed discretionary rather than mandatory DPNAs and terminations. Discretionary DPNAs and terminations provide nursing homes with a shorter opportunity to correct deficiencies before the sanction goes into effect compared to mandatory DPNAs and terminations. Despite this shorter correction period, both of these SFFs were typically able to correct their deficiencies and avoid these sanctions.

Most SFFs Did Eventually Graduate, but States and me CMS Regions Kept So SFFs in the Program beyond the Program’s Expecte Frame

Although most SFFs eventually graduated, CMS regions and states often chose to keep SFFs in the program beyond the 18-month time frame. Mor homes have graduated from the SFF Program than were terminated, but some graduates did not meet CMS’s criteria. States we interviewed indicated that some SFFs were able to improve their performance while others lingered in the program for various reasons, including not improving enough to meet the graduation criteria nor performing poorly enough to terminate from Medicare and Medicaid. As a result, other nursing homes whose performance may have been worse could not be selected for the program.

Of the 355 nursing homes placed in the SFF Program from Janua through February 2009, 181 graduated (51 percent) and 41 were terminated (12 percent). CMS terminated 21 SFFs involuntarily, and 20 left the program voluntarily. We found that nursing homes sharing the same physical location as 13 of the 41 SFFs that terminated have since been certified to participate in Medicare or Medicaid again. However, at least 24 of the 181 SFF graduates did not actually meet elements of CMS’s graduation criteria—two consecutive standard surveys and any intervening complaint investigations with no deficiencies at the F level or higher; 15 of these 24 SFFs should not have graduated because they had deficiencies at the F level or higher. For example, a Florida SFF had four deficiencies at the F level on the two standard surveys that occurred prior to its graduation.

We found that CMS’s regions and states often chose to keep SFFs in the program instead of terminating them, even though CMS’s SFF Program guidance establishes the expectation that SFFs will be terminated from Medicare and Medicaid if they fail to make significant progress after being in the program for 18 months. The fact that some homes did not gradu ate or terminate after being in the program for 18 months prevented other nursing homes whose perf selected for the program. ormance may have been worse from being SFFs added to the program from 2005 through 2007. Of the 254 homes that became SFFs during this time frame, 102 (40 p the program for 25 months or more as of February 2009.

Active SFFs. Of the 133 active SFFs as of February 2009, 23 (17 per cent) were in the program f program or 25 months or more, and 11 had been in the since 2005.

However, the length of time that SFFs spent in the program before graduating or terminating has decreased since 2005. (See fig. 5.) For example, as of the end of February 2009, SFFs added to the program in 2005 graduated on average after 28 months, compared to an average of 20 and 13 months for SFFs that were added to the program in 2006 and 2007, respectively.

Some CMS regions and states we interviewed offered several reasons that might help to explain why they chose to keep some SFFs in the program beyond 18 months. First, nursing homes may not improve enough to meet CMS’s criteria for graduation, nor be performing poorly enough to terminate from Medicare and Medicaid. Second, closing a nursing home requires the state to transfer residents to another nursing home, and some residents with special care needs—such as children or those that have behavioral health issues—may be difficult to relocate. Third, if the SFF recently had a change in ownership, it may have the potential to improve and therefore should not be terminated: new management or capital investment in a poorly performing nursing home may bring about needed changes in quality. For example, officials in CMS’s New York regional office described a case in which all three reasons influenced their decision to allow a nursing home to remain in the program for over 45 months. The home had initially demonstrated improvement until two consecutive surveys resulted in deficiencies at the F level or higher, had a behavioral health population that was large, making it difficult to place them in other homes, and experienced two changes of ownership while in the SFF Program. The most recent owners invested millions of dollars to improve the physical environment at the facility and hired a nurse consultant.

Regional office officials indicated that a more recent survey had shown improvement with no deficiencies higher than the D level, which they attributed to the investments made by the most recent owner.

Conversely, some SFFs were able to improve enough to graduate in less than 19 months. Our analysis shows that between 2005 and February 2009, 32 states graduated at least one facility in less than 19 months. According to some state and regional office officials we interviewed, such SFFs showed improvement after being selected for the program because of factors such as the home’s ability to establish stable leadership, a willingness to adopt new improvements, or the hiring of outside consultants. Some state and regional office officials indicated that homes might be able to graduate earlier if the graduation criteria were less stringent. For example, one regional office and one state did not think that F-level deficiencies in kitchen sanitation should prevent homes from graduating. Two regions suggested that a more appropriate graduation criterion would be to allow homes with F-level deficiencies to graduate as long as the deficiencies were not in SQC areas, rather than the current criterion of no F-level or higher deficiencies. We found that 27 percent of the 328 SFFs (90 homes) had at least one instance where one or more F- level deficiency prevented them from graduating and over half of those SFFs (54 homes) had F-level deficiencies in kitchen sanitation.

