ORIGINAL ARTICLE

Examining the Value of Inpatient Nurse Staffing An Assessment of Quality and Patient Care Costs Grant R. Martsolf, PhD, MPH, RN,* David Auerbach, PhD,w Richele Benevent, MS,z Carol Stocks, MHSA, RN,y H. Joanna Jiang, PhD,y Marjorie L. Pearson, PhD, MSHS,8 Emily D. Ehrlich, MPH,z and Teresa B. Gibson, PhDz

Background: Inpatient quality deficits have important implications for the health and well-being of patients. They also have important financial implications for payers and hospitals by leading to longer lengths of stay and higher intensity of treatment. Many of these costly quality deficits are particularly sensitive to nursing care. Objective: To assess the effect of nurse staffing on quality of care and inpatient care costs. Design: Longitudinal analysis using hospital nurse staffing data and the Healthcare Cost and Utilization Project State Inpatient Databases from 2008 through 2011. Subjects: Hospital discharges from California, Nevada, and Maryland (n = 18,474,860). Methods: A longitudinal, hospital-fixed effect model was estimated to assess the effect of nurse staffing levels and skill mix on patient care costs, length of stay, and adverse events, adjusting for patient clinical and demographic characteristics. Results: Increases in nurse staffing levels were associated with reductions in nursing-sensitive adverse events and length of stay, but did not lead to increases in patient care costs. Changing skill mix by increasing the number of registered nurses, as a proportion of licensed nursing staff, led to reductions in costs. Conclusions: The study findings provide support for the value of inpatient nurse staffing as it contributes to improvements in inpatient care; increases in staff number and skill mix can lead to improved quality and reduced length of stay at no additional cost. From the *RAND Corporation, Pittsburgh, PA; wFormerly of RAND Corporation, Boston, MA; zTruven Health Analytics, Santa Barbara, CA; yAgency for Healthcare Research and Quality (AHRQ) Center for Delivery, Organization, and Markets (CDOM), Rockville, MD; 8RAND Corporation, Santa Monica, CA; and zTruven Health Analytics, Ann Arbor, MI. Supported by the Agency for Healthcare Research and Quality (AHRQ) (Contract HHSA-290-2006-00009-C) through intramural research. The findings and conclusions in this document are those of the authors, who are responsible for its content, and do not necessarily represent the views of AHRQ, or the US Department of Health and Human Services. No official endorsement by any agency of the federal or state governments, RAND Corporation, or Truven Health Analytics is intended or should be inferred. The authors declare no conflict of interest. Reprints: Teresa B. Gibson, PhD, Truven Health Analytics, 777 E Eisenhower Pkwy, Ann Arbor, MI 48108. E-mail: tbgibson1@gmail. com. Copyright r 2014 by Lippincott Williams & Wilkins ISSN: 0025-7079/14/5211-0982

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Key Words: nursing, nurse staffing, patient care costs, adverse events, quality, length of stay (Med Care 2014;52: 982–988)

T

he cost and quality of hospital care is a significant concern for a range of health care stakeholders. Deficits in quality of care have contributed to substantial levels of patient morbidity and mortality. For example, the Institute of Medicine has estimated that as many as 44,000–98,000 deaths can be attributed to inpatient medical errors.1 These quality deficits have important implications for the health and well-being of patients, as well as important financial implications for payers and hospitals. Quality deficits— particularly adverse events—have been associated with increased length of stay and intensity of treatment,2 which can result in significant increases in costs for hospitals and payers. Policymakers, researchers, and practitioners have argued that many of these potentially deadly and costly quality deficits are particularly sensitive to nursing care.3 Increases in nurse staffing levels or adjustments to nurse staffing skill mix may increase labor-related patient care costs; however, additional costs for increased staffing could also lead to reductions in patient care costs driven by associated reductions in adverse events and length of stay. If the latter effect is significant, these quality improvements may result in an offset of direct labor-related patient care costs associated with nurse staffing by improving outcomes relative to costs, a common definition of value.4 Despite the potential importance of the size and skill mix of nurse staffing in reducing patient care costs, more research is necessary to fully understand this relationship. A relatively large number of studies have demonstrated that nurse staffing is associated with reductions in adverse events5–16 and length of stay.5,14,17–19 However, the relationship between nurse staffing and patient care costs is relatively less developed.2,20–24 The few studies that have been published are limited in a number of ways: using a cross-sectional22 or simulation design,2,20 focusing on a single hospital system,22,23 or using data that are now >20 years old.21 Furthermore, no recent study has integrated quality, length of stay, and costs into a single study to obtain a better picture of the value of nurse staffing. The purpose of this study is to provide a comprehensive investigation of the role of nurse staffing in improving hospital Medical Care



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care by examining both quality and inpatient care costs. We linked hospital nurse staffing data to the Agency for Healthcare Research and Quality (AHRQ) Healthcare Cost and Utilization Project (HCUP) State Inpatient Databases to estimate a longitudinal model of the causal relationship between nurse staffing (level and skill mix) and quality (adverse events), length of stay, and cost. Our study has advantages over previous work in that it uses longitudinal data, uses fixed effects to control for hospital-level unobservable confounders, and is based on recent multistate data. The findings from this study have important implications for the role of nurses in promoting value in health care.

METHODS Data Sources We used state hospital financial and utilization reports in 3 states (California,25,26 Maryland,27,28 and Nevada29) to extract nurse staffing data from 2008 to 2011. We chose these 3 states because they had consistent nurse staffing data for all 4 years in a format that could be linked using hospital identifiers. We obtained counts of licensed registered nurses (RNs), licensed practical nurses (LPNs), and nursing aides. We recognize that the term “aides” can refer to various staff titles, including nurse assistants or patient care technicians. However, for the purposes of clarity and brevity, we use the term aides throughout our analysis. The nurse staffing data were linked to discharges in the HCUP State Inpatient Databases by hospital identifier. Hospitals were included if they were nonfederal, general acute care, nonrehabilitation hospitals. Hospitals were excluded if they did not meet nurse staffing threshold values (described in detail in the Measures section); did not have nurse staffing reported; reported 100% change), or less than halving (<  50% change), of staff levels from one year to the next. We measured 3 outcomes variables at the discharge level: adverse events, length of stay, and patient care costs. The adverse events measure was a binary variable that represented the occurrence of any of 8 nursing-sensitive quality indicators that could be measured using the HCUP data. The 8-item index was comprised of 6 AHRQ Patient Safety Indicators (PSIs)30: PSI 2, death in low-mortality diagnosis-related groups (DRGs); PSI 4, death rate among surgical inpatients with serious treatable complications; PSI 7, central line catheter–related blood stream infection; PSI 11, postoperative respiratory failure rate; PSI 12, perioperative pulmonary embolism or deep vein thrombosis rate; and PSI 13, postoperative sepsis rate. These indicators were previously identified to be sensitive to nurse staffing.3 The remaining 2 measures were postoperative iatrogenic complications that the nursing practice experts among the authors also deemed to be likely nursing sensitive— postoperative urinary complications and postoperative pneumonia.31 We used AHRQ specifications for these measures; an important implication of this is that the adverse events are restricted to patients who are above 18 years of age, so children were excluded from the measure of adverse events.30,31 If the patient experienced any of these adverse events, they were coded as 1; otherwise, they were coded as 0. Patient care costs were calculated for each discharge using the AHRQ Cost-to-Charge ratio files derived from hospital accounting reports collected by the Centers for Medicare & Medicaid Services.32 Length of stay was measured as the difference between the date of admission and the date of discharge. For control variables, we included a number of discharge-level covariates that were likely correlated with the outcomes of interest, to account for changes over time in the mix of patients in any given hospital. We included the patient’s sex, age, and the urban-rural classification of the patient’s county of residence (central counties of metro areas with Z1 million population, fringe counties of metro areas with >1 million population, counties in metro areas with 250,000–999,999 population, and other counties).33 We also included primary payer (Medicare, Medicaid, private, or other) and emergency department admission source. Finally, we entered 2 variables in the model that were intended to control for the relative health of the patient associated with each discharge. Specifically, we included the Medicare Severity-Diagnosis Related Group weight34 and 20 comorbidity flags based on AHRQ comorbidity groups35 (eg, metastatic cancer, renal failure). We decided to control for patient mix (eg, sex, age, comorbidities, and DRGs) directly in the regression models, rather than using the risk adjustment available in the AHRQ PSI software. The primary reason for doing this is because our measure of quality is a composite of 8 different adverse events (of which 2 are not in the PSI set). Although risk adjustment algorithms exist for the individual adverse event, no such algorithm exists for a composite measure of selected nursing-sensitive adverse events. We also examined hospital-level characteristics from the American Hospital Association (AHA) (bed size, www.lww-medicalcare.com |

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teaching status) and other sources (magnet status, urban/rural location). However, our analytic method controls implicitly for any time-invariant characteristics of hospitals. We analyzed these variables and found them to be largely time invariant. Hence, we did not include them in the models.

Analysis To estimate the effect of nurse staffing on length of stay, costs, and adverse events, we utilized a hospital-fixed effects panel data model–measuring changes in patient-level outcomes as they related to changes in staffing in a given hospital from one year to the next. By estimating a unique intercept for each hospital, the fixed effect design accounted for any time-invariant hospital characteristics that were correlated with nurse staffing and the outcomes of interest. The model also accounted for time-variant characteristics that might affect nurse staffing over time equally across all hospitals—such as changes to the Medicare payment rates—by including a separate intercept for each year. This approach is consistent with previous health services research studies.36,37 We also included observable time-varying characteristics that might be correlated with nurse staffing and outcomes over time by adding patient covariates to the model, as described in the Measures section. We used a robust sandwich estimator to adjust the SEs to account for clustering within a hospital.38 We estimated linear (OLS) hospital-fixed effects models using the model specification: Yiht ¼ b0 þ b1 Staffht þ b2 Mixhtþ b3 Dischiht þ dh þ dt þ eiht where, Y represents each of the 3 outcomes of interest, Staff represents staffing levels, Mix represents staffing mix, Disch represents a vector of discharge characteristics, d represents a vector of hospital and year-fixed effects, and E represents stochastic error term. The variables are indexed by discharge (i), hospital (h), and time (t). We chose a linear model for the cost analysis, despite the fact that the data had a skewed distribution. We note that there is no consensus regarding the most effective model for cost data analysis,39 and linear models can perform as well as more complicated models.40 Furthermore, in large samples, OLS does not rely on normality to construct confidence intervals and perform hypothesis testing.38 Given that there are no clearly superior models and that we can invoke large sample properties of OLS based on our large effective sample size, we decided to use a simpler model (OLS) that provides more interpretable marginal effects without the need to perform complicated data retransformations. Importantly, we entered the nurse staffing variables into the model estimation in pairs: model 1 included RNs and LPNs per 1000 inpatient days and the proportion of nursing staff that are RNs; model 2 included total nursing staff (RNs, LPNs, and aides) per 1000 inpatient days and the proportion of all nursing staff that are licensed nurses. In this way, we could examine the effects of an additional nurse per 1000 inpatient days while holding skill mix constant, or we could examine the effects of changing skill mix while holding nurses staffing levels constant. Previous literature has noted

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that the effect of nurse staffing on costs may be different for medical and surgical discharges.22 We performed a number of sensitivity analyses. We estimated the models separately for medical and surgical patients. We also calculated adjusted nurse staffing levels39 and aggregated all of the variables to the hospital level and rerunning the analyses. We also used various approaches to controlling for patient mix (risk adjustment) related to DRGs. Specifically, we repeated the analyses by adding indicator variables for the 25 different MDC categories as well as the 103 different DRGs used to risk adjust each individual PSI included in our composite measure.41 Finally, to provide information about the magnitude of the effect size, we estimated the effect on the outcomes of interest of increasing nurse staffing levels from the mean to the 75th percentile. To do so, we first used the above regression model to calculate predicted values for each outcome measure for each hospital by setting the staffing and mix variable and all covariates to the mean; this is presented in the results tables as the “baseline” score. We estimated a second set of predicted values after increasing only the nurse staffing and mix variables to the 75th percentile. This is presented in the results tables as the estimate after increasing to 75th percentile.

