- Email: [email protected]
Nurse staffing and patient outcomes: Are we asking the right research question?
International Journal of Nursing Studies 63 (2016) A1–A2
Contents lists available at ScienceDirect
International Journal of Nursing Studies journal homepage: www.elsevier.com/ijns
Guest Editorial
Nurse staffing and patient outcomes: Are we asking the right research question?
In this issue of the Journal, Peter Griffiths and colleagues address the important issue of the research methods used to study nurse staffing levels and the link to patient care quality and outcomes of care (Griffiths et al., 2016). There has been a plethora of studies investigating the outcomes of nurse staffing levels or nurse to patient ratios such as infections, injuries, pressure ulcers, clinical errors and death (Choi and Staggs, 2014; D’Amour et al., 2014; Kane et al., 2007; Lee et al., 2014). However, the authors raise a potential problem with bias from the methods used in these studies. Nurse staffing researchers have primarily used observational data and not randomized clinical trials methods, therefore cause and effect between nursing care with quality and outcomes of that care cannot be established (Griffiths et al., 2011). There are a number of potential issues with bias such as unobserved variables that can influence and separately explain the findings associated with nursing care. For example, Aiken and colleagues examined nurse staffing levels in 4 states in the US and concluded that hospitals with lower nurse to patient ratios had better risk adjusted surgical mortality (Aiken et al., 2011). The study did not control for surgical volume, a well know factor associated with outcomes of complex surgeries (unobserved variable), therefore the conclusion to improve nurse staffing levels to decrease mortality may be misguided. An alternative would be to have high risk surgeries performed in hospitals with demonstrated excellence (Welton, 2010). Adequate Staffing unto itself is a necessary but insufficient condition for safe, high quality and cost effective nursing care.One unanswered question is who is the best nurse for any situation given the needs of the patient and abilities of the nurse? To date, most of the nurse staffing studies have used units of analysis either at the hospital or ward (inpatient unit) level. Recently, access to large inpatient data sets has allowed analysis of each nurse and the effect on individual patients. A study by Yakusheva et al. found patients who had a higher portion of nurses with experience or BSN degree had lower lengths of stay and cost (Yakusheva et al., 2014). Each patient is exposed to a number of nurses during a hospitalization (or other care settings), so the actual effects of workload, experience, academic preparation, etc., can be measured by the added or cumulative effects of care by individual nurses. The key question is, are we measuring nursing care at the hospital, ward, or individual patient level? Patient level analysis of nursing care has not been feasible until recently. Emerging new methods in big data science in nursing may be able to help overcome the http://dx.doi.org/10.1016/j.ijnurstu.2016.08.015 0020-7489/ã 2016 Elsevier Ltd. All rights reserved.
previous methodologic challenges, for example identifying all of the nurses who cared for a patient, their characteristics and the short term changes in patient condition using standardized nursing terminologies (Birmingham et al., 2010). Further study is indicated at the individual nurse-patient encounter as well as the assignment patterns of nurses to patients within a shift in acute care settings. Considering the actual nurses and other providers caring for each patient, what are the patient level, nurse level, and ward level effects that can influence outcomes of care and what future studies and methods can integrate the many factors and personnel surrounding a patient to discover how best to optimize outcomes and lower costs? The staffing perspective ultimately focuses on nurse workload, characteristics of the nurse that are associated with good outcomes, and the ability of the nurse to do all the things needed in a shift for the patient (Aiken et al., 2013; Cho et al., 2014; Schubert et al., 2013; Voepel-Lewis et al., 2013). How well do all team members work together and identify priorities, set patient specific goals, and work together towards a good outcome? The methodologic question is how to measure individual contributions of nurses along with all other providers as well as measure team cohesion, unity, and interactions. Key unresolved research questions are whether team characteristics and individual