Beginning in 2007, CMS introduced two program changes that may contribute to a decline in the amount of time that SFFs remain in the program. For example, CMS began identifying SFFs on its Nursing Home Compare Web site each month. The majority of CMS regional offices and states we interviewed believed CMS’s identification of SFFs had a positive impact on the SFF Program because of the media and public attention. Florida indicated that there had been little concern by providers about being in the SFF Program until SFFs were publicly identified. An additional factor may also have influenced the amount of time SFFs remain in the program. As noted earlier, CMS began requiring states to formally notify homes in 2007 that they had been placed in the program, including each home’s governing body, owner, operator, and administrator. Michigan has applied a more aggressive notification policy since 2005, sending a notification letter to each SFF candidate explaining that they are at risk of being selected as an SFF if they fail to address performance problems. A copy of the letter is also sent to the owner and operator of the nursing home the first time the home appears on CMS’s candidate list. The Michigan official we interviewed believed that this notification policy has motivated the state’s SFF candidates to improve. In December 2008, the state of Missouri also began notifying SFF candidates that they were at risk of being selected as SFFs.

Most SFFs Improved, but Some Graduates Failed to Sustain Their Performance

While in the program, most SFFs improved their performance, but some failed to sustain their improved performance after graduation. The scores of SFF graduates showed a statistically significant improvement when compared to their scores before entering the program, as did the scores of active SFFs, but to a lesser degree than graduates. Officials from some CMS regions and states told us that the SFF Program was helping nursing homes to improve their performance, but others were unsure of the program’s effectiveness. For example, officials from New York, which graduated 8 of 13 SFFs from January 2005 to February 2009, told us that more frequent surveys and the state’s presence at the facilities had a positive effect on the behavior of SFFs. Conversely, officials in Alabama, which graduated 2 of 5 SFFs in the same period, believed that it was difficult to say whether the program had improved nursing home performance. Alabama officials noted that while the program had motivated a few of the states’ SFFs to take a closer look at their operations, SFFs did not always understand the consequences that might result for failing to improve their performance.

Despite the improvement in scores, we also found that 75 (50 percent) of the 149 graduates from calendar years 2005 through 2008 that had a standard or complaint survey after graduation received at least one F-level or greater deficiency after graduation, which would have prevented their graduation had they still been SFFs. For example, one graduate had one F-level deficiency, two G-level deficiencies, and two J-level deficiencies on its first standard survey after graduating. A smaller subset of these 149 SFF graduates—22 homes (15 percent)—received at least one J- through L-level deficiency (immediate jeopardy) on either a standard or a complaint survey after they graduated.

Other Quality Improvement Strategies to Assist SFFs Have Merit, and Some Could Inform CMS Efforts

While the core of the SFF Program is more frequent surveys and stringent enforcement, CMS and others have adopted a variety of other quality improvement strategies to help address care problems identified at SFFs and other nursing homes; both the HHS OIG and state strategies hold potential lessons that could help inform CMS’s efforts. Through Systems Improvement Agreements (SIA) and the Nursing Homes in Need (NHIN) initiative, CMS has attempted to deal more directly with the root causes of poor quality in a limited number of SFFs. In contrast to CMS’s focus on individual facilities, the HHS OIG has identified corporations that own poorly performing homes and negotiated Corporate Integrity Agreements (CIA) to bring about quality improvements across the homes in the corporation. States have adopted quality improvement strategies that sometimes resemble CMS’s efforts but that may also be offered to homes that have not exhibited performance problems.

SIAs and the NHIN Initiative Hold Promise, but the Use of Such Interventions Has Been Limited

While both SIAs and the NHIN initiative have the potential to help SFFs improve the quality of care provided to residents, their use has been limited. More than 2 years after the first SIA was implemented, CMS’s central office has not yet collected and shared information about this intervention with its 10 regional offices. Moreover, the measures used by CMS to monitor the impact of the NHIN initiative are inconsistent with those used to measure whether an SFF is improving.

Systems Improvement Agreements with SFFs. SIAs are agreements between CMS and SFFs that identify concrete actions that the homes are required to take, generally within a specific time, in order to avoid termination and to improve and maintain quality. CMS did not develop the first SIA with an SFF until 2007—9 years after the creation of the SFF Program. Overall, four CMS regional offices have negotiated a total of 10 SIAs. SFFs that agreed to SIAs retained their SFF designation and were still subject to more frequent surveys than other homes.

SIAs share some common requirements and sometimes describe similar elements using different terminology. SIAs may differ because regional offices attempt to craft agreements that reflect each SFF’s particular circumstances or the specific regional office goals for the home. Common SIA elements include hiring an independent consultant to provide technical assistance with quality improvement activities at the home; requiring the consultant to identify solutions to issues that were preventing the SFF from attaining or maintaining compliance, often referred to as a root-cause analysis, and to develop an action plan to address quality problems; and providing periodic reports prepared by either the consultant or the home to the regional office on the status of actions or recommendations to improve quality.

Two of the SIAs required the SFFs to commit funds ranging from $850,000 to $956,000 for the improvement of clinical care systems or facility capital improvements. For example, one SIA required the SFF to dedicate $956,000, primarily for capital improvements such as a new roof and a heating and cooling system but also to go into an escrow fund to pay for quality improvements consistent with the consultant’s root-cause analysis and action plan.

