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Is Hospital Nurse Staffing Legislation in the Public’s Interest?

An Observational Study in New York State

Lasater, Karen B. PhD, RN*,†; Aiken, Linda H. PhD, RN, FAAN*,†; Sloane, Douglas M. PhD*; French, Rachel BSN, RN*,†; Anusiewicz, Colleen V. PhD, RN*,†; Martin, Brendan PhD; Reneau, Kyrani MS; Alexander, Maryann PhD, RN, FAAN; McHugh, Matthew D. PhD, RN, FAAN*,†

Author Information
doi: 10.1097/MLR.0000000000001519

Abstract

A bipartisan coalition of New York (NY) state assembly members sponsored legislation in 2019, Safe Staffing for Quality Care Act, to require hospitals to staff enough nurses to safely care for patients.1 A July 2020 Harris national public opinion poll found 91% of respondents agreed that hospitals should be required to meet safe staffing standards for nurses.2 Yet a similar policy requiring hospitals in Massachusetts to meet minimum nurse staffing standards was rejected in 2018 after a contentious debate in which some stakeholders warned of disastrous consequences absent information on whether the legislation was in the public’s interest. The current study provides timely and relevant evidence related specifically to nurse staffing in NY hospitals— information necessary to guide the policy decision process that was absent in Massachusetts and other states considering similar policies.

We provide new evidence to inform the public and their elected officials of variability in nurse staffing across hospitals in NY and the consequences and associated costs of high nurse staffing ratios on clinical outcomes among older adults hospitalized with common medical and surgical conditions. This examination provides much needed evidence about the need in NY for mandated safe nurse staffing legislation. While our findings are specific to the circumstances and legislation under consideration in NY, the relationships between staffing ratios, patient outcomes, and cost savings may be relevant to other states looking to undertake safe staffing laws.

METHODS

Study Design and Data

This cross-sectional study utilizes data on hospital patient-to-nurse staffing ratios collected between December 16, 2019 and February 24, 2020 from a survey of all actively licensed registered nurses (RNs) in NY. RNs completed an email-based survey and provided the name of their employer and detailed information about their inpatient workloads during their most recent shift worked, including: the type of unit (eg, medical or surgical, intensive care) and how many patients they were assigned to care for at one time. The name of their employing hospital and type of unit that nurses reported being assigned to allowed us to generate hospital-level estimates of staffing ratios from responses of nurses working in the same hospitals.

Hospital staffing data were linked with the American Hospital Association (AHA) Annual Survey which included other hospital information such as size, teaching status, and technology availability. The Medical Provider Analysis and Review File (MEDPAR) was used to identify Medicare patients discharged from each NY hospital, between January 1, 2017 and December 31, 2018, the 2 years for which data most proximate to our survey were available.

Sample

This study included 2 distinct samples of Medicare patients discharged over the 2-year study period: medical and surgical. The sample of medical patients included those admitted for 1 of 5 common medical conditions: acute myocardial infarction, congestive heart failure, pneumonia, stroke, and sepsis (Appendix Table 1, Supplemental Digital Content 1, http://links.lww.com/MLR/C192). These medical conditions account for ∼23.5% of the total Medicare medical admissions in NY. The sample of surgical patients included those admitted for a general, orthopedic, or vascular procedure (Appendix Table 2, Supplemental Digital Content 1, http://links.lww.com/MLR/C192), accounting for ∼15.5% of the total Medicare surgical admissions in the state. Medicare beneficiaries younger than 65 years old and older than 99 years old were excluded. If a patient had more than 1 hospitalization during the study period, the first hospitalization for a medical and surgical condition was selected as the index admission to avoid double counting.

The analytic sample of hospitals included 116 acute care general hospitals in NY. Inclusion in the analysis was determined by whether enough nurse respondents from each hospital had been obtained to reliably estimate measures of patient-to-nurse staffing ratios for medical-surgical nurses. On average, there were 24 direct-care medical-surgical RN survey respondents per hospital, though larger hospitals (ie, those with 500+ beds) averaged more than 50 medical-surgical RN survey respondents per hospital. The 116 hospitals in our sample account for ∼81% of the acute care general hospitals in NY state. Hospitals omitted were smaller hospitals with fewer inpatient admissions.

