quantitative research nurse to patient ratios

Patient-to-nurse ratios: Balancing quality, nurse turnover, and cost

• Published: 29 November 2023
• Volume 26 , pages 807–826, ( 2023 )

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• David D. Cho 1 ,
• Kurt M. Bretthauer 2 &
• Jan Schoenfelder 3 , 4  

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We consider the problem of setting appropriate patient-to-nurse ratios in a hospital, an issue that is both complex and widely debated. There has been only limited effort to take advantage of the extensive empirical results from the medical literature to help construct analytical decision models for developing upper limits on patient-to-nurse ratios that are more patient- and nurse-oriented. For example, empirical studies have shown that each additional patient assigned per nurse in a hospital is associated with increases in mortality rates, length-of-stay, and nurse burnout. Failure to consider these effects leads to disregarded potential cost savings resulting from providing higher quality of care and fewer nurse turnovers. Thus, we present a nurse staffing model that incorporates patient length-of-stay, nurse turnover, and costs related to patient-to-nurse ratios. We present results based on data collected from three participating hospitals, the American Hospital Association (AHA), and the California Office of Statewide Health Planning and Development (OSHPD). By incorporating patient and nurse outcomes, we show that lower patient-to-nurse ratios can potentially provide hospitals with financial benefits in addition to improving the quality of care. Furthermore, our results show that higher policy patient-to-nurse ratio upper limits may not be as harmful in smaller hospitals, but lower policy patient-to-nurse ratios may be necessary for larger hospitals. These results suggest that a “one ratio fits all” patient-to-nurse ratio is not optimal. A preferable policy would be to allow the ratio to be hospital-dependent.

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Agency for Healthcare Research and Quality (2016) Access and disparities in access to health care. Rockville, MD. https://archive.ahrq.gov/research/findings/nhqrdr/nhqdr15/access.html . Accessed 15 Sept 2022

Aiken LH, Clarke SP, Sloane DM, Lake ET, Cheney T (2008) Effects of hospital care environment on patient mortality and nurse outcomes. J Nurs Adm 38(5):223–229

Article   Google Scholar  

Aiken LH, Clarke SP, Sloane DM, Sochalski J, Silber JH (2002) Hospital nurse staffing and patient mortality, nurse burnout, and job dissatisfaction. J Am Med Assoc 288(16):1987–1993

Aiken LH, Sloane DM, Bruyneel L, Van den Heede K, Griffiths P, Busse R, Diomidous M, Kinnunen J, Kózka M, Lesaffre E, McHugh MD, Moreno-Casbas MT, Rafferty AM, Schwendimann R, Scott PA, Tishelman C, van Achterberg T, Sermeus W (2014) Nurse staffing and education and hospital mortality in nine European countries: A retrospective observational study. The Lancet 383(9931):1824–1830

Aiken LH, Sloane DM, Cimiotti JP, Clarke SP, Flynn L, Seago JA, Spetz J, Smith HL (2010) Implications of the California nurse staffing mandate for other states. Health Serv Res 45(4):904–921

Aiken LH, Sloane DM, Griffiths P, Rafferty AM, Bruyneel L, McHugh M, Maier CB, Moreno-Casbas T, Ball JE, Ausserhofer D, Sermeus W (2017) Nursing skill mix in European hospitals: cross-sectional study of the association with mortality, patient ratings, and quality of care. BMJ Qual Saf 26:559–568

Aiken LH, Sermeus W, Van den Heede K, Sloane DM, Busse R, McKee M, Bruyneel L, Rafferty AM, Griffiths P, Moreno-Casbas MT, Tishelman C, Scott A, Mrzostek T, Kinnunen J, Schwendimann R, Heinen M, Zikos D, Sjetne IS, Smith HL, Kutney-Lee A (2012) Patient safety, satisfaction, and quality of hospital care: Cross sectional surveys of nurses and patients in 12 countries in Europe and the United States. BMJ 344:e1717

Aiken LH, Xue Y, Clarke SP, Sloane DM (2007) Supplemental nurse staffing in hospitals and quality of care. J Nurs Adm 37(7–8):335–342

Ang BY, Lam SWS, Pasupathy Y, Ong MEH (2018) Nurse workforce scheduling in the emergency department: a sequential decision support system considering multiple objectives. J Nurs Manag 26(4):432–441

Bae SH, Mark B, Fried B (2010) Use of temporary nurses and nurse and patient safety outcomes in acute care hospital units. Health Care Manage Rev 35(4):333–344

Ball JE, Bruyneel L, Aiken LH, Sermeus W, Sloane DM, Rafferty AM, Lindqvist R, Tishelman C, Griffiths P (2018) Post-operative mortality, missed care and nurse staffing in nine countries: across-sectional study. Int J Nurs Stud 78:10–15

Benzaid M, Lahrichi N, Rousseau LM (2020) Chemotherapy appointment scheduling and daily outpatient–nurse assignment. Health Care Manag Sci 23(1):34–50

Bond C., Raehl CL, Pitterle ME, Franke T (1999) Health care professional staffing, hospital characteristics, and hospital mortality rates. Pharmacother: J Human Pharmacol Drug Ther 19(2):130–138

Bretthauer KM, Heese HS, Pun H, Coe E (2011) Blocking in healthcare operations: A new heuristic and an application. Prod Oper Manag 20(3):375–391

Brooks Carthon JM, Hatfield L, Brom H, Houton M, Kelly-Hellyer E, Schlak A, Aiken LH (2021) System-level improvements in work environments lead to lower nurse burnout and higher patient satisfaction. J Nurs Care Qual 36(1):7–13

Bureau of Labor Statistics, U.S. Department of Labor (2022) Occupational outlook handbook, registered nurses. http://www.bls.gov/ooh/healthcare/registered-nurses.htm . Accessed 15 Sept 2022

Burke EK, Curtois T (2014) New approaches to nurse rostering benchmark instances. Eur J Oper Res 237(1):71–81

Campbell GM (1999) Cross-utilization of workers whose capabilities differ. Manage Sci 45(5):722–732

Chang AM, Cohen D, Lin A, Augustine J, Handel D, Howell E, Kim H, Pines J, Schuur JD, McConnell KJ, Sun BC (2018) Hospital strategies for reducing emergency department crowding: a mixed-methods study. Ann Emerg Med 71(4):497-505.e4

Cheang B, Li H, Lim A, Rodrigues B (2003) Nurse rostering problems––a bibliographic survey. Eur J Oper Res 151(3):447–460

Chhatwal J, Alagoz O, Burnside ES (2010) Optimal breast biopsy decision-making based on mammographic features and demographic factors. Oper Res 58(6):1577–1591

Cho D, Cattani K (2019) The patient patient: the performance of traditional versus open-access scheduling policies. Decis Sci 50(4):756–785

Cho E, Sloane DM, Kim EY, Kim S, Choi M, Yoo IY, Lee HS, Aiken LH (2015) Effects of nurse staffing, work environments, and education on patient mortality: An observational study. Int J Nurs Stud 52(2):535–542

Cho SH, Ketefian S, Barkauskas VH, Smith DG (2003) The effects of nurse staffing on adverse events, morbidity, mortality, and medical costs. Nurs Res 52(2):71–79

Dall TM, Chen YJ, Seifert RF, Maddox PJ, Hogan PF (2009) The economic value of professional nursing. Med Care 47(1):97–104

Daskin MS, Dean LK (2005) Location of health care facilities. Operations research and health care: a handbook of methods and applications. In: Brandeau M, Sainfort F, Pierskalla WP (eds). Kluwer Academic Publishers. Chapter 3, pp 43–76

De Véricourt F, Jennings OB (2011) Nurse staffing in medical units: a queueing perspective. Oper Res 59(6):1320–1331

Denton BT, Miller AJ, Balasubramanian HJ, Huschka TR (2010) Optimal allocation of surgery blocks to operating rooms under uncertainty. Oper Res 58(4):802–816

Dobson G, Hasija S, Pinker EJ (2011) Reserving capacity for urgent patients in primary care. Prod Oper Manag 20(3):456–473

Dobson G, Lee HH, Pinker E (2010) A model of ICU bumping. Oper Res 58(6):1564–1576

Dobson G, Pinker E, Van Horn RL (2009) Division of labor in medical office practices. Manuf Serv Oper Manag 11(3):525–537

Easton FF (2011) Cross-training performance in flexible labor scheduling environments. IIE Transaction 43(8):589–603

Faridimehr S, Venkatachalam S, Chinnam RB (2021) Managing access to primary care clinics using scheduling templates. Health Care Manag Sci 24(3):482–498

Fagerström L, Kinnunen M, Saarela J (2018) Nursing workload, patient safety incidents and mortality: an observational study from Finland. BMJ Open 8(e016367):1–10

Google Scholar  

Gnanlet A, Gilland WG (2009) Sequential and simultaneous decision making for optimizing health care resource flexibilities. Decis Sci 40(2):295–326

Gnanlet A, Gilland WG (2014) Impact of productivity on cross-training configurations and optimal staffing decisions in hospitals. Eur J Oper Res 238(1):254–269

Green, L.V. (2005). Capacity Planning and Management in Hospitals. In: Brandeau, M.L., Sainfort, F., Pierskalla, W.P. (eds) Operations Research and Health Care. International Series in Operations Research & Management Science, vol 70. Springer, Boston, MA. pp 15-41

Green LV, Savin S (2008) Reducing delays for medical appointments: A queueing approach. Oper Res 56(6):1526–1538

Griffiths P, Ball J, Bloor K, Böhning D, Briggs J, Dall’Ora C, De Iongh A, Jones J, Kovacs C, Maruotti A, Meredith P, Prytherch D, Saucedo AR, Redfern O, Schmidt P, Sinden N, Smith G (2018) Nurse staffing levels, missed vital signs and mortality in hospitals: Retrospective longitudinal observational study. Health Serv Deliv Res 6(38)

Gupta D, Denton B (2008) Appointment scheduling in health care: Challenges and opportunities. IIE Transaction 40(9):800–819

Gurses AP, Carayon P, Wall M (2009) Impact of performance obstacles on intensive care nurses’ workload, perceived quality and safety of care, and quality of working life. Health Serv Res 44(2p1):422–443

Halawa F, Madathil SC, Gittler A, Khasawneh MT (2020) Advancing evidence-based healthcare facility design: A systematic literature review. Health Care Manag Sci 23(3):453–480

Halm EA, Lee C, Chassin MR (2002) Is volume related to outcome in health care? A systematic review and methodologic critique of the literature. Ann Intern Med 137(6):511–520

Hopp WJ, Tekin E, Van Oyen MP (2004) Benefits of skill chaining in serial production lines with cross-trained workers. Manage Sci 50(1):83–98

Hugonnet S, Chevrolet J, Pittet D (2007) The effect of workload on infection risk in critically ill patients. Crit Care Med 35(1):76–81

Institute of Medicine (2001) Crossing the quality chasm: a new health system for the 21st century. National Academies Press, Washington D.C

Iwashyna TJ, Kramer AA, Kahn JM (2009) Intensive care unit occupancy and patient outcomes. Crit Care Med 37(5):1545–1557

Jones CB (2005) The costs of nurse turnover, part 2: application of the nursing turnover cost calculation methodology. J Nurs Adm 35(1):41–49

Jordan WC, Inman RR, Blumenfeld DE (2004) Chained cross-training of workers for robust performance. IIE Trans 36(10):953–967

Kahn JM, Goss CH, Heagerty PJ, Kramer AA, O’Brien CR, Rubenfeld GD (2006) Hospital volume and the outcomes of mechanical ventilation. N Engl J Med 355(1):41–50

Kanai Y, Takagi H (2021) Markov chain analysis for the neonatal inpatient flow in a hospital. Health Care Manag Sci 24(1):92–116

Kane RL, Shamliyan TA, Mueller C, Duval S, Wilt TJ (2007) The association of registered nurse staffing levels and patient outcomes: systematic review and meta-analysis. Med Care 45(12):1195–1204

Kosel K, Olivo T (2002) The business case for workforce stability. VHA Research Series 7:1–16

Lankshear AJ, Sheldon TA, Maynard A (2005) Nurse staffing and healthcare outcomes: a systematic review of the international research evidence. Adv Nurs Sci 28(2):163–174

Lee DKK, Zenios SA (2009) Optimal capacity overbooking for the regular treatment of chronic conditions. Oper Res 57(4):852–865

Legrain A, Omer J, Rosat S (2020) An online stochastic algorithm for a dynamic nurse scheduling problem. Eur J Oper Res 285(1):196–210

Lim GJ, Mobasher A, Côté MJ (2012) Multi-objective nurse scheduling models with patient workload and nurse preferences. Management 2(5):149–160

Lin H (2014) Revisiting the relationship between nurse staffing and quality of care in nursing homes: An instrumental variables approach. J Health Econ 37:13–24

Lin YK, Chou YY (2020) A hybrid genetic algorithm for operating room scheduling. Health Care Manag Sci 23(2):249–263

Lopez V, Anderson J, West S, Cleary M (2022) Does the COVID-19 pandemic further impact nursing shortages? Issues Ment Health Nurs 43(3):293–295

Ma C, McHugh MD, Aiken LH (2015) Organization of hospital nursing and 30-day readmissions in Medicare patients undergoing surgery. Med Care 53(1):65–70

Mandelbaum A, Momcilovic P, Tseytlin Y (2012) On fair routing from emergency departments to hospital wards: QED queues with heterogeneous servers. Manage Sci 58(7):1273–1291

May JH, Spangler WE, Strum DP, Vargas LG (2011) The surgical scheduling problem: Current research and future opportunities. Prod Oper Manag 20(3):392–405

McCue M, Mark BA, Harless DW (2003) Nurse staffing, quality, and financial performance. J Health Care Finance 29(4):54–76

Mousavi H, Darestani SA, Azimi P (2021) An artificial neural network based mathematical model for a stochastic health care facility location problem. Health Care Manag Sci 24(3):499–514

Musy SN, Endrich O, Leichtle AB, Griffiths P, Nakas CT, Simon M (2020) Longitudinal study of the variation in patient turnover and patient-to-nurse ratio: Descriptive analysis of a Swiss University Hospital. J Med Internet Res 22(4):e15554

Needleman J, Buerhaus P, Mattke S, Stewart M, Zelevinsky K (2002) Nurse-staffing levels and the quality of care in hospitals. N Engl J Med 346(22):1715–1722