In general, regional offices have used SIAs as an alternative to terminating homes that had been in the program for 18 months. Five of the 10 homes with SIAs had been in the SFF Program from 27 to 42 months when they signed SIAs. (See app. IV for a chronological summary of all SIAs as of August 2009.) One regional office negotiated an SIA to avoid bed shortages that would have resulted had the SFF been terminated from Medicare and Medicaid. Another SFF at risk of termination had recently undergone a change in ownership, and the new owner had made substantial clinical and capital improvements. With 6 of the 10 SIAs, Dallas is the only regional office with an SIA that has established specific criteria for the use of the agreements. Any SFF that has failed to graduate from the SFF Program in 18 months and that has no deficiencies above the F level is given one last chance by signing an SIA. Dallas regional office officials stressed that in applying these criteria they have also begun to assess the SFF’s commitment to improve through the SIA process.

Whether or not the agreements result in improved nursing home performance, they have the potential to address the problem of homes that linger in the SFF Program for years. Although three of the regional offices with SIAs indicated that SIAs were effective, the Dallas regional office thought it was too early to say because the future performance of the homes was unknown.

Four homes met the terms of their SIAs and graduated from the SFF Program. As of August 2009, one of these homes was above average according to CMS’s Five-Star System, and three were below or much below average.

Two homes were terminated, one for failing to meet terms of the SIA. The other home voluntarily left the Medicare and Medicaid programs before CMS could terminate it.

Two homes are still SFFs even though the SIAs have ended. These homes were rated as below and much below average in August 2009.

Two homes are still subject to the terms of their SIAs, and both were rated as much below average in August 2009.

Although the first SIA was implemented more than 2 years ago, CMS central office officials told us they had not yet disseminated information to the regions describing the elements that should be part of SIAs or catalogued any lessons learned from their use. We found that most SIAs were initiated by regional offices with limited CMS central office involvement and varying levels of state involvement. As of May 2009, CMS’s central office was not aware of some SIAs that we identified in an interview with officials in the Dallas regional office. According to officials at three of the six regional offices that have not used SIAs, they prefer other agreements to improve and maintain quality, including agreements between state licensing authorities and SFFs. Of the three remaining regional offices, two indicated they would be likely to develop an SIA when they have facilities deemed appropriate and the other had not heard of SIAs.

Nursing Homes in Need Initiative. The technical assistance provided by QIOs to a limited number of nursing homes (one per year per state) from August 1, 2008, through July 31, 2011, under the NHIN initiative resembles the actions outlined in SIAs. For example, QIOs are required to conduct an on-site assessment of each home to identify the underlyin causes of poor quality of care, prepare a root-cause analysis based on those findings, and develop an action plan to address the home’s problems. Given the voluntary nature of the QIO program, SFFs must agree to work with QIOs; however, CMS did direct QIOs to contact SFFs recommended by state survey agencies instead of relying on homes to request QIO assistance. According to CMS, 44 QIOs assisted one SFF per state from August 2008 through July 2009, the first year of the NHIN initiative, and the remaining 6 QIOs assisted other homes, including SFF candidates. CMS told us that these states’ QIOs were not working with SFFs for various reasons, such as the SFFs were already showing improvement. Officials from a majority of the states we interviewed thought it would be beneficial for QIOs to work with every SFF instead of just one per state per year.

In addition to the small number of SFFs that participate each year of the contract, the initiative’s effectiveness is also limited by the misalignment between the measures CMS uses to monitor the effect of QIO assistance and those used to measure whether an SFF is improving. Specifically, CMS monitors the improvement in two quality of care measures to determine the effect of QIO assistance during the initiative—the percentage of long- term residents of a facility who were either physically restrained or were high risk and had pressure ulcers. An alternative approach would be to use the measures that CMS uses to evaluate whether homes in the SFF Program are improving, that is, the number or scope and severity of deficiency citations. CMS officials responsible for the QIO program indicated that they were aware of this discrepancy and would consider alternative methods for measuring QIO performance.

HHS OIG’s Experience with Quality of Care CIAs Is Relevant to CMS Activities to Address Poor Quality at SFFs and Other Nursing Homes

CMS officials told us that SIAs are not modeled on the HHS OIG quality of care CIAs—corporate-level interventions to address quality problems— and CMS and HHS OIG officials have indicated that they have had preliminary discussions regarding SIAs. SIAs and CIAs are similar in that they are intended to improve and maintain the quality of participating facilities in lieu of termination or exclusion, respectively, but quality of care CIAs are more commonplace, have been in use since 2000, and are generally in effect for longer periods. According to a 2009 HHS OIG report, by June 2008, 35 nursing home corporations had entered into such agreements, which are generally in effect for 3 to 5 years. In its report, HHS OIG examined 15 of these nursing home corporations under quality of care CIAs, which collectively operated 1,104 nursing homes as of December 31, 2006. Both SIAs and quality of care CIAs require homes or corporations to seek outside technical assistance to identify changes that will help address quality problems. In addition, quality of care CIAs may require corporations to take other actions to help improve quality, such as the establishment of corporate-level compliance officers, quality assurance committees, and quality assurance monitoring committees. The HHS OIG requires the corporations to hire an independent entity, or monitor, to assess the quality assurance and quality improvement systems in place in the corporations’ homes. According to an official with one such entity, staff members are embedded in the nursing home corporation to help ensure that planned quality improvements are actually implemented. In addition, the corporations must provide the HHS OIG with a list of all homes that belong to the same corporation at the beginning of the CIA and update the HHS OIG with information on the homes bought and sold; the independent quality monitor commonly contracts with a data analysis organization, subject to HHS OIG approval, to analyze state survey results and quality of care measures across the homes in the corporation. The 2009 HHS OIG report evaluated the extent to which required quality of care structures and processes were implemented through CIAs. Based on its review of 15 such CIAs, the OIG found that all 15 corporations had enhanced their quality of care structures and processes while subject to these agreements. As of September 2008, an HHS OIG official was aware of three corporations with quality of care CIAs that had homes that were also in the SFF Program, and two of these SFFs had SIAs. However, there is little coordination between CMS and the HHS OIG to determine whether there is overlap among homes with these agreements.

