Personnel Protection: Advance Procedures: Proven Practices

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.

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.

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. In these papers, reviewers identify specific measurement types and established criteria for study inclusion in terms of design and reporting and examined a relatively complete group of the studies one by one to provide an overview of the state of findings as an integrated whole. The contrasts in the conclusions are interesting but are probably less important than the overall trend: An additional important point is that nearly all studies connecting staffing parameters with outcomes have been conducted at the hospital rather than the unit level.

In a 2-year AHRQ Working Conditions and Patient Safety study built on the work of CalNOC, Donaldson and colleagues 17 engaged acute care hospitals using ANA nursing indicators for reporting staffing, patient safety, and quality indicators in a research, repository development, and benchmarking project. Data were drawn from 25 acute care, not-for-profit California hospital participants in the regional CalNOC. The sample included urban and rural hospitals with an average daily census from to more than patients.

The aims of the study were to test associations between daily nurse staffing on adult medical-surgical units and hospital-acquired pressure ulcers, patient falls, and other significant adverse events, if they were of sufficient volume to analyze. A prospective, descriptive, correlational design tested associations between patient outcome measures and daily unit-level nurse staffing, skill mix, hours of care along with hours covered by supplemental agency staff , and workload.

Unit activity index and hospital complexity measured by bed size were also significant predictors of falls. In another analysis, Donaldson and colleagues 39 traced daily, unit-level direct care nurse staffing in 77 units across 25 hospitals over a 2-month period using data on staffing effectiveness the match between hours of care and hours provided.

By law in California, each hospital unit uses an institutionally selected, acuity-based workload measurement system to determine required hours of care for each patient. For each patient-care unit, the ratio of actual to required hours of care, was expressed as both a mean ratio and as a percentage of days on which required hours exceeded actual hours over the 7 days prior to a pressure ulcer prevalence study.

These analyses linked unit-level staffing and safety-related outcomes data, and measured for time periods at the unit level closely and logically connected staffing measures relevant to conditions before the outcome occurred. Both researchers and research consumers need to reflect on the time frames involved in the evolution of various outcomes when assessing the validity of data linkages across time and units.

For instance, in contrast to the lags between quality problems in care and evidence of their impact on outcomes such as infections and pressure ulcers, practice conditions will tend to have more immediately observable impacts on outcomes like falls with injury and most adverse drug reactions. Recent legislation in California that introduced mandated nurse-to-patient ratios at the unit level provides an interesting context for studying the association of staffing and outcomes. CalNOC has reported early comparisons of staffing and outcomes in medical-surgical and step-down units in 68 California hospitals during two 6-month intervals Q1 and Q2 of and Q1 and Q2 of before and after introduction of the ratios.

Data were stratified by hospital size and unit type. On medical-surgical units, mean total RN hours per patient day increased by However, there were no statistically significant changes in the rate of patient falls or pressure ulcers on these units. Researchers have generally found that lower staffing levels are associated with heightened risks of poor patient outcomes.

Staffing levels, particularly those related to nurse workload, also appear related to occupational health issues like back injuries and needlestick injuries and psychological states and experiences like burnout that may represent precursors for nurse turnover from specific jobs as well as the profession. Associations are not identified every time they are expected in this area of research. Other aspects of hospital working conditions beyond staffing, as well individual nurse and patient characteristics, affect outcomes since negative outcomes are relatively uncommon even at the extremes of staffing and do not occur in every circumstance where staffing is low.

A critical mass of studies established that nurse staffing is one of a number of variables worthy of attention in safety practice and research. There is little question that staffing influences at least some patient outcomes under at least some circumstances. Future research will clarify more subtle issues, such as the preferred methods for measuring staffing and the precise mechanisms through which the staffing-outcomes relationship operates in practice.

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Nurse executives and frontline managers make decisions about numbers of staff to assign to the various areas of their facilities. They also establish models of care to be used in caring for patients in terms of the constellation of nursing staff and distribution of responsibilities among professional nurses and other types of nursing staff.