Needleman J, Buerhaus P, Pankratz VS, Leibson CL, Stevens SR, Harris M (2011) Nurse staffing and inpatient hospital mortality. N Engl J Med 364(11):1037–1045

Needleman J, Buerhaus PI, Stewart M, Zelevinsky K, Mattke S (2006) Nurse staffing in hospitals: Is there a business case for quality? Health Aff 25(1):204–211

Newhouse RP, Johantgen M, Pronovost PJ, Johnson E (2005) Perioperative nurses and patient outcomes—mortality, complications, and length of stay. AORN J 81(3):508–528

NSI Nursing Solutions, Inc. (2021) 2021 NSI national health care retention & rn staffing report. https://www.emergingrnleader.com/wp-content/uploads/2021/04/NSI_National_Health_Care_Retention_Report.pdf . Accessed 12 Mar 2023

Oakley D, Onggo BS, Worthington D (2020) Symbiotic simulation for the operational management of inpatient beds: model development and validation using Δ-method. Health Care Manag Sci 23(1):153–169

OECD/European Union (2014) Health at a glance: Europe 2014. OECD Publishing. https://doi.org/10.1787/health_glance_eur-2014-en

Olivares M, Terwiesch C, Cassorla L (2008) Structural estimation of the newsvendor model: an application to reserving operating room time. Manage Sci 54(1):41–55

Papanicolas I, Woskie LR, Jha AK (2018) Health care spending in the United States and other high-income countries. JAMA 319(10):1024–1039

Peelen L, de Keizer N, Peek N, Jan Scheffer G, van der Voort P, de Jonge E (2007) The influence of volume and intensive care unit organization on hospital mortality in patients admitted with severe sepsis: a retrospective multicentre cohort study. Crit Care 11(2):1–10

Phibbs C, Bartel A, Giovannetti B, Schmitt S, Stone P (2009) The impact of nurse staffing and contract nurses on patient outcomes: New evidence from longitudinal data. Working Paper, Columbia Business School

Phibbs CS, Baker LC, Caughey AB, Danielsen B, Schmitt SK, Phibbs RH (2007) Level and volume of neonatal intensive care and mortality in very-low-birth-weight infants. N Engl J Med 356(21):2165–2175

Pinker EJ, Shumsky RA (2000) The efficiency-quality trade-off of cross-trained workers. Manuf Serv Oper Manag 2(1):32–48

Pronovost PJ, Dang D, Dorman T, Lipsett PA, Garrett E, Jenckes M, Bass EB (2001) Intensive care unit nurse staffing and the risk for complications after abdominal aortic surgery. Eff Clin Pract 4(5):199–206

Rauner MS, Gutjahr WJ, Heidenberger K, Wagner J, Pasia J (2010) Dynamic policy modeling for chronic diseases: Metaheuristic-based identification of pareto-optimal screening strategies. Oper Res 58(5):1269–1286

Rerkjirattikal P, Huynh VN, Olapiriyakul S, Supnithi T (2020) A goal programming approach to nurse scheduling with individual preference satisfaction. Math Probl Eng 2020:2379091

Rothberg MB, Abraham I, Lindenauer PK, Rose DN (2005) Improving nurse-to-patient staffing ratios as a cost-effective safety intervention. Med Care 43(8):785–791

Schoenfelder J, Bretthauer KM, Wright PD, Coe E (2020) Nurse scheduling with quick-response methods: Improving hospital performance, nurse workload, and patient experience. Eur J Oper Res 283(1):390–403

Shehadeh KS, Padman R (2021) A distributionally robust optimization approach for stochastic elective surgery scheduling with limited intensive care unit capacity. Eur J Oper Res 290(3):901–913

Silber JH, Rosenbaum PR, McHugh MD, Ludwig JM, Smith HL, Niknam BA, Even-Shoshan O, Fleisher LA, Kelz RR, Aiken LH (2016) Comparison of the value of nursing work environments in hospitals across different levels of patient risk. JAMA Surg 151(6):527–536

Sims CE (2003) Increasing clinical, satisfaction, and financial performance through nurse-driven process improvement. J Nurs Adm 33(2):68–75

Sloane DM, Smith HL, McHugh MD, Aiken LH (2018) Effect of changes in hospital nursing resources on improvements in patient safety and quality of care: a panel study. Med Care 56(12):1001–1008

Spence Laschinger HK, Leiter MP (2006) The impact of nursing work environments on patient safety outcomes: the mediating role of burnout engagement. J Nurs Adm 36(5):259–267

Spetz J (2004) California’s minimum nurse-to-patient ratios: the first few months. J Nurs Adm 34(12):571–578

​Stanton MW, Rutherford MK (2004) Hospital nurse staffing and quality of care. Research in Action, Issue 14. Agency for Healthcare Research and Quality, Rockville, Maryland

Sturm H, Rieger MA, Martus P, Ueding E, Wagner A, Holderried M, Maschmann J (2019) Do perceived working conditions and patient safety culture correlate with objective workload and patient outcomes: A cross-sectional explorative study from a German university hospital. Plos One 14(1):e0209487 (1-19)

Theokary C, Ren ZJ (2011) An empirical study of the relations between hospital volume, teaching status, and service quality. Prod Oper Manag 20(3):303–318

Thompson S, Nunez M, Garfinkel R, Dean MD (2009) OR practice–-Efficient short-term allocation and reallocation of patients to floors of a hospital during demand surges. Oper Res 57(2):261–273

Tourangeau AE, Doran DM, Hall LMG, O’Brien Pallas L, Pringle D, Tu JV, Cranley LA (2007) Impact of hospital nursing care on 30-day mortality for acute medical patients. J Adv Nurs 57(1):32–44

Tsai SC, Yeh Y, Kuo CY (2021) Efficient optimization algorithms for surgical scheduling under uncertainty. Eur J Oper Res 293(2):579–593

U.S. General Accounting Office (GAO) (2009) Hospital emergency departments: crowding continues to occur, and some patients wait longer than recommended time frames. Report GAO-09-347

Valouxis C, Gogos C, Goulas G, Alefragis P, Housos E (2012) A systematic two phase approach for the nurse rostering problem. Eur J Oper Res 219(2):425–433

Volland J, Fügener A, Schoenfelder J, Brunner JO (2017) Material logistics in hospitals: a literature review. Omega 69:82–101

Wang WY, Gupta D (2011) Adaptive appointment systems with patient preferences. Manuf Serv Oper Manag 13(3):373–389

White DL, Froehle CM, Klassen KJ (2011) The effect of integrated scheduling and capacity policies on clinical efficiency. Prod Oper Manag 20(3):442–455

Wolbeck L, Kliewer N, Marques I (2020) Fair shift change penalization scheme for nurse rescheduling problems. Eur J Oper Res 284(3):1121–1135

Wright PD, Bretthauer KM (2010) Strategies for addressing the nursing shortage: Coordinated decision making and workforce flexibility. Decis Sci 41(2):373–401

Wright PD, Bretthauer KM, Côté MJ (2006) Reexamining the nurse scheduling problem: Staffing ratios and nursing shortages. Decis Sci 37(1):39–70

Wynendaele H, Willems R, Trybou J (2019) Systematic review: Association between the patient–nurse ratio and nurse outcomes in acute care hospitals. J Nurs Manag 27(5):896–917

Yankovic N, Green LV (2011) Identifying good nursing levels: A queuing approach. Oper Res 59(4):942–955

Yu X, Bayram A (2021) Managing capacity for virtual and office appointments in chronic care. Health Care Manag Sci 24(4):742–767

Zhang X, Tai D, Pforsich H, Lin VW (2018) United States registered nurse workforce report card and shortage forecast: a revisit. Am J Med Qual 33(3):229–236

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David D. Cho

Operations and Decision Technologies Department, Kelley School of Business, Indiana University, Bloomington, IN, 47405, USA

Kurt M. Bretthauer

Health Care Operations / Health Information Management, University of Augsburg, 86159, Augsburg, Germany

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All authors contributed to the study conception and design. Material preparation, data collection, analysis, and manuscript writing were performed by David D. Cho, Kurt M. Bretthauer, and Jan Schoenfelder. All authors read and approved the final manuscript.

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1.1 Three case study hospitals

We collected nursing data from three hospitals in the United States. One is located in California and two are located in Indiana. They range in size from 350 to 550 beds. We obtained information on nurse wages, shift types, staff size and mix, shift preferences and availability, patient-to-nurse ratios, and limited bed demand data. Note that detailed and extensive historical patient flow and demand data were not available. Due to the limited bed demand data, we also use data from the American Hospital Association and California Office of Statewide Health Planning and Development to estimate inpatient demand and create hospital size categories, as described in the next subsection. The three hospitals differ in size and nurse wages. Table 5 summarizes the data.

1.2 American hospital association (AHA) data

In addition to the three case study hospitals, we acquired 2015 AHA Annual Survey data from California, New York, and Texas for our numerical experiments. From the dataset, we consider hospitals with the primary service code of “general medical and surgical” and that are coded as either “nongovernment, not-for-profit” or “corporation-owned, for-profit”. We exclude hospitals that do not have any general medical and surgical adult beds. After filtering, the data set contains information on 493 hospitals across the three states of California, New York, and Texas.

Based on the 2015 AHA Annual Survey data, we created four hospital size categories, as shown in Table 6 . While the range of total facility inpatient days for category 3 is relatively wide, the impact of hospital size on the policy patient-to-nurse ratio is still captured effectively with the four categories, as shown by the results in Section  5.1 .

Figure  11 reports the proportion of general medical and surgical beds in the included hospitals according to the AHA data. The AHA data provides total hospital-wide inpatient days, but not unit-specific inpatient days, which is what we need. Therefore, based on Fig.  11 , we estimate that the inpatient days for med/surg units are around 50–80% of the total hospital-wide inpatient days.

Distribution of medical and surgical bed proportion for hospitals in AHA data set

1.3 California office of statewide health planning and development (OSHPD) data

To further support our estimate of med/surg inpatient days, we also acquired data from the “2014–2015 Fiscal Year Hospital Annual Financial Disclosure Report” provided by California’s Office of Statewide Health Planning and Development (OSHPD). While this data set is limited to hospitals in California, it includes unit-specific information regarding beds and patient (census) days. After applying the identical filter as used for the AHA data set, the OSHPD data set provides information on 198 hospitals in California. Figure  12 shows that our assumption of inpatient days for the med/surg unit being around 50–80% of the total hospital-wide inpatient days is reasonable.

Distribution of medical and surgical patient days proportion for California hospitals in OSHPD data set

Appendix B. Limiting undesirable shifts for each nurse

In Section  5.3 , we minimize the total number of undesirable shifts without incurring any additional schedule costs, but we do not limit the number of undesirable shifts for each nurse. Thus, it is theoretically possible for the remaining undesirable shifts to be assigned disproportionately to a small number of nurses. While this was not a major issue for our numerical experiments in Section  5.3 due to the very low number of remaining undesirable shifts with the second objective function, we can also add constraints ( 27 ) and ( 28 ) that limit the number of undesirable shifts along with second objective function ( 23 ).

where \({\overline{US} }_{i}^{UN}\) and \({\overline{US} }_{i}^{FN}\) are upper limits on the number of undesirable shifts assigned to unit and float nurse \(i\) , respectively.

Because we still do not allow additional schedule costs, our optimal costs do not change in this case. Furthermore, we also do not observe any meaningful differences in total number of undesirable shifts compared to the results presented in Section  5.3 as long as \({\overline{US} }_{i}^{UN}\) and \({\overline{US} }_{i}^{FN}\) are not too low. We note that when the limit is too low (for example, 0 or 1 undesirable shift per nurse), the problem sometimes becomes unsolvable for policy PTN ratio of 4:1 due to the insufficient number of available and desirable shifts to stay under the policy PTN for every shift since we do not allow any increase in costs.

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Cho, D.D., Bretthauer, K.M. & Schoenfelder, J. Patient-to-nurse ratios: Balancing quality, nurse turnover, and cost. Health Care Manag Sci 26 , 807–826 (2023). https://doi.org/10.1007/s10729-023-09659-y

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Issue Date : December 2023

DOI : https://doi.org/10.1007/s10729-023-09659-y

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Evidence that Reducing Patient-to-Nurse Staffing Ratios Can Save Lives and Money

Having more nurses can increase patient safety and improve quality of care, yet hospitals often differ in the number of nurses they have per patient. A recent study, funded in part by NINR, examined variation in patient-to-nurse staffing in NY hospitals and its association with adverse outcomes (i.e., mortality and avoidable costs). Findings revealed that nurse staffing varied considerably across hospitals ranging from having 4.3 to 10.5 patients per nurse. Importantly, each additional patient per nurse increased the likelihood of death, length of hospital stays, and chances of being readmitted to the hospital within 30 days. The authors concluded that improving hospital nurse staffing would likely save thousands of lives per year, and that the associated cost would be offset by savings achieved by reducing hospital readmissions and length of hospital stays. This study provides important information for administrators and policymakers to consider when determining ways to improve healthcare.

Lasater KB, Aiken LH, Sloane DM, French R, Anusiewicz CV, Martin B, Reneau K, Alexander M, McHugh MD. Is hospital nurse staffing legislation in the public's interest? An observational study in New York State. Med Care. 2021 May 1;59(5):444-450. PMID: 33655903

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Introduction, declaration of conflicting interests.

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The effect of nurse-to-patient ratios on nurse-sensitive patient outcomes in acute specialist units: a systematic review and meta-analysis

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Andrea Driscoll, Maria J Grant, Diane Carroll, Sally Dalton, Christi Deaton, Ian Jones, Daniela Lehwaldt, Gabrielle McKee, Theresa Munyombwe, Felicity Astin, The effect of nurse-to-patient ratios on nurse-sensitive patient outcomes in acute specialist units: a systematic review and meta-analysis, European Journal of Cardiovascular Nursing , Volume 17, Issue 1, 1 January 2018, Pages 6–22, https://doi.org/10.1177/1474515117721561

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Nurses are pivotal in the provision of high quality care in acute hospitals. However, the optimal dosing of the number of nurses caring for patients remains elusive. In light of this, an updated review of the evidence on the effect of nurse staffing levels on patient outcomes is required.

To undertake a systematic review and meta-analysis examining the association between nurse staffing levels and nurse-sensitive patient outcomes in acute specialist units.