nurse characteristics together improve patient care outcomes along with other unit and hospital factors such as nursing staff turnover, use of contract nursing labor, the unit specific experience of nursing staff, and the overall care and work environment in which the nursing care delivery systems operates (Spilsbury et al., 2011). Another important variable only sparsely studied to date is the effects of nurse staffing and costs of care. There is a clear and linear relationship between nurse to patient ratios and daily patient level costs. Is there a positive cost to benefit ratio with better nurse staffing levels? The traditional use of the midnight census as a metric to predict future (next shift) staffing levels is at best inadequate and seriously flawed in guiding operational decision making (Simon et al., 2011). For example, with inadequate staffing levels patients have higher risk of developing infections, pressure ulcers, and are subject to medical errors by nurses. The cost of patients who develop adverse events is higher than other patients (Pappas, 2008). The interesting public policy question is whether reimbursement or payment for care should be aligned with optimum nursing care? In the US, nursing care is subsumed within daily room and board charges. As many nations move towards a prospective payment system, e.g. Diagnosis Related Group (DRG),
A2
J.M. Welton / International Journal of Nursing Studies 63 (2016) A1–A2
should there be an independent adjustment for nursing intensity of care as originally envisioned (Thompson et al., 1979)? Further study is needed to identify the optimum relationship between nurse staffing and assignment levels, patient level nursing costs, and outcomes of care. It is possible to develop real-time quality and performance metrics that would link nursing workload and ability to meet patient care needs? For example, bar coded medication administration data could identify when nurses are getting late in giving medication due to workload or ward activity such as a high number of admissions and discharges (Welton, 2013). Other potential performance indicators could include when and how often nurses assess and manage pain, surveillance of patient blood glucose levels, ambulating patients after surgery, etc. The data contained in the electronic health record can be used to study the relationship between staffing levels, ward activities, nurse workload and overall real-time performance. The use of the large volume of data collected in the clinical setting can be studied and inform clinicians, nurse and healthcare leaders as well as payers and policy makers (Westra et al., 2015). This effort to share and compare relevant data across many healthcare settings can provide new findings and new methods that can address the limitations of prior nurse staffing studies. Lastly, one of the primary goals of studying nursing care delivery systems is to measure and identify the added value nurses bring to the healthcare system. This value orientation encompasses both the quality, costs and outcomes of care (Pappas, 2013). There are ongoing efforts to identify and measure nursing care value based on emerging capability to measure the clinical care of patients at many different and simultaneous levels (Welton and Harper, 2016). New research methods will need to be developed to allow posing and answering multiple research questions that encompass not only nursing centric data, but a wide range of integrated clinical and operational data. The ultimate ability of these emerging methods is to compare nursing care wherever it may occur and identify and benchmark best nursing care to allow others to emulate the success and value added by nurses across the broad spectrum of healthcare. References Aiken, L.H., Cimiotti, J.P., Sloane, D.M., Smith, H.L., Flynn, L., Neff, D.F., 2011. Effects of nurse staffing and nurse education on patient deaths in hospitals with different nurse work environments. Med. Care 49 (12), 1047–1053. Aiken, L.H., Sloane, D.M., Bruyneel, L., Van den Heede, K., Sermeus, W., Consortium, R.C., 2013. Nurses’ reports of working conditions and hospital quality of care in 12 countries in Europe. Int. J. Nurs. Stud. 50 (2), 143–153. Birmingham, S.E., Nell, K., Abe, N., 2010. Determining staffing needs based on patient outcomes versus nursing interventions. In: Cowen, P.S., Moorhead, S. (Eds.), Current Issues in Nursing. Mosby, St. Louis, MO, pp. 391–404. Cho, E., Sloane, D.M., Kim, E.Y., Kim, S., Choi, M., Yoo, I.Y., Lee, H.S., Aiken, L.H., 2014. Effects of nurse staffing, work environments, and education on patient mortality: an observational study. Int. J. Nurs. Stud..
