Characteristics of Nursing Home Providers With Distinct Patterns of Physical and Occupational Therapy Staffing

Abstract

Previous work found a substantial growth in therapy staffing among nursing home providers following the introduction of Medicare’s Prospective Payment System (PPS). Since the PPS, however, several new Medicare policies have been implemented that may impact the provision of rehabilitative care in nursing homes. In view of the rising focus on patient outcomes and provider performance, it is worthwhile to explore more recent therapy staffing patterns following the introduction of these Medicare programs. While our results show stable staffing levels through prior policy changes, upcoming Medicare payment changes will likely have a stronger impact that may result in reduced therapy staffing. In addition, given that our findings show that staffing patterns vary across provider type, we may see greater variation as a result of the upcoming changes. Thus, therapy staffing should continue to be monitored and deeper explorations into the impact of staffing changes on patient outcomes should be undertaken.

Keywords

health services long-term services & supports nursing homes therapy staffing post-acute care Medicare policy Patient Driven Payment Model

Introduction

Advances in medicine and technology have allowed individuals to live longer, but many are doing so with chronic conditions. This has led to a more medically complex older adult population and an increase in utilization of long-term care (LTC) and post-acute care (PAC) services (Institute of Medicine [IOM], 2001). In the United States, the populations 65 years and older and 85 years and older are expected to double and triple, respectively, by 2050 (Reaves & Musumeci, 2015). Among those 65 years and older, 70% are estimated to utilize nursing home services and those 85 years and older are 4 times as likely to use services from nursing home providers compared to their younger (65–84) counterparts (Reaves & Musumeci, 2015). We can expect these populations to use care in nursing homes to rehabilitate during a short, post-acute stay prior to community discharge and/or to optimize function as long-stay residents who live in the facility permanently.

Historically, consumers and government regulators have criticized the nursing home industry for not providing reliable, effective, high-quality services (Castle & Ferguson, 2010; IOM, 2001; Mukherjee et al., 2010; Vladeck, 1980). In efforts to improve quality over the last two decades, the Centers for Medicare and Medicaid Services (CMS) has implemented changes to reimbursement levels, mandated several quality initiative programs, and established minimum nurse staffing laws over time (Castle & Ferguson, 2010; Mor et al., 2004).

The nursing home PAC and LTC services provided are largely and respectively reimbursed by Medicare and Medicaid (Kaiser Family Foundation, 2016). Over the last two decades, updates to Medicare reimbursement policies for PAC services have influenced nursing home staffing of physical therapy (PT) and occupational therapy (OT) services (White, 2003; Zinn et al., 2003). Under the retrospective cost-based reimbursement system, therapy flourished throughout the 1990s. However, a surge in Medicare spending facilitated the phased implementation of the Prospective Payment System (PPS), which began in 1998 (Jette et al., 2005). This transition from a retrospective to prospective-based payment system altered the provision of therapy services and restructured how rehabilitative care was offered in nursing homes.

PT and OT staffing provide restorative-based care to Medicare-covered patients in nursing homes to reduce impairment, rehabilitate from an injury or illness, improve functioning and cognition, and prevent adverse effects or onset of disease (Jette et al., 2005; Leland et al., 2018). More specifically, OT staff provide more holistic and alternative approaches to care. OT staff assist patients (and caregivers) to improve the safety of their environment and support patients by recommending modifications to daily activities and educating them on their condition, which leads to an increase in physical activity, engagement in self-care, reduced pain, and maintaining independence (Livingstone et al., 2019). PT staff, on the other hand, engage patients in strength training to increase strength and endurance, offer high-intensity exercises to improve balance and gait, and provide therapeutic services to improve functioning and mobility (Boyd et al., 2008). Both OT and PT staff serve an important role in nursing homes and have been associated with improved health outcomes for recipients of their care in the form of lower prevalence of falls, higher functioning, reduced impairment, improved quality of life, and shorter length of stay (Boyd et al., 2008; Jette et al., 2005; Leland et al., 2017; Livingstone et al., 2019; Wong & Leland, 2016).