Nine electronic databases were searched for English articles published between 2006 and 2017. The primary outcomes were nurse-sensitive patient outcomes.

Of 3429 unique articles identified, 35 met the inclusion criteria. All were cross-sectional and the majority utilised large administrative databases. Higher staffing levels were associated with reduced mortality, medication errors, ulcers, restraint use, infections, pneumonia, higher aspirin use and a greater number of patients receiving percutaneous coronary intervention within 90 minutes. A meta-analysis involving 175,755 patients, from six studies, admitted to the intensive care unit and/or cardiac/cardiothoracic units showed that a higher nurse staffing level decreased the risk of inhospital mortality by 14% (0.86, 95% confidence interval 0.79–0.94). However, the meta-analysis also showed high heterogeneity (I 2 =86%).

Nurse-to-patient ratios influence many patient outcomes, most markedly inhospital mortality. More studies need to be conducted on the association of nurse-to-patient ratios with nurse-sensitive patient outcomes to offset the paucity and weaknesses of research in this area. This would provide further evidence for recommendations of optimal nurse-to-patient ratios in acute specialist units.

Over the past decade there has been a renewed focus on what constitutes an adequate level of nurse staffing. This is in part due to some spectacular failures that have occurred in care provision for hospital inpatients leading to loss of life. 1 , 2 Organisations across countries have adopted different approaches to managing the nursing workforce. In Victoria, Australia, and California, USA, standardised and mandatory nurse staffing levels have been in place for over a decade. In the UK and Ireland there are national nurse staffing recommendations, but these are not mandated by law. 3 – 5 Wales has a similar situation, they recently introduced the Nurse Staffing Levels Act 2016; however, there are no mandated nurse-to-patient ratios (NPRs) only recommendations to guide decisions about nurse staffing levels. 6 The notion of an optimal level of nurse staffing is somewhat controversial because there is no one-size-fits-all approach to assessing staffing levels. This lack of clarity is further aggravated by a lack of consensus about the most appropriate way of estimating the size and mix of nursing teams because all measurement approaches have limitations. 4 , 7

One of the challenges faced by managers responsible for staffing is finding a way to understand the influence of the multiple factors that make up each individual care environment which are likely to differ across organisations and countries. Donabedian grouped potential factors into three broad domains: structural factors (the people, paraphernalia and place that make up the healthcare delivery system); processes of care (how care is done through the interactions between health professionals and patients); and subsequent outcomes (the end results of the care that takes place in the context of the organisation). 8

To determine nurse staffing levels, managers need to understand the underlying determinants which are patient factors (patient nursing need according to acuity and dependency levels), ward factors (patient throughput) and nursing staff factors (number and skill level). 9 Findings from a systematic review and meta-analysis, now a decade old, reported a significant association between increased nursing staffing in hospitals and improved nurse-sensitive patients outcomes. 10 A more recent literature review by Penoyer found an association between nurse staffing levels and patient outcomes in the intensive care unit (ICU). 11 However, their review only included studies from 1998 to 2008. In light of this an updated literature review is warranted. This review will examine recently published studies investigating associations between nurse staffing levels and nurse-sensitive patient outcomes in acute specialist units.

To support the quality of the systematic review, a protocol was developed based on the PRISMA statement. 12 The review protocol was not registered.

Review objective

To identify studies conducted in acute specialist units, which examine the association between nurse staffing levels (NPRs) and nurse-sensitive patient outcomes (as defined below).

Definitions

Nurse-to-patient ratio.

NPRs are typically expressed in two ways: the number of nurses working per shift or over a 24 hour period divided by the number of beds occupied by a patient over the same time period; or the number of nursing hours per patient bed days (NHPPD). There are other more complex approaches to measure nurse staffing requirements but there is no single recommended approach. 3 Many of the studies included in this review have determined NPRs. A higher level of nursing staff indicates more nurses (or higher proportion of nurses) for assigned patients. Lower nurse staffing is defined as fewer nurses (or lower proportion) for the number of assigned patients. 11

Moreover, little is known about how nurse staffing levels are managed across hospitals in Europe. NPRs are easily and cheaply measured but it is a relatively blunt instrument that can function as one indicator, and can be triangulated with other measurement approaches to establish safe nurse staffing levels.

Nurse-sensitive patient outcome measures

The nurse-sensitive patient outcomes measures included in this study were based on adverse events from previous studies that have been sensitive to changes in nurse staffing. 10 , 13 The nurse-sensitive patient outcome measures we included were: mortality, failure to rescue (FTR), shock (including sepsis resuscitation), cardiac arrest, unplanned extubation, hospital acquired pneumonia, respiratory failure, surgical bleeding, heart failure/fluid overload, catheter-associated urinary tract infection, pressure sores, patient falls, nosocomial bloodstream infection, medication error, length of stay, hospital-acquired sepsis, deep vein thrombosis, central nervous system complications, death, wound infection, pulmonary failure, and metabolic derangement.

Search strategy

The search strategy was developed by the research team with input from expert information technologists (see Supplementary Appendix 1 ). Electronic databases and grey literature were searched (Medline (OvidSP), Medline in Process (OvidSP), CINAHL (Cumulative Index to Nursing and Allied Health Literature) (EBSCO), PsycInfo (OvidSP), Embase (OvidSP), HMIC (Health Management Information Consortium) (OvidSP), Cochrane Database of Systematic Reviews, Web of Science; Science Citation Index Expanded (ISI Web of Knowledge), Web of Science; Social Sciences Citation Index (ISI Web of Knowledge), Web of Science; Conference Proceedings Citation Index – Science (ISI Web of Knowledge), Web of Science; Conference Proceedings Citation Index- Social Science and Humanities (ISI Web of Knowledge), Index to Theses, Proquest Dissertations and Theses). A combination of keywords was used and controlled vocabulary such as MeSH (medical subject headings) when available. Search terms included 18 terms on settings, i.e. coronary care, high dependency, critical care, intensive care, cardiac ward, intensive treatment unit and 17 terms relating to nursing or manpower or skill mix, i.e. nurse staffing, nurse ratio, nurse mix, nurse dose, nurse workload and 78 nurse-sensitive outcomes, i.e. wound infection, pulmonary failure, shock, pneumonia, length of stay, outcome, patient safety. The search was limited to English language and conducted from January 2006 to February 2017. Conference abstracts and reference lists of included studies were manually searched and additional studies identified.

Inclusion criteria

Following the literature search, a team of reviewers worked in pairs to screen titles and abstracts independently according to the inclusion criteria. Any disagreement between reviewers was resolved by a third reviewer. Studies that met the following inclusion criteria were included:

Patients admitted to acute specialist units (e.g. intensive therapy units/critical care/intensive care/coronary care, high dependency, and cardiothoracic surgery units, where a proportion of the nurses are required to have a postgraduate critical care qualification) with care provision for adults (over 18 years of age). Studies with a mixed population ward were included.

Investigating the effect of NPRs using either the number of nurses divided by the number of patients over 24 hours or the NHPPD.

Published from January 2006 to February 2017 in English.

Quantitative methodology.

at least one nurse-sensitive outcome such as mortality, FTR, shock, cardiac arrest, unplanned extubation, hospital acquired pneumonia, respiratory failure, surgical bleeding, heart failure/fluid overload/imbalance, urinary tract infection, pressure sores, patient falls, nosocomial bloodstream infection, medication error, pain control, unplanned readmission.

Data extraction

A tailor-made data extraction tool was developed a priori and piloted and refined.

The tool included six screening questions to ensure papers fit with the review inclusion criteria (see Supplementary Appendix 2 ). Information was also extracted from each study to record under the following headings: bibliographic details; setting/country; study design; outcomes, findings/conclusions and quality assessment.

Quality assessment

All included studies were assessed by the Newcastle–Ottawa scale (NOS) to determine the quality of non-randomised studies. 14 This tool was designed to facilitate the incorporation of quality assessment into the systematic review. This tool has been used in previous Cochrane reviews for assessment of risk of bias in non-randomised studies. The content validity and inter-rater reliability of this scale was previously established. The NOS consists of eight items: representativeness of cohort, selection of cohort, ascertainment of exposure, outcome of interest was not present at baseline, comparability of cohorts, assessment of outcome, length of follow-up and adequacy of follow-up. 14 Each item was awarded a ‘*’ for meeting the criterion. A study was also awarded an additional ‘*’ if the analysis was adjusted for potential confounding variables. The quality of each study was graded as low, medium or high according to the number of stars (*). The quality assessment was conducted independently by two reviewers. Disagreements were resolved by a third reviewer.

Statistical analysis

As this systematic review involved cross-sectional studies we used adjusted measures, as reported by authors, as the primary effect measures to control for confounding when it was available. Odds ratios (ORs) were used as an appropriate effect measure if available. Other effect measures were: hazard ratios or risk ratios.

A meta-analysis was conducted on homogenous studies using a random-effect model with inhospital mortality as the primary outcome. In studies where patient-to-nurse ratios were used, these were converted to NPRs by calculating the inverse ratio. The overall effect sizes will be presented in a forest plot. In studies in which a pooled meta-analysis was unable to be performed, a narrative analysis will be undertaken.

Clinical homogeneity was assessed in terms of study cohort, hospital units, diagnosis and risk of bias. The I 2 was also used to determine statistical heterogeneity. If I 2 is greater than 40% a random effects model will be used. A sensitivity analysis will also be conducted using a fixed effects model to determine if the conclusions were different.

Data analysis was conducted using Review Manager version 5.3. 15

We identified a total of 4472 studies from the literature search. After duplicates were removed, 3429 records were screened using title and abstract. Of these, we identified 196 full-text articles for retrieval. We included 35 articles in the final analysis (see Figure 1 ). Reasons for exclusion included research relating to neonates, non-acute settings, no NPRs and no nurse-sensitive patient outcomes being reported.

Flow diagram of study selection

The effect of nurse-to-patient ratios (NPRs) on inhospital mortality

Description of studies

All of the 35 papers were cross-sectional studies except for one point prevalence study. All of the studies had a large sample size derived from administrative datasets ( Table 1 ). Fourteen studies were conducted in the USA/Canada/Mexico, 17 studies in Europe, three studies in China and one in Thailand. In terms of study setting, 11 studies included patients throughout the hospital including critical care, 19 studies restricted their cohort to ICUs only (included cardiovascular patients), and five studies were in specialist cardiac units. 16 – 46

Characteristics of included studies

CI: confidence interval; CCU: critical care unit; DVT: deep vein thrombosis; ED: emergency department; HR: hazard ratio; ICU: intensive care unit; NHPPD: nursing hours per patient day; NPR: nurse-to-patient ratio; OR: odds ratio; PCI: percutaneous coronary intervention; RR: relative risk; VAP: ventilator-associated pneumonia.

Quality appraisal

The NOS consists of three principal domains: case selection, representativeness of cohorts, and measurement of outcome. 14 All 35 cohort studies met the criterion for representativeness of cohort selection, five studies received one star and 24 studies received two stars for comparability of cohorts, 24 studies discussed outcome assessment and 35 studies defined their length of follow-up ( Table 2 ). 16 – 46

Summary of NOS quality assessment: cross-sectional studies

Also includes controlling for potential confounders.

Evidence quality:

Low: downgrading from moderate to low based on design or lack of information in report.

Moderate: study met selection criteria (4 stars), comparability (1 star and upgraded a level for 2 stars), and outcome assessment.

High: upgrading from moderate to high based on comparability of 2 stars.

There were 24 studies that rated highly on the NOS for assessing the quality of non-randomised trials ( Table 2 ). All of these studies controlled for several confounding factors in either their methodology or data analysis. The majority of these studies adjusted for age, comorbidities and hospital characteristics as potential confounders. Seven studies were rated as low quality mainly due to the lack of comparability of cohorts.

Nurse-to-patient ratios

Various approaches were used to measure NPRs. Schwab et al. calculated the NPR per shift (number of nurses per day/three (per shift)/number of patients per day) using monthly census data. 38 Other studies used similar approaches. 19 , 25 , 26 , 31 , 33 , 37 Several authors provided less detail about how the NPR was calculated. 18 , 28 , 30 , 32 Valentin et al. calculated both the NPR by shift and the occupancy rate (maximum number of occupied beds divided by allocated beds), NPR for each shift in each unit and the relative turnover (number of admitted and discharged patients divided by the number of unit beds). 43 Cho et al. calculated the NPR based on the bed occupancy rate and then categorised it into grades. 21 Grade 1 indicated the number of beds per nurse was less than 0.5 up to grade 9 when the ratio was greater than 2.0. In Cho et al., 20 the ratio of bed occupancy rate to the number of full-time equivalent (FTE) nurses was used for calculation. This bed occupancy rate was extracted from the ICU survey data over a 3-month period. Tourangeau et al. calculated the ‘nursing staff dose’ rather than the NPR. 42 This was calculated as the total nursing worked hours divided by the sum of weighted patient cases discharged from each hospital.

Stone et al. calculated the NHPPD from payroll and ICU census data. 41 Diya et al. 22 calculated the NHPPD but did not stipulate how this was calculated. Van den Heede and colleagues 44 , 45 calculated the NHPPD daily for each ward. It was based on daily ward census data. A similar approach was adopted by Shuldham et al. 40 and Hart and Davis 23 both of whom made the distinction between the numbers of hours worked by permanent staff versus temporary staff. Adjustment for staff sick leave and annual leave was not always accounted for, suggesting that staffing ratios may have been overestimated. 16 Sometimes day-to-day staffing levels were unobtainable in which case a proxy of the highest NPR in a 24-hour period was used. 17

Nurse-sensitive outcomes

There were 19 studies that examined mortality. Thirteen studies had a primary outcome of inhospital mortality, one study examined 28-day mortality and five studies examined 30-day mortality. Of the 19 studies, 10 were conducted in ICUs, two studies in an acute cardiac unit, two in the emergency department and seven studies recruited patients throughout the hospital regardless of unit including ICU/critical care units (CCUs). Six studies reported ORs on all-cause inhospital mortality of 175,755 patients admitted to ICUs and/or cardiac/cardiothoracic units. 20 , 21 , 29 , 31 , 37 , 46 A meta-analysis was conducted on the six studies using a random effects model. The pooled analysis showed that a higher level of nurse staffing decreased the risk of inhospital mortality by 14%, (95% confidence interval (CI) 0.79–0.94). However, the meta-analysis also showed high heterogeneity (I 2 =86%), with one study showing a wide confidence interval. The pooled analysis was influenced by four of the six studies each ranging from 21% to 24%. 20 , 29 , 31 , 46

As the I 2 was greater than 40% a sensitivity analysis was performed using a fixed effects model. The pooled analysis of the fixed effects model (OR 0.90, 95% CI 0.88–0.92) was similar to the random effects model (OR 0.86, 95% CI 0.79–0.94) despite the high heterogeneity.