Choi, J., Staggs, V.S., 2014. Comparability of nurse staffing measures in examining the relationship between RN staffing and unit-acquired pressure ulcers: a unitlevel descriptive, correlational study. Int. J. Nurs. Stud. 51 (10), 1344–1352. D’Amour, D., Dubois, C.A., Tchouaket, E., Clarke, S., Blais, R., 2014. The occurrence of adverse events potentially attributable to nursing care in medical units: cross sectional record review. Int. J. Nurs. Stud. 51 (6), 882–891. Griffiths, P., Maben, J., Murrells, T., 2011. Organisational quality, nurse staffing and the quality of chronic disease management in primary care: observational study using routinely collected data. Int. J. Nurs. Stud. 48 (10), 1199–1210. Griffiths, P., Ball, J., Drennan, J., Dall’Ora, C., Jones, J., Maruotti, A., Pope, C., Recio Saucedo, A., Simon, M., 2016. Nurse staffing and patient outcomes: strengths and limitations of the evidence to inform policy and practice. A review and discussion paper based on evidence reviewed for the National Institute for Health and Care Excellence Safe Staffing guideline development. Int. J. Nurs. Stud. 63, 213–225. Kane, R.L., Shamliyan, T.A., Mueller, C., Duval, S., Wilt, T.J., 2007. The association of registered nurse staffing levels and patient outcomes: systematic review and meta-analysis. Med. Care 45 (12), 1195–1204. Lee, H.Y., Blegen, M.A., Harrington, C., 2014. The effects of RN staffing hours on nursing home quality: a two-stage model. Int. J. Nurs. Stud. 51 (3), 409–417. Pappas, S.H., 2008. The cost of nurse-sensitive adverse events. J. Nurs. Adm. 38 (5), 230–236. Pappas, S.H., 2013. Value, a nursing outcome. Nurs. Adm. Q. 37 (2), 122–128. Schubert, M., Ausserhofer, D., Desmedt, M., Schwendimann, R., Lesaffre, E., Li, B., De Geest, S., 2013. Levels and correlates of implicit rationing of nursing care in Swiss acute care hospitals–a cross sectional study. Int. J. Nurs. Stud. 50 (2), 230– 239. Simon, M., Yankovskyy, E., Klaus, S., Gajewski, B., Dunton, N., 2011. Midnight census revisited: reliability of patient day measurements in US hospital units. Int. J. Nurs. Stud. 48 (1), 56–61. Spilsbury, K., Hewitt, C., Stirk, L., Bowman, C., 2011. The relationship between nurse staffing and quality of care in nursing homes: a systematic review. Int. J. Nurs. Stud. 48 (6), 732–750. Thompson, J.D., Averill, R.F., Fetter, R.B., 1979. Planning, budgeting, and controlling– one look at the future: case-mix cost accounting. Health Serv. Res. 14 (2), 111– 125. Voepel-Lewis, T., Pechlavanidis, E., Burke, C., Talsma, A.N., 2013. Nursing surveillance moderates the relationship between staffing levels and pediatric postoperative serious adverse events: a nested case-control study. Int. J. Nurs. Stud. 50 (7), 905–913. Welton, J.M., Harper, E.M., 2016. Measuring nursing care value. Nurs. Econ. 34 (1), 7– 14. Welton, J.M., 2010. Response to nurse staffing and quality of care with direct measurement of inpatient staffing. Med. Care 48 (10), 940. Welton, J.M., 2013. Nursing and the value proposition: how information can help transform the healthcare system. Proceedings of the Conference: Nursing Knowledge: Big Data Research for Transforming Health Care, University of Minnesota School of Nursing, Minneapolis, MN, August 12–13, 2013 (Retrieved from http://www.nursing.umn.edu/prod/groups/nurs/@pub/@nurs/documents/content/nurs_content_452544.dot). Westra, B.L., Latimer, G.E., Matney, S.A., Park, J.I., Sensmeier, J., Simpson, R.L., Swanson, M.J., Warren, J.J., Delaney, C.W., 2015. A national action plan for sharable and comparable nursing data to support practice and translational research for transforming health care. J. Am. Med. Inform. Assoc. 22 (3), 600– 607. Yakusheva, O., Lindrooth, R., Weiss, M., 2014. Nurse value-added and patient outcomes in acute care. Health Serv. Res. 49 (6), 1767–1786.
John. M. Welton PhD RN FAAN (Dr. Professor Senior Scientist Health Systems Research) University of Colorado College of Nursing Education, 2 North, Room 4230, Mail Stop C288-18, 13120 E. 19th Avenue, Aurora, CO, 80045, USAE-mail address: [email protected] (J. Welton).
