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- Nurse Staffing and Patient Care Quality and Safety - Patient Safety and Quality - NCBI Bookshelf
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Contemporary Australian and New Zealand midwifery and maternity services Chapter 3: Human rights in childbirth Chapter 4: Fear and risk Chapter 5: Midwifery as primary health Chapter 6: Birth place and birth space Chapter 7: Social and environmental determinations of women's health Chapter 9: Midwives and Maori women: Professional frameworks for practice in Australia and New Zealand Chapter Legal frameworks for practice in Australia and New Zealand Chapter Supporting midwives, supporting each other Chapter Midwifery partnership Chapter Working in collaboration Chapter Overview of reproductive physiology Chapter Nutrition and physical activity foundations for pregnancy, childbirth and lactation Chapter Screening and assessment Chapter Working with women in pregnancy Chapter Applied physiology for labour and birth Chapter Nurse staffing is a crucial health policy issue on which there is a great deal of consensus on an abstract level that nurses are an important component of the health care delivery system and that nurse staffing has impacts on safety , much less agreement on exactly what research data have and have not established, and active disagreement about the appropriate policy directions to protect public safety.
The purpose of this chapter is to summarize and discuss the state of the science examining the impact of nurse staffing in hospitals and other health care organizations on patient care quality, as well as safety-focused outcomes. To address some of the inconsistencies and limitations in existing studies, design issues and limitations of current methods and measures will be presented. The chapter concludes with a discussion of implications for future research, the management of patient care and public policy.
For several decades, health services researchers have reported associations between nurse staffing and the outcomes of hospital care. There has been remarkable growth in this body of literature since the IOM report. Over the course of the last decade, hospital restructuring, spurred in part by a move to managed care payment structures and development of market competition among health care delivery organizations, led to aggressive cost cutting.
Human resources, historically a major cost center for hospitals, and nurse staffing in particular, were often the focus of work redesign and workforce reduction efforts. Cuts in nursing staff led to heavier workloads, which heightened concern about the adequacy of staffing levels in hospitals.
A few years ago, reports began documenting a new, unprecedented shortage of nurses linked to growing demand for services, as well as drops in both graduations from prelicensure nursing education programs and workforce participation by licensed nurses, linked by at least some researchers to deteriorating working conditions in hospitals. An expected deepening of the shortage in coming years 12 has increased the urgency of understanding the staffing-outcomes relationship and offering nurses and health care leaders evidence about the impacts of providing care under variable nurse staffing conditions.
This chapter includes a review of related literature from early The availability of data on measures of quality that can be reasonably attributed to nurses, nursing care, and the environments in which care is delivered has constrained research studying the link between staffing and outcomes. No matter what label these measures are given, measures that have conceptual and clinical links to the practice of nursing and are sensitive to variations in the structure and processes of nursing care are an essential ingredient in this area of research.
Data sources from which to construct these measures must be identified, and exact definitions indicating how measures are to be calculated must be drafted. This is particularly critical if different individuals or groups are involved in compiling quality measures. There have been calls for standardization of measures of the quality of health care for some time, 1 , 15 along with outcome measures related to the quality of nursing care. Inconsistent definitions have slowed progress in research and interfered with comparability of results across studies.
A paper, now under review, examines and compares common measures of adult, acute care nurse staffing, including unit-level hospital-generated data gleaned from the California Nursing Outcomes dataset, hospital-level payroll accounting data obtained from the California Office of Statewide Health Planning and Development, hospital-level personnel data submitted to the American Hospital Association, and investigator research data obtained from the California Workforce Initiative Survey.
Findings reveal important differences between measures that may explain at least some inconsistencies in results across the literature Spetz, Donaldson, Aydin, personal communication February, This initiative began with a literature search to identify potential nurse-sensitive quality indicators. Next, expert reviewers examined and validated a smaller, selected group of indicators and measures from among these.
All four groups currently collect and analyze unit-level data related to the associations between nurse staffing and the quality and safety of patient care. Together, they have formed an unofficial collaborative of nursing quality database projects. The most recent initiative in standardizing staffing and outcomes measures for quality improvement and research purposes was undertaken by the National Quality Forum NQF.