RESULTS Table 1 provides descriptive statistics for the sample. On average, hospitals staffed 6.3 licensed nurses per 1000 inpatient days and 7.8 nursing staff (licensed nurses plus aides) per 1000 inpatient days. RNs comprised 94.6% of licensed nurses, and 81.5% of all nursing staff (including aides) were licensed nurses. Patient ages were well distributed across age categories ranging from newborn to older than 75 years, and female patients had a greater percentage of discharges (58.4%) than male patients. Of the 18,474,860 hospital discharges in the 3 states, approximately half (49.3%) originated in the emergency department. Medicare was the primary payer for approximately one third of discharges, private payers accounted for another third of discharges, Medicaid accounted for one quarter, and slightly under one tenth were paid by other sources (including self-pay). Four of the most common comorbidities were hypertension (36.5%), anemia (19.4%), fluid and electrolyte disorders (17.7%), and diabetes (17.7%). Discharges were almost evenly divided across years. Across the 3 states, 77.9% of discharges occurred in hospitals in California, 16% in Maryland, and 6.1% in Nevada. Roughly 1.5% of discharges that were at risk of any nursing-sensitive adverse events experienced at least one of these events. The average length of stay was 4.43 days, and each discharge cost an average of $11,141. The primary results of the fixed effect models are shown in Table 2. In model 1 (where nursing staff included licensed staff, RNs, and LPNs), we find that increases in licensed nurse staffing levels led to reductions in adverse events and length of stay but no significant changes in patient care costs. A higher staff skill mix led to significant reductions in patient care costs but no changes in adverse events or length of stay. In model 2 (where nursing staff included aides as well), increases in all r

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TABLE 1. Descriptive Statistics for Discharges, 2008–2011 Variables Total no. discharges Nurse staffing Total no. licensed nurses (RN + LPN) per 1000 inpatient days All nursing staff (including aides) per 1000 inpatient days Licensed nurses (RN + LPN) that are RNs Nursing staff (including aides) that are licensed nurses (RN + LPN) Patient-level variables Age 0–17 18–34 35–44 45–54 55–64 65–74 75 + Sex Male Female Patient location “Central” counties of metro areas of Z1 million population “Fringe” counties of metro areas of Z1 million population Counties in metro areas of 250,000–999,999 population Other Discharge-level variables Admission source Not emergency department Emergency department Diagnosis-related group weight Primary payer Medicare Medicaid Private Other Health status variables Comorbidities Hypertension (combine uncomplicated and complicated) Deficiency anemia/chronic blood loss anemia/ coagulopathy Fluid and electrolyte disorders Diabetes, uncomplicated/diabetes with chronic complications Chronic pulmonary disease/pulmonary circulation disorders Obesity/weight loss Depression/psychoses Renal failure Hypothyroidism Alcohol abuse/drug abuse Congestive heart failure Other neurological disorders Peripheral vascular disorders Valvular disease Liver disease Paralysis Rheumatoid arthritis/collagen vascular diseases Metastatic cancer Solid tumor without metastasis

Mean (SD) or % 18,474,860 6.31 (1.30) 7.81 (1.75) 94.62 (6.26) 81.49 (7.97)

18.6 18.0 9.1 11.2 12.2 11.7 19.2

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TABLE 1. Descriptive Statistics for Discharges, 2008–2011 (continued) Mean (SD) or %

Variables Acquired immune deficiency syndrome/lymphoma/ peptic ulcer disease excluding bleeding Year 2008 2009 2010 2011 State California Maryland Nevada Outcome measures Adverse events (N = 11,754,844) Length of stay (N = 18,466,880) Costs (N = 16,971,758) (US $)

0.80 25.4 24.9 25.5 24.2 77.9 16.0 6.1 0.015 (0.12) 4.43 (7.58) $11,141 ($19,806)

LPN indicates licensed practical nurses; RN, registered nurses.

41.6 58.4 56.4 20.1 15.0 8.5 50.7 49.3 1.26 (1.31) 32.7 25.0 33.4 8.8 36.50 19.40 17.70 17.70 14.30 12.40 10.20 9.10 7.90 7.00 6.00 5.70 4.40 2.90 2.80 2.30 1.80 1.70 1.50 (Continued )

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nurse staffing levels also led to reductions in adverse events and length of stay, and there were no reductions in patient care costs. Skill mix was not significantly associated with adverse events, length of stay, or costs in this model. The results from the sensitivity analysis showed no differences between medical and surgical discharges. The sensitivity analyses resulted in estimates in the same direction and of similar magnitude. However, the statistical significance of the results, particularly related to the adverse events composite measure, was sensitive to various specification approaches. The results pertaining to length of stay and costs were relatively robust. We also estimated the impact on length of stay, adverse events, and costs estimates if hospitals moved from the mean levels on nurse staffing variables to the 75th percentile. The results are presented in Table 3. We found that, if hospitals moved from the mean count of licensed nurses per 1000 inpatient days to the 75th percentile (from 6.3/1000 to 7.0 nurses/ 1000 d; an 11.1% increase above the mean), there was an associated 0.5% decrease in length of stay (from 4.427 to 4.404 d), a 1.2% decrease in nursing-sensitive quality indicators (from 0.0149 to 0.0147), and no change in cost. The results were similar for nurse staffing per 1000 inpatient days. We found that, if hospitals moved from the mean nurse staffing per 1000 inpatient days to the 75th percentile (from 7.8/1000 d to 8.7 staff members/1000 d; a 11.5% increase), there was a 0.7% decrease in length of stay (from 4.427 to 4.397 d), a 1.2% decrease in nursing-sensitive quality indicators (from 0.0149 to 0.0147), and no change in cost. We found that, if hospitals moved from the mean for RNs as a proportion of licensed nurses to the 75th percentile (from 94.6% to 98.6%; a 4.2% increase), there was a 3.1% decrease in costs (from $11,141 to $10,793 per discharge) but no change in length of stay or adverse events. We found that, if hospitals moved from the mean for nurses as a proportion of all nursing staff including aides to the 75th percentile (from 81.5% to 87.0%; a 6.7%

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TABLE 2. Fixed Effect Model Results for Nurse Staffing, Length of Stay, and Total Cost Measures Models Nurse Staffing Measures % with any nursing-sensitive adverse event Total no. discharges Total no. licensed nurses (RN + LPN) per 1000 inpatient days All nursing staff (including aides) per 1000 inpatient days Percentage of licensed nurses (RN + LPN) that are RNs Percentage of nursing staff (including aides) that are licensed nurses (RN + LPN) Length of stay Total no. discharges Total no. licensed nurses (RN + LPN) per 1000 inpatient days All nursing staff (including aides) per 1000 inpatient days Percentage of licensed nurses (RN + LPN) that are RNs Percentage of nursing staff (including aides) that are licensed nurses (RN + LPN) Total cost ($) Total no. discharges Total no. licensed nurses (RN + LPN) per 1000 inpatient days All nursing staff (including aides) per 1000 inpatient days Percentage of licensed nurses (RN + LPN) that are RNs Percentage of nursing staff (including aides) that are licensed nurses (RN + LPN)

1

2

11,754,487  0.252* (0.444, 0.059) — 0.094 (0.019, 0.206) —

11,754,487 —  0.191* (0.364, 0.019) — 0.007 (0.036, 0.021)

18,466,880  0.033* (0.059, 0.007) — 0.009 (0.021, 0.003) —

18,466,880 —  0.031** (0.051, 0.011) — 0.001 (0.004, 0.006)

16,971,758 166.5 (35.0, 368.1) — 87.0* (153.6, 20.4) —

16,971,758 — 63.1 (70.5, 196.7) — 41.2 (25.6, 108.0)

*0.01rP < 0.05. **0.001rP < 0.01. LPN indicates licensed practical nurses; RN, registered nurses.

increase), we observed no change in length of stay, nursingsensitive quality indicators, nor change in cost.

DISCUSSION This study examines the value of inpatient nurse staffing on inpatient quality of care and patient care costs. Despite the potential impact of nurse staffing on inpatient hospital costs, there is little evidence in the literature that examines this relationship. Our study uses longitudinal data and fixed effects to control for hospital-level observable and unobservable, time-invariant confounders. Our findings have important implications related to the role of nurse staffing. We find that increasing nurse staffing levels is associated with a reduction in adverse events and length of stay. Although our findings are consistent with previous literature,5–17,19 they are small in size, and, in the case of adverse

events, relatively sensitive to different modeling decisions. We also find that adding more licensed nurses or other nursing staff is not associated with increases in average patient care costs. These null results are estimated with a high degree of certainty given the large sample size of our dataset. Therefore, increases in nurse staffing may be cost neutral due to a cost reduction associated with a decline in adverse events and length of stay. This suggests that increasing nurse staffing may promote value by improving outcomes while not increasing costs. Nurse staffing skill mix also affects patient care costs. Specifically, as the proportion of nursing staff that are RNs increases, patient care costs decrease. Previous studies similarly suggest that increasing RN staff as a proportion of all licensed nurses can lead to reductions in costs,20,22 suggesting that value be further promoted by shifting from LPNs to RNs. Our findings do not provide clear insight into the