Contents lists available at ScienceDirect
International Journal of Nursing Studies journal homepage: www.elsevier.com/ijns
Guest Editorial
Nurse staffing and patient outcomes: Are we asking the right research question?
In this issue of the Journal, Peter Griffiths and colleagues address the important issue of the research methods used to study nurse staffing levels and the link to patient care quality and outcomes of care (Griffiths et al., 2016). There has been a plethora of studies investigating the outcomes of nurse staffing levels or nurse to patient ratios such as infections, injuries, pressure ulcers, clinical errors and death (Choi and Staggs, 2014; D’Amour et al., 2014; Kane et al., 2007; Lee et al., 2014). However, the authors raise a potential problem with bias from the methods used in these studies. Nurse staffing researchers have primarily used observational data and not randomized clinical trials methods, therefore cause and effect between nursing care with quality and outcomes of that care cannot be established (Griffiths et al., 2011). There are a number of potential issues with bias such as unobserved variables that can influence and separately explain the findings associated with nursing care. For example, Aiken and colleagues examined nurse staffing levels in 4 states in the US and concluded that hospitals with lower nurse to patient ratios had better risk adjusted surgical mortality (Aiken et al., 2011). The study did not control for surgical volume, a well know factor associated with outcomes of complex surgeries (unobserved variable), therefore the conclusion to improve nurse staffing levels to decrease mortality may be misguided. An alternative would be to have high risk surgeries performed in hospitals with demonstrated excellence (Welton, 2010). Adequate Staffing unto itself is a necessary but insufficient condition for safe, high quality and cost effective nursing care.One unanswered question is who is the best nurse for any situation given the needs of the patient and abilities of the nurse? To date, most of the nurse staffing studies have used units of analysis either at the hospital or ward (inpatient unit) level. Recently, access to large inpatient data sets has allowed analysis of each nurse and the effect on individual patients. A study by Yakusheva et al. found patients who had a higher portion of nurses with experience or BSN degree had lower lengths of stay and cost (Yakusheva et al., 2014). Each patient is exposed to a number of nurses during a hospitalization (or other care settings), so the actual effects of workload, experience, academic preparation, etc., can be measured by the added or cumulative effects of care by individual nurses. The key question is, are we measuring nursing care at the hospital, ward, or individual patient level? Patient level analysis of nursing care has not been feasible until recently. Emerging new methods in big data science in nursing may be able to help overcome the http://dx.doi.org/10.1016/j.ijnurstu.2016.08.015 0020-7489/ã 2016 Elsevier Ltd. All rights reserved.
previous methodologic challenges, for example identifying all of the nurses who cared for a patient, their characteristics and the short term changes in patient condition using standardized nursing terminologies (Birmingham et al., 2010). Further study is indicated at the individual nurse-patient encounter as well as the assignment patterns of nurses to patients within a shift in acute care settings. Considering the actual nurses and other providers caring for each patient, what are the patient level, nurse level, and ward level effects that can influence outcomes of care and what future studies and methods can integrate the many factors and personnel surrounding a patient to discover how best to optimize outcomes and lower costs? The staffing perspective ultimately focuses on nurse workload, characteristics of the nurse that are associated with good outcomes, and the ability of the nurse to do all the things needed in a shift for the patient (Aiken et al., 2013; Cho et al., 2014; Schubert et al., 2013; Voepel-Lewis et al., 2013). How well do all team members work together and identify priorities, set patient specific goals, and work together towards a good outcome? The methodologic question is how to measure individual contributions of nurses along with all other providers as well as measure team cohesion, unity, and interactions. Key unresolved research questions are whether team characteristics and individual nurse characteristics together improve patient care outcomes along with other