The HHS OIG’s experience with quality of care CIAs is relevant and may contain lessons that could improve the effectiveness of CMS’s efforts to deal with poorly performing nursing homes even though CMS central office officials told us that the agency’s relationship is with the individual home and not the parent company. Thus, we found that some regional offices do interact with corporations. One SIA we reviewed was signed by the Vice President of Clinical Services of a large nursing home chain. Moreover, officials from two regional offices told us that they are aware of and monitor problems at chains operating in their regions, such as by conducting federal monitoring surveys at homes belonging to a problem chain. In fact, one of these regions indicated that CMS’s central office had directed closer scrutiny of certain nursing home corporations in the past. Officials from two regions provided a concrete example of their interactions with officials from one corporation. These two regions noticed that several homes with compliance problems had a common name, and one regional office determined through a Google search that the homes were part of the same chain; this chain operated almost 50 homes in five states, and some of this chain’s homes were SFFs. In one instance, regional office officials met with the chief executive officer and regional operations manager to discuss an improvement plan for one of the chain’s homes after this home became an SFF. In another instance, officers from the same chain approached a regional office. Ultimately, the chain developed a quality improvement plan intended to cover all of its facilities across three CMS regions; however, regional office officials did not consider this plan to be an SIA because it did not provide for enforcement.

Some States Have Also Adopted Nursing Home Quality Improvement Strategies That Are Relevant to CMS’s Efforts to Address SFF Quality Problems

Officials from the majority of the 14 states we interviewed detailed strategies they use to promote quality improvement in nursing homes, including poorly performing homes. Their strategies have incorporated a range of activities, such as having nurse monitors visit all nursing homes, training nursing home staff on clinical best practices, conducting expedited or more frequent surveys of nursing homes with performance problems, and hiring consultants to provide technical assistance to homes. These activities can be voluntary, and some states target certain poorly performing nursing homes for participation. According to some states, the SFF Program is more effective when combined with state-based quality improvement activities, which may make it difficult to determine whether performance improvements are attributable to the SFF Program or to state activities. Officials from three states described the following noteworthy quality activities: Florida. Florida law requires the state agency to survey nursing homes with serious performance problems more frequently (every 6 months for 2 years). Such homes must pay a fine of $6,000 in installments over the 2-year period to cover the expenses associated with the additional surveys. As of August 2008, state officials told us that 26 nursing homes, including 3 SFFs, met these criteria.

Missouri. The state’s Quality Improvement Program for Missouri’s Long Term Care Facilities offers a voluntary nurse consultant program that provides in-person, on-site technical assistance for nursing homes deemed to be at risk. While any nursing home in the state can ask to participate in the program, the state also determines which homes it deems eligible to participate and has included SFFs in this determination since July 2008. As of October 2008, 85 nursing homes were targeted for program participation because the state deemed them to be at risk.

Arkansas. Arkansas’s Innovative Performance Program, which began in August 2005, provides most nursing homes with (1) monthly training opportunities, covering a variety of topics for nursing home staff, such as the top 10 most frequently cited deficiencies or clinical best practices; and (2) intensive reviews of a home’s operations to help identify and correct systemic problems and put controls in place to ensure the changes are implemented. Under its contract with the Arkansas Office of Long Term Care, the state’s QIO administers this voluntary program and helps identify the homes most in need of assistance. As of September 2009, 141 nursing homes were participating in the program, including 5 of 6 SFFs.

Conclusions

Improving the performance of poorly performing nursing homes through efforts such as the SFF Program is essential to protecting highly vulnerable elderly and disabled residents. Paradoxically, we found that many SFFs improved their performance and graduated from the program even though some may not always have been surveyed as frequently as required or subjected to more robust enforcement—actions called for in CMS SFF Program guidance to states and regional offices. Important program changes made by CMS since 2004, such as the 2007 decisions to publicly report the names of SFFs and ensure that owners and boards of directors are explicitly informed of their facility’s SFF status, have given homes additional incentives to improve their performance. The effectiveness of the SFF Program could be further improved if CMS implemented our August 2009 recommendation to target scarce resources by placing more emphasis on the worst homes nationally rather than on the worst homes in each state.

We found that state discretion in selecting SFFs is a key element of the program, but that limitations in the candidate list restrict that discretion. For example, homes that are selected as SFFs remain on each state’s list of 15 candidates, giving states with six SFFs, such as California and Texas, fewer options when homes graduate from the program or are terminated, compared to states with only one or two SFFs. Moreover, at least two states—Michigan and Missouri—have expanded on CMS’s public disclosure strategy in an attempt to influence the performance of SFF candidates by informing them that they are at risk of being selected as an SFF if they fail to address performance problems.