TABLE 3. Results of Estimating at the 75th Percentile for Nurse Staffing Measures % With Any Nursing-sensitive Adverse Event Length of Stay Total Cost Model (1) Mean Estimate after increasing to the 75th percentile (increase above mean) Total no. nurses per 1000 inpatient days (mean = 6.3, p75 = 7.0) Percentage of nurses (RN+LPN) that are RNs (mean = 94.6, p75 = 98.6) Model (2) Mean Estimate after increasing to the 75th percentile (increase above mean) All nursing staff per 1000 inpatient days (mean = 7.8, p75 = 8.8) Percentage of nursing staff (including aides) that are nurses (mean = 81.5, p75 = 87.0)

0.0149

4.427

$11,141

0.0147* 0.0153

4.404* 4.392

$11,257 $10,793*

0.0149

4.427

$11,141

0.0147* 0.0149

4.397** 4.431

$11,202 $11,369

*0.01rP < 0.05. **0.001rP < 0.01. ***P < 0.001. LPN indicates licensed practical nurses; RN, registered nurses.

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mechanism through which skill mix (ie, RNs as proportion of all nursing staff) contribute to reductions in patient care costs. Although we found staff skill mix was associated with reductions in patient care costs, skill mix was not significantly associated with reductions in length of stay or adverse events. Thus, the observed relationship does not seem to be driven by a reduction in length of stay or adverse events, as suggested in previous work.22 Additional research is needed to better understand the relationship between RNs and reductions in patient care costs. The present study focuses only on the value of care for a given discharge within the hospital itself. The study cannot speak to the full value of inpatient nurse staffing on the health care system as a whole or a hospital’s overall financial performance. Patients exposed to increased levels of nursing or enhanced nurse staffing skill mix while in the hospital might be healthier when they are discharged and require less intensive postacute care. In addition, a growing evidence suggests that increases in nurse staffing contribute to reductions in readmissions,23,42,43 which may lead to reductions in costs to the health care system overall. Our study is not without limitations. First, our patient care cost measure is not directly observed; instead, it is calculated from hospital charges, which are known to have measurement error. To the extent that this error is uncorrelated with changes in nurse staffing over time, the estimates of the effect of nurse staffing would be unbiased but inefficient. We have no reason to believe that the error inherent in calculating charges is correlated with changes in nurse staffing. However, any inefficiency in the estimates would reduce the likelihood that we observe statistically significant results in the cost models. Second, although we have multiple states, most of the observations (78%) are from California, meaning that the observed results are largely being driven by observations from that state. Third, although the analysis is performed at the discharge level, the nurse staffing variables are calculated at the hospital-year level. Therefore, we are unable to tie the nurse staffing levels directly to the outcomes of any individual discharge. Fourth, clinical coding practices can vary across hospitals and states, which may affect the cross-sectional comparability of hospital adverse event rates. Similarly, we did not use the Present on Admission indicator given as it was not available for every state. However, our study focuses on changes in events over time and we believe our use of hospital-fixed effects largely addresses these issues by controlling for cross-sectional, timeinvariant differences in coding practice. Fifth, as noted previously, many of the results (particularly those related to adverse events) had small effect sizes and were sensitive to different specification decisions. Therefore, readers should interpret the results related to the adverse events with caution. However, previous studies have found evidence of the effect of nurse staffing on quality. Future research should continue to focus on identifying the unique and particular contribution that nurses make to quality of care on measures other than PSIs, such as patient care experience. Finally, we did not simultaneously estimate the effect of nurse staffing while holding cost constant in a single regression. This approach would arguably be the most precise estimate of the value of r