Other nurse-sensitive outcomes

Fifteen studies examined the effect of NPRs on nurse-sensitive outcomes other than mortality. Three studies examined mortality as a primary end point and nurse-sensitive outcomes as their secondary end point. 39 , 41 , 44 However, none of the studies combined all of the nurse-sensitive patient outcomes, rather they typically selected three or four outcome measures. Three studies conducted in CCUs, reported an association between a higher number of NHPPD 35 , 41 or a higher level of nurse staffing 33 resulting in a reduction in events for nurse-sensitive patient outcomes. Another study reported on medication errors and found that as the number of nurses decreased, the OR for parenteral medication errors increased, some of which caused harm and death. 43 A higher level of nurse staffing in CCUs was associated with a lower incidence of pressure ulcer development, 23 , 41 use of physical restraints 16 and incidence of nosocomial infection 25 , 38 , 41 including late onset ventilator assisted pneumonia. 26 In the emergency department, a higher level of nurse staffing increased the prescribing of aspirin on arrival to the emergency department and a percutaneous coronary intervention within 90 minutes of arrival. 27

Evidence was less clear in studies in which results were combined across setting such as high dependency and CCUs. One such study examined the association between NPRs and a range of nurse-sensitive patient outcomes; there were few significant results. 40 However, as the number of permanent staff compared to temporary staff increased, the rates of sepsis decreased. 40 Hart and Davis found that the use of agency staff was associated with a higher incidence of hospital acquired pressure ulcers but only in medical surgical units rather than CCUs and coronary care settings. 23 A statistically significant association was also reported between a higher level of nurse staffing on the ward and CCU settings and lower rates of FTR. 35 Three studies reported no association between NPRs and nurse-sensitive patient outcomes, after adjusting for confounding variables. 17 , 30 , 44 Merchant et al. reported no association between NPRs and inhospital cardiac arrests rates. 30 Similarly Blot et al. reported no association between NPRs and ventilator-associated pneumonia, after adjusting for confounding variables. 17 Due to the heterogeneity in outcome measures no meta-analysis was performed.

This analysis found that a higher level of nurse staffing was associated with a decrease in the risk of inhospital mortality (OR 0.86, 95% CI 0.79–0.94) and nurse-sensitive outcomes. Due to the heterogeneity between studies, particularly in NPRs, no recommendation can be made regarding the optimal ratio required to improve patient outcomes. However, studies do report the higher the level of nurse staffing, the greater the reduction in inhospital mortality. Unfortunately, all of these studies were cross-sectional so no causal relationship can be determined. This systematic review builds on work conducted previously by Kane et al. 10 who found a higher level of nurse staffing was associated with a lower mortality in ICUs (OR 0.91, 95% CI 0.86–0.96), surgical wards (OR 0.84, 95% CI 0.8–0.89) and medical wards (OR 0.94, 95% CI 0.94–0.95) per additional 1.0 FTE nurse per patient day. 10 Our meta-analysis found a decrease in risk of 14% in inhospital mortality for every additional one decrease in patient load over 24 hours. All of the studies included in the meta-analysis rated high in the NOS quality assessment tool.

We also examined the effect of NPRs on nurse-sensitive patient outcomes. There was a large degree of heterogeneity in the type of nurse-sensitive patient outcomes that were measured as an end point so no meta-analysis was conducted. Park et al. examined the effect of nurse staffing and FTR rates. 35 FTR rates were defined as mortality after an adverse event associated with post-surgical complications. Park et al. analysed data from an administrative dataset of 159 non-ICUs and 158 ICUs from 42 hospitals. 35 In ICUs, they found a higher number of NHPPD was associated with a lower FTR rate (OR −0.022, 95% CI −0.39 to −0.005 (adjusted)). 35 Stone et al. also examined the effect of NPRs on nurse-sensitive outcomes. 41 These outcomes included: central line bloodstream infections, ventilator-assisted pneumonia, catheter-associated urinary tract infection, 30-day mortality, and the presence of decubitus pressure ulcers. Their sample consisted of 15,846 patients from 51 ICUs in 31 hospitals. Stone et al. found that patients cared for with a higher number of NHPPD were 68% less likely to experience bloodstream infections (95% CI 0.15–0.17), 79% less likely to experience pneumonia (95% CI 0.08–0.53) and there was a 31% reduction in risk for a decubitus pressure ulcer (95% CI 0.49–0.98). 41 Cardiac outcomes were also improved with a higher level of nurse staffing. Every 10% increase in the number of nurses was associated with a 7.1% increase in prescribing of aspirin on arrival and a 6.3% decrease in time for a percutaneous coronary intervention within 90 minutes of arriving in hospital. 27

O’Brien-Pallas et al. investigated the association of NPRs with nurse-sensitive patient outcomes. 33 Their outcomes included: deep vein thrombosis, pressure ulcers, falls with injury, medical errors with consequences, pneumonia, catheter-associated urinary tract infection and wound infections. O’Brien-Pallas et al. analysed an administrative dataset of 1230 patients from 24 cardiac and cardiovascular units from six hospitals. 33 They calculated the NPR as the average number of patients cared for daily by a nurse on day shift during the data collection period. They found that for every additional patient per nurse, patients were 22% less likely to experience ‘excellent or good quality care’ and 35% more likely to experience a longer than expected length of stay. 33

Limitations/weakness of the evidence base

The results of this systematic review and meta-analysis should be interpreted with caution. There were several limitations associated with the review. Several studies combined patients from non-specialist units with special units, which may have skewed the results. Stone et al. conducted a separate analysis for ICU and non-ICU units. 41 They found that in non-ICUs, NPRs were not statistically associated with the rate of nurse-sensitive patient outcomes. However, there was a reduction in the rate of nurse-sensitive patient outcomes in patients in an ICU with a higher level of nurse staffing.

There was also a large degree of heterogeneity in how the NPRs were calculated. For example, Perez et al. did not stipulate how they calculated the NPR, 36 Van Den Heede and colleagues calculated the number of NHPPD 44 , 45 and Cho and colleagues calculated the number of patients per bed to total FTE. 20 , 21

This systematic review found that there may be an association between a higher level of nurse staffing and improved patient outcomes. For every increase of one nurse, patients were 14% less likely to experience inhospital mortality.

More studies need to be conducted on the association of NPRs with nurse-sensitive patient outcomes. However, there needs to be greater homogeneity in the nurse-sensitive end points measured and the calculation of the NPR. Such metrics should not be used in isolation but can contribute to a ‘triangulated’ approach to the decision-making process about safe and sustainable nurse staffing levels.

The authors declare that there is no conflict of interest.

This review was supported by the Council of Cardiovascular Nursing and Allied Professionals (CCNAP) and the European Society of Cardiology (ESC).

Andrea Driscoll was supported by a Heart Foundation Future Leader fellowship 100472 from the National Heart Foundation of Australia, Melbourne, Australia.

A higher level of nurse staffing will lower the risk of inhospital mortality. For every increase of one nurse, patients were 14% less likely to experience inhospital mortality. In addition to nurse-patient ratios, it is also important to incorporate skill mix within a critical care unit particularly when planning workforce shifts.

Patients will also be less likely to experience an adverse event in units with a high nurse-to-patient ratio. This has important implications for clinical practice and the optimisation of patient outcomes.

These studies highlight the need for some agreement, at an international level, about the most appropriate way to measure nurse staffing levels. For many countries facing financial constraints in healthcare delivery complex and expensive techniques to address this challenge are unlikely to be adopted.

The Mid Staffordshire NHS Foundation Trust Public Inquiry . Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry: Executive Summary . London : Stationery Office , 2013 .

Google Scholar

Google Preview

Grey E , Kennedy I   The Report of the Independent Review of Children’s Cardiac Services in Bristol . London : NHS England , 2016 .

Royal College of Nursing . Guidance on safe nurse staffing levels in the UK . London : Royal College of Nursing (RCN), 2010 .

National Quality Board . Supporting NHS providers to deliver the right staff, with the right skills, in the right place at the right time, safe sustainable and productive staffing . National Quality Board (NQB) , 2016 .

Department of Health . Interim Report and Recommendations by the Taskforce on Staffing and Skill Mix for Nursing on a Framework for Safe Nurse Staffing and Skill Mix in General and Specialist Medical and Surgical Care Settings in Adult Hospitals in Ireland . London : Department of Health , 2016 .

Welsh Government . Nurse Staffing Levels Act 2016 . Cardiff : Welsh Government , 2016 .

Hurst K   Selecting and applying methods for estimating the size and mix of nursing teams . Leeds : Nuffield Institute for Health , 2003 .

Donabedian A   Structural attributes of care setting, processes of care and outcomes . JAMA: Journal of the American Medical Association   1988 ; 260 : 1743 – 1748 .

National Institute for Health and Care Excellence . Safe staffing for nursing in adult in-patient wards in acute hospitals: safe staffing guideline 1 . London : National Institute for Health and Care Excellence (NICE) , 2014 .

Kane R L , Shamliyan T A , Mueller C et al.  . The association of registered nurse staff levels and patient outcomes: systematic review and meta-analysis . Med Care   2007 ; 45 : 1195 – 1204 .

Penyoyer D A   Nurse staffing and patient outcomes in critical care: a concise review . Crit Care Med   2010 ; 38 : 1521 – 1528 .

Moher D , Liberati A , Tetzlaff J et al.  .; The PRISMA Group . Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement . PLoS Med   2009 ; 6 : e1000097 .

Needleman J , Buerhaus P , Mattke S et al.  . Nurse-staffing levels and the quality of care in hospitals . N Engl J Med   2002 ; 346 : 1715 – 1722 .

Wells G A , Shea B , O’Connell D et al.  . The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses . Ottawa : Ottawa Hospital Research Institute . 2014 . http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp ( accessed 6 July 2017 ).

Cochrane Collaboration . Review Manager (RevMan) (Version 5.4) . Copenhagen : Nordic Cochrane Centre . 2016 . http://ims.cochrane.org/revman ( accessed 6 July 2017 ).

Benbenbishty J , Adam S , Endacott R   Physical restraint use in intensive care units across Europe: the PRICE study . Intens Crit Care Nurs   2010 ; 26 : 241 – 245 .

Blot S I , Serra M L , Koulenti D et al.  . Patient to nurse ratio and risk of ventilator-associated pneumonia in critically ill patients . Am J Crit Care . 2011 ; 20 : e1 – e9 .

Checkley W , Martin G S , Brown S M et al.  . Structure, process and annual intensive care unit mortality across 69 centers: United States Critical Illness and Injury Trials Group Critical Illness Outcomes Study (USCIITG-CIOS) . Crit Care Med   2014 ; 42 : 344 – 356 .

Chittawatanarat K , Permpikul C , Chaiwat O et al.  . Effects of ICU characters, human resources and workload to outcome indicators in thai ICUs: the results of ICU-RESOURCE i study . J Med Assoc Thailand   2014 ; 97 ( 1   Suppl. 1 ): S22 – S30 .

Cho S H , Hwang J H , Kim J   Nurse staffing and patient mortality in intensive care units . Nurs Res   2008 ; 57 : 322 – 330 .

Cho S H , Yun S C   Bed-to-nurse ratios, provision of basic nursing care, and in-hospital and 30-day mortality among acute stroke patients admitted to an intensive care unit: Cross-sectional analysis of survey and administrative data . Int J Nurs Studies   2009 ; 46 : 1092 – 1101 .

Diya L , Van den Heede K , Sermeus W et al.  . The relationship between in-hospital mortality, readmission into the intensive care nursing unit and/or operating theatre and nurse staffing levels . J Adv Nurs   2012 ; 68 : 1073 – 1081 .

Hart P , Davis N   Effects of nursing care and staff skill mix on patient outcomes within acute care nursing units . J Nurs Care Quality   2011 ; 26 : 161 – 168 .

He J , Almenoff P L , Keighley J et al.  . Impact of Patient-Level Risk Adjustment on the Findings About Nurse Staffing and 30-Day Mortality in Veterans Affairs acute care hospitals . Nurs Res   2013 ; 62 : 226 – 232 .

Hugonnet S , Chevrolet J-C , Pittet D   The effect of workload on infection risk in critically ill patients . Crit Care Med   2007 ; 35 : 76 – 81 .

Hugonnet S , Uckay I , Pittet D   Staffing level: a determinant of late-onset ventilator-associated pneumonia . Crit Care   2007 ; 11 : R80 .

Johansen M L , de Cordova P B , Cimiotti J P   The implications of nurse resources on cardiac care in the emergency department . Appl Nurs Res   2015 ; 28 : 210 – 212 .

Kim J H , Hong S-K , Kim K C et al.  . Influence of full-time intensivist and the nurse-to-patient ratio on the implementation of severe sepsis bundles in Korean intensive care units . J Crit Care   2012 ; 27 : 414.e11 – 414e21 .

McHugh M D , Rochman M F , Sloane D M et al.  . Better nurse staffing and nurse work environments associated with increased survival of in-hospital cardiac arrest patients . Med Care   2016 ; 54 : 74 – 80 .

Merchant R M , Yang L , Becker L B et al.  . Variability in case-mix adjusted in-hospital cardiac arrest rates . Med Care   2012 ; 50 : 124 – 130 .

Metnitz B , Metnitz PG H , Bauer P et al.  . Patient volume affects outcome in critically ill patients . Wiener Klin Wochenschr   2009 ; 121 : 34 – 40 .

Neuraz A , Guerin C , Payet C et al.  . Patient Mortality is Associated with Staff Resources and Workload in the ICU: a multicenter observational study . Crit Care Med   2015 ; 43 : 1587 – 1594 .

O’Brien-Pallas L , Li X M , Wang S et al.  . Evaluation of a patient care delivery model: system outcomes in acute cardiac care . Can J Nurs Res   2010 ; 42 : 98 – 120 .

Ozdemir B A , Sinha S , Karthikesalingam A et al.  . Mortality of emergency general surgical patients and associations with hospital structures and processes . Br J Anaesth   2016 ; 116 : 54 – 62 .