Since the implementation of PPS, there has been increased scrutiny on therapy staffing levels from stakeholders with respect to trends in PAC treatment patterns and provider behavior with respect to escalating expenditures (Wodchis, 2004). Research suggest that providers were more likely to offer therapy services in-house under the PPS, compared with the previous cost-based system under which providers utilized outside vendors to offer contract therapy services (Wodchis, 2004). This change in delivery was likely done to reduce costs and manage the uncertainty associated with changes to reimbursement methodology (Leland et al., 2017; Tyler et al., 2013).

Other findings suggest that providers also targeted residents who were associated with more profitable therapy levels (Wodchis, 2004). Using data from 2001 to 2010, Tyler et al. (2013) evaluated staffing levels and found an increase in average total hours per resident day (HPRD) for PT/OT staff and PT/OT assistants in freestanding facilities. This suggests that the two types of staff complement each other and that PT/OT assistants were not used as substitutes for more highly skilled staff. However, the same study found a decline in these types of staff in hospital-based facilities, indicating differences in the impact across different types of facilities. Tyler and her team also found that PT/OT aide staffing levels remained relatively stable during this period (Tyler et al., 2013). Since 2010, however, several new Medicare policies and program changes have occurred that may impact the use of PT/OT staff, including the 2014 Improving Medicare Post-Acute Care Transformation (IMPACT) Act, the introduction of the Skilled Nursing Facility Value-Based Purchasing (SNF VBP) program in 2014, and changes to the Five-Star Quality Rating system in 2016. Given the implementation of these new policies since the work from Tyler et al. (2013), examining more recent PT/OT staffing levels in nursing homes is worthwhile to determine if staffing patterns continued to change in response to these competing policies.

The IMPACT Act was signed into law to establish quality reporting programs for PAC providers using standardized assessment data. Through shared decision-making and improvements in care coordination and discharge planning, the intent was to increase transparency of performance for consumers to improve outcomes for Medicare patients, the direct targets for therapy services in PAC settings (CMS, 2018a). The SNF VBP program is a performance-based initiative that awards SNFs based on their quality performance. Providers are evaluated on the care provided to Medicare patients, and if deemed appropriate, incentive payments are included in Medicare claims paid under the SNF PPS (CMS, 2019). Finally, the Five-Star Quality Rating system was updated by adding five new short-stay quality measures to expand the number of short-stay measures used in the calculation. Medicare patients comprise a substantial proportion of the short-stay population in nursing homes, and quality of care will now be evaluated using more quality domains that capture the care provided to Medicare patients (CMS, 2017).

Given the growing emphasis on provider performance and optimizing patient outcomes in the recent years, it is important to explore staffing patterns subsequent to Tyler et al. (2013) since their investigation of therapy staffing occurred prior to the introduction to value-based payments in PAC. Previous work has examined staffing trends at the national level only and has not differentiated between PT and OT staff types (Tyler et al., 2013; Wodchis, 2004; Wong & Leland, 2016). There is a need to examine geographic variation in therapy staffing and variation across different types of providers, given the differences in training and responsibilities across therapy staff types (Tyler et al., 2013; Wodchis, 2004; Wong & Leland, 2016). For example, PT and OT staff are licensed and have obtained a graduate degree (i.e., occupational and physical therapists). These staff are responsible for evaluating and creating customized care plans for each patient/resident (Leland et al., 2017). Occupational and physical therapy assistants (OTAs and PTAs) are also licensed but have a minimum of an associate’s degree and must work under the supervision of the PT or OT. These staff can carry out the plan of care established by the respective physical or occupational therapist and can independently treat residents. In contrast, occupational and physical therapy aides require no license or degree and are not permitted to independently deliver treatment. Instead, these staff provide support to the therapy team under the direct supervision of PTs and OTs (American Physical Therapy Association, 2017; Leland et al., 2017; Tyler et al., 2013).