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inpatient nurse staffing. However, we believe that the estimates here are suggestive of increased nurse staffing leading to improvements in quality and costs, and that more work in this area is warranted. In summary, this study finds that nurse staffing levels are associated with reductions in adverse events and length of stay while remaining cost neutral. These results provide further support for the business case for increasing nurse staffing—suggesting that outcomes can be improved without increasing patient care costs. We also find that staff skill mix (specifically, the number of RNs as a proportion of all nursing staff) leads to reductions in patient care costs. This suggests that hospitals may be able to reduce patient care costs by shifting staff skill mix toward RNs. However, the impact of this shift on the overall financial performance of a given hospital may depend on the reimbursement mechanisms (eg, DRG) and the presence of other reporting and incentive systems (eg, pay-for-performance). In addition, this study is limited to a definition of value based on a given discharge within a given hospital. To have a more complete view of the implications of inpatient nurse staffing on health care more broadly, future studies should investigate the impact of nurse staffing on hospital readmissions and other postacute care costs. REFERENCES 1. Institutes of Medicine. To Err is Human: Building A Safer Health System. Washington, DC: National Academy Press; 1999. 2. Dall TM, Chen YJ, Seifert RF, et al. The economic value of professional nursing. Med Care. 2009;47:97–104. 3. Savitz L, Jones C, Bernard S. Quality Indicators Sensitive to Nurse Staffing in Acute Care Settings. Rockville, MD: Agency for Health Care Research and Quality; 2005. 4. Porter ME. What is value in health care? N Engl J Med. 2010;363: 2477–2481. 5. Blegen MA, Goode CJ, Spetz J, et al. Nurse staffing effects on patient outcomes: safety-net and non-safety-net hospitals. Med Care. 2011;49:406–414. 6. Harless DW, Mark BA. Nurse staffing and quality of care with direct measurement of inpatient staffing. Med Care. 2010;48:659–663. 7. Breckenridge-Sproat S, Johantgen M, Patrician P. Influence of unit-level staffing on medication errors and falls in military hospitals. West J Nurs Res. 2012;34:455–474. 8. Frith KH, Anderson EF, Tseng F, et al. Nurse staffing is an important strategy to prevent medication error in community hospitals. Nurs Econ. 2012;30:288–294. 9. Mark BA, Harless DW, Spetz J, et al. California’s minimum nurse staffing legislation: results from a natural experiment. Health Serv Res. 2013;48(pt 1):435–454. 10. Patrician PA, Loan L, McCarthy M, et al. The association of shift-level nurse staffing with adverse patient events. J Nurs Adm. 2011;41: 64–70. 11. Kendall-Gallagher D, Blegen MA. Competence and certification of registered nurses and safety of patients in intensive care units. J Nurs Adm. 2010;40(suppl):S68–S77. 12. Lake ET, Shang J, Klaus S, et al. Patient falls: association with hospital Magnet status and nursing unit staffing. Res Nurs Health. 2010;33: 413–425. 13. Stratton KM. Pediatric nurse staffing and quality of care in the hospital setting. J Nurs Care Qual. 2008;23:105–114. 14. Frith KH, Anderson EF, Caspers B, et al. Effects of nurse staffing on hospital-acquired conditions and length of stay in community hospitals. Qual Manag Health Care. 2010;19:147–155. 15. Mark BA, Harless DW, Berman WF. Nurse staffing and adverse events in hospitalized children. Policy Polit Nurs Pract. 2007;8:83–92.

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16. Spetz J, Harless DW, Herrera CN, et al. Using minimum nurse staffing regulations to measure the relationship between nursing and hospital quality of care. Med Care Res Rev. 2013;70:380–399. 17. Tschannen D, Kalisch BJ. The effect of variations in nurse staffing on patient length of stay in the acute care setting. West J Nurs Res. 2009; 31:153–170. 18. Mark BA, Harless DW, McCue M. The impact of HMO penetration on the relationship between nurse staffing and quality. Health Econ. 2005;14:737–753. 19. Newhouse RP, Johantgen M, Pronovost PJ, et al. Perioperative nurses and patient outcomes—mortality, complications, and length of stay. AORN J. 2005;81:508–509. 513-522, 525-508. 20. Needleman J, Buerhaus PI, Stewart M, et al. Nurse staffing in hospitals: is there a business case for quality? Health Aff (Millwood). 2006;25:204–211. 21. McCue M, Mark BA, Harless DW. Nurse staffing, quality, and financial performance. J Health Care Finance. 2003;29:54–76. 22. Li YF, Wong ES, Sales AE, et al. Nurse staffing and patient care costs in acute inpatient nursing units. Med Care. 2011;49:708–715. 23. Weiss ME, Yakusheva O, Bobay KL. Quality and cost analysis of nurse staffing, discharge preparation, and postdischarge utilization. Health Serv Res. 2011;46:1473–1494. 24. Thungjaroenkul P, Cummings GG, Embleton A. The impact of nurse staffing on hospital costs and patient length of stay: a systematic review. Nurs Econ. 2007;25:255. 25. State of California Office of Statewide Health Planning & Development. Healthcare Information Division. Annual financial data. 2012. Available at: http://www.oshpd.ca.gov/HID/Products/Hospitals/AnnFinanData/ PivotProfles/default.asp. Accessed April 11, 2013. 26. State of California Office of Statewide Health Planning & Development. Healthcare Information Division. Hospital annual utilization data. 2013. Available at: http://www.oshpd.ca.gov/hid/Products/Hospitals/Utili zation/Hospital_Utilization.html. Accessed April 11, 2013. 27. The Maryland Health Services Cost Review Commission. HSCRC financial databases. Wage and salary survey results. Available at: http://www.hscrc.state.md.us/hsp_Data2.cfm. Accessed April 14, 2013. 28. The Maryland Health Services Cost Review Commission. HSCRC financial databases. Revenue and volumes report—fiscal years 20072011. 2011. Available at: http://www.hscrc.state.md.us/hsp_Data2.cfm. Accessed July 6, 2012. 29. University of Las Vegas. Center for Health Information Analysis (CHIA). NHQR Utilization and Financial Reports. 2013. Available at: http://www.chiaunlv.com/Reports/NHQRUtilizationAndFinancial.php. Accessed March 27, 2013.