CMS’s guidance to states regarding enforcement for SFFs is vague and interpreted inconsistently by regional offices and states. We found that for six SFFs with consecutive noncompliance, severe deficiencies did not consistently result in more severe or immediate sanctions. For example, one SFF received no civil money penalties even though it was cited for consecutive deficiencies that could have resulted in fines ranging from $300 to $825 per day of noncompliance, while a home with a similar compliance history received CMPs that increased from $300 to $600 per day of noncompliance, suggesting that CMS’s monitoring of enforcement is insufficient.

The significant percentage of SFFs that remained in the program for considerably longer than CMS’s 18-month time frame for improved performance or termination is troubling because it prevents other poorly performing nursing homes from receiving enhanced attention by becoming SFFs since the program is limited by resources to 136 at a time. For a limited number of homes that failed to show improvement or where termination was seen as an option of last resort, 4 of CMS’s 10 regional offices have negotiated agreements requiring homes to address quality problems. Although the first SIA was implemented over 2 years ago, CMS has not disseminated information to the regional offices describing the elements that should be part of SIAs, or catalogued any lessons learned from their use. It is probably too early to evaluate the longer-term effectiveness of SIAs, but they do have the potential to address the problem of homes that linger in the program for extended periods.

Other efforts also have the potential to help address performance problems at poorly performing nursing homes, and some may hold lessons for improving the operation of the SFF Program.

Nursing Homes in Need Initiative. QIOs are employing interventions similar to those used in SIAs at poorly performing nursing homes, primarily SFFs, at the rate of one home per state each year for 3 years. However, the QIO measures used to monitor the effect of QIO assistance are inconsistent with the measures CMS uses to evaluate whether SFFs are improving.

Corporate Integrity Agreements. The HHS OIG has considerable experience negotiating legal agreements with corporations that own poorly performing nursing homes, agreements that also employ more direct interventions to address quality problems across the homes in a chain. Currently, there is little coordination between CMS and the HHS OIG, even though we found that some regional offices also work with chain corporate offices and a few homes were subject to both SIAs and CIAs.

State Quality Improvement Initiatives. Some states have adopted quality improvement strategies to address problems at poorly performing homes. For example, Florida requires homes with serious performance problems to pay $6,000 to cover the costs associated with more frequent surveys over a 2-year period. Although SFFs and other poorly performing nursing homes that frequently harm residents or place them in immediate jeopardy increase survey costs, CMS does not currently have the authority to charge them for additional survey activities. Charging such a fee could improve the linkage between the costs associated with more frequent surveys and the nursing homes that give rise to these costs.

Recommendations for Executive Action

To increase the SFF Program’s effectiveness in helping to address quality of care problems at poorly performing nursing homes, we recommend that the Administrator of CMS take the following five actions: Expand the SFF Program’s public disclosure strategy by directing states to notify nursing homes that have been identified as SFF Program candidates that they are at risk of being selected as an SFF.

Revise the SFF candidate list by removing homes that states have selected as SFFs and including additional homes so that states with a large number of SFFs have a full complement of candidates to choose from each time they select a new SFF.

Ensure that states impose more stringent enforcement, such as higher CMPs or termination; clarify SFF Program guidance regarding appropriate sanctions; and monitor SFF sanctions more closely.

Provide CMS regional offices with a description of the elements that should be part of SIAs and catalogue any lessons learned from their use.

Coordinate more systematically with the HHS OIG regarding its experiences with CIAs.

To offset the additional costs imposed by SFFs and create incentives for poorly performing nursing homes to improve resident care more quickly, we recommend that the Administrator of CMS seek legislative authority to charge SFFs for the costs associated with conducting additional surveys.

Agency Comments and Our Evaluation

We obtained written comments on our draft report from HHS, which are reprinted in appendix V. HHS noted its commitment to further strengthening the SFF Program and indicated that our report would help to further improve what could work better and reaffirm what is already working well. HHS agreed fully with four of our six recommendations, agreed in principle with a fifth recommendation, and indicated that it would take a sixth recommendation under advisement.

In its comments, HHS reported on additional analyses it had conducted, which found that in comparing SFF graduates to candidates, (1) a higher percentage of graduates had no deficiencies at the F-level or higher both 6 months and 12 months after graduation from the program, and (2) an average of 21.5 months and 15.6 months, respectively, elapsed before 50 percent of the homes in either group had an F-level or higher deficiency. HHS concluded that while these analyses did not suggest that SFF graduates had transformed into high-quality nursing homes, they did indicate that the greater efforts made by SFF graduates had some lasting and positive effects on quality of care. Despite the improvements demonstrated by SFFs, HHS expressed concern about those SFF graduates that relapse into a pattern of serious deficiencies. HHS noted that it will re-examine the 18-month time frame for SFFs to demonstrate improved performance given our finding that a significant percentage of SFFs remain in the program beyond 18 months.

HHS fully concurred with the following four recommendations: (1) directing states to notify SFF candidates that they are at risk of being selected for the SFF Program; (2) clarifying SFF Program guidance on appropriate sanctions and monitoring SFF sanctions more closely; (3) providing CMS regional offices with a description of the elements of an SIA and cataloguing lessons learned from their use; and (4) coordinating more systematically with the HHS OIG regarding its experiences with CIAs. In addition, HHS agreed in principle with our recommendation that CMS revise the SFF candidate list by removing homes that have already been selected as SFFs and including additional homes so that states have a full complement of candidates from which to choose; however, HHS said that CMS will operationalize the recommendation in a different manner by sizably increasing the SFF candidate list for most states. If implemented, CMS’s proposed approach would address our recommendation. Finally, HHS indicated it would take under advisement our recommendation that the Administrator of CMS seek legislative authority to charge SFFs for the costs associated with conducting additional surveys.

HHS also provided technical comments, which we incorporated as appropriate. We also provided excerpts of the report to the HHS OIG and the three state survey agencies whose specific quality improvement strategies we described in the report—Arkansas, Florida, and Missouri— and incorporated technical comments from those agencies as appropriate.

As we agreed with your offices, unless you publicly announce the contents of this report earlier, we plan no further distribution of it until 30 days from the date of this letter. At that time, we will send copies to the Administrator of the Centers for Medicare & Medicaid Services and appropriate congressional committees. The report also is available at no charge on the GAO Web site at http://www.gao.gov.

If you or your staff have any questions about this report, please contact me at (202) 512-7114 or at dickenj@gao.gov. Contact points for our Offices of Congressional Relations and Public Affairs may be found on the last page of this report. GAO staff who made key contributions to this report are listed in appendix VI.

Appendix I: Special Focus Facility Financial Performance

This appendix addresses your interest in the financial performance of Special Focus Facilities (SFF). Officials from the Centers for Medicare & Medicaid Services’s (CMS) central office told us that they do not attempt to determine whether poor financial performance contributed to the poor quality of care provided by SFFs. However, three of the four regional offices we interviewed did not believe that poor financial performance was more common for SFFs than for other nursing homes. An official from the remaining regional office did not comment because he told us the office assesses only quality of care. Regional offices told us that they may learn about homes’ financial problems on an ad hoc basis. For example, they might learn that a home failed to meet payroll, that lenders were unwilling to extend credit to a home, or that a resident’s complaint, such as a low building temperature or limited food supply, might indicate financial problems. In addition, regional offices are made aware of potential financial problems when a home requests a delay or reduction in payment on civil money penalties (CMP) based on poor financial condition. CMS guidance provides a suggested list of sources for information to determine if a CMP should be delayed or reduced due to a home’s financial condition, but does not indicate how the regional office should make this determination. Furthermore, two regional offices told us that requests for delayed or reduced CMPs are rare.

CMS’s central office also has access to another source of financial information—annual Medicare cost reports, which nursing homes receiving Medicare payments are required to submit to Medicare Administrative Contractors. However, according to the Medicare Payment Advisory Commission (MedPAC), the financial information provided in these reports has several limitations for assessing total financial performance of nursing homes. For example, the cost reports do not follow the format of standard audited financial statements or receive serious audit attention. Despite these limitations, the Medicare Administrative Contractor we interviewed, which processes Medicare claims in addition to receiving Medicare cost reports, told us that it could conduct a financial analysis of nursing homes if CMS were to request and fund this activity, but noted that it would first want to verify their information.

We did identify two entities that have initiatives that collect information about nursing home financial performance. First, Virginia passed legislation on health care data reporting in 1996, which resulted in the state’s contracting with Virginia Health Information to report both nursing home financial information and an assessment of homes’ efficiency and productivity, among other information, on its Web site. The financial information that providers, including nursing homes, are required to file annually includes details about revenue, expenses, and assets. This information is summarized from either certified audited financial statements or, if the nursing home is part of a publicly held company, unconsolidated unaudited financial statements submitted by the home. Second, the Department of Housing and Urban Development (HUD) classifies the financial performance of all facilities that participate in the Section 232 Mortgage Insurance for Residential Care Facilities program. According to HUD officials, 12 SFFs as of June 2009 had insured mortgages and the officials classified 8 of them as financially troubled based on various criteria, such as the home making a late mortgage payment or concerns about the home’s ability to make future mortgage payments.

Appendix II: Scope and Methodology

This appendix provides additional details regarding our scope and methodology.

State interviews. We selected a nongeneralizable sample of 14 states to interview based on a combination of factors, including: (1) the number of SFFs allocated to the state, (2) the inclusion of at least one state from each CMS region, (3) states that had many homes with high SFF scores, (4) the number of SFFs that either graduated from the program or were terminated from Medicare and Medicaid for failing to improve their performance, (5) the use of Systems Improvement Agreements, and (6) the existence of state ranking methodologies. We interviewed officials from the following 14 states: Alabama, Arkansas, California, Colorado, Connecticut, Florida, Illinois, Indiana, Massachusetts, Missouri, New York, Pennsylvania, Texas, and Washington.

SFF candidate lists. CMS provided us with 16 SFF candidate lists generated from December 2005 to January 2009, which contain the score and rank of all nursing homes using CMS’s SFF methodology. Since 2006, CMS has issued candidate lists quarterly, but it generates additional lists when necessary.

To determine the factors that states considered in selecting SFFs from January 2006 through February 2009, we determined the rank of each home on the candidate list that was generated before, but closest to, the date of that home’s selection for the program.

To determine the factors that states considered in selecting SFFs, we analyzed the scores of SFF candidates from CMS’s January 2009 candidate list.

To determine the SFF Program’s impact on homes’ performance, we analyzed the scores for each of the three cycles provided on CMS’s candidate lists from December 2005 to January 2009 by calculating state average indexed scores. We then created a mean indexed score for all SFF graduates for three points in time—before the home entered the program, while the home was in the program, and after graduation—and compared them to each other. For SFFs that were active in the program as of February 2009, we also compared the mean indexed score before the home entered the program to the mean indexed score while the home was in the program.

OSCAR survey and deficiency data. We conducted several analyses using data from CMS’s On-Line Survey, Certification, and Reporting system (OSCAR) for standard surveys and complaint investigations generally conducted between January 2005 and July 31, 2009. We analyzed survey dates, deficiencies cited on standard surveys and complaint investigations, and any revisits associated with the standard surveys. To be consistent with the SFF methodology and avoid potential double-counting, we did not include deficiencies that also appeared on complaint surveys conducted within 15 days of a standard survey (either prior to or after the standard survey).

To determine how SFFs differ from other nursing homes, we calculated the average number of deficiencies and revisits for certain SFFs compared to other nursing homes. We identified SFFs that were added to the SFF Program in 2008 and that also had a 2008 standard survey prior to their entry into the program. We compared these SFFs to other homes that had a standard survey conducted in 2008. Our analysis included deficiencies identified during the 2008 standard survey, deficiencies from complaint investigations that occurred in the year prior to the 2008 standard survey, and revisits associated with the standard survey.

To determine whether states followed CMS’s guidance to survey SFFs twice a year, we analyzed the dates of the standard surveys conducted while nursing homes were in the SFF Program. We determined the number of surveys that should have been conducted each year by multiplying the state’s SFF allotment by two. We then summed the number of SFF surveys conducted by each state for fiscal years 2007 and 2008 and determined if the number of surveys conducted was fewer than the total number of surveys that should have been conducted. In addition, we calculated the number of states that did not survey at least one of their SFFs within the first 7 months after the home entered the program and that had at least one SFF with more than 10 months between surveys from calendar year 2005 through 2008.

To determine whether CMS regional offices and the states followed the agency’s SFF Program graduation criteria, we analyzed survey dates and data on deficiencies from standard surveys and complaint investigations. We determined which SFFs did not graduate appropriately because (1) states conducted standard surveys zero or only one time while the home was in the SFF Program; (2) the home had deficiencies at the F level or higher on the two standard surveys preceding their graduation; or (3) the home had deficiencies at the F level or higher on any complaint surveys that occurred from the two standard surveys preceding their graduation through the date of their graduation.

To determine if SFFs had F-level deficiencies that may have contributed to the length of time they were in the SFF Program, we determined the number of SFFs that had various combinations of standard and complaint surveys that resulted in deficiencies at the F level and lower.

To determine the program’s impact on SFF graduates, we analyzed deficiencies from standard and complaint surveys for SFFs after they graduated from the program. Specifically, we determined the number of SFFs that after graduation had F-level or higher deficiencies on any survey, F-level or higher deficiencies on consecutive standard surveys, and immediate jeopardy deficiencies.

Nursing home enforcement. To determine whether CMS regional offices and states followed the agency’s SFF Program enforcement guidance, we first identified homes that were SFFs as of January 1, 2007, and that had two consecutive standard surveys, both of which cited at least one G-level or higher deficiency; we then analyzed Nursing Home Enforcement History reports and Nursing Home Enforcement Case Profiles for these homes that we obtained from the regional offices and which summarize data from CMS’s Automated Survey Processing Environment Enforcement Manager (AEM). In addition, we analyzed AEM data on CMPs and denials of payment for new Medicare or Medicaid admissions (DPNA) that were in effect for calendar years 2006 through 2008 for each of three categories of nursing homes—SFFs, SFF candidates, and other nursing homes. We determined the percentage of homes in each category that had at least one CMP or DPNA by year. In addition, we calculated mean CMP amounts (per day and per instance) by home type and year by summing each home’s CMPs for the year. For SFFs, we included only sanctions that occurred while the home was in the SFF Program. SFF candidates were those homes ranked as among the worst 15 homes per state based on the CMS candidate lists for that same year.

Termination. To determine the number of SFFs that were terminated— voluntarily or involuntarily—and later became certified to participate in Medicare or Medicaid again, we analyzed information available from CMS’s Provider of Service file dated December 2008. To do so, we identified the addresses of SFFs that terminated, and then we identified nursing homes that shared the same physical location as the terminated SFFs and that later began participating in Medicare or Medicaid.