The purpose of this study is to characterize nursing homes associated with distinct therapy staffing patterns, including PT and OT staff, PT and OT assistants, and PT and OT aides, and to examine individual trends of each therapy staff type, separately, over time and by region since Tyler et al. (2013). Depicting staffing patterns since the implementation of several new Medicare policies is important to identify the type of therapy staff that are utilized by different provider types to deliver rehabilitative services to patients/residents in this new health care environment. Building from the work of previous studies, results from this study can offer additional insight about facility-level therapy staffing patterns in nursing homes and help in our understanding of provider approaches to care and helping families identify an appropriate provider for a family member based on their service needs. In addition, examining staffing data over time allows for the identification of changes in utilization patterns, including any potential changes in staffing patterns following Medicare payment updates. This may help to explain how providers have responded over time to changes in policy and guide our understanding for how providers may respond to future changes to Medicare policies.

Method

Secondary data sources were used to capture characteristics of nursing home providers and track facility-level staffing patterns longitudinally from 2013 to 2016. The Certification and Survey Provider Enhanced Reporting (CASPER) system and Long-Term Care: Facts on Care in the US (LTCFocus) data sets were used to capture staffing patterns and provider characteristics. The CASPER system is an administrative data set maintained by CMS that was used for the survey and certification process for all Medicare- and Medicaid-participating facilities during the study period (American Health Care Association [AHCA], 2017). These data have been used in numerous studies to examine staffing in the nursing home setting and include information about staffing patterns, services offered, and various ownership characteristics (Castle et al., 2007; Castle & Ferguson, 2010; Feng et al., 2005). The LTCFocus data set was developed by Brown University to present information about nursing home demographics, resident characteristics, and other provider attributes. LTCFocus combines data from various sources to present information about nursing home at the facility, county, and state level (Brown University, 2017).

The CASPER data served as the source for finalizing the sample of nursing homes. Between 2013 and 2016 we identified 15,635 U.S. nursing homes participating in Medicare and/or Medicaid. The sample was limited to facilities residing within the 50 U.S. states, and providers were excluded if they were not present in all 4 years of the study (n = 2,227) or if data were missing on all key analytic variables (n = 1,656). Excluding providers for not being present in all 4 years was done to control for external effects, such as competition or state policies, that may influence provider behavior and nursing home closures/openings (AHCA, 2017; Brown University, 2017). The final sample is stable across the study period and includes a total of 12,237 nursing homes.

The CASPER data were used to calculate therapy staffing levels for the six separate therapy staff types: PTs and OTs, PT and OT assistants, and PT and OT aides, and to capture facility-level characteristics of providers. Full-time equivalents for therapy staff were converted into HPRD, to examine staffing pattern trends for each staff type over time, by applying the CMS approach used for the overall five-star measure (CMS, 2017). The continuous measures of HPRD for PTs and OTs and PT and OT assistants were then recoded into terciles to distinguish between high, medium, and low staffing levels of each staff type, and the aide staffing variables were recoded into a dichotomous variable to identify which providers utilized PT and OT aides. These categorical/dichotomous variables were used to characterize providers associated with different patterns of therapy staffing. Variables indicating provider characteristics include facility size, measured in total number of beds; for-profit and non-profit status; chain affiliation status; ownership type, a categorical variable created from interacting profit status and chain affiliation; freestanding and hospital-based status; occupancy rate, measured in number of occupied beds; and the proportion of residents who are Medicare- and Medicaid-pay.

LTCFocus data were used to identify several facility-level resident attributes and geographic location. We used Resource Utilization Groups (RUGs), which are used for Medicare reimbursement under the PPS and are defined as a continuous measure of the resources needed based on the intensity of care for all residents within a facility (Levinson & General, 2006). We also used average acuity level, a continuous measure of the care needed by short- and long-stay nursing home residents. This facility-level measure is calculated based on the level of care needed for activities of daily living and special treatments, such as the proportion of residents receiving respiratory care, tracheotomy care, or intravenous therapy, for example (Feng et al., 2006). We used the 10 CMS geographic regions to define geographic location.

First, we performed univariate analyses on all variables to describe the sample of providers used in this study. All continuous variables are described using means, and all dummy and categorical variables are described using percentages. We then stratified the sample into terciles by staffing level for each therapy staffing category. The means and proportions from the overall sample serve as a reference for comparing characteristics of facilities that differ in their therapy staffing levels. Second, bivariate analyses between each therapy staffing pattern variable and facility-level characteristic were then conducted to examine provider characteristics associated with distinct therapy staffing patterns and the proportion of providers in each staff type category by provider characteristics. Significant differences in staffing patterns were determined using F tests, t tests, and χ² tests and row percentages are reported to determine the distribution of facilities by staffing level within each facility characteristic. Row percentages were used instead of column percentages because column percentages were a function of the overall sample within each facility characteristic and would not offer insightful information about the distribution of facilities within each staffing category. Last, average therapy staffing HPRD were also tracked longitudinally, separately for freestanding and hospital-based facilities, to depict overall trends and compare average staffing levels across years. Distinguishing between freestanding and hospital-based facilities is important given the difference in case mix and provider responses following the implementation of Medicare’s PPS (Rahman et al., 2016; Tyler et al., 2013). Displaying both OT and PT staffing patterns by facility type not only is beneficial to compare staffing patterns between freestanding and hospital-based facilities but also allows for a comparison of each therapy staff and depicts the overall distribution of staffing within each facility type.

Results

Our final sample includes 43,469 observations from 12,237 U.S. nursing homes (see Tables 1 and 2). The average nursing home size is 110 beds and the average facility-level occupancy rate is 81.0%. The sample of facilities are predominately freestanding homes (96.8%), for-profit (74.7%), part of a chain (60.0%), and over 50% of facilities are included in three regions: approximately 25% in Region 5 (i.e., IL, IN, MI, MN, OH, WI), 18% in Region 4 (i.e., AL, FL GA, KY, MS, NC, SC, TN), and 14% in Region 6 (i.e., AR, LA, NM, OK, TX). The proportion of Medicaid- and Medicare-covered residents ranges between 0% and 100%, with an overall mean of 59.0% and 15.0%, respectively. The mean facility-level RUG case mix for all residents is 1.2, and the average acuity level is 12.1.

Table 1.

Facility-Level Characteristics of Nursing Home Providers, by Certified and Assistant Staffing Levels (HPRD) and Presence of Aides.

Facility characteristics^a	Overall sample	PT staffing categories			PT assistant staffing categories			PT aides
	Overall sample	High	Medium	Low	High	Medium	Low	Yes	Nov
	N = 12,237	n = 4,077	n = 4,077	n = 4,083	n = 4,079	n = 4,078	n = 4,080	n = 3,737	n = 8,500
	M/%	M/%			M/%			M/%
Beds (size)	110.5	108.9	121.9	101.9	104.9	119.7	108.1	127.6	102.7
Occupancy rate	80.9	80.6	82.7	79.4	79.7	81.7	81.3	82.8	80.1
Prop. Medicaid	59.1	46.8	63.7	66.3	48.5	62.6	65.8	56.8	60.1
Prop. Medicare	15.2	24.0	12.8	9.2	23.7	12.6	9.7	17.6	14.1
RUG score	1.2	1.2	1.2	1.1	1.2	1.2	1.1	1.2	1.2
Acuity levels	12.1	12.3	12.3	11.9	12.2	12.2	11.9	12.3	12.1
Facility characteristics^a	Overall sample	OT staffing categories			OT assistant staffing categories			OT aides
	Overall sample	High	Medium	Low	High	Medium	Low	Yes	No
	N = 12,237	n = 4,079	n = 4,079	n = 4,079	n = 4,077	n = 4,080	n = 4,080	n = 1,319	n = 10,918
	M/%	M/%			M/%			M/%
Beds (size)	110.5	107.4	122.7	102.6	106.8	121.3	104.6	130.8	107.8
Occupancy rate	80.9	80.4	82.9	79.4	79.6	82.0	81.1	82.6	80.7
Prop. Medicaid	59.1	47.5	63.3	66.1	50.3	63.4	63.1	60.1	58.9
Prop. Medicare	15.2	24.0	12.9	9.2	22.8	12.7	10.6	16.4	15.1
RUG score	1.2	1.2	1.2	1.1	1.2	1.2	1.1	1.2	1.2
Acuity levels	12.1	12.3	12.3	11.9	12.3	12.2	11.9	12.2	12.1

Note. HPRD = hours per resident day; PT = physical therapy; RUG = Resource Utilization Group; OT = occupational therapy.