Park S H , Blegen M A , Spetz J et al.  . Patient turnover and the relationship between nurse staffing and patient outcomes . Res Nurs Health   2012 ; 35 : 277 – 288 .

Perez A , Dennis R J , Rondon M A et al.  . A Colombian survey found intensive care mortality ratios were better in private vs. public hospitals . J Clin Epidemiol   2006 ; 59 : 94 – 101 .

Sakr Y , Moreira C L , Rhodes A et al.  . The impact of hospital and ICU organizational factors on outcome in critically ill patients: results from the Extended Prevalence of Infection in Intensive Care Study . Crit Care Med   2015 ; 43 : 519 – 526 .

Schwab F , Meyer E , Geffers C et al.  . Understaffing, overcrowding, inappropriate nurse:ventilated patient ratio and nosocomial infections: which parameter is the best reflection of deficits?   J Hosp Infect   2012 ; 80 : 133 – 139 .

Sheetz K H , Dimick J B , Ghaferi A A   Impact of hospital characteristics on failure to rescue following major surgery   Ann Surg   2016 ; 263 : 692 – 697 .

Shuldham C , Parkin C , Firouzi A et al.  . The relationship between nurse staffing and patient outcomes: a case study . Int J Nurs Studie s 2009 ; 46 : 986 – 992 .

Stone P W , Mooney-Kane C , Larson E L et al.  . Nurse working conditions and patient safety outcomes . Med Care   2007 ; 45 : 571 – 578 .

Tourangeau A E , Doran D M , Hall L M et al.  . Impact of hospital nursing care on 30-day mortality for acute medical patients . J Adv Nurs   2007 ; 57 : 32 – 44 .

Valentin A , Capuzzo M , Guidet B et al.  . Errors in administration of parenteral drugs in intensive care units: multinational prospective study . BMJ   2009 ; 338 : 928 – 931 .

Van den Heede K , Sermeus W , Diya L et al.  . Nurse staffing and patient outcomes in Belgian acute hospitals: cross-sectional analysis of administrative data . Int J Nurs Studies   2009 ; 46 : 928 – 839 .

Van den Heede K , Lesaffre E , Diya L et al.  . The relationship between inpatient cardiac surgery mortality and nurse numbers and educational level: analysis of administrative data . Int J Nurs Studies   2009 ; 46 : 796 – 803 .

West E , Barron D N , Harrison D et al.  . Nurse staffing, medical staffing and mortality in intensive care: an observational study . Int J Nurs Studies   2014 ; 51 : 781 – 794 .

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Nurse staffing and nurse outcomes: A systematic review and meta-analysis

Affiliations.

• 1 College of Nursing, Ewha Womans University, Seoul, Republic of Korea.
• 2 College of Nursing, Daegu Catholic University, Daegu, Republic of Korea.
• 3 College of Nursing, Ewha Womans University, Seoul, Republic of Korea. Electronic address: [email protected].
• PMID: 29685321
• DOI: 10.1016/j.outlook.2017.12.002

Background: A great number of studies have been conducted to examine the relationship between nurse staffing and patient outcomes. However, none of the reviews have rigorously assessed the evidence about the effect of nurse staffing on nurse outcomes through meta-analysis.

Purpose: The purpose of this review was to systematically assess empirical studies on the relationship between nurse staffing and nurse outcomes through meta-analysis.

Methods: Published peer-reviewed articles published between January 2000 and November 2016 were identified in CINAHL, PubMed, PsycINFO, Cochrane Library, EBSCO, RISS, and DBpia databases.

Findings: This meta-analysis showed that greater nurse-to-patient ratio was consistently associated with higher degree of burnout among nurses (odds ratio: 1.07; 95% confidence interval [CI]: 1.04-1.11), increased job dissatisfaction (odds ratio: 1.08; 95% CI: 1.04-1.11), and higher intent to leave (odds ratio: 1.05; 95% CI: 1.02-1.07). With respect to needlestick injury, the overall effect size was 1.33 without statistical significance.

Discussion: The study findings demonstrate that higher nurse-to-patient ratio is related to negative nurse outcomes. Future studies assessing the optimal nurse-to-patient ratio level in relation to nurse outcomes are needed to reduce adverse nurse outcomes and to help retain nursing staff in hospital settings.

Keywords: Meta-analysis; Nurse outcomes; Nurse staffing; Nurse-to-patient ratio; Systematic review.

Copyright © 2018 Elsevier Inc. All rights reserved.

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Nurse-to-patient ratio and nurse staffing norms for hospitals in India

A critical analysis of national benchmarks.

Sharma, Suresh K. 1, ; Rani, Ritu 1

1 College of Nursing, AIIMS, Rishikesh, Uttarakhand, India

Address for correspondence: Dr. Suresh K. Sharma, College of Nursing, AIIMS, Rishikesh, Uttarakhand, India. E-mail: [email protected]

Received February 11, 2020

Received in revised form March 13, 2020

Accepted March 20, 2020

Optimum nurse-to-patient ratio is the concern of most of the nurse leaders globally. It has benefits both for nurses and patients; which is essential for patient's safety and quality of care. Some parts of the world such as California, USA, and Queensland, Australia has passed the law for the minimum nurse-to-patient ratio, which has scientifically found to be beneficial for the patients and healthcare system. Indian nurse staffing norms given by the Staff Inspection Unit, Indian Nursing Council, and Medical Council of India are developed through professional judgement models and are not updated. Five electronic databases were considered for literature search; in addition, grey literature and books were also searched. The primary outcome was to summarise exiting national nurse-to-patient norms and to find out the ideal nurse-to-patient ratio and nurse staffing norms as per Indian resources. It is concluded that nurse staffing norms must be immediately revised in the light of international norms and research evidence available in this regard. Further, there is a need for workload analysis based research evidence to have true nurse-to-patient ratio estimation for hospitals in India.

Introduction

The enactment of a standardized nurse to patient ratio is an ongoing discussion all over the world that would necessitate a precise nurse-patient ratio for hospitals to employ.[ 1 ] Studies have shown that appropriate nurse staff helps to achieve clinical and economic improvements in patient care, including enhanced patient satisfaction, reduction in medication errors, incidences of fall, pressure ulcers, healthcare-associated infections, patient mortality, hospital readmission and duration of stay, patient care cost, nurses’ fatigue, and burnout.[ 2 3 4 5 6 7 ]

It is a difficult question to answer how many nurses will be sufficient for a particular type of unit/ward of a hospital. However, the decision on the optimum level of nurse-to-patient ratio for a particular unit depends on several factors such as intensity of patients’ needs, the number of admissions, discharges, and transfers during a shift, level of experience of nursing staff, layout of the unit, and availability of resources, such as ancillary staff and technology. The American Nurses Association supports a legislative model in which nurses are empowered to create staffing plans specific to each unit.[ 8 ] Victoria state in Australia was the first region in the world to introduce mandated minimum nurse/midwife-to- patient ratios during 2000 in its public sector enterprise agreement of nurse-to-patient ratio, 1:4 on morning shifts, 1:5 on afternoon shifts and 1:8 on night duty shifts, plus an in-charge nurse on all shifts, who have flexibility to allocate even fewer number of patients to a nurse based on patients’ level of dependency.[ 9 ] Later in 2004, California became the first state of USA to legally define required minimum nurse-to-patient ratio, i.e., general medical-surgical ward 1:5, emergency-1:4, and critical care units-1:2 or fewer in all the shifts,[ 8 ] which was found to be beneficial for both patients and nurses, and now other US states have also considered laws on minimum nurse staffing standards.[ 10 ]

The nurse–patient ratio is calculated using various approaches as no single approach would find its place in all settings. Over many years, staffing was determined by the census, i.e. the volume of patients indicated the volume of nurses needed to care for them. This was indeed rigid enough to meet the health needs of the patients during unforeseen emergencies etc.[ 11 ] The other approach, workload analysis or timed assignment or activity method includes the types and frequency of activities of nursing care. The World Health Organization has developed an approach to estimate the nurses’ manpower requirement popularly named as workload indicator of staffing need; which is also known as the bottom-up approach that utilizes activity assessment to assess the need for nursing staff. It calculates the number of health care workers per cadre based on the available workload in the hospital.[ 12 ] Although it is an objective technique; it requires a committed and skilled team to assess significant personnel estimation information.[ 13 ] It depends upon the amount of workload available in a particular department. Some activities, however, trigger unforeseen delays, such as lagging reaction from others, changes in the condition of the patient, changes in the nursing team and skill mix, etc., There are three different aspects of workload such as task level, job-level and unit level where emotional and physical workload should be taken into consideration and these aspects have a direct effect on burnout, job satisfaction, and medication error.[ 14 ]

Various Indian committees and regulatory bodies have provided recommendations on benchmarks of the nurse-to-patient ratio. However, there is a paucity of critical analysis of the existing norms. Therefore, the present paper is providing a critical analysis of existing national nurse staffing norms by comparing with international norms, guidelines and legislations and discussion with national and international research evidence in this regard.

The search strategy was developed by the research team. Five electronic databases (Embase, Ovid, ClinicalKey, PubMed, and MEDLINE) were considered for literature search; in addition, grey literature and books were also searched. Controlled vocabularies such as MeSH (medical subject headings) terms were used wherever available; otherwise, a combination of keywords as boolean operators were used for electronic literature search. The search terms used were nurse staffing, nurse-to-patient ratio, nurse workload, nursing workforce, measures for nurses’ workload, manpower requirement, nurse staffing norms. The search was limited to the English language; however, all the published data related to Indian recommendation of nurse-to-patient ratio/nurse staffing norms and recently published (since independence till 2020) national and international evidence related to nurse staffing norms were considered. The primary outcome was to find out the ideal nurse to patient ratio and nurse staffing norms.

Nurse staffing norms in India

The Bhore Committee, Shetty Committee, Bajaj Committee, High power committee, and Cadre review committee on nursing and nursing profession have provided recommendations about nurse-to-population ratio and nurse staffing norms for the hospitals. Summaries about the nurse-to-patient recommendations of these committees have been illustrated in Table 1 .

Nurse staffing norms also have been enacted by nursing and medical regulatory/accrediting bodies in India. Summaries of norms for nurse-to-patient ratio provided by Indian Nursing Council (1985),[ 17 ] Medical Council of India (1990),[ 18 ] Staff Inspection Unit (1991 - 92)[ 19 ] and National Accreditation Board for Hospitals and Healthcare providers (2005)[ 20 ] has been presented in Table 2 .

Research evidence on nurse-to-patient ratio

Some of the research studies conducted to assess the gap between required and exiting nurse-to-patient ratios in selected units of acute care hospitals in India. The summary of these studies has been presented in Table 3 .

International nurse staffing norms

The norms or legislations for minimum nurse-to-patient ratio prescribed by different organization in selected developed countries like UK, USA, Australia, and Canada has been presented in Table 4 .

The nurse-to-patient ratio is one of the determining factors of the patient outcome. The higher workload and lower nurse-to-patient ratio increases the risk of medication errors, iatrogenic complications, hospital morbidity, prolonged hospital stay and compromised patient safety.[ 26 ] A study was conducted in 168 general hospitals of Israel and found that an increase in the nurse-to-patient ratio from 1:4 to 1:6 raised the patient mortality rate by 7% and with further increase in nurse-patient ratio to 1:8, the mortality rate increased to 14%.[ 1 ] The world authority in nurse-to-patient ratio research Professor Linda Aiken and her team (2002) found that for every extra patient over four patients per nurse in a general medical or surgical ward, there is a direct impact on a patient's recovery and the risk of serious complications and/or death.[ 2 ]

Surprisingly, the existing recommended nurse-to-patient ratio for the general wards in India is 1:6 by SIU[ 19 ] and NABH,[ 20 ] and 1:5 by INC[ 17 ] for non-teaching hospitals; which is significantly lower when compared to international norms. The understaffing results in more “task-oriented” nursing care with minimal consideration of the emotional well-being and quality of care.[ 34 ] However, in the present scenario of higher care complexity and advancement in technologies, the concept of an optimal level of nurse staff planning fails to estimate the nurse–patient ratio as no one size can fit all.[ 1 35 ] Even the recommendations of nurse-to-patient ratios from the United States, United Kingdom, Australia, Canada, and other developed nations are also not consistent, however, the studies suggested that 1:4 nurse-to-patient ratio is best for patients’ health outcomes.[ 36 ] Optimum nurse-to-patient ratio not only reduces the workload of the nurses but also improves patients’ satisfaction and quality of health care.[ 1 ]

The nurse-to-patient ratio for intensive care units recommended by SIU is 1:1; while NABH recommended 1:1 for ventilated patients and 1:2 for non-ventilated patients. These recommendations are in line with international norms. However, the ratio recommended by INC was significantly lower, i.e. only 1:3 or 1:1. Most of the research studies conducted in critical care units of the selected tertiary care hospitals in India also highlighted the required nurse-patient ratio of less than 1:1 in different ICUs.[ 23 24 27 ] The norms recommended by these Indian committees, and Statutory/Accrediting bodies were about 30–35 years back, based on census and professional judgement method to estimate the nurse-patient ratio. These approaches of nurse-to-patient ratio estimation have serious drawbacks of under or overestimation of direct nursing care activities.[ 37 ] SIU norms are most frequently used for nurses manpower estimation in India but it is also not flawless, for example, it has clubbed the post of the nursing sisters and the staff nurses together, which makes staff estimation confusing.[ 17 ]

Several time and activity studies recommend that the requirement of the nurses for meeting the minimum standards of care should be based on the degree of the patients’ illness, i.e. completely dependent, partially dependent and ambulatory.