The mission of the NQF is to improve American health care through consensus-based standards for quality measurement and public reporting related to whether health care services are safe, timely, beneficial, patient centered, equitable, and efficient.
The aim of the expert panel was to explicate and endorse national voluntary consensus standards as a framework for measuring nursing-sensitive care and to inform related research. These measures represent a first but by no means final attempt to make nurse-sensitive outcomes visible to the broader community of payers and policymakers. The first 15 voluntary consensus standards for nursing-sensitive care intended for use in public reporting and policy initiatives included Figure 2 illustrates a set of conceptual relationships between the key variables in this review, including influences on staffing levels and factors influencing outcomes.
These relationships form a set of interrelated pathways that link nurse staffing to patient care quality, safety, and outcomes.
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Notable is that each of the elements enclosed in a box—specifically administrative decisions, quality of nursing care, care needs, and safety and clinical outcomes—is influenced by a host of factors that are not detailed in the diagram and could each be the subject of its own literature review. The quality of care that nurses provide is influenced by individual nurse characteristics such as knowledge and experience, as well as human factors such as fatigue.
The quality of care is also influenced by the systems nurses work in, which involve not only staffing levels, but also the needs of all the patients a nurse or nursing staff is responsible for, the availability and organization of other staff and support services, and the climate and culture created by leaders in that setting. The same nurse may provide care of differing quality to patients with similar needs under variable staffing conditions and in different work environments.
The sheer number of variables and myriad linkages depicted suggest why precise evidence-based formulas for deploying nursing staff to ensure safe, high-quality patient care are impossible based on the knowledge on hand. In fact, such prescriptions may never be possible. Certainly, evidence-based guidelines for allocating resources to ensure optimal outcomes in acute care and other health care settings cannot be offered until working environments, staffing beyond head counts and skill mix , patient needs, processes, and outcomes of care can be measured with precision.
Research investigating links between hospital nurse staffing and patient outcomes began with studies examining patient mortality. Reviews now include research examining a broad range of outcomes, including specific adverse events other than mortality. Although many studies support a link between lower nurse staffing and higher rates of negative nurse-sensitive safety outcomes, 25—27 reviews of two decades of research revealed inconsistent results across studies.
Before examining the state of the scientific literature on the relationship between nurse staffing and clinical outcomes, it is important to consider common challenges of research in this arena. Investigators face at least two fundamental problems when designing staffing-outcomes studies: As noted earlier in this chapter, because of limitations in measures, data sources, and analytic methods, researchers generally ask a different question in their studies Is there a correlation between staffing and patient care outcomes?
As clinical trials or controlled experiments are difficult if not impossible to conduct in this area, observational designs must be optimized as much as possible. When outcomes are compared across hospitals or other health care organizations as a whole or their clinical units or microsystems, frequently the research design that results from data linkages and analyses is cross-sectional and correlational in nature.
Staffing levels and patient outcomes from approximately the same time are analyzed to determine whether a correlation exists between the two. As all students of research methods know, correlational designs are more limited than experiments for determining the extent to which causal links exist between staffing levels and outcomes.
Factors other than nurse staffing can vary alongside staffing levels, so whether or not certain different staffing levels directly lead to better or worse outcomes cannot be determined with certainty from correlational designs. Statistical methods can control for obvious factors that influence or are otherwise associated with staffing levels such as hospital size, academic affiliation, or rural-urban location.
Nonetheless, it is impossible to measure and account for all possible confounding variables or competing explanations for findings in the typical designs of these studies. Tables 1 and 2 provide brief overviews of types of measures and the questions consumers of staffing outcomes research might consider in appraising individual studies.
The discussion that follows is intended to emphasize a few fundamental points before turning to the findings in the literature itself. Staffing levels can be reported or calculated for an entire health care organization or for an operational level within an organization a specific unit, department, or division. Specific time frames at the shift level and as a daily, weekly, or yearly average must be identified to ensure common meaning among collectors of the data, those analyzing it, and individuals attempting to interpret results of analyses.