Differences in therapy staffing categories across each therapy staff type and facility characteristic were significant (p < .05).

Table 2.

Proportions of Nursing Homes in Each Category of Facility, by Facility Type and Region.

Facility characteristics^a	Overall sample	PT staffing categories			PT assistant staffing categories			PT aides
	Overall sample	High	Medium	Low	High	Medium	Low	Yes	No
	N = 12,237	n = 4,077	n = 4,077	n = 4,083	n = 4,079	n = 4,078	n = 4,080	n = 3,737	n = 8,500
	%	Row % (total = 100%)			Row % (total = 100%)			Row % (total = 100%)
FP	74.7	31.8	34.2	34.1	34.2	34.0	31.9	30.5	69.5
NP	25.3	37.7	31.0	31.4	31.0	31.6	37.4	33.0	67.0
Chain owned	59.8	34.3	34.7	31.0	37.3	34.3	28.4	30.2	69.8
Non–chain owned	40.2	31.9	31.2	36.9	27.4	31.8	40.8	32.7	67.3
FP, chain	48.3	33.6	34.9	31.5	38.6	34.5	26.9	30.0	70.0
FP, no chain	25.4	28.4	32.7	38.9	25.7	32.9	41.4	31.5	68.5
NP, chain	11.7	37.2	34.0	28.8	31.9	33.5	34.6	30.6	69.4
NP, no chain	14.6	38.0	28.6	33.5	30.3	30.0	39.6	34.9	65.1
Freestanding	96.8	32.8	33.6	33.6	33.2	33.8	33.1	30.9	69.1
Hospital based	3.2	50.3	23.9	25.9	38.3	19.7	41.9	39.1	60.9
Region 1^c	5.7	37.9	38.6	23.5	25.6	36.8	37.6	29.8	70.2
Region 2^c	7.9	48.7	39.4	11.9	22.1	33.7	44.2	47.7	52.3
Region 3^c	10.5	42.3	39.4	18.4	35.1	39.4	25.5	38.9	61.1
Region 4^c	17.6	35.9	36.9	27.2	42.2	33.2	24.6	34.0	66.0
Region 5^c	23.5	29.8	32.1	38.2	41.6	32.1	26.4	27.8	72.2
Region 6^c	14.3	19.7	32.5	47.8	26.6	33.4	40.0	28.7	71.3
Region 7^c	10.3	15.4	25.8	58.7	19.8	36.1	44.2	17.2	82.8
Region 8^c	2.8	31.5	24.7	43.8	19.7	22.8	57.5	28.7	71.3
Region 9^c	4.4	54.9	24.3	20.8	32.8	26.2	41.1	34.8	65.2
Region 10^c	3.0	62.6	25.3	12.1	47.2	25.4	27.4	31.7	68.3
Facility characteristics^a	Overall sample	OT staffing categories			OT assistant staffing categories			OT aides^b
	Overall sample	High	Medium	Low	High	Medium	Low	Yes	No
	N = 12,237	n = 4,079	n = 4,079	n = 4,079	n = 4,077	n = 4,080	n = 4,080	n = 1,319	n = 10,918
	%	Row % (total = 100%)			Row % (total = 100%)			Row % (total = 100%)
FP^b	74.7	32.7	34.1	33.2	34.8	34.3	30.9	11.6	88.4
NP^b	25.3	35.2	31.1	33.7	29.1	30.7	40.2	11.7	88.3
Chain owned	59.8	35.9	33.7	30.4	37.2	34.4	28.5	10.4	89.7
Non–chain owned	40.2	29.5	32.8	37.7	27.6	31.8	40.6	13.5	86.5
FP, chain	48.3	35.5	34.2	30.2	38.7	34.8	26.5	10.2	89.8
FP, no chain	25.4	27.2	33.9	38.9	27.6	33.2	39.2	14.2	85.8
NP, chain	11.7	37.3	31.5	31.2	31.0	32.3	36.7	11.1	89.0
NP, no chain	14.6	35.7	30.8	33.5	27.6	29.4	43.1	12.3	87.7
Freestanding	96.8	32.9	33.7	33.4	33.4	33.9	32.7	11.5	88.5
Hospital based	3.2	46.6	22.3	31.0	31.0	16.7	52.3	13.9	86.1
Region 1^c	5.7	43.5	36.2	20.3	25.5	37.3	37.2	8.6	91.5
Region 2^c	7.9	39.4	38.5	22.2	20.7	32.8	46.5	18.0	82.0
Region 3^c	10.5	42.2	38.5	19.4	36.0	39.4	24.6	11.5	88.6
Region 4^c	17.6	36.4	39.2	24.5	42.9	34.4	22.6	11.6	88.4
Region 5^c	23.5	31.6	31.7	36.7	40.5	32.9	26.6	10.7	89.3
Region 6^c	14.3	19.8	32.0	48.3	32.1	35.2	32.7	13.7	86.3
Region 7^c	10.3	17.0	27.5	55.5	17.9	30.7	51.5	7.2	92.8
Region 8^c	2.8	28.7	21.7	49.6	17.5	21.9	60.6	11.7	88.3
Region 9^c	4.4	52.7	25.6	21.7	27.4	25.2	47.5	15.1	84.9
Region 10^c	3.0	60.2	23.7	16.1	42.2	26.2	31.6	8.3	91.7