Variables

The predictor of primary interest is patient-to-nurse staffing ratios. Nurse survey respondents were asked, “How many patients were assigned to you on that [your last] shift?” The response was open-ended. The numbers of patients reported by direct-care medical-surgical staff nurses during their last shift were aggregated within hospitals and divided by the number of medical-surgical nurse respondents to create a hospital-level measure representing average patient-to-nurse staffing. This approach has been widely used and validated.3,4

Patient outcomes were derived from Medicare claims data and included in-hospital mortality, length of stay, and 30-day readmission. In-hospital mortality was defined as a death occurring during the first 30 days of the index hospitalization. Length of stay represented the number of days spent in the hospital during the index admission. Readmissions were defined as a hospital admission occurring within 30 days from the index hospital discharge date. Costs were estimated using Medicare-specific cost-to-charge ratios using patient-level charge data from the MEDPAR file and hospital-level cost reports from the Centers for Medicare and Medicaid Services Impact Files.

The Medicare claims data also provided detailed information for risk-adjustment including patients’ age, sex, 32 Elixhauser comorbidities, transfer status, and dummy variables for each surgical diagnostic-related group and each medical category. AHA data provided hospital-level variables for risk-adjustment including size, teaching status, and technology status. Hospital size was defined by the number of licensed beds (small ≤100 beds; medium 101–250 beds; large >250 beds). Teaching status categories were based on the ratio of medical residents or fellows to hospital beds (nonteaching, no medical trainees; minor teaching, 0–4 per bed; major teaching, ≥4 per bed). High technology hospitals performed major organ transplants and/or open-heart surgery. Skill mix was also controlled for. While skill mix did not have a significant effect on the different outcomes, net of the effect of medical-surgical nurse staffing, its inclusion in our models did appear to stabilize our staffing effect estimates across various model specifications.

Statistical Analyses

We use descriptive statistics (percentages, means, medians, SD, and ranges) to show (1) the region, size, teaching status, and technology of the study hospitals; (2) the average hospital medical-surgical staffing derived from the reports of the medical-surgical nurses in the hospitals; (3) the numbers of medical-surgical nurses whose survey responses produced these average hospital staffing measures; and (4) the numbers of medical and surgical patients admitted. We show the characteristics of Medicare patients with medical and surgical conditions and the same study hospitals, including the region of their admitting hospital as well as their age, sex, and comorbidities. We also show, before taking account of any potentially confounding factors, how mortality, readmissions, and lengths of stay vary across groups of hospitals with different staffing levels (eg, <6, 6–7, and >7 patients per nurse).

Ultimately, we use multi-level random effects logistic regression models to estimate the size and significance of the association of hospital nurse staffing with individual patient mortality and readmissions. Zero-truncated negative binomial regression models were used to test the association of staffing with length of stay, which is a count variable that ranged in value from 1 to 60 days. We estimated these associations before and after adjusting for other hospital characteristics and potentially confounding characteristics of the patients. We then used the adjusted estimates to approximate how many lives and how much money would have been saved over the 2 years from shorter lengths of stay and the reduced number of readmissions that would be produced by staffing at the proposed policy level (4:1 on medical-surgical units) rather than the current observed levels.

To compute cost savings to hospitals from reductions in length of stay, we first computed the total observed days and charges for patients in each hospital, and then, using regression modeling, estimated the predicted days per hospital if that hospital were to have staffed at a 4:1 patient-to-nurse ratio. The percentage reduction in length of stay between the observed and predicted was then applied to a percentage reduction in charges, and then converted to cost savings using hospital-level Medicare-specific cost-to-charge ratios. Payments avoided from reduced readmissions were calculated by first computing the difference between the observed and predicted readmissions in each hospital, and then applying the percentage reduction in readmissions to the payments from observed readmissions.

RESULTS

The 7 regions into which the hospitals were classified, as well as the number of hospitals in each region are presented in Table 1 and in Figure 1 of the Appendix, Supplemental Digital Content 1 (http://links.lww.com/MLR/C192). Thirty percent of the study hospitals were located in NY City, and another 30% in the 2 contiguous regions consisting of counties along the Mid-Hudson and on Long Island. The remaining hospitals—some 40% of the total—were scattered across the Western, Finger Lakes, Central, and North Eastern regions of the state. Roughly 60% of the hospitals had 250 beds or more, a slightly larger percentage were major or minor teaching hospitals, and more than half (53.5%) of hospitals were characterized as non–high-technology hospitals.

TABLE 1 - Selected Characteristics of the 116 New York Hospitals in the Study Sample, and Medical-Surgical Staffing in Hospitals With Different Characteristics
Medical-Surgical Staffing (Patients Per Nurse)
Range in Staffing
Hospital Characteristics Number of Hospitals Percent of Hospitals Mean SD Median Minimum Maximum
Region
 1 Western NY 12 10.3 6.1 0.6 6.2 5.0 7.0
 2 Finger Lakes 8 6.9 5.7 0.6 5.5 5.1 6.6
 3 Central 14 12.1 5.8 0.7 5.8 4.3 6.9
 4 North Eastern 12 10.3 5.4 0.3 5.3 4.9 5.9
 5 New York City 35 30.2 6.9 1.0 6.9 5.1 8.7
 6 Mid-Hudson 17 14.7 6.2 1.2 6.0 4.7 10.5
 7 Nassau-Suffolk 18 15.5 6.5 1.0 6.5 4.9 8.6
 Total 116 100.0 6.3 1.0 6.1 4.3 10.5
Hospital size
 ≤100 beds 12 10.3 5.8 0.5 5.7 5.3 6.9
 101–250 beds 36 31.0 6.3 1.2 6.1 4.3 10.5
 >250 68 58.6 6.4 0.9 6.2 4.8 8.7
 Total 116 100.0 6.3 1.0 6.1 4.3 10.5
Teaching status
 Nonteaching 31 26.7 5.9 0.7 5.8 4.9 7.5
 Minor teaching 43 37.1 6.3 1.1 6.1 4.3 10.5
 Major teaching 36 31.0 6.5 1.1 6.5 4.8 8.6
 Missing 6 5.2 6.5 1.2 6.2 5.2 8.4
 Total 116 100.0 6.3 1.0 6.1 4.3 10.5
Technology
 Non–high technology 62 53.5 6.2 0.9 6.1 4.3 8.5
 High technology 32 27.6 6.0 0.8 5.9 4.8 8.3
 Missing 22 19.0 6.9 1.4 6.7 4.7 10.5
 Total 116 100.0 6.3 1.0 6.1 4.3 10.5
Patients discharged from 116 study hospitals
Number Mean SD Median Minimum Maximum
Surgical patients 185,169 1596 1739 887 35 8707
Medical patients 232,692 2,006 1,726 1,416 140 8820
Nurses completing surveys in 116 study hospitals
Number Mean SD Median Minimum Maximum
Medical-surgical nurses 2747 23.7 24.3 14.0 5 139
Staffing data are from the nurse survey and other hospital characteristics are from AHA data. Data related to patients are from MEDPAR.

Medical-surgical staffing in these hospitals was estimated using individual workloads from 2747 medical-surgical RNs in the 116 hospitals, or a mean of 24 RNs per hospital. The medical-surgical staffing ratios ranged across hospitals from 4.3 to 10.5 patients per nurse and were somewhat worse (or patients per nurse were somewhat higher) in NY City (P/N=6.9) than elsewhere (P/N=6.0).