In many cases, staffing measures are calculated for entire hospitals over a 1-year period. It is fairly common to average or aggregate staffing across all shifts, for instance, or across all day shifts in a month, quarter, or year and sometimes also across all the units of hospitals. However, staffing levels on different units reflect differences in patient populations and illness severity the most striking of which are seen between general care and critical care units.
Nurse Staffing and Patient Care Quality and Safety - Patient Safety and Quality - NCBI Bookshelf
Furthermore, in practice, staffing is managed on a unit-by-unit, day-by-day, and shift-by-shift basis, with budgeting obviously done on a longer time horizon. For these reasons, some researchers argue that at least some research should be conducted where staffing is measured on a shift-specific and unit-specific basis instead of on a yearly, hospitalwide basis. A distinct, but growing, group of studies examined staffing conditions in subunits or microsystems of organizations such as nursing units within hospitals over shorter periods of time for example, monthly or quarterly.
In addition to three sources of staffing data, there are also two basic types of staffing measures or variables.
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The first type divides a volume of nurses or nursing services by a quantity of patient care services. Common examples include patient-to-nurse ratios, hours of nursing care delivered by various subtypes of personnel per patient day HPPD , and full-time equivalent FTE positions worked in relation to average patient census ADC over a particular time period. The second major type of measure examines the credentials or qualifications of those staff members and expresses them as a proportion of staff with more versus less training or vice-versa.
Commonly, the composition of the nursing staff employed on a unit or in a hospital in terms of unlicensed personnel, practical or vocational nurses, and registered nurses RNs is calculated. The specific types of educational preparation held by RNs baccalaureate degrees versus associate degrees and diplomas have also begun to be studied. Additional staffing-related characteristics studied include years of experience and professional certification. The incidence of voluntary turnover and the extent to which contract or agency staff provide care have also been studied.
As will be discussed, the majority of the evidence related to hospital nurse staffing focuses on RNs rather than other types of personnel. For the most common measures, ratios and skill-mix, determining which staff members should be included in the calculations is important, given the diversity of staffing models in hospitals. Most researchers feel these statistics should reflect personnel who deliver direct care relevant to the patient outcomes studied. Whether or not to count charge nurses, nurse educators involved in bedside care, and nurses not assigned a patient load but who nevertheless deliver important clinical services can present problems, if not in principle, then in the reality of data that institutions actually collect.
Outcomes research examining the use of advanced practice nurses in acute care—for instance, nurse practitioners and nurse anesthetists—to provide types of care traditionally delivered by medical staff and medical trainees has been done in a different tradition analyzing the experiences of individual patients cared for by specific providers and does not tend to focus on outcomes relevant to staff nurse practice; therefore these studies are not reviewed here. No studies were found that examined advanced practice nurse-to-patient ratios or skill mix in predicting acute care patient outcomes.
There have been calls to examine advanced practice nurses supporting frontline nurses in resource roles for instance, clinical nurse specialists who consult and assist in daily nursing care, staff development, and quality assurance and their potential impact on patient outcomes.
No empirical evidence of this type was found. Clearly, capturing data about patient outcomes prospectively i. This approach is the most challenging because of practical, ethical, and financial considerations. However, researchers can sometimes capitalize on prospective data collections already in progress. For instance, hospital-associated pressure ulcer prevalence surveys and patient falls incidence are commonly collected as part of standard patient care quality and safety activities at the level of individual nursing units in many institutions.
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Patients are not all at equal risk of experiencing negative outcomes. Elderly, chronically ill, and physiologically unstable patients, as well as those undergoing lengthy or complex treatment, are at much greater risk of experiencing various types of adverse events in care. For instance, data on falls may be consistently collected for all hospitalized patients but may not be particularly meaningful for obstetrical patients. Accurately interpreting differences in rates across health care settings or over time requires understanding the baseline risks patients have for various negative outcomes that are beyond the control of the health care providers.
Ultimately this understanding is incorporated into research and evaluation efforts through risk adjustment methods, usually in two phases: Without sound risk adjustment, any associations between staffing and outcomes may be spurious; what may appear to be favorable or unfavorable rates of outcomes in different institutions may no longer seem so once the complexity or frailty of the patients being treated is considered. The focus of this review is on staffing and safety outcomes.