unit and hospital factors such as nursing staff turnover, use of contract nursing labor, the unit specific experience of nursing staff, and the overall care and work environment in which the nursing care delivery systems operates (Spilsbury et al., 2011). Another important variable only sparsely studied to date is the effects of nurse staffing and costs of care. There is a clear and linear relationship between nurse to patient ratios and daily patient level costs. Is there a positive cost to benefit ratio with better nurse staffing levels? The traditional use of the midnight census as a metric to predict future (next shift) staffing levels is at best inadequate and seriously flawed in guiding operational decision making (Simon et al., 2011). For example, with inadequate staffing levels patients have higher risk of developing infections, pressure ulcers, and are subject to medical errors by nurses. The cost of patients who develop adverse events is higher than other patients (Pappas, 2008). The interesting public policy question is whether reimbursement or payment for care should be aligned with optimum nursing care? In the US, nursing care is subsumed within daily room and board charges. As many nations move towards a prospective payment system, e.g. Diagnosis Related Group (DRG),
A2
J.M. Welton / International Journal of Nursing Studies 63 (2016) A1–A2
should there be an independent adjustment for nursing intensity of care as originally envisioned (Thompson et al., 1979)? Further study is needed to identify the optimum relationship between nurse staffing and assignment levels, patient level nursing costs, and outcomes of care. It is possible to develop real-time quality and performance metrics that would link nursing workload and ability to meet patient care needs? For example, bar coded medication administration data could identify when nurses are getting late in giving medication due to workload or ward activity such as a high number of admissions and discharges (Welton, 2013). Other potential performance indicators could include when and how often nurses assess and manage pain, surveillance of patient blood glucose levels, ambulating patients after surgery, etc. The data contained in the electronic health record can be used to study the relationship between staffing levels, ward activities, nurse workload and overall real-time performance. The use of the large volume of data collected in the clinical setting can be studied and inform clinicians, nurse and healthcare leaders as well as payers and policy makers (Westra et al., 2015). This effort to share and compare relevant data across many healthcare settings can provide new findings and new methods that can address the limitations of prior nurse staffing studies. Lastly, one of the primary goals of studying nursing care delivery systems is to measure and identify the added value nurses bring to the healthcare system. This value orientation encompasses both the quality, costs and outcomes of care (Pappas, 2013). There are ongoing efforts to identify and measure nursing care value based on emerging capability to measure the clinical care of patients at many different and simultaneous levels (Welton and Harper, 2016). New research methods will need to be developed to allow posing and answering multiple research questions that encompass not only nursing centric data, but a wide range of integrated clinical and operational data. The ultimate ability of these emerging methods is to compare nursing care wherever it may occur and identify and benchmark best nursing care to allow others to emulate the success and value added by nurses across the broad spectrum of healthcare. References Aiken, L.H., Cimiotti, J.P., Sloane, D.M., Smith, H.L., Flynn, L., Neff, D.F., 2011. Effects of nurse staffing and nurse education on patient deaths in hospitals with different nurse work environments. Med. Care 49 (12), 1047–1053. Aiken, L.H., Sloane, D.M., Bruyneel, L., Van den Heede, K., Sermeus, W., Consortium, R.C., 2013. Nurses’ reports of working conditions and hospital quality of care in 12 countries in Europe. Int. J. Nurs. Stud. 50 (2), 143–153. Birmingham, S.E., Nell, K., Abe, N., 2010. Determining staffing needs based on patient outcomes versus nursing interventions. In: Cowen, P.S., Moorhead, S. (Eds.), Current Issues in Nursing. Mosby, St. Louis, MO, pp. 391–404. Cho, E., Sloane, D.M., Kim, E.Y., Kim, S., Choi, M., Yoo, I.Y., Lee, H.S., Aiken, L.H., 2014. Effects of nurse staffing, work environments, and education on patient mortality: an observational study. Int. J. Nurs. Stud..