The fixed nurse-to-patient ratio is followed in most parts of the world and it has even become legislation in some parts of the world like California,[ 32 ] Queensland,[ 38 ] and Australia.[ 9 ] Further, other states of the USA and Australia are trying to get such legislations implemented in their states. However, the American Organization of Nurse Executives objects fixed mandatory laws for nurse staffing with an argument that it reduces the flexibility in the working conditions of the nurses.[ 39 ] The American Nurses Association supports a legislative model in which nurses are empowered to develop nurses staffing plans which are particular to their unit, more flexible and can change according to the health needs of the patient, expertize level of the nurses, working environment, availability of resources with the provision of minimum upwardly adjustable staffing levels in order to achieve safe and apt staffing strategies.[ 40 ]

It has been observed in the research studies that patient care load does not remain constant during all three shifts; it is highest during the morning shift and progressively lesser during the evening and night shift.[ 41 ] Thus, Australian Nursing and Midwifery Federation, Victoria recommended varying nurse-to-patient ratio in general wards for each shift, i.e. morning-1:4; afternoon-1:5, and night-1:8.[ 9 ] However, existing Indian norms are not as per workload of the different shifts, which could contribute into an overestimation of the required number of nurses and higher healthcare cost. In line with a study, done in the maternity ward of the Medical College Hospital in Kolkata, which aimed to find out the nurses’ requirement based on the workload analysis method, showed that there was over staffing and less work pressure.[ 42 ]

The high power committee for nursing and INC recommended 30% leave reserve considering 24 offs, 30 EL, 10 CL, and 02 RH to be provided to the nurses.[ 43 ] However, as per new norms, nurses working in the public sector are expected to give 99 offs in a year and there is also the provision of Child Care Leave for female nurses; thus additional 15% leave reserve is required.[ 44 ] In this line, SIU norms recommended 45% posts added for the area of 365 days working including 10% leave reserve (maternity leave, earned leave, and days off) as nurses are entitled for 8 days off per month and 3 national holidays per year when doing 3 shift duties.[ 17 ]

The estimations of the nurse-to-patient ratios are primarily done based on the projected workload of nurses for direct patient care.[ 17 ] However, nurses are involved in various indirect care activities such as in documentation, communication, meetings, rounds, reporting, administrative, and other logistics-related activities; which are generally not taken into consideration while estimating nurse-to-patient ratio. Studies highlighted that nurses are found to spend their 30–50% time in indirect care actives.[ 41 45 ] Thus, nurses should be provided with technological and supportive staff help, so that they can spend more time on direct patient care.

A different approach that would optimize the nursing performance needs to be developed, like constituting a team of nurses with a range of skill levels and experiences. Instead of one nurse working exclusively with one patient, a team of nurses could work for a group of patients including the most senior team member who guides, facilitates and offers patient care as well.[ 46 ]

• Nurse staffing norms in India are not updated since a long and they are far behind from international norms and estimated ratios in some of the research studies conducted in India. However, recommendations given by NABH are most recent and realist, practical and feasible to use in India
• A single norm for all the wards and hospitals cannot be used for a fair estimation of nursing human resource needs. While estimating nurse-to-patient ratio estimation different factors such as unit workload, patients’ dependency, skill mix, available proportion of nurses’ productive and non-productive activities, and variations in time and nursing care activities during the shift should be considered
• Considering Indian resources, best international norms and Indian research evidence, we recommend following nurse-to-patient ratio in each shift for Indian hospitals.

General wards: 1:6; Super speciality wards: 1:4; high dependency units: 1:3; ICUs and Post-op recovery rooms: 1:1 (ventilator beds) and 1:2 (non-ventilator beds); Emergency and Trauma: 1:1 (ventilator beds) and 1:2 (non-ventilator beds); Labor room: 02 nurse per labor table; antenatal/postnatal ward: 1:4; Pediatric ward:- 1:5; neonatal ICU 1:1; acute respiratory/burns unit: 1:2; palliative care unit: 1:4; major OT: 02 nurses for each table; minor OT: 1:1; Chemotherapy/Daycare Unit: 1:3; OPD procedure rooms: 1:1 and OPDs: 1:50 patients; Infection control nurse: 01 for every 100 beds; and 10–15 nurses for the work of diabetes nurse educator, wound care nurse, stoma nurse, dialysis nurse, organ transplant coordinator nurse, Peripherally inserted central venous catheter (PICC) line care nurse and nurse research assistants. Further, there must be 45% additional nurses for the leave reserve and in-charge nurses must have the flexibility to distribute nurses as per workload in each shift. Further extensive studies are needed to provide staffing standards for nurses, based on the available workload of tertiary care hospitals.

Institutional clearance

This study was duly approved by the competent authority but being a review did not require ethical approval.

Financial support and sponsorship

This study received no specific grant from any funding agency.

Conflicts of interest

The authors declare that they have no conflicts of interest.

Acknowledgement

We are sincerely thankful to Dr. Shiv Kumar Mudgal and Ms. Km. Madhu for their contributions during the article preparation.

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Health Care Access & Coverage

What Patient-To-Nurse Ratios Mean for Hospital Patient Health and Outcomes

Pennsylvania legislature pushed to take up patient safety issue it has long avoided.

• Hoag Levins
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“Should Hospitals be Required to Have a Certain Number of Nurses?” asks a Philadelphia Inquirer headline about the controversy brewing in Harrisburg around the latest efforts to have the Pennsylvania legislature pass a law requiring minimum patient-to-nurse ratios in its hospitals. It’s the latest general media story that seems to infer that this patient-to-nurse issue is a vague, unsettled thing essentially about a nursing labor grievance. But it isn’t. Hard scientific evidence since the 1980s has shown that having insufficient numbers of nurses on a given hospital unit kills and injures more patients than when there are enough nurses available to adequately attend and monitor those patients. The University of Pennsylvania’s School of Nursing and its Center for Health Outcomes and Policy Research (CHOPR) have played a leading international role in these decades of research.

In recent years, the number of highly trained nurses in hospitals has been affected by severe and repeated budget cuts that save money by increasing the patient-to-nurse ratios so that more patients are assigned to each nurse, and/or by using less trained and skilled aides to replace registered nurses. After California became the first state to enact a minimum required nurse staffing law for its hospitals in 1999, other hospitals and their lobbying organizations across the country worked hard to prevent similar legislation from being enacted in other states–despite the evidence that not having adequate patient-to-nurse ratios leads to higher mortality rates and worse patient outcomes.

A 12-Year effort

The latest effort to enact a minimum required nurse staffing law in Pennsylvania began early in May with the announcement of both House Bill 106 and Senate Bill 240 , which together are known as the Pennsylvania Patient Safety Act. Prior to this, similar bills have been introduced every year in the Statehouse since 2010. All have died in Republican-controlled committees.

“It is really an example of how in our democracy a couple of individuals for their own personal reasons can deny legislation that is in the public interest from coming up for a vote,” said Founding Director of CHOPR and LDI Senior Fellow Linda Aiken, PhD, RN .

But now, after last November’s elections, Democrats hold the majority in the Pennsylvania House for the first time in 12 years and this new round of nurse ratio bills is being heavily lobbied by nursing organizations, unions, and public health advocates.

The Pennsylvania Patient Safety Act would set the minimum numbers of patients that could be assigned to individual nurses in a hospital’s various departments. Those ratios vary depending upon the nature of the unit’s focus and severity of patients’ conditions and treatment. ( See the list of the exact ratios the Act specifies for various hospital units .)

Nursing Surveillance

It isn’t all that difficult to understand why patient-to-nurse ratios matter if you think of the times you yourself have been in a hospital bed. Nurses function as your minute-to-minute biomedical and wellbeing surveillance system. Although they may appear to be just taking your temperature, providing scheduled pills, or checking your IV set up, they are doing much more invisibly — for every patient under their care.

The wide variety of conditions and illnesses treated in hospitals are all prone to various sorts of disastrous, and often unexpected complications that, if not recognized and immediately addressed, can lead to increased patient deaths, injury, or permanent disability. Together across a ward or unit, nurses function as a critical surveillance system constantly monitoring each patient for the subtle signs that something in their condition has or is about to change for the worse. This invisible surveillance system by highly trained and experienced registered nurses is the most critical–but least understood–of the services they provide.

But the intensity and effectiveness of that surveillance is determined by how many patients a single nurse is charged with caring for. For instance, a registered nurse caring for four seriously ill patients on a shift can conduct a far more comprehensive surveillance on each than if caring 10 or more seriously ill patients on a shift. Research has shown that each additional patient assigned to a registered nurse beyond the optimum ratio significantly increases the risk of preventable death, longer stays, readmissions, and unfavorable patient satisfaction. It directly results in less effective care, poorer patient outcomes, and higher costs of care.

State-Wide PA Hospital Study

In her testimony earlier this month as lead witness before the Pennsylvania House Health Committee hearing on the Patient Safety Act, Aiken detailed the findings of CHOPR’s recent study of patient-to-nurse variations and health outcomes in 114 Pennsylvania hospitals. Conducted according to a National Institutes of Health-funded research protocol, the project used data from more than half a million patients.

In adult medical and surgical units in the 114 hospitals, researchers found patient-to-nurse ratios variations from 3-11. “This is huge variation in a hospital resource that has been shown in hundreds of studies to be associated with a wide range of patient outcomes including mortality, failure to rescue patients with complications, hospital acquired infections, patient satisfaction, length of stay, readmissions, and patient safety,” they noted.

Further analyzing 33 different aspects of patient severity of illness and hospital organizational characteristics, the researchers determined that “in-hospital mortality increased by 7% for each additional medical patient and 8% for each surgical patient added to nurses’ workloads.” They also found that hospital readmissions increased by 2% for medical patients and 4% for surgical patients for each 1 patient increase in nurses’ patient workloads.”

Preventing 1,155 Unnecessary Deaths

The researchers estimated that if the Patient Safety Act was passed and implemented it would:

• Prevent 1,155 hospital deaths annually in Pennsylvania hospitals
• Avoid 771 hospital readmissions annually
• Reduce length of stay in the aggregate by 39,919 days annually, which would alone save Pennsylvania hospitals $93 million
• Accrue additional savings for hospitals with higher patient satisfaction, avoid Medicare readmission penalties, and reduce nurse turnover which costs Pennsylvania hospitals many millions of dollars annually.

Aiken also provided evidence in the hearing that Pennsylvania has a sufficiently large supply of nurses to meet the standards set by House Bill 106. She said Pennsylvania has a larger supply of nurses per 1,000 residents than all but five other states and Washington, D.C., and a significantly larger supply of nurses than California which has had mandated minimum hospital nurse staffing for 20 years.

“Impossible and Dangerous Workloads”

“The root cause of nurse burnout and turnover is impossible and dangerous workloads and setting a safe nurse staffing level will bring more nurses back to the hospital bedside,” said Aiken.

“The common finding in all our policy outcomes research on nurse staffing,” said Aiken, “is that there is significant variation across hospitals in nurse staffing adequacy with substantial adverse outcomes for the public and that establishing mandated minimum safe hospital nurse staffing standards saves lives and money. Further delays in mandating safe nurse staffing in hospitals are not in the public’s interest and elected officials should act now on the basis of the evidence.”

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Nurse Staffing, Missed care, Quality of Care and Adverse Events: A Cross Sectional Study

Apiradee nantsupawat.

1. Chiang Mai University, Faculty of Nursing, Chiang Mai, Thailand

Lusine Poghosyan

2. School of Nursing, Columbia University, New York, United States

Orn-Anong Wichaikhum

Wipada kunaviktikul.

3. Assistant to the President in Health Science Panyapiwat Institute of Management, Nonthaburi, Thailand.

Yaxuan Fang

6. School of Nursing, Southern Medical University, Guangzhou, China

Supakorn Kueakomoldej

Hunsa thienthong.

4. Nursing Director, Nursing Service Division, Maharaj Nakorn Chiang Mai Hospital, Chiang Mai, Thailand.

5. Visiting Professor, Faculty of Nursing, Chiang Mai, Thailand.

To illustrate the relationship between nurse staffing and missed care, and how missed care affects quality of care and adverse events in Thai hospitals.

Background:

Quality and safety are major priorities for health care system. Nurse staffing and missed care are associated with low quality of care and adverse events. However, examination of this relationship is limited in Thailand.

This cross-sectional study collected data from 1,188 nurses in 5 university hospitals across Thailand. The participants completed questionnaires that assessed the patient-to-nurse ratio, adequacy of staffing, missed care, quality of care, and adverse events. Logistic regression models were used to estimate associations.

Higher patient-to-nurse ratio, poor staffing, and lack of resource adequacy were significantly associated with higher odds of reporting missed care. Higher nurse-reported missed care was significantly associated with higher odds of adverse events and poor quality of care.

Conclusions:

Poor nurse staffing was associated with missed care and missed care was associated adverse events and lower quality of care in Thai university hospitals.

Implications for Nursing Management:

Improving nurse staffing and assuring adequate resources are recommended to reduce missed care, adverse events, and increase quality of care.

1. Background

Safety and quality are foundational components of health care system, which are also used as indicators to measure the quality of care in hospitals ( Lam et al.,2018 ). However, enhancing safety and quality are challenging since they represent a significant burden in many countries ( Ball et al., 2018 ). Strong evidence reveals that nurses play a critical role in ensuring patient safety while providing direct patient care ( Malliaris, Phillips, & Bakerjian, 2021 ). Nurses are frontline health care professionals who are constantly present at the bedside. They bring clinical expertise to monitor patients for deterioration, detect errors and near misses, design care process that protect patient safety, and accomplish the goals of patient safety management. Nurse managers are tasked with the challenge of helping bedside nurses ensure quality patient care and safety and improve hospital quality and performance.

Systematic reviews have demonstrated the association of missed care and patient safety and quality of care ( Recio-Saucedo et al., 2018 ; Zhao et al., 2019 ; Kalánková et al., 2020 ). ‘Missed care’ is termed by Kalisch, Landstrom, and Hinshaw (2009) --also called ‘unfinished care’ ( Lucero, Lake, & Aiken, 2009 ) or ‘rationed care” ( Schubert, Glass, Clarke, Schaffert-Witvliet, & DeGeest, 2007 )--to describe important patient care tasks that are omitted. Missed care reflects nurses’ decision-making processes and the prioritization of care when resources are not sufficient to provide all the needed care to patients. Missed nursing care is an issue worldwide and previous studies in the United States ( Campbell et al., 2020 ), Europe ( Eskin Bacaksiz, Alan, Taskiran Eskici, & Gumus, 2020 ; Senek et al., 2020 ), Asia ( Labrague et al., 2020 ), and Australia ( Henderson, Willis, Xiao, & Blackman, 2017 ) have shown that large numbers of nurses leave care undone. Further significant international research studies have demonstrated the impact of missed nursing care on patient outcomes, including poor overall quality of care, increased mortality, decreased patient satisfaction, and increased patient adverse events such as medication errors, falls, pressure ulcers, critical incidents, infections, and readmission ( Recio-Saucedo et al., 2018 ; Aiken et al., 2018 ; Bail et al., 2020 ; Chaboyer, Harbeck, Lee, & Grealish, 2021 ).

Reasons for higher levels of missed care can often be traced to organizational factors, such as inadequate staffing levels, and poor work environment, teamwork, and hospital safety climate. Among those factors, nurse staffing and work environment have been explicitly identified as contributing factors to missed care. Empirical evidence documents that poor nurse staffing, staffing and resource inadequacy ( Park, Hanchett, & Ma, 2018 ; Smith, Morin, Wallace, & Lake, 2018 ; Lee & Kalisch, 2021 ) and higher patient-to-nurse ratio ( Griffiths et al., 2018 ; Al-Faouri, Obaidat, & AbuAlRub, 2020 ; Lee & Kalisch, 2021 ) are associated with increased missed care. A recent longitudinal study by Lake, Riman, & Sloane (2020) found that, with improved work environments and nurse staffing, the prevalence and frequency of missed care decreased significantly.

The conceptual framework to guide the understanding of how nurse staffing is related to missed care, and how missed care is related quality and safety is the missed nursing care model ( Kalisch, Landstrom, & Hinshaw, 2009 ). This framework is based on Donabedian’s model ( Donabedian, 1988 ) that linear conception of quality that structure affect processes, which in turn affects outcomes. The model proposes a direct relationship between nursing staffing, missed care, and outcomes ( Lucero, Lake, & Aiken, 2010 ). The missed nursing care model describes how the structure (e.g., nurse staffing) may influence nursing care processes (e.g., missed care) which potentially impact patient outcomes (e.g., quality of care, adverse events). It is possible that when the number of nurses is limited, there is a heavy workload burden; nurses may not be able to properly carry out tasks that require professional skills, such as training their patients and their family, and lack the time to provide patients with necessary care. This, in turn, may affect the quality of the healthcare service provided at the hospital as illustrated in Figure 1 .

Diagram of hypothesized relationships

The public hospitals under the Ministry of Public Health are the main healthcare service providers in Thailand. These hospitals deliver care services for Thai peoples with accessibility, equality, and service excellence. However, nursing shortage is still an issue and data from healthcare facilities show that in order to effectively meet the demand 122,170 nurses are required and there are currently only 98,070 nurses in the system. The hospitals need to recruit an additional 24,100 nurses but turnover rate is around 4.45% among nurses ( Sawangdee, 2017 ). Moreover, on the supply side, 86 nursing schools can produce around 11,000–12,000 nurses per year which may not be enough to meet the demand. Thus, it is necessary to understand how existing nurse staffing affects healthcare quality in Thai hospitals. Such evidence will help hospitals to administer strategies to retain nursing workforce and maintain the quality of care.

While previous studies provide the empirical knowledge that poor nurse staffing is associated with increased missed care, and increase missed care is associated with increased adverse events, little is known about the relationship between nurse staffing and adverse events via missed care in Thailand.

To illustrate the relationship between nurse staffing and missed care, and how missed care affects quality of care and adverse events in Thai university hospitals.

3.1. Study design, sampling method, and setting

This was a cross-sectional study with data collected by paper questionnaires from 43 units in five university hospitals ( Nantsupawat et al., 2015 ). The sample was selected using multi-stage random sampling. First, five university hospitals were selected from five regions across Thailand using simple random sampling. Then, proportional stratified random sampling was used to select 50 nurses from each in-patient hospital’s unit. Inclusion criteria included nurses who provided direct patient care with at least one year of bedside experience. Exclusion criteria included nurses having a managerial position. The questionnaires were distributed to 1,750 nurses and 1,450 nurses returned the questionnaires (82.86% response rate). Total of 1,188 questionnaires were completed (67.89% usable data) and used in this study. Based on the number of study parameters to analysis, a sample size of 1,188 nurses was verified as acceptable.

3.2. Research instrument

Nurse staffing, patient to nurse ratio.

Patient to Nurse Ratio was measured based on a question about the nurse-reported number of patients assigned to each nurse. That is, nurses were asked how many patients were assigned to them on their last shift. This question has been previously used in a study in Thailand ( Nantsupawat et al., 2011 ). Nurse responses were calculated as the mean patient load across all registered nurses who reported having responsibility for at least one patient, but fewer than 30 patients, on the last shift they worked.

Staffing and Resource Adequacy

Staffing and Resource Adequacy was measured with The Practice Environment Scale of the Nursing Work Index (PES-NWI) ( Lake, 2002 ). The PES-NWI is a validated instrument often used in international studies to measure the work environment of nurses ( Aiken et al., 2011 ). The subscale had previously been translated into Thai and has been used in previous research ( Nantsupawat et al., 2011 ). Nurses rate each item on a 4-point Likert scale from strongly disagree to strongly agree. (i.e., 1=strongly disagree, 2= somewhat disagree, 3=somewhat agree and 4=strongly agree). For this study, we used the staffing and resource adequacy subscale of the PES-NWI. This subscale measures the adequacy of unit staffing and consist of items such as “I have enough staff to get the work done”, “Enough opportunity to discuss patient care problems with other nurses”, “Adequate support services allow me to spend time with my patients”, and “Enough registered nurses on staff to provide quality patient care. Cronbach’s alpha was 0.85 for the Thai version of the staffing and resource adequacy. Additionally, in this study the staffing and resource adequacy was significantly correlated with the number of patients assigned to nurse (r= −.38; p<.05).

Missed care

Missed care reflects the process of care and was defined as necessary nursing activities that were missed due to a lack of time. Items for this measure were informed by missed care instruments used in USA and European studies ( Ausserhofer et al., 2014 ; Ball et al., 2018 ). Nurses were asked whether 7 nursing care activities: adequately document nursing care, comfort /talk to patients, develop or update the nursing care plan, prepare patient and family for discharge, educate patients and family, provide oral hygiene, and provide skin care were necessary but left undone because they lacked the time to complete them. Nurses responded on a 4-point Likert scale, and responses were dichotomized into missed care (occasionally and frequently) or not missed care (never and rarely). Missed care frequency was the number of activities missed, which was summed for each nurse and averaged. The items measuring nursing activities were translated into Thai using a back translation process. The Cronbach alpha on the tool in this study was 0.88. We created a global dichotomous missed care indicator for each patient on each shift if a nurse reported missing any of the 7 items during the shift.

Adverse events

Nurse-reported patient adverse events included medication error, infection, fall, patient complaint, and verbal abuse toward nurses ( Lucero, Lake, & Aiken, 2010 ). The 4-point Likert response options were: never, rarely, sometimes, and often. Nurse-reported patient adverse events were categorized as frequent (sometime, and often) and infrequently (never and rarely) to facilitate the interpretation of the results from the logistic regression models. The adverse events were translated into Thai using a back translation process. We calculated the Cronbach alpha which was 0.84.

Quality of care

Nurses rated the overall quality of care provided on their unit using a 4-point scale (poor to excellent). This questionnaire’s reliability has been confirmed with analysis of administrative patient data, showing correlation between nurse report of quality and hospital quality ( McHugh & Stimpfel, 2012 ). The questionnaire was translated and has been used in Thai context ( Nantsupawat et al., 2011 ). The Cronbach alpha value in this study was 0.81. Reponses of poor or fair were classified as poor quality of care and good or excellent as excellent quality of care.

3.3. Data Collection

Data were collected after ethical committee approval from the Faculty of Nursing, Chiang Mai University, Thailand (EXP:016-2014) and from the hospital and nursing directors of the participating hospitals. Questionnaires and informed consent forms were distributed to hospital coordinators via mail and then they distributed the questionnaires to nurses. Nurses returned the completed questionnaires in a sealed envelope to hospital coordinators and completed questionnaires were sent to researchers by mail. The data were de-identified for analysis.

3.4. Data Analysis

Data were cleaned carefully for missing data and checked for normality. We calculated descriptive statistics. Logistic regression models with and without control variables (adjusted and unadjusted) were used to describe how patient-to-nurse ratio and resource adequacy related to missed care. We also used logistic regression to examine whether, and to what extent missed care affected quality of care and adverse patient events. Control variables included nurses’ age, education, years of experience as RN, and unit type which influence outcomes ( Audet, Bourgault, & Rochefort, 2018 ). Analyses were performed using STATA 14.0 (StataCorpLP, College Station, TX, USA).

4.1. Prevalence of nurse staffing missed care, quality of care as poor, and adverse events.

The majority (97.4%) of participants were female, with average age of 34 years (SD=0.27). The majority held a bachelor’s degree (87.7%) and the average years of RN experience was 11 (SD=0.25) and average number of years worked in unit was 9 (SD=0.22). The average number of patients per nurse was 8 (SD=0.17). The staffing and resource adequacy subscale had the mean score of approximately 2.40 (SD=0.15). Only 11% of nurses reported that they “adequately document nursing care” and 14–18% of nurses reported that “comforting/talking with patients”, and “developing or updating nursing care plan” were left undone. Around 21–24% of nurses reported that “preparing patient and family for discharge”, “educating patients and family”, and “oral hygiene” were left undone. Around 50% of nurses reported that “skincare” was left undone. Roughly 10% of nurses perceived that the quality of care in their units had deteriorated in the last shift. Around 4–25.6% of nurses reported that adverse events of medication error, infection, patient fall, patient complaint and verbal abuse occurred occasionally or frequently (see Table 1 ).

Nurse characteristics and distribution of nurse staffing, missed care, quality of care, and adverse events ( n =1,188)

4.2. Nurse staffing and missed care

The results of the logistic regression analysis are shown in Table 2 . Adjusted models revealed that each additional patient per nurse was associated with an increase in the odds of nurses reporting missed care in terms of providing comfort for patients (OR = 1.05, 95% CI 1.02–1.08), documentation of care (OR = 1.04, 95% CI 1.01–1.08), providing skincare (OR = 1.05, 95% CI 1.03–1.08), providing oral care (OR = 1.05, 95% CI 1.03–1.08), updating nursing care plan (OR = 1.03, 95% CI 1.00–1.05), and total missed care (OR = 1.06, 95% CI 1.02–1.08).

Odds ratio estimating the relationship between nurse staffing and missed care ( n =1,188)

CI=confidence interval

Adjusted models revealed that the odds of missed care for patient teaching (OR = 1.47, 95% CI 1.20–1.79), providing patient and family for discharge (OR = 1.30, 95% CI 1.06–1.60), comforting patients (OR = 2.18, 95% CI 1.70–2.79), documenting care (OR = 2.10, 95% CI 1.60–2.75), providing skincare (OR = 1.36, 95% CI 1.15–1.61), providing oral care (OR = 1.69, 95% CI 1.38–2.06), updating nursing care plan (OR = 1.57, 95% CI 1.26–1.95), and total missed care (OR = 1.39, 95% CI 1.15–1.67) were significantly higher for nurses who worked in units with lower staffing and resource adequacy scores than nurses who worked in units with higher staffing and resource adequacy scores.

4.3. Missed care and the relationship with quality of care and adverse events

The results of the logistic regression analysis are shown in Table 3 . Adjusted models revealed that the odds of nurses’ reporting quality of care as poor (OR = 1.40, 95% CI 1.25–1.58) were significantly higher for nurses who reported higher missed care scores. Adjusted models revealed that the odds of nurse-reported adverse events including medication error (OR = 2.28, 95% CI 1.25–4.13), infection (OR = 1.66, 95% CI 1.21–2.26), patient complaint (OR = 2.31, 95% CI 1.01–5.25), verbal abuse toward nurses (OR = 1.53, 95% CI 1.01–5.25), and overall adverse events (OR = 1.68, 95% CI 1.29–2.20) were significantly higher for nurses who reported higher missed care scores than for nurses who reported lower missed care scores.

Odds ratio estimating the effect of missed care on quality of care and adverse events ( n =1,188)

5. Discussion

This is the first study in Thailand to explore missed care, the relationship between nurse staffing and missed care, and the relationship between missed care and quality of care and adverse events. Our findings indicate that patient-to-nurse ratio in the university hospitals was 8 to 1 which is less than the patient-to-nurse ratio in general hospitals where each nurse cares for 10 patients ( Nantsupawat et al., 2011 ) and in community hospitals where each nurse cares for 11 patients ( Nantsupawat et al., 2015 ). University hospitals deliver tertiary health care services where nurse care for complex patients and also the hospitals are focused more on research and education, which may explain the lower patient-to-nurse ratio. All university hospitals are accredited by the standards from International Society for Quality in Healthcare. These standards determine the number of patients. When compared with international studies, patient-to-nurse ratio in Thai university hospitals are similar to those in hospitals in nine European countries ( Ball et al., 2018 ), Jordan ( Al-Faouri, Obaidat, & AbuAlRub, 2020 ), but less than those in other settings such as South Korea ( Cho et al., 2020 ).

The staffing and resource adequacy subscale had the mean score of approximately 2.40 which higher than the score in hospitals in South Korea ( Kim, Yoo, & Seo, 2018 ) and less than in settings such as South Western U.S. hospitals ( Smith et al., 2018 ). University hospitals provide health care to complex patients and nurses need to deal with learning new technologies and coordinate with interdisciplinary health care professionals. These challenging environments may make nurses report high workload and lack of enough time or staff to get the work done. In our study, skin care and oral hygiene were the most left undone activities. It is possible that university hospitals have a nursing skill mix. Moreover, the study results show that infection was the highest adverse events happening in university hospitals and these findings are consistent with the findings from another Thai study ( Indrawattana & Vanaporn, 2015 ). It is possible that patients who admitted to university hospitals are those with complicated diseases treated with a variety of antibiotics.

Both patient-to- nurse ratio, staffing and resource adequacy are significantly associated with missed care after controlling for potential confounders. Each increase of one patient per nurse during the shift was associated with a 6% increase in likelihood of missed care. The study’s findings are consistent with previous studies that reported associations between high patient-to-nurse ratio and missed care ( Henderson et al., 2017 ; Griffiths et al., 2018 ; Aiken et al., 2018 ; Tubbs-Cooley, Mara, Carle, Mark, & Pickler,2019 ; Al-Faouri et al., 2020 ; Lee & Kalisch, 2021 ). Additionally, one unit increase of poor staffing and resource adequacy score was associated with a 39% increase in likelihood of missed care. These findings are consistent with previous studies ( Park et al., 2018 ; Smith, et al., 2018 ).

Moreover, this study showed a significant association between missed care and patient adverse events. After controlling for RN age, education, years as RN, and unit type, one unit increase in missed care score increased the relative proportion of nurses reported frequency of quality of care as poor (40%), medication error (128%), infection (66%), complaint (131%), verbal abuse (53%), and adverse event (68%). Similarly, previous studies have found that higher missed care was also associated with patient adverse events such as medication errors, falls, pressure ulcers, critical incidents and nosocomial infections ( Simpson & Lyndon, 2017 ; Aiken et al., 2018 ; Bail et al., 2020 ; Cho et al., 2020 ; Chaboyer et al., 2021 ), and quality of care as poor and nurses’ lower perception of quality of care ( Recio-Saucedo et al., 2018 ; Smith, Lapkin, Sim, & Halcomb, 2020 ).

Like prior studies, this study demonstrates evidence of significant associations between nurse staffing and missed care, and between missed care and quality of care and adverse events. The potential explanation for this pattern is reflected in the Missed Nursing Care Model ( Kalisch et al., 2009 ). This model describes that the structure (e.g., nurse staffing) influences nursing care processes (including missed care) which in turn potentially impacts patient outcomes (e.g. quality of care, adverse events). It may possible that nurses in university hospitals function as gatekeepers of patient care through their roles as planners, coordinators, providers, and evaluators of care. Nurses carry out orders prescribed by other providers to treat illness and treatment complications; they provide nursing care which include surveillance and early detection of deterioration in patient status. If the flow of care through nurses to patients is blocked, patients may not receive all services as prescribed by nurses and/or other health care providers, leaving the care processes unfinished. This may result in adverse events and poor care delivery by nurses.

6. Strength and Limitations

This study was the first to examine nurse staffing, missed care, quality of care, and adverse events in university hospitals in Thailand. The findings addressed the relationship between nurse staffing, missed care, quality of care, and patient outcomes which supported the evidence of the missed care model. Limitations of this study include its cross-sectional design which explain the association among variables rather than causation. Thus, it is not possible to establish a causal link between nurse staffing and missed care. In addition, the findings were from self-report instruments which relied on nurses’ responses. Lastly, the study sites are parts of university hospital serving tertiary care with academic medical center therefore may not representatives of other hospitals. We recommend that further research examine a casual model, utilize clinical documentation or other objective data sources, and study larger and more diverse samples of nurses and hospitals.

7. Conclusions

Our study revealed that nurse staffing is associated with missed care, and missed care is associated with lower quality of care and adverse patient outcomes. Decreasing missed care could help decrease patient adverse outcomes and improve the quality of care.

8. Implication for Nursing Management

Nurse managers are challenged with ensuring quality improvement and safety in nursing units. The findings of this study suggest that nurse managers should develop effective strategies to support nurse staffing and design regulations on safe staffing. In addition, considering staffing factors such as skill mix and elimination of non-nursing tasks should be considered. Moreover, in order to improve missed care on the unit, nurse managers should encourage transparency and communication around missed care events. Creating a non-punitive culture of transparency around missed care events may promote missed care reporting and monitoring ( McCauley et al., 2020 ).

Acknowledgement:

This study was supported by the Chiang Mai University Visiting Professor Fellowship Program. SK is supported by NIH-NINR T32NR014205 training grant.

Ethical Approval: Approval to conduct the study was obtained from the Faculty of Nursing, Chiang Mai University, Thailand. (Approval no. EXP:016-2014)

• Aiken LH, Cimiotti JP, Sloane DM, Smith HL, Flynn L, & Neff DF (2011). Effects of nurse staffing and nurse education on patient deaths in hospitals with different nurse work environments . Medical care , 49 ( 12 ), 1047–1053. 10.1097/MLR.0b013e3182330b6e [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Aiken LH, Sloane DM, Ball J, Bruyneel L, Rafferty AM, & Griffiths P (2018). Patient satisfaction with hospital care and nurses in England: an obsrvational study . BMJ Open , 8 , e019189. 10.1136/bmjopen-2017-019189 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Al-Faouri I, Obaidat DM, & AbuAlRub RF (2020). Missed nursing care, staffing levels, job satisfaction, and intent to leave among Jordanian nurses . Nursing Forum ,1–11. https://onlinelibrary.wiley.com/doi/full/10.1111/nuf.12537 [ PubMed ] [ Google Scholar ]
• Audet LA, Bourgault P, & Rochefort CM (2018). Association between nurse education and experience and the risk of mortality and adverse events in acute care hospitals: A systematic review of observational studies . International Journal of Nursing Studies , 80 , 128–146. https:// doi: 10.1016/j.ijnurstu.2018.01.007 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ausserhofer D, Zander B, Busse R, Schubert M, De Geest S, Rafferty A … Schwendimann R. (2014). Prevalence, patterns and predictors of nursing care left undone in European hospitals: results from the multicountry cross-sectional RN4CAST study . BMJ Qualigy & Safety , 23 ( 2 ),126–135. 10.1136/bmjqs-2013-002318 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bail K, Willis E, Henderson J, Blackman I, Verrall C, & Roderick A (2020). Missed infection control care and healthcare associated infections: A qualitative study . Collegian (in press) . 10.1016/j.colegn.2020.11.007 [ CrossRef ] [ Google Scholar ]
• Ball JE, Bruyneel L, Aiken LH, Sermeus W, Sloane DM, Rafferty AM, … Consortium RNC (2018). Post-operative mortality, missed care and nurse staffing in nine countries: A cross-sectional study . International Journal of Nursing Studies , 78 , 10–15. 10.1016/j.ijnurstu.2017.08.004 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Campbell CM, Prapanjaroensin A,, Anusiewicz C,V,, Baernholdt M,, Jones T, & Patrician PA., (2020). Variables associated with missed nursing care in Alabama: A cross-sectional analysis . Journal of Nursing Management , 28 ( 8 ), 2174–2184. https:// doi: 10.1111/jonm.12979 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chaboyer W, Harbeck E, Lee BO, & Grealish L (2021). Missed nursing care: An overview of reviews . Kaohsiung Journal of Medical Science , 37 ( 2 ),82–91. 10.1002/kjm2.12308 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cho SH, Lee JY, You SJ, Song KJ, & Hong KJ (2020). Nurse staffing, nurses prioritization, missed care, quality of nursing care, and nurse outcomes . International Journal of Nursing Practice , 26 ( 1 ), e12803. 10.1111/ijn.12803 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Donabedian A (1988). The quality of care. How can it be assessed? . JAMA , 260 ( 12 ), 1743–8. doi: 10.1001/jama.260.12.1743 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Eskin Bacaksiz F, Alan H, Taskiran Eskici G, & Gumus E (2020). A cross-sectional study of the determinants of missed nursing care in the private sector: Hospital/unit/staff characteristics, professional quality of life and work alienation . Journal of Nursing Management , 28 ( 8 ), 1975–1985. https://doi: 10.1111/jonm.12999 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Griffiths P, Recio-Saucedo A, Dall’Ora C, Briggs J, Maruotti A, Meredith P, … Ball J (2018). The association between nurse staffing and omissions in nursing care: A systematic review . Journal of Advanced Nursing , 74 ( 7 ), 1474–1487. 10.1111/jan.13564 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Henderson J, Willis E, Xiao L, & Blackman I (2017). Missed care in residential aged care in Australia: An exploratory study . Collegian , 24 ( 5 ), 411–416. 10.1016/j.colegn.2016.09.001 [ CrossRef ] [ Google Scholar ]
• Indrawattana N, & Vanaporn M (2015). Nosocomial Infection . Journal of Medicine and Health Sciences , 22 ( 1 ), 81–92. [ Google Scholar ]
• Kalánková D, Kirwan M, Bartoníčková D, Cubelo F, Žiaková K, Kurucová R (2020). Missed, rationed or unfinished nursing care: A scoping review of patient outcomes . Journal of Nursing Management , 28 , 1783–1797. 10.1111/jonm.12978 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kalisch BJ, Landstrom GL, & Hinshaw AS (2009). Missed nursing care: a concept analysis . Journal of Advanced Nursing , 65 ( 7 ),1509–1517. http://doi: 10.1111/j.1365-2648.2009.05027.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kim KJ, Yoo MS, & Seo EJ (2018). Exploring the Influence of Nursing Work Environment and Patient Safety Culture on Missed Nursing Care in Korea . Asian Nurs Res (Korean Soc Nurs Sci) , 12 : 121–126. 10.1016/j.anr.2018.04.003 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Labrague LJ, De Los Santos JAA, Tsaras K, Galabay JR, Falguera CC, Rosales RA, & Firmo CN (2020). The association of nurse caring behaviors on missed nursing care, adverse patient events and perceived quality of care: A cross-sectional study . Journal of Nursing Management . 28 ( 8 ), 2257–2265. doi: 10.1111/jonm.12894 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lake ET (2002). Development of the practice environment scale of the Nursing Work Index . Research in Nursing & Health , 25 ( 3 ), 176–188. 10.1002/nur.10032. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lake ET, Riman KA, & Sloane DM (2020). Improved work environments and staffing lead to less missed nursing care: A panel study . Journal of Nursing Management , 28 ( 8 ), 2157–2165. 10.1111/jonm.12856 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lam MB, Figueroa JF, Feyman Y, Reimold KE, Orav EJ, & Jha AK (2018). Association between patient outcomes and accreditation in US hospitals: observational study . BMJ , 18 ; 363 :k4011. https://doi: 10.1136/bmj.k4011 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lee E, & Kalisch BJ (2021). Identification and comparison of missed nursing care in the United States and South Korea . Journal of Clinical Nursing , 00 : 1–11. 10.1111/jocn.15712 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lucero RJ, Lake ET, & Aiken LH (2009). Variations in nursing care quality across hospitals . Journal of Advanced Nursing , 65 ,2299–2310. 10.1111/j.1365-2648.2009.05090.x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lucero RJ, Lake ET, & Aiken LH (2010). Nursing care quality and adverse events in US hospitals . Journal of Clinical Nursing , 19 ( 15–16 ), 2185–2195. 10.1111/j.1365-2702.2010.03250.x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Malliaris AP, Phillips J, & Bakerjian D (2021, April ). Nursing and Patient Safety . https://psnet.ahrq.gov/primer/nursing-and-patient-safety [ Google Scholar ]
• McHugh MD, & Stimpfel AW (2012). Nurse reported quality of care: A measure of hospital quality . Research in Nursing & Health , 35 ( 6 ),566–575. 10.1002/nur.21503 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• McCauley L, Kirwan M, Riklikiene O Hinno S (2020). A SCOPING REVIEW: The role of the nurse manager as represented in the missed care literature . Journal of Nursing management , 28 ( 8 ), 1770–1782 10.1111/jonm.13011 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nantsupawat A, Kunaviktikul W, Nantsupawat R, Wichaikhum O-A, Thiengthong H, & Poghosyan L, (2015). Factors affecting patient and nurse outcomes in university hospitals in Thailand . Unpublished data. [ Google Scholar ]
• Nantsupawat A, Nantsupawat R, Kulnaviktikul W, McHugh MD (2015). Relationship between nurse staffing levels and nurse outcomes in community hospitals , Thailand. Nursing and Health Science , 17 ( 1 ), 112–118. https:// doi: 10.1111/nhs.12140 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nantsupawat A, Srisuphan W, Kunaviktikul W, Wichaikhum OA, Aungsuroch Y, & Aiken LH (2011). Impact of nurse work environment and staffing on hospital nurse and quality of care in Thailand . Journal of Nursing Scholarship , 43 ( 4 ), 426–433. 10.1111/j.1547-5069.2011.01419.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Park SH, Hanchett M, & Ma C (2018). Practice Environment Characteristics Associated With Missed Nursing Care . Journal of Nursing Scholarship , 50 ( 6 ), 722–730. 10.1111/jnu.12434 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Recio-Saucedo A, Dall’Ora C, Maruotti A, Ball J, Briggs J, Meredith P, … Griffiths P (2018). What impact does nursing care left undone have on patient outcomes? Review of the literature . Journal of Clinical Nursing , 27 ( 11–12 ), 2248–2259. 10.1111/jocn.14058 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sawangdee K (2017). Crisis of Nursing Shortage in Health Services Facilities under Office of Permanent secretary, Ministry of Public Health: Policy Recommendations . Journal of Health Science , 26 ( 2 ), 456–68. [ Google Scholar ]
• Schubert M, Glass TR, Clarke SP, Schaffert-Witvliet B, & De Geest S (2007). Validation of the Basel Extent of Rationing of Nursing Care instrument . Nursing Research , 56 ( 6 ), 416–24. https://doi: 10.1097/01.NNR.0000299853.52429.62. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Senek M, Robertson S, Ryan T, King R, Wood E, & Tod A (2020). The association between care left undone and temporary Nursing staff ratios in acute settings: a cross-sectional survey of registered nurses . BMC health services research , 20 ( 1 ), 637. 10.1186/s12913-020-05493-y [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Simpson KR, & Lyndon A (2017). Consequences of Delayed, Unfinished, or Missed Nursing Care During Labor and Birth . The Journal of Perinatal & Neonatal Nursing , 31 ( 1 ), 32–40. doi: 10.1097/JPN.0000000000000203 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Smith S, Lapkin S, Sim J, & Halcomb E (2020). Nursing care left undone, practice environment and perceived quality of care in small rural hospitals . Journal of Nursing Management , 28 ( 8 ), 2166–2173. doi: 10.1111/jonm.12975 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Smith JG, Morin KH, Wallace LE, & Lake ET (2018). Association of the nurse work environment, collective efficacy, and missed care . Western Journal of Nursing Research , 40 ( 6 ), 779–798. 10.1077/0193945917734159 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tubbs-Cooley HL, Mara CA, Carle AC, Mark BA, & Pickler RH (2019). Association of Nurse Workload With Missed Nursing Care in the Neonatal Intensive Care Unit . JAMA Pediatrics , 173 ( 1 ), 44–51. https:// doi: 10.1001/jamapediatrics.2018.3619 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zhao Y, Ma D, Wan Z, Sun D, Li H, & Jiao S (2019). Association between work environment and implicit rationing of nursing care: An integrative literature review . Journal of Nursing Management , 28 ( 8 ),1841–1850.https://doi: 10.1111/jonm.12895 [ PubMed ] [ CrossRef ] [ Google Scholar ]