However, as was noted earlier, quality of care and clinical outcomes and by extension, the larger domain of nursing-sensitive outcomes include not only processes and outcomes related to avoiding negative health states, but also a broad category of positive impacts of sound nursing care. Knowledge about positive outcomes of care that are less likely to occur under low staffing conditions or are more likely under higher levels is extremely limited.
The findings linking functional status, psychosocial adaptation to illness, and self-care capacities in acute care patients are at a very early stage 37 but eventually will become an important part of this literature and the business case for investments in nurse staffing and care environments. In staffing-outcomes studies, researchers must match information from data sources about the conditions under which patients were cared for with clinical outcomes data on a patient-by-patient basis or in the form of an event rate for an organization or organizational subunit during a specific period of time.
Ideally, errors or omissions in care would be observed and accurately tracked to a particular unit on a particular shift for which staffing data were also available. Most, but not all, large-scale studies have been hospital-level analyses of staffing and outcomes on an annual basis and have used large public data sources. Linkages of staffing with outcomes data involve both a temporal time component and a departmental or unit component. These include some types of complications as well as patient deaths. Attribution of outcomes is complicated by the reality that patients are often exposed to more than one area of a hospital.
For instance, they are sometimes initially treated in the emergency department, undergo surgery, and either experience postanesthesia care on a specialized unit or stay in an intensive care unit before receiving care on a general unit. Unfortunately, in hospital-level datasets, it is impossible to pinpoint the times and locations of the errors or omissions most responsible for a clinical endpoint.
In the end, if outcomes information is available only for the hospital as a whole which is the case in discharge abstracts, for instance , data linkage can happen only at the hospital level, even if staffing data were available for each unit in a facility.
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Similarly, if staffing data are available only as yearly averages, linkage can be done only on an annual basis, even if outcomes data are available daily or weekly. Linkages can be done only at the broadest levels on the least-detailed basis or at the highest level of the organization available in a dataset.
Many patient outcomes measures such as potentially preventable mortality may actually be more meaningful if studied at the hospital level, while others such as falls may be appropriately examined at the unit level. One should recognize that common mismatches between the precision of staffing measures and the precision of outcome measures i. This finding is particularly relevant when staffing statistics span a long time frame and therefore contain a great deal of noise—information about times other than the ones during which particular patients were being treated.
High-quality staffing data, as well as patient assessment and intervention data—all of which are accurately date-stamped and available for many patients, units, and hospitals—will be necessary to overcome these linkage problems. Such advances may come in the next decades with increased automation of staffing functions and the evolution of the electronic medical record. Recent prospective unit-level analyses, now possible with datasets developed and maintained by the NDNQI, CalNOC, and the military hospital systems, make it possible to overcome some of these issues.
These databases, although not risk adjusted, stratify data by unit type and hospital size and have adopted standardized measures of nurse staffing and quality of care. The resulting datasets provide opportunities to study how variations in unit-level staffing characteristics over time can influence patient outcomes for instance, pressure ulcers and falls, as discussed later.
As data sources do not exist for all types of staffing and outcomes measures at all levels of hospital organization nor will they ever , research at both the unit level and the hospital level will continue, and both types of studies have the potential to inform understanding of the staffing-outcomes relationship.
Perhaps staffing and outcomes research has such importance and relevance for clinicians and educators as well as for managers and policymakers, staffing-outcomes research is a frequently reviewed area of literature. As was just detailed, a diversity of study designs, data sources, and operational definitions of the key variables is characteristic of this literature, which makes synthesis of results challenging. Many judgments must be made about which studies are comparable, which findings if any contribute significantly to a conclusion about what this literature says, and perhaps regarding how to transform similar measures collected differently so they can be read side by side.
The review of evidence here builds on a series of recent systematic reviews with well-defined search criteria. These findings have appeared in studies conducted using a variety of designs and examining hospital care in different geographical areas and over different time periods. The evidence table summarizes four major systematic reviews of the literature, approaches, and conclusions regarding the state of the evidence for specific outcomes or outcome types. Collaborative Management tables concisely summarize key points while incorporating nationally recognized guidelines.
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