Eight percent of the 232,692 medical patients and 1.5% of the 185,169 surgical patients died in the hospital within 30 days of admission (Table 2). The average length of stay, excluding patients who died, were transferred, or stayed beyond 60 days, was 6.7 days (median: 5 d) for medical patients and 5.0 days on average for surgical patients (median: 3 d). Of the 206,259 medical patients who were discharged alive and not transferred to another acute care hospital, 15.3% were readmitted within 30 days; while 9.2% of surgical patients were readmitted within 30 days. Roughly one-third of patients were in NY City hospitals, and a similar percentage were in 2 contiguous regions combined. Similar percentages of patients were found in the various age groups, slightly more than half were female, and the most common comorbidities were hypertension, fluid/electrolyte disorders, chronic pulmonary disease, and renal failure.

TABLE 2 - Outcomes and Selected Characteristics of the Surgical and Medical Patients in the Study Hospitals
Numbers (%)
Patient Outcomes Surgical Patients Medical Patients
Deaths/cases 2730/185,169 (1.5%) 19,184/232,692 (8.2%)
30 d readmissions/cases 16,756/182,437 (9.2%) 31,534/206,259 (15.3%)
No. of cases Mean (SD) No. of cases Mean (SD)
Length of stay 182,304 5.0 (5.4) 205,855 6.7 (6.1)
Patient characteristics Number (%) Number (%)
Region of their admitting hospital
 1 Western NY 17,794 (9.6) 20,120 (8.7)
 2 Finger Lakes 16,561 (8.9) 17,780 (7.6)
 3 Central 18,852 (10.2) 21,096 (9.1)
 4 North Eastern 18,720 (10.1) 19,840 (8.5)
 5 New York City 57,745 (31.2) 78,543 (33.8)
 6 Mid-Hudson 17,880 (9.7) 27,185 (11.7)
 7 Nassau-Suffolk 37,617 (20.3) 48,128 (20.7)
 Total 185,169 (100.0) 232,692 (100.0)
Age (y)
 65–69 45,143 (24.4) 37,923 (16.3)
 70–74 44,351 (24.0) 37,484 (16.1)
 75–79 36,664 (19.8) 38,814 (16.7)
 80–84 27,189 (14.7) 39,860 (17.1)
 85–89 19,243 (10.4) 39,000 (16.8)
 90+ 12,579 (6.8) 39,611 (17.0)
 Total 185,169 (100.0) 232,692 (100.0)
Sex
 Female 107,045 (57.8) 121,985 (52.4)
 Male 78,124 (42.2) 110,707 (47.6)
 Total 185,169 (100.0) 232,692 (100.0)
Transfer status
 Nontransfer 176,992 (95.6) 222,051 (95.4)
 Transfer 8,177 (4.4) 10,641 (4.6)
 Total 185,169 (100.0) 232,692 (100.0)
Common comorbidities
 Hypertension 133,073 (71.9) 141,560 (60.8)
 Fluid and electrolyte disorders 35,442 (19.1) 103,508 (44.5)
 Chronic pulmonary disease 35,533 (19.2) 67,699 (29.1)
 Obesity 33,907 (18.3) 30,406 (13.1)
 Hypothyroidism 32,300 (17.4) 42,587 (18.3)
 Diabetes without complications 28,780 (15.5) 37,053 (15.9)
 Renal failure 26,775 (14.5) 66,797 (28.7)
 Deficiency anemias 24,307 (13.1) 55,180 (23.7)
 Diabetes with complications 24,409 (13.2) 52,129 (22.4)
 Depression 19,837 (10.7) 26,138 (11.2)
 Peripheral vascular disease 15,225 (8.2) 21,299 (9.2)
 Congestive heart failure 15,612 (8.4) 39,830 (17.1)
 Other neurological disorders 14,294 (7.7) 30,835 (13.3)
 Valvular disease 13,109 (7.1) 18,320 (7.9)
 Weight loss 9365 (5.1) 23,476 (10.1)
 Coagulopathy 6889 (3.7) 19,786 (8.5)
 Paralysis 4022 (2.2) 14,570 (6.3)
The percentage of deaths are for all surgical cases admitted for general, orthopedic and vascular surgeries, and medical cases with acute myocardial infarction, congestive heart failure, pneumonia, stroke, and sepsis. Readmissions are based on cases that exclude cases that died in the hospital or were transferred to another hospital. Cases used to calculate length of stay exclude cases involving in-hospital deaths, patients transferred to another acute care facility, and lengths of stay longer than 60 days. Comorbidities shown are those that involved at least 5% of the patients in either patient group, ordered according to their prevalence among surgical patients. The percentages of cases with different comorbidities do not sum to 100% due to patients with no comorbidities and multicomorbidities.