Choi, J., Staggs, V.S., 2014. Comparability of nurse staffing measures in examining the relationship between RN staffing and unit-acquired pressure ulcers: a unitlevel descriptive, correlational study. Int. J. Nurs. Stud. 51 (10), 1344–1352. D’Amour, D., Dubois, C.A., Tchouaket, E., Clarke, S., Blais, R., 2014. The occurrence of adverse events potentially attributable to nursing care in medical units: cross sectional record review. Int. J. Nurs. Stud. 51 (6), 882–891. Griffiths, P., Maben, J., Murrells, T., 2011. Organisational quality, nurse staffing and the quality of chronic disease management in primary care: observational study using routinely collected data. Int. J. Nurs. Stud. 48 (10), 1199–1210. Griffiths, P., Ball, J., Drennan, J., Dall’Ora, C., Jones, J., Maruotti, A., Pope, C., Recio Saucedo, A., Simon, M., 2016. Nurse staffing and patient outcomes: strengths and limitations of the evidence to inform policy and practice. A review and discussion paper based on evidence reviewed for the National Institute for Health and Care Excellence Safe Staffing guideline development. Int. J. Nurs. Stud. 63, 213–225. Kane, R.L., Shamliyan, T.A., Mueller, C., Duval, S., Wilt, T.J., 2007. The association of registered nurse staffing levels and patient outcomes: systematic review and meta-analysis. Med. Care 45 (12), 1195–1204. Lee, H.Y., Blegen, M.A., Harrington, C., 2014. The effects of RN staffing hours on nursing home quality: a two-stage model. Int. J. Nurs. Stud. 51 (3), 409–417. Pappas, S.H., 2008. The cost of nurse-sensitive adverse events. J. Nurs. Adm. 38 (5), 230–236. Pappas, S.H., 2013. Value, a nursing outcome. Nurs. Adm. Q. 37 (2), 122–128. Schubert, M., Ausserhofer, D., Desmedt, M., Schwendimann, R., Lesaffre, E., Li, B., De Geest, S., 2013. Levels and correlates of implicit rationing of nursing care in Swiss acute care hospitals–a cross sectional study. Int. J. Nurs. Stud. 50 (2), 230– 239. Simon, M., Yankovskyy, E., Klaus, S., Gajewski, B., Dunton, N., 2011. Midnight census revisited: reliability of patient day measurements in US hospital units. Int. J. Nurs. Stud. 48 (1), 56–61. Spilsbury, K., Hewitt, C., Stirk, L., Bowman, C., 2011. The relationship between nurse staffing and quality of care in nursing homes: a systematic review. Int. J. Nurs. Stud. 48 (6), 732–750. Thompson, J.D., Averill, R.F., Fetter, R.B., 1979. Planning, budgeting, and controlling– one look at the future: case-mix cost accounting. Health Serv. Res. 14 (2), 111– 125. Voepel-Lewis, T., Pechlavanidis, E., Burke, C., Talsma, A.N., 2013. Nursing surveillance moderates the relationship between staffing levels and pediatric postoperative serious adverse events: a nested case-control study. Int. J. Nurs. Stud. 50 (7), 905–913. Welton, J.M., Harper, E.M., 2016. Measuring nursing care value. Nurs. Econ. 34 (1), 7– 14. Welton, J.M., 2010. Response to nurse staffing and quality of care with direct measurement of inpatient staffing. Med. Care 48 (10), 940. Welton, J.M., 2013. Nursing and the value proposition: how information can help transform the healthcare system. Proceedings of the Conference: Nursing Knowledge: Big Data Research for Transforming Health Care, University of Minnesota School of Nursing, Minneapolis, MN, August 12–13, 2013 (Retrieved from http://www.nursing.umn.edu/prod/groups/nurs/@pub/@nurs/documents/content/nurs_content_452544.dot). Westra, B.L., Latimer, G.E., Matney, S.A., Park, J.I., Sensmeier, J., Simpson, R.L., Swanson, M.J., Warren, J.J., Delaney, C.W., 2015. A national action plan for sharable and comparable nursing data to support practice and translational research for transforming health care. J. Am. Med. Inform. Assoc. 22 (3), 600– 607. Yakusheva, O., Lindrooth, R., Weiss, M., 2014. Nurse value-added and patient outcomes in acute care. Health Serv. Res. 49 (6), 1767–1786.
John. M. Welton PhD RN FAAN (Dr. Professor Senior Scientist Health Systems Research) University of Colorado College of Nursing Education, 2 North, Room 4230, Mail Stop C288-18, 13120 E. 19th Avenue, Aurora, CO, 80045, USAE-mail address: [email protected] (J. Welton).