Note. PT = physical therapy; NP = non-profit; FP = for-profit; OT = occupational therapy; CMS = Centers for Medicare and Medicaid Services.

Differences in therapy staffing categories across each therapy staff type and facility characteristic were significant (p < .05) unless otherwise noted. ^bThe relationship between profit status and presence of OT aides was nonsignificant (p = .61). ^cStates for each region defined by CMS: CT, ME, MA, NH, RI, and VT (Region 1); NJ and NY (Region 2); DE, DC, MD, PA, VA, and WV (Region 3); AL, FL GA, KY, MS, NC, SC, and TN (Region 4); IL, IN, MI, MN, OH, and WI (Region 5); AR, LA, NM, OK, and TX (Region 6); IA, KS, MO, and NE (Region 7); CO, MT, ND, SD, UT, and WY (Region 8); AZ, CA, HI, and NV (Region 9); AK, ID, OR, and WA (Region 10).

The mean staffing level for PT staff is 0.10 HPRD (0–1.14), PT assistants is 0.11 HPRD (0–9.04), and PT aides is 0.02 HPRD (0–1.14). Comparably, the mean staffing level for OT staff is 0.09 HPRD (0–0.91), OT assistants is 0.09 HPRD (0–11.80), and OT aides is 0.01 HPRD (0–3.17). The range in HPRD for the PT staffing tercile categories are low (0–0.05), medium (0.05–0.10), and high (0.10–1.14) and PT assistant tercile categories are low (0–0.06), medium (0.06–0.12), and high (0.12–9.04). The range in HPRD for the OT staffing tercile categories are low (0–0.05), medium (0.05–0.10), and high (0.10–0.91) and OT assistant tercile categories are low (0–0.05), medium (0.05–0.10), and high (0.10–11.80). Approximately 31% of providers use PT aides, whereas only 11% use OT aides. With the exception to the relationship between OT aides and profit status in Table 2, differences in therapy staffing patterns across each staff type and provider characteristic were significant using an alpha level of .05.

When comparing facilities by staffing levels, on average, the characteristics of providers are alike across similar staffing levels. For example, bed size and occupancy rates for facilities with high PT staffing resemble those of facilities with high OT staffing. Nursing homes with high HPRD for both PT and OT staff and PT and OT assistants have the highest proportion of Medicare-covered residents and lowest proportion of Medicaid-covered residents compared to providers with lower staffing HPRD. The payer compositions show similar proportions of Medicare and Medicaid across providers that utilize PT or OT aides and those that do not. Also, facilities with high HPRD for PT and OT staff and PT and OT assistants are linked to higher average RUG scores compared to providers with lower HPRD for each staff type.

