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. Author manuscript; available in PMC: 2017 Mar 1.
Published in final edited form as: Nurs Econ. 2016 Mar-Apr;34(2):82–89.

Staff Efficiency Trends Among Pediatric Hospices, 2002–2011

Melanie J Cozad 1, Lisa C Lindley 2, Sandra J Mixer 2
PMCID: PMC5045247  NIHMSID: NIHMS807731  PMID: 27265950

Abstract

Delivering care for children at end of life often takes considerable time and effort by the hospice staff. The purpose of this study was to examine trends in staff technical efficiency among California pediatric hospice providers from 2002 and 2011.

INTRODUCTION

Delivering care for children at end of life often takes considerable time and effort by the hospice staff. In providing hospice care for children, the team of nurses, social workers, counselors, physicians, and others often must quickly develop and implement a plan of care in just a few short days because children typically enroll in hospice for only a few days or weeks (National Hospice and Palliative Care Organization, 2009). For example, the hospice team must conduct the initial comprehensive physical and psychosocial assessment of the child and family upon entering hospice (Senft, 2011). The team assesses current medications and stabilizes pain and symptoms (i.e., dyspnea, cachexia, seizures, pruritus) (Hendricks-Ferguson, 2008). The hospice staff also assists the child and family with psychosocial issues and provides spiritual care (Schulman-Green, McCorkle, Curry et al., 2004; Neilson, Macarthur, & Greenfield, 2011). Finally, the team cares for and comforts the child and family during the active dying phase (Stajduhar, Funk, & Outcalt, 2013). Hospice care for children at end of life can be complicated and time-consuming. Thus, hospice staff efficiency may be critical in delivering quality end-of-life care for children and their families.

Generally, examinations of staff efficiency have focused on the acute care setting (Bates, Mukherjee, & Santerre, 2006; Dexter, Epstein, McIntosh, & O’Neil, 2007; Mark, Lindley, & Jones, 2009; Wilson, Kerr, Bastian, & Fulton, 2012) and efficiency has not been well studied in hospices. Lorenz and colleagues (2002) investigated the association between profit status and the delivery of care among hospices. The outcomes measured were patient selection, length of stay, and number of visits by registered nurses, licensed visiting nurses, and home health aides. The study found that for-profit hospices delivered more nursing visits than nonprofit hospices; however, they were often not skilled nursing visits (i.e., nursing assistants). Although this study shed some light on the issues of productivity and process efficiency, it is still unclear whether pediatric hospices are technically efficient. Technical efficiency is a measure of productivity developed by Koopmans (1951), Debreu (1951), and Farrell (1957) that gauge’s a hospice’s ability to utilize inputs (e.g., capital, labor, and equipment) to produce a target output (e.g., staff visits). Additionally, no studies were identified that explored technical efficiency over time. Knowledge of technical efficiency trends may be particularly important to nurse managers within pediatric hospices where staff efficiency may impact the overall quality of care patients receive, hospice costs, and other clinical outcomes. Therefore, the purpose of this study was to examine trends in staff efficiency among pediatric hospices over a 10-year period between 2002 and 2011 among California pediatric hospice providers.

METHODS

Data Source and Study Sample

The study was a panel data analysis of hospices providing pediatric care, using the 2002–2011 California State Utilization Data File of Home Health Agencies and Hospice Facilities. The data files are maintained and made publicly available by the California Office of Statewide Health Planning and Development (CA OSHPD), which routinely compiles survey data on hospices and home health agencies licensed in the state of California. The dates 2002 to 2011 were used because they were the most current data available. The survey targets hospice and home health agency administrators and asks them to respond to a list of questions about organizational demographics, services, patients, payment sources, and financials. In this study, the data utilized contain information for unduplicated hospice facilities that provide pediatric care. Hospices’ responses were dropped from the study if they were coded as unknown business entities or lacked financial data. The final sample over the 10-year time frame was 634 hospice/year responses.

Primary Outcome Variable

The primary outcome variable was pediatric hospice staff technical efficiency. The term “efficiency” implies the best use of resources in the production of goods or services (Shone, 1981). In particular, technical efficiency measures how well a hospice uses inputs such as physical capital and labor to produce a given output such as visits to patients’ bedsides. Thus, when a hospice is technically efficient, it is using the minimum amount of inputs to deliver the maximum amount of output. To obtain an empirical estimate of technical efficiency, this study used Data Envelopment Analysis (DEA) (Cooper, Seiford, & Tone, 2007).

DEA constructs an efficiency frontier by maximizing the weighted output/input ratio for each hospice/year survey response, given the constraint that the ratio can equal but never exceed 1 (Chirikos & Sear, 2000; Ozcan, 2008; Mark et al, 2009). Hospices that receive an efficiency score of 1.0, lie on the efficiency frontier and are considered “best practice units,” when compared to others in the sample (Mark et al 2009). Hospices with efficiency scores less than 1.0 are operating inefficiently and using too many inputs in production. The lower the efficiency score the more inefficient the hospice. DEA was selected to assess pediatric hospice staff efficiency trends because it can account for multiple inputs and outputs used in production as opposed to more traditional efficiency measures that rely on a unidimensional approach requiring the selection of a single output and input to capture the production process. The DEA measure of efficiency also can help hospice managers improve performance by identifying specific sources of inefficiency and best practice hospice units. DEA is a reliable and valid measure for the assessment of efficiency that has been used extensively to evaluate efficiency of health care delivery of hospitals (Ozcan, 1992; Chirikos & Sear, 2000; Clement et al, 2008), acute care nursing units (Mark et al, 2009), and as well as more aggregate health care systems (Hollingsworth, 2008; Cozad & Wichmann, 2013).

In the application of DEA, real operating expenses and daily census represent capital and labor inputs the hospice utilizes in the production process. Hospice operating expenses were converted to real 2006 dollars to account for inflation over the study’s time period. 2006 dollars were selected because this year represented the middle of the study’s time period. Visits per day by physicians, registered nurses, social services, and chaplains represent output measures of production. These selected measures of inputs and outputs capturing the production process are advantageous because they can be readily assessed by nurse managers and are commonly selected measures within the health care sector (Ozcan, 1992).

Independent Variables

Since the study focused on how pediatric hospice staff efficiency changes over time, individual year variables for 2002 through 2011 were the main variables of interest. These variables take on a value of 1 in the year of interest and 0 otherwise. Year 2002 served as the reference year and a variable for this year does not appear in the regression of the first model. In a second model, a second time variable was considered that captures the cumulative changes in efficiency from 2008 to 2011. This variable takes on a 1 for the years 2008 to 2011 and a 0 otherwise.

Other control variables included patient characteristics representing child’s age and patient length of stay (LOS). Child’s age was divided into the following classifications: 1 year and under, 2–5 years, 6–10 years, and 11–20 years. The variable representing each classification is given a value of 1 when the hospice cared for a child in that age bracket. Otherwise, the value is 0. LOS is defined as the number of patients that stayed at the hospice for the following duration period: 0–30 days, 31–90 days, 91–179 days, and over 180 days. Organizational characteristics were profit status, service location, and entity type. Based on the CA OSHPD definitions, profit status was classified as for-profit or non-profit. For this variable, a non-profit hospice took on a value of 1 and a 0 otherwise. Hospice service location was characterized by a categorical variable that took on a 0 for urban and 1 for rural and mixed areas. Entity type distinguished between a hospice only and a hospice/home health agency combination. For this variable, the hospice/home health agency took on a value of 1 and the hospice only a value of 0.

Statistical Analysis

Standard descriptive statistics for all study variables were calculated to identify anomalies in the data and to ensure that the assumptions of all analyses were met. The means, percentiles, and standard deviations were used to describe sample characteristics. Graphs were constructed to visually demonstrate the trends in staff technical efficiency during the study time period. For the multivariate analysis, two models were used. The first model (Model 1) examines the association between individual years and staff efficiency, while the second model (Model 2) examines the cumulative effect of multiple years on staff efficiency. Both models are estimated using a random-effects tobit estimator. The random-effects tobit estimator exploits the variation of the longitudinal data and accounts for the fact that the primary variable of interest, staff efficiency, is bounded between 0 and 1. This estimator is appropriate because censored regression models of this nature are commonly used for estimating the impacts of environmental variables on efficiency (Kooreman, 1994; Hoff, 2007). Results are reported as marginal effects with standard errors. Analysis was performed using Stata 12.0 software (Statacorp LP, College Station, TX). Institutional Review Board approval for this study was obtained from the University of Tennessee, Knoxville, USA.

RESULTS

Sample Descriptive Statistics for Pediatric Hospices

Table 1 presents the summary statistics for the variables in the analytical models. Overall, average staff efficiency for the entire sample was 0.76. Hospice staff efficiency increased from an average of 0.78 in 2002 to 0.82 in 2011.

Table 1.

Descriptive Statistics of Study Variables (N=634)

Variables Percentage/Mean Entire Sample Percentage/Mean 2002 Percentage/Mean 2011
Outcome Variable
Staff Efficiency 0.76 (0.21) 0.78 (0.21) 0.82 (0.16)
Independent Variables
Years
 Year 2002 (%) 9.4
 Year 2003 (%) 8.5
 Year 2004 (%) 9.1
 Year 2005 (%) 9.9
 Year 2006 (%) 10.4
 Year 2007 (%) 9.6
 Year 2008 (%) 11.0
 Year 2009 (%) 9.6
 Year 2010 (%) 11.0
 Year 2011 (%) 11.5
 Cumulative Effect (2008–2011) (%) 43
Control Variables
Child’s Age
 1 and Under (%) 55.3 49.2 52.1
 2–5 (%) 32.0 22.0 37.0
 6–10 (%) 33.1 32.2 24.7
 11–20 (%) 65.9 72.9 64.3
Length of Stay
 0–30 Days 387.0 (376.2) 378.3 (344.1) 397.3 (366.0)
 31–90 Days 119.1 (104.8) 114.5 (96.8) 129.0 (106.7)
 91–180 Days 52.1 (48.9) 45.3 (41.6) 56.4 (52.9)
 >181 Days 48.7 (60.3) 29.3 (36.4) 58.0 (72.8)
Profit Status
 For-profit (%) 53.2 55.9 37.0
 Non-profit (%) 46.8 44.1 63.0
Service Location
 Urban (%) 55.0 57.6 53.4
 Rural/Mixed (%) 45.0 42.4 46.6
Entity Type
 Hospice Only (%) 77.9 64.4 86.3
 Hospice/HHA (%) 22.1 35.6 13.7
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In terms of the control variables, Table 1 shows that approximately 55% of pediatric hospices provided care to children under 1, one third (32%–33%) provided care for children ages 2–5 and 6–10, and two-thirds (65.9%) provided care to children ages 11–20. All patients in hospice generally had a length of stay of 0–30 days. Slightly over half (53.2%) of the hospices were non-profit and operated in urban areas (55%). Most hospices (77.9%) provided only hospice care; however, this percentage grew over the sample period from 64.4% in 2002 to 86.3% in 2011.

Trends in Pediatric Hospice Staff Efficiency

Within the sample, 163 hospice/year observations had an efficiency score of 1, meaning these observations are on the efficiency frontier and represent best practice units because they obtained the maximum amount of patient visits using the least amount of inputs. 85 different hospices attained a score of 1.0, and 37 hospices had this score in multiple years. Figure 1 displays the mean staff efficiency scores across hospices for each individual year from 2002 through 2011. The trend in staff efficiency demonstrated considerable variability over the study time period. In 2002, staff efficiency was 0.78, which means the average hospice was inefficient. Specifically, the hospice could reduce input consumption by 22% without affecting the number of patient visits. The increase in mean efficiency to 0.90 in 2003 suggests there was a 12% reduction in input utilization over the course of a year. From 2003 to 2005, efficiency fell to 0.79 and then increased again in 2006, but dropped dramatically to a low of 0.57 in 2009/2010. This overall decline in efficiency means the average hospice experienced a dramatic increase in their input utilization; however, these inputs did not improve efficiency by increasing patient visits. In 2011, staff efficiency increased 0.82 meaning that only 18% of inputs where not being translated into patient visits.

Figure 1.

Figure 1

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Average Staff Efficiency for Pediatric Hospices

Associations Between Individual Years and Pediatric Hospice Staff Efficiency

Table 2 shows the results of the two different multivariate models estimating the associations between specific time periods and pediatric hospice staff efficiency. Among all the years of the study, 2003, 2008, 2009, and 2010 were significantly related to staff efficiency (Model 1). Relative to 2002, staff efficiency increased by 15% in 2003 (ME 0.15, P<.01). However, staff efficiency decreased 74% in 2008 (ME −0.74, P<.05), 25% in 2009 (ME −0.25, P<.01), and 24% in 2010 (ME −0.24, P<.01), compared with 2002. The other years of the study were not significantly related to pediatric hospice staff efficiency. Because of the significant negative effects of individual years on staff efficiency in Model 1, the study also explored the cumulative effects of multiple years (2008 to 2011) in Model 2. Overall, cumulative effect from 2008 to 2011 significantly decreases staff efficiency by 17% (ME −0.17, P<0.01).

Table 2.

Effects of Individual Years on Pediatric Hospice Staff Efficiency

Independent Variables Model 1 Model 2
Independent Variables
Year 2003 0.15(0.038)***
Year 2004 0.06 (0.038)
Year 2005 −0.026(0.0308)
Year 2006 0.041(0.037)
Year 2007 0.040(0.039)
Year 2008 −0.74(0.037)**
Year 2009 −0.25 (0.03)***
Year 2010 −0.24(0.038)***
Year 2011 0.045(0.038)
Cumulative Effect (2008–2011) −0.170 (0.022)***
Control Variables
Child’s Age
 1 and Under 0.051 (0.020) *** 0.050 (0.022) **
 2–5 0.013 (0.019) 0.011 (0.021)
 6–10 −0.042 (0.019)** −0.047 (0.022)**
 11–20 0.029 (0.019) 0.007 (0.021)
Length of Stay
 0–30 Days 0.0002 (0.000009) ** 0.0016 (0.0001)*
 31–90 Days −0.0006 (0.0004) −0.0004 (0.0004)
 91–180 Days 0.0002(0.0005) 0.0005(0.0007)
 >181 Days 0.0006 (0.0005) 0.0004 (0.0005)
Non-Profit −0.052(0.027) * −0.041(0.027)
Rural/Mixed −0.018(0.013) −0.021(0.014)
Hospice&HHA −0.012(0.036) −0.027(0.036)
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*

p< 0.10,

**

p<0.05,

***

p<0.01

The analysis also revealed that several control variables were significantly related to staff efficiency among pediatric hospices in both specifications. Hospices that provided care for infants, one year of age or less, had a significant probability of increased efficiency of 5% (both models), while hospices that provided care for children ages 6–10 experienced a 4 and 5% decrease in efficiency (Model 1 and 2, respectively). Hospices with patients admitting to hospice for 0 to 30 days were likely to have increased staff efficiency (both models). Compared to for-profit hospices, non-profits had a decrease in staff efficiency by 5% (Model 1), though this variable was not significant in Model 2. No other control variables demonstrated a significant relationship with staff efficiency.

DISCUSSION

This was the first study to examine staff efficiency trends among pediatric hospices. The analysis showed that pediatric staff efficiency demonstrated large variability from 2002 to 2011 reaching a high of 0.90 in 2003 to a low of 0.57 in 2009 and 2010 (Figure 1). This variation represents a 36 percent decline in efficiency over a six-year period. The decline means that on average hospices had higher operating expenses and used more capacity (as represented by daily census), but greater amounts of these resources did not translate into greater output as measured by visits per patient from physicians, registered nurses, social services, and chaplains.

The declining trend of pediatric hospice staff efficiency particularly during 2008 to 2010 represents a novel finding, and there are several potential explanations. First, staff efficiency may have declined during this period due to hospice regulatory changes. The 2008 changes in the Centers for Medicare and Medicaid Service’s (CMS) hospice Conditions of Participation (CoP) initiated widespread modifications in the basic rulebook of hospice care delivered for patients. These much anticipated changes took effect in December of 2008 and in the case of some provisions were phased in during 2009. The 2008 CoPs required hospice staff to make significant structural and process changes in delivering care, including mandates for contracts with nursing homes, rules for hospice inpatient care, guidelines for quality assessment and performance, and clarifications of service requirements (42CFR418, 2008). For example, the 2008 CoPs required that hospices designate a registered nurse to coordinate each hospice patient’s care, a task previously undefined or unassigned. The CoP compliance often resulted in new agency-level protocols, policies, and training that may have initially redirected the attention of staff away from patient care, thus temporarily decreasing their efficiency. In Table 2, Model 2 demonstrates that the entire time period of 2008 to 2011 had a significant and negative impact on efficiency. However, the cumulative effect is smaller in absolute magnitude than the individual-year coefficients in Model 1. This suggests that hospices may have adapted or responded to the regulatory changes over time. This adaptation is also a possible reason efficiency returned close to previous levels by 2011 (Figure 1). Given the recent policy mandate regarding the Hospice Quality Reporting Program under the Affordable Care Act of 2010 (Centers for Medicare and Medicaid Services, 2013), additional research may be warranted on the relationship between regulatory policy and pediatric hospice staff efficiency.

Additionally, structural changes within the hospice industry may have influenced hospice staff efficiency among pediatric providers from 2008 to 2010. These findings are consistent with recent reports on the growth of the hospice industry (Thompson, Carlson, & Bradley, 2012). Researchers found that from 1999 to 2009, there was a significant increase in the number of hospice providers. In addition, hospices became larger, with the number of full-time employees doubling during this same time period. An increase in staff suggests that hospices may have experienced an increase in operating expenses. Hiring new workers takes time and effort. New staff may be less experienced in hospice and often need intensive orientation and on-the-job training with a co-worker prior to delivering care independently (Lynch, & Buckner-Hayden, 2010). As a result, new staff may increase a hospice’s operating expenses temporarily before these employees are able to contribute towards increasing output as measured by more visits per patient. Additionally, experienced staff ability to visit patients may be lowered while time is invested in training new employees (DeLia, Cantor, & Duck, 2002). This would explain the decrease in efficiency from 2008 to 2010 because greater operating expenses do not translate into more visits per patient. As new employees begin to deliver care on their own and experienced staff members return to their normal workload, there is an increase in visits, which would explain the increase in efficiency by 2011. This timing of the structural change in the hospice industry is also another potential reason for the difference in absolute magnitude of individual year and cumulative time effects in Model 1 and Model 2.

Specific Strategies for Nurse Managers

The findings from this study have implications for hospice administrators, in particular nurse leaders. Nurse managers and executives must develop strategies for dealing with known (regulatory) and unknown (growth) changes in the industry. They may also want to explore alternative staffing models and work flow planning, in anticipation of regulatory disruptions. For example, hospices may need to develop a PRN (as needed) staffing pool that provides flexibility in weathering such workload peaks and valleys. Given the shortages of key hospice staff such as registered nurses (Hospice and Palliative Nurses Association, 2011), workforce planning may be especially critical for pediatric hospice efficiency during these times. In addition, hospice nurse leaders should take advantage of seminars and toolkits developed by hospice associations such as the National Hospice and Palliative Care Organization to assist in them in preparing agency-level structures and processes for regulatory changes. Lastly, as the U.S. hospice industry continues to experience growth, it is important to recognize the hospice sector’s unique employment relationship. Namely, industry growth tends to increase employment of new workers. Therefore, strategic planning focusing on flexibility of the workforce to promote staff experience may benefit the hospice in weathering growth cycles. Activities may include identifying core staff and keeping them committed to the agency through increased staff communication, training, and total compensation as well as improved assimilation of new workers. Thus, administrative planning may assist pediatric hospice nurse leaders in providing efficient care for patients during times of change.

Limitations

The study had several data and methodological limitations. The data represents hospices within California; therefore, generalization of the findings to other geographic regions should be interpreted with care. In addition, there are several limitations inherent to the DEA methodology that should be recognized. First, the efficiency score represents a measure of relative efficiency, meaning that hospices with an efficiency score of 1 obtained the highest possible level of efficiency compared to their peers within the sample. This means that DEA cannot evaluate efficiency based on any other benchmark aside from the information on inputs and outputs contained within the data set. Second, the results of DEA are sensitive to the selection of input and output measures. Therefore, measures should be reflective of metrics that can be readily assessed and used by hospice managers such as those selected in this study. Third, because of the tangible numerical results DEA provides, hospice administrators might be tempted to implement management practices that directly target the metrics identified as needing attention, but these may or may not meet patient and staff needs and well as other strategic goals of the hospice. Instead, hospice administrators should use DEA results to begin conversations with staff to identify where improvements in the delivery of care could occur.

CONCLUSION

This study provided the first examination of staff efficiency trends among pediatric hospices. Although pediatric staff efficiency demonstrated large variability from 2002 to 2011, the general trend in efficiency was a 36 percent decline in efficiency from 2003 to 2010. The decline in efficiency means that on average pediatric hospices had higher operating expenses and used more capacity, but greater amounts of these resources did not translate into greater outputs as measured by visits per patient. The downward trend in efficiency coincides with increases in regulation and growth within the hospice industry. This finding is important because regulation and growth are both factors beyond pediatric hospice nurse leader’s control; yet, they potentially impact the productive capabilities of the hospice. Future studies should elucidate the effects of these and other such external factors on hospice staff efficiency. In addition, the study also highlights the crucial role pediatric hospice nurse managers’ play in developing effective workforce strategies that allow for responsive changes to workload fluctuations. Due to the associations between efficiency, regulation, and growth, nurse leader’s abilities to develop effective strategies are more imperative than ever to ensure quality of end-of-life care for children and their families.

Acknowledgments

Special thanks to Beth Schewe for her assistance with the manuscript.

Funding: Research reported in this publication was supported by the National Institute of Nursing Research of the National Institutes of Health under award number K01NR014490.

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