Table 3 shows that, before adjusting for any potentially confounding factors, the average percentages of deaths and readmissions, as well as the average length of stay were lowest in the group of better-staffed hospitals with fewer than 6 patients per nurse and highest in the worst-staffed hospitals with 7 or more patients per nurse. Table 4 provides odds ratios (for mortality and readmissions) and incident rate ratios (for length of stay) indicating the effects of medical-surgical staffing for outcomes of medical and surgical patients separately. Coefficients after adjusting for different hospital and patient characteristics indicate that each patient added to the average nurse’s workload increase the odds of death by a factor of 1.13 (or 13%) for both surgical patients and medical patients. The odds of 30-day readmissions increase by a factor of 1.08 (or 8%) and 1.06 (or 6%) for surgical and medical patients respectively; and the odds of staying a day longer at all intervals increased by a factor of 1.09 (or 9%) and 1.05 (or 5%) for surgical and medical patients, respectively.

TABLE 3 - Average Deaths, Readmissions and Lengths of Stay (LOS), for Medical and Surgical Patients in 3 Groups of Hospitals With Different Patient/Nurse Ratios
Medical Surgical
Patient/Nurse Ratio N Mortality Readmissions LOS Mortality Readmissions LOS
<6 52 7.6% 14.4% 6.1 1.4% 8.4% 4.8
6–<7 39 8.6% 15.6% 6.7 1.9% 9.6% 5.8
7+ 25 9.5% 16.5% 7.3 2.4% 12.3% 7.5
Total 116 8.3% 15.3% 6.6 1.8% 9.6% 5.7
Slight discrepancies between some of the percentages reported in this table versus Table 2 result from the fact that numbers and percentages in Table 2 are calculated from individual patients rather than by averaging numbers and percentages across hospitals.

TABLE 4 - Effect of Medical Surgical Staffing (Patient-to-Nurse Ratio) on Surgical and Medical Patient Outcomes
Surgical Patients Medical Patients
Patient Outcome Coefficient Unadjusted Models Fully Adjusted Models Unadjusted Models Fully Adjusted Models
In-hospital mortality Odds ratio 1.18 1.13 1.11 1.13
95% CI 1.08, 1.30 1.01, 1.26 1.05, 1.18 1.05, 1.22
P>|z| 0.0004 0.0262 0.0003 0.0019
30-day readmission Odds ratio 1.14 1.08 1.05 1.06
95% CI 1.08, 1.21 1.04, 1.13 1.03, 1.08 1.03, 1.09
P>|z| <0.0001 0.0002 <0.0001 0.0003
Length of stay IRR 1.13 1.09 1.05 1.05
95% CI 1.05, 1.22 1.04, 1.14 1.01, 1.08 1.02, 1.09
P>|z| 0.0022 0.0008 0.0066 0.0023
CI indicates confidence interval; IRR, incidence rate ratio.

We use the adjusted effects to estimate, for the 2-year study period, the number of lives that would have been saved and the amount of money that might have been saved by hospitals from shorter lengths of stay and by payers from avoided readmissions, if medical-surgical staffing had been 4:1 (as prescribed by the legislation) rather than an average of roughly 6:1 (as was observed). As shown in Table 5, for medical patients (who account for roughly one-quarter of the medical hospital stays among Medicare beneficiaries, and considerably fewer among hospitalizations of all payer-types), there would have been 3,821 fewer deaths and nearly $298 million saved from shorter stays over the two years. Avoided payments from reduced readmissions following a medical admission would have been more than $37 million over two years. Among the surgical patients studied, who account for roughly 15% of the total surgical Medicare volume in hospitals, we estimate that if hospitals were to have staffed at the 4:1 ratio, 549 lives would have been saved, $360 million could have been saved by hospitals from shortened stays, and nearly $24 million payments avoided from avoided readmissions.

TABLE 5 - Lives Saved and Cost Savings From Reduced Readmissions and Shorter Lengths of Stay With 4:1 Staffing Ratios Over 2 Years (2017–2018)
Surgical Patients Medical Patients
Variables Used to Estimate Lives Saved and Cost Savings Mortality 30-Day Readmissions Length of Stay Mortality 30-Day Readmissions Length of Stay
Number of patients at risk of experiencing outcomes 185,169 182,437 182,304 232,692 206,259 205,855
Observed number of patients experiencing outcomes 2730 16,756 19,184 31,534
Expected number of patients experiencing outcomes with 4:1 patient/nurse ratio 2181 14,727 15,363 28,520
Difference between observed and expected patients experiencing outcomes 549 2029 3821 3014
Observed total payments $195,598,928 $381,565,063
Projected savings in payments $23,985,739 $37,555,479
Observed number of patient days 909,555 1,377,496
Expected number of patient days with 4:1 patient/nurse ratio 805,015 1,254,798
Difference between observed and expected patient days 104,540 122,698
Observed total charges $14,290,847,096 $13,538,143,902
Projected reduction in total charges $1,353,413,218 $1,166,470,040
Projected cost savings after applying cost-to-charge ratio $360,155,287 $297,836,798

DISCUSSION

We found wide variation in patient-to-nurse staffing across a large representative sample of NY hospitals; with staffing on medical-surgical units ranging from 4.3 to 10.5 patients per nurse and an average of 6.3 patients per nurse. Staffing on intensive care units ranged from 1.8 to 4.3, with an average of 2.5 patients per nurse, which is more than the proposed legislation for ICU staffing of 2:1 (Appendix Table 3, Supplemental Digital Content 1, http://links.lww.com/MLR/C192). While we observed variation in average staffing levels across hospitals, medical-surgical nurses in most study hospitals reported caring for more patients than would be permissible under the 4:1 ratio for medical-surgical nurses in the proposed NY staffing legislation. In fact, medical-surgical nurses in only a few NY hospitals reported workloads of <5:1, which is the current mandated ratio in California passed 20 years ago—the only US state to have implemented hospital-wide staffing legislation.5–7

We demonstrate higher odds of in-hospital mortality and 30-day readmission, and longer lengths of stay for medical and surgical patients in hospitals where nurses care for more patients each. If NY hospitals had staffed medical-surgical nurses at the 4:1 ratio as proposed in the current legislation, we project there would have been 4370 fewer in-hospital deaths in the 2-year period among Medicare patients. With roughly 388,160 fewer hospital days annually, we project $658 million in cost savings to hospitals annually from shortened lengths of stay. Likewise, we estimate 1.5% and 1.1% fewer 30-day readmissions among medical and surgical patients, respectively, with a resultant annual estimated savings of roughly $61.5 million. These estimates are produced by only about 25% of the Medicare hospitalizations in NY, so the actual savings when applied to all inpatients—both Medicare and non-Medicare—would likely be many-fold higher.

Previous research on larger groups of medical and surgical inpatients has demonstrated an association between lower patient-to-nurse staffing ratios and better patient outcomes including lower in-hospital mortality, avoided readmission, and reductions in length of stay8–15; as well as the value of patient outcomes relative to costs of investment in hospital nurse staffing.16–18 Shorter lengths of stay and avoided readmissions are not only favorable outcomes for patients, but have favorable cost implications for hospitals including making available more beds to accept a case-mix consistent with hospitals’ strategic priorities.