988 | www.lww-medicalcare.com

Medical Care



Volume 52, Number 11, November 2014

30. Agency for Healthcare Research and Quality (AHRQ). Patient safety indicators technical specifications. Version 4.5. 2013. Available at: http:// www.qualityindicators.ahrq.gov/modules/PSI_TechSpec.aspx. Accessed May 22, 2013. 31. McDonald KM, Romano PS, Geppert J, et al. Measures of Patient Safety Based on Hospital Administrative Data—The Patient Safety Indicators. Technical Reviews, No. 5. Rockville, MD: Agency for Healthcare Research and Quality; 2002. 32. Healthcare Cost and Utilization Project. Cost-to-charge ratio files. 2013. Available at: http://www.hcup-us.ahrq.gov/db/state/costtocharge.jsp. 33. Centers for Disease Control and Prevention. National Center for Health Statistics (NCHS) urban-rural classification scheme for counties. 2013. Available at: http://www.cdc.gov/nchs/data_access/urban_rural.htm. Accessed October 16, 2013. 34. United States Department of Veterans Affairs. MS-Diagnosis-Related Group (MS-DRG) weights (2008-2013). 2013. Available at: http://www. herc.research.va.gov/resources/faq_f03.asp. Accessed August 27, 2013. 35. Healthcare Cost and Utilization Project. Comorbidity software. Version 30.7. 2013. Available at: http://www.hcup-us.ahrq.gov/toolssoftware/ comorbidity/comorbidity.jsp. Accessed October 18, 2013. 36. Stange K. How does provider supply and regulation influence health care markets? Evidence from nurse practitioners and physician assistants. J Health Econ. 2014;33:1–27. 37. Carpenter C, Cook PJ. Cigarette taxes and youth smoking: New evidence from national, state, and local Youth Risk Behavior Surveys. J Health Econ. 2008;27:287–299. 38. Wooldridge JM. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press; 2002. 39. Basu A, Manning WG. Issues for the next generation of health care cost analyses. Med Care. 2009;47(suppl 1):S109–S114. 40. Buntin MB, Zaslavsky AM. Too much ado about two-part models and transformation? Comparing methods of modeling Medicare expenditures. J Health Econ. 2004;23:525–542. 41. Agency for Healthcare Research and Quality. AHRQ quality indicator: risk adjustment coefficients for the PSI. 2010. Available at: http:// www.qualityindicators.ahrq.gov/Downloads/Modules/PSI/V41/PSI_Risk_ Adjustment_Tables_V41a.pdf. Accessed August 6, 2014. 42. McHugh MD, Berez J, Small DS. Hospitals with higher nurse staffing had lower odds of readmissions penalties than hospitals with lower staffing. Health Aff (Millwood). 2013;32:1740–1747. 43. McHugh MD, Ma C. Hospital nursing and 30-day readmissions among Medicare patients with heart failure, acute myocardial infarction, and pneumonia. Med Care. 2013;51:52–59.

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2014 Lippincott Williams & Wilkins

Examining the value of inpatient nurse staffing: an assessment of quality and patient care costs.

Inpatient quality deficits have important implications for the health and well-being of patients. They also have important financial implications for ...
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