Non-profit providers were overrepresented in the high PT staffing category and underrepresented in the low PT staffing category. In contrast, non-profit providers were underrepresented in the high PT assistant category and overrepresented in the low PT assistant category. The opposite pattern is observed for for-profit facilities, with underrepresentation in the high PT staffing categories and overrepresentation in the low PT staffing category. The same patterns were observed for the OT and OT assistant categories and apply to both chain-owned and non–chain owned providers. Similar to non-profit providers, hospital-based facilities were also overrepresented in the high PT staffing category and low PT assistant category. This same pattern was also observed for the OT and OT assistant categories. While significant differences were found, the proportions of freestanding homes were more evenly distributed across the PT and PT assistant categories compared to hospital-based facilities. This pattern also held true for the OT and OT assistant categories.

Among CMS regions, over half of all providers in Regions 9 (i.e., AZ, CA, HI, NV) and 10 (i.e., AK, ID, OR, WA) were captured in the high PT and OT staffing category, and providers in Regions 4 (i.e., AL, FL GA, KY, MS, NC, SC, TN), 5 (i.e., IL, IN, MI, MN, OH, WI), and 10 (i.e., AK, ID, OR, WA) captured the largest proportion of facilities in the high PT and OT assistant categories. Region 2 had the highest proportion of nursing homes with PT aides (48%) while Region 7 had the fewest (17%). This difference is smaller for OT aides: Region 2 includes the highest proportion of providers that utilize OT aides (18%) and Region 7 with the fewest (7%), compared with all other CMS regions.

Staffing rates for the six staffing groups was stable among freestanding and hospital-based facilities between 2013 and 2016 (Figures 1 and 2). Hospital-based facilities had substantially higher average staffing levels, with average HPRD for PTs and OTs being double that of freestanding facilities. Within hospital-based facilities, comparisons across disciplines showed higher average PT HPRD staffing, compared with OT staff across all staff types. Yet, when comparing the different levels of types of providers within each discipline, HPRD for PT and OT staff, on average, are highest, followed by PT and OT assistants and PT and OT aides. In contrast, in freestanding facilities, PT and OT assistants account for the largest proportion of therapy staffing HPRD.

Figure 1.

Average total PT and OT staffing HPRD in freestanding facilities, 2013–2016.

Figure 2.

Average total PT and OT staffing HPRD in hospital-based facilities, 2013–2016.

Discussion

Previous work found that changes to Medicare payment policies led to an increase in PAC services in nursing homes and a growth in therapy staffing to accommodate for this change (Tyler et al., 2013). However, several new Medicare policies have been implemented since the work from Tyler and her team. Our study adds to the literature by examining individual staffing patterns of PT and OT staff, separately, within the context of this new health care environment by (a) characterizing facilities associated with distinct therapy staffing patterns, (b) examining recent temporal trends, and (c) describing regional variation in staffing.

While we found differences in staffing patterns across providers, the overall findings demonstrate that average PT and OT therapy staffing HPRD for each staff type were stable between 2013 and 2016, a period during which several Medicare policies and programs, which focused on value-based care, were introduced. However, these policies did not alter reimbursement for therapy services and instead included payment incentives based on performance, which may help explain the consistency we found (CMS, 2017, 2018a, 2019). Providers may have perceived therapy staffing levels to be sufficient given the increases that occurred prior to the implementation of these policy changes (Leland et al., 2017; Tyler et al., 2013).

However, a new SNF payment model, referred to as the Patient Driven Payment Model (PDPM), took effect October 1, 2019 and will directly affect payments to providers for PAC services (CMS, 2018b). Under this new payment policy, CMS has removed the inclusion of service intensity from the payment determination in an effort to remove the financial incentive to deliver high-volume services (CMS, 2018b). The PDPM will no longer determine payment classification based on therapy minutes. Instead, the daily payment rate will be determined by patient characteristics, not service delivery (CMS, 2018b). This new approach has generated concerns about the adequacy of reimbursements, which may affect the quality of care offered to the Medicare population (Livingstone et al., 2019). Such a change in payment has not occurred since the introduction of the PPS in 1998. As a result, providers may modify their therapy staffing strategies, either by reducing overall staffing levels or by employing more aides/assistants to deliver therapy services instead of PTs and OTs (Livingstone et al., 2019).