Strengths and Limitations

This study describes hospital nurse staffing using data collected from nurses employed in NY hospitals during the period that staffing legislation is under consideration. The timeliness of the staffing data is a major strength of this study since it provides current evidence to inform state policy decisions. There is, however, a lag in available patient claims data. While we use hospital staffing data collected during 2019 and 2020, we relied on the most recent available Medicare claims data from 2017 and 2018. While the timing of the patient and hospital data do not overlap, previous research demonstrates that nurse staffing changes modestly over a 10-year period.4 Thus, it is likely that the staffing data collected in 2019–2020 closely resembles staffing in 2017–2018.

Our estimates for predicted numbers of lives saved, readmissions avoided, reduced lengths of stay, and resultant cost-savings are conservative estimates of the overall potential impact of improving patient-to-nurse ratios. Our estimates make use of data from Medicare patients with 1 of 5 common medical conditions or a common surgery; a sample which accounts for roughly 25% of the total hospitalizations among Medicare beneficiaries in NY, not accounting for hospitalizations among non-Medicare patients. Previous research has shown that more favorable nurse staffing ratios are associated with better outcomes for all kinds of patients, including adults undergoing surgical procedures,11,19,20 adults with a range of medical conditions,3,15,21,22 as well as special populations like very low–birth-weight infants23 and Black patients.10 This study examines a limited number of patient outcomes and their cost-implications; however, with the increasing shift towards value-based payments, reductions in other unfavorable outcomes such as hospital-acquired infections which are known to be associated with lower patient-to-nurse staffing24 might also result in cost-savings to hospitals. We also do not account for nurse outcomes, such as burnout and job turnover, which are known to be associated with higher staffing ratios19,24,25,26 and negatively impact hospitals financially.

In this study, we demonstrate a need for and the potential implications of implementing mandated hospital nurse staffing ratios on the health of the public and associated cost-savings to hospitals and payers. While we do not study the number of additional nurses needed in NY state to achieve the recommended ratios, previous research suggests there are more than enough nurses to safely staff hospitals. Nursing graduations have doubled over the past 20 years with more than 160,000 newly educated nurses entering the US workforce every year.27 The nurse to population ratio in NY (18.7 RNs per 1000 population) is higher than in California (11.3 RNs per 1000 population), where ratios have been successfully implemented.28 The unfunded mandate in California was successfully implemented in safety net hospitals5 as well as other hospitals, and there was no evidence of hospital closures associated with state-wide staffing requirements. Finally, 34 states, but not NY, have joined the Nurse Licensure Compact, state legislation which permits nurses to practice in any Compact state.29 Passage of the Compact in NY would facilitate timely recruitment of nurses to the relatively few communities likely to have an actual shortage of nurses. All together, these trends in nurse workforce development suggest that passing the proposed safe nurse staffing legislation will not create a nursing shortage in hospitals or other clinical settings that would disrupt access to or quality of care.

CONCLUSIONS

Our findings demonstrate considerable variation in the patient-to-nurse staffing ratios on adult medical and surgical units in a large, representative sample of NY hospitals. The majority of hospitals do not meet the minimum nurse staffing required by legislation currently under consideration in NY. We find associations between worse nurse staffing and poorer outcomes for NY patients, consistent with a large body of research which shows the clinical benefit to patients of being cared for by a nurse with fewer assigned patients.8,10–12,19,30 If NY state enacted the Safe Staffing for Quality Care Act, our evidence projects many lives would be saved and shorter hospital stays would translate into cost-savings for hospitals.

ACKNOWLEDGMENTS

The authors wish to acknowledge Tim Cheney for his contributions to data management and analysis in April through September of 2020. All meetings were held virtually.

REFERENCES

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Keywords:

health policy; health services research; hospital staffing; nurse staffing; nursing

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