Nursing home characteristics data. To determine how SFFs differ from other nursing homes, we analyzed CMS data that describe the characteristics of nursing homes: a December 17, 2008, extract of OSCAR variables, such as type of organization and number of beds; nurse staffing hours, which were case-mix adjusted by CMS for use in its Five-Star Quality Rating System and which were dated January 2009; and nursing home ratings from the Five-Star System available from Nursing Home Compare, which were dated December 2008. Following are highlights of how we analyzed certain characteristics: Nursing homes self-report their ownership type. We created the ownership type of for-profit by combining for-profit individual, for-profit partnership, for-profit corporation, and limited liability corporation; the ownership type of nonprofit by combining nonprofit corporation, nonprofit church-related, and nonprofit other; and the ownership type of government by combining the six government designations (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 types described above with CMS’s designation of multi-nursing home (chain) ownership.

We used the number of beds certified for payment for Medicare and/or Medicaid to calculate the average number of beds per nursing home.

We calculated the percentage of residents by 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 of nursing homes by dividing the total number of residents by the number of certified beds.

We analyzed the following case-mix adjusted nurse staffing hours: 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 registered nurse hours as a share of the total. Unadjusted nurse staffing hours data are collected by CMS, are self-reported by nursing homes, and represent staffing levels for a 2-week period prior to 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 Nursing Home Compare.

Appendix III: Characteristics of Special Focus Facilities and Other Nursing Homes

Average number of residents per home (number)

For-profit (individual, partnership, or corporation)

Nonprofit (corporation, church, or other)

Type of staff (average hours per resident-day)

Licensed practical and vocational nurses Registered nurse hours as a share of total hours (percentage)

Individual entries may not sum to 100 percent because of rounding.

Appendix IV: Chronological Summary of All 10 Systems Improvement Agreements (SIA)

Appendix IV: Chronological Summary of All 10 Systems Improvement Agreements (SIA)

Duration in SFF Program (prior to SIA)

date of SIA Duration of SIA(date graduated/ terminated)

CMS’s Five-Star System Rating 8 months Graduated (10/08) 5 months Graduated (3/08) 24 months Graduated (4/08) 2 months Graduated (3/09) 4 months Terminated (10/08) 6 months Terminated (3/09)

Appendix V: Comments from the Department of Health and Human Services

Appendix VI: GAO Contact and Staff Acknowledgments

Acknowledgments

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

Related GAO Products

Nursing Homes: Addressing the Factors Underlying Understatement of Serious Care Problems Requires Sustained CMS and State Commitment. GAO-10-70. Washington, D.C.: November 24, 2009.

Nursing Homes: Opportunities Exist to Facilitate the Use of the Temporary Management Sanction. GAO-10-37R. Washington, D.C.: November 20, 2009.

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. GAO-09-689. Washington, D.C.: August 28, 2009.

Medicare and Medicaid Participating Facilities: CMS Needs to Reexamine Its Approach for Funding State Oversight of Health Care Facilities. GAO-09-64. Washington, D.C.: February 13, 2009.

Nursing Homes: Federal Monitoring Surveys Demonstrate Continued Understatement of Serious Care Problems and CMS Oversight Weaknesses. GAO-08-517. Washington, D.C.: May 9, 2008.

Nursing Home Reform: Continued Attention Is Needed to Improve Quality of Care in Small but Significant Share of Homes. GAO-07-794T. Washington, D.C.: May 2, 2007.

Nursing Homes: Efforts to Strengthen Federal Enforcement Have Not Deterred Some Homes from Repeatedly Harming Residents. GAO-07-241. Washington, D.C.: March 26, 2007.

Nursing Homes: Despite Increased Oversight, Challenges Remain in Ensuring High-Quality Care and Resident Safety. GAO-06-117. Washington, D.C.: December 28, 2005.

Nursing Home Quality: Prevalence of Serious Problems, While Declining, Reinforces Importance of Enhanced Oversight. GAO-03-561. Washington, D.C.: July 15, 2003.

Nursing Homes: Public Reporting of Quality Indicators Has Merit, but National Implementation Is Premature. GAO-03-187. Washington, D.C.: October 31, 2002.

Nursing Homes: Federal Efforts to Monitor Resident Assessment Data Should Complement State Activities. GAO-02-279. Washington, D.C.: February 15, 2002.

Nursing Homes: Sustained Efforts Are Essential to Realize Potential of the Quality Initiatives. GAO/HEHS-00-197. Washington, D.C.: September 28, 2000.

Nursing Home Care: Enhanced HCFA Oversight of State Programs Would Better Ensure Quality. GAO/HEHS-00-6. Washington, D.C.: November 4, 1999.

Nursing Home Oversight: Industry Examples Do Not Demonstrate That Regulatory Actions Were Unreasonable. GAO/HEHS-99-154R. Washington, D.C.: August 13, 1999.

Nursing Homes: Proposal to Enhance Oversight of Poorly Performing Homes Has Merit. GAO/HEHS-99-157. Washington, D.C.: June 30, 1999.

Nursing Homes: Complaint Investigation Processes Often Inadequate to Protect Residents. GAO/HEHS-99-80. Washington, D.C.: March 22, 1999.

Nursing Homes: Additional Steps Needed to Strengthen Enforcement of Federal Quality Standards. GAO/HEHS-99-46. Washington, D.C.: March 18, 1999.

California Nursing Homes: Care Problems Persist Despite Federal and State Oversight. GAO/HEHS-98-202. Washington, D.C.: July 27, 1998.