As history has demonstrated, payment changes negatively impact therapy staffing levels, which was found to be more evident among for-profit facilities compared with non-profits. Moreover, PT unemployment increased, after the implementation of PPS in 1998 (Goldstein, 2001; White, 2005). Yet, the response differed between hospital-based and freestanding facilities where therapy staffing levels decreased slightly among hospital-based providers while PT and OT and PT and OT assistant staffing levels increased substantially among freestanding homes.

This study found that hospital-based and non-profit facilities, on average, have higher PT and OT staffing levels compared with freestanding homes and for-profits, respectively. Our results also show staffing levels remained stable despite Medicare policy changes. However, the potentially stronger, more direct impacts of the PDPM may create new instabilities. Specifically, the differences in staffing levels between provider types may be reduced following implementation of the PDPM. Akin to strategies implemented after the introduction of the PPS, providers may continue to modify the provision of therapy services to manage uncertainties and reduced payments for services. This could result in employing fewer licensed/certified PT/OT staff or, despite earlier findings that suggest PT/OT staff and PT/OT assistants complement each other, these major changes may result in providers utilizing more PT/OT assistants and PT/OT aides as lower cost substitutes (Tyler et al., 2013). Alternatively, some providers may maintain or bolster their therapy staff team to continue to provide high-quality care to patients and residents. Therefore, future work is needed to examine the impact of these new payment policies on staffing, quality of care, and patient outcomes.

Limitations

It is important to note the limitations of this study. First, the CASPER data set was used in this study. These data are collected through state surveys and the certification process, where much of the data are self-reported (AHCA, 2017). These data do not undergo a routine auditing schedule and are instead reviewed periodically by state inspectors. Although concerns around the validity of these data have been generated, the CASPER data have since been validated and widely used in a number of nursing home-related studies (Castle & Anderson, 2011).

Second, facilities were excluded if they were not present, or were missing data, in all 4 years of the study period. This may occur due to nursing home closures, openings, or being acquired by a competitor. Excluding these facilities was a way to verify that differences in PT/OT staffing levels across the study period were not linked to external influences at the state or county level. However, these exclusion criteria may cause some survivor bias (Berry et al., 2010). Having data in each year may be related to a greater financial performance, higher quality, information gathering to remain ahead of economic trends, or greater access to resources through economies of scale. Limiting the data in this way skews the sample toward facilities that were able to succeed throughout the study period, yielding a sample of potentially higher performing facilities compared to those that were excluded (Berry et al., 2010).

Conclusion and Implications

Previous changes to Medicare reimbursement policies have been linked to shifts in the provision of therapy services in nursing homes. Yet, our results suggest that average therapy staffing levels have remained stable during more recent changes to Medicare policies and programs, though differences in staffing levels exist across providers. However, these changes did not directly affect payments; the new PDPM directly affects reimbursements for therapy services and may provide a bigger incentive for providers to alter how therapy is delivered to residents.

It is recommended that future research continue to track trends in therapy staffing to evaluate if and how the PDPM policy influences staffing patterns. Research should also explore patient outcomes to determine if changes to reimbursements and/or therapy staffing patterns resulted in changes in the quality of care offered to patients. Studies should distinguish between therapy staff types to understand the effects of each staff on quality and to assess any potential unintended consequences that may be associated with altering the delivery of therapy services in nursing homes.

Footnotes

Authors’ Note

Jennifer Hefele is also affiliated with Booz Allen Hamilton, McLean, VA, USA.

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Jennifer Gaudet Hefele is an employee of Booz Allen Hamilton and receives regular pay as part of her employment. The views and opinions expressed in this article are those of the author and do not necessarily reflect the official policy or position of her employer.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Ian Livingstone

Natalie Leland

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