Policymakers want assurances that the nursing workforce in their jurisdictions is adequate; they also want to know whether or not regulatory intervention is necessary to ensure acceptable staffing levels and desirable patient outcomes. Staffing researchers are ultimately constrained by the limitations of their data in answering many questions of relevance to the real worlds of health care delivery and public policy.

Investigators most commonly examined the correlations of complex patient outcomes with staffing measures derived at some distance from the delivery of care perhaps aggregated over time. Researchers then asked whether measures of staffing and outcomes were statistically associated with each other.

A clear distinction between direct conclusions from research findings and the opinions of particular authors or interest groups must be made. It is impossible to specify parameters for staffing that will ensure safety based on current evidence without many qualifiers. The adequacy of staffing the degree to which staffing covers patient needs even for the same patients and nurses may change from hour to hour, particularly in acute care settings. Nurse-to-patient ratios and skill mixes in specific settings that are too low for safety still cannot be identified on the basis of the research literature, but decisions must be made on the basis of the judgments by frontline staff and their managers.

On a related note, the specific nursing care processes that are more likely to be omitted or rendered less safe under different staffing conditions are not well understood, empirically speaking, and deserve further attention. A number of other areas identified in the staffing literature are relatively underdeveloped. Most research on staffing has been conducted in acute care settings; however, not all clinical areas within acute care have been equally well studied.

Data regarding settings for the care of children, childbearing families, and patients with mental health problems are currently very thin. The majority of nurses working in hospitals in the United States are, of course, registered nurses. Available evidence suggested that patients in hospitals that use more licensed practical nurses LPNs or vocational nurses may see worse outcomes. There is no direct evidence that it is unsafe to employ LPNs in acute care settings, 42 , 43 nor is there empirical support that the use of unlicensed personnel is intrinsically related to poor outcomes.

Use of practical nurses and UAPs can be driven by any and all of the factors outlined in Figure 2. The models of care under which LPNs and unlicensed care providers are employed i. While RNs have the broadest scope of practice of frontline nursing workers, it is far from established that percent RN staffing is effective in all situations. Until then and even when it does , local labor market realities, experience, and judgment will need to be used by leaders to establish skill mix and to define the models of care under which RNs, LPNs, and UAPs work.

Early studies have offered early, tantalizing insights regarding a number of variables conceptually close to staffing. These findings include the educational preparation of RN staff in hospitals. Two recent studies 44 , 45 found that mortality in surgical and medical patients was lower in hospitals where higher proportions of staff nurses held baccalaureate degrees. Additionally, in this latter work, units where higher percentages of RNs held specialty certification had lower proportions of restrained patients. Should these findings be borne out in future studies, there are important potential local and national policy implications.

There is a clear need for more research. Similarly, while many feel experience and specialty training have logical associations with quality of care and patient safety, empirical data regarding their impact are very limited at present. Yet another area where data related to patient outcomes are thin relates to the impact of specific types of work environments on nurse-sensitive outcomes, and in particular the impact of the Magnet hospital model, which has been argued to produce superior patient outcomes and safer care.

To our knowledge, there are no studies yet to directly support a connection between safety and specific managerial approaches or to link Magnet status with patient outcomes in the current era of certification. However, early findings with respect to questions around the outcomes of the program are expected in the coming years. There has been intense interest in identifying staffing-outcomes relationships in long-term care settings.

RNs are, of course, in the minority among the nursing staff in long-term care, with unlicensed providers providing the bulk of physical care in these facilities. There are many challenges in using existing documentation and databases to measure outcomes in long-term care facilities, 48 some of which are shared with outcomes measurement in acute care.

Long-term care researchers face special issues, specifically with respect to data reliability and measure stability, skewedness of measures, and selection and ascertainment bias where types of patients at high risk for poor outcomes or who are more closely observed are concentrated in certain nursing homes. Despite these problems, a critical mass of studies suggests that long-term care facilities with the lowest licensed and unlicensed staffing levels among their peers show disproportionately worse patient outcomes.

A study sponsored by the Centers for Medicare and Medicaid Services CMS suggested that among short-stay patients, skilled nursing facilities with the lowest staffing levels were 30 percent more likely to fall in the worst 10 percent of facilities for transfers to acute care for acute heart failure, electrolyte imbalances, sepsis, respiratory infection, and urinary tract infection.

Facilities with staffing below thresholds of 2. In 1, residents of 82 long-term care facilities, patients in facilities with more direct RN time 30—40 minutes per patient day and more had fewer pressure ulcers, acute care hospitalizations, urinary tract infections, and urinary catheters, and less deterioration in ability to perform activities of daily living.

These researchers suggested that administrative practices other than staffing may play an important role in determining long-term care quality. Home health is a growing sector in U. Staffing models fall somewhere between acute care hospitals and long-term care in terms of the proportions of unlicensed personnel and practical nurses. Allocation of nursing time to patients presumably influences quality and thoroughness of nursing acts and assessments.

There may be skill-mix issues as well. However, to date there have been no studies of home health agency staffing models, nurse workloads, or skill mix.

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OASIS Outcomes Assessment and Information Set data gathered by home health providers by mandate from the Medicare program, skillfully analyzed and interpreted, will offer opportunities to examine safety in home care in relation to staffing decisions. The general conclusion of these studies conducted in various settings is that differences in outcomes are often observed between situations or institutions where staffing is high and those where it is low.

A critical mass of data suggests that staffing at the lower end of the continuum may place patients and nurses at heightened risk of poor outcomes. Therefore, it appears hazardous to patients and staff to staff at the lowest levels relative to peer units and health care organizations. Limitations of cross-sectional observational designs that predominate in this literature have been reviewed extensively in the chapter. Prominent among these is that there is no guarantee that increasing staffing alone improves the process or outcomes of care. Nonetheless, it would appear wise to continue the widespread practice of adjusting staffing levels for setting, specialty, model of care, client needs, special circumstances, and trends in the frequency of outcomes potentially sensitive to nurse staffing.

A key implication arising from this review is that as much as possible, investigators should align their studies with emerging taxonomies and specifications of measures promulgated by authoritative sources e. Continued proliferation of measures is slowing progress in this field. Standardized measurement will advance meta-analytic efforts and facilitate aggregation of data across studies. As hospitals and health systems are inundated with data-reporting demands, wise investigators will leverage ongoing measurement efforts by selecting core measures and common metrics already collected by hospitals.

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There is value for researchers to forge strategic partnerships with professional sponsors of public and private data repositories. Agencies and researchers alike will be served well by study designs that use already de-identified data and make minimal use of protected health information, particularly since the Health Insurance Portability and Accountability Act took effect in Likewise, both researchers and clinical administrators must fully harness the potential of new health information systems to capture clinical data. Some authors suggested that competing on the analytics is a characteristic of high-performing organizations.

Leaders at all levels in the health care system must decide how to apply the findings of this literature. It is impossible to read and discuss this area of research without considering whether regulation of nurse staffing is a valid application of the findings, especially in the current climate in health care. As in all aspects of health care management, empirical evidence needs to be interpreted in the context of local data and experience. Even absent any specific legal mandates to do so, benchmarking staffing and outcomes against peers and attempting to avoid extremes of low staffing and high adverse events, keeping in mind important contextual factors when making comparisons, is undoubtedly the best administrative practice.

Executives and managers make a host of decisions beyond those involving staffing that affect the clinical effectiveness of nursing staff. Thought leaders in the arena of patient safety practices have identified a number of organizational strategies that may constitute better practice in managing the impact of nurse staffing on patient care quality and safety.

For example, efforts to optimize clinical, throughput flow and reduce practice variability may reduce threats to staff and patients due to system and personnel overload. Engaging staff in self-governance related to patient flow has also been cited as a promising best practice. Considered key to safe staffing, professional judgment as the gold standard establishes the threshold for safe patient care in a given clinical setting, 59 as nurses use a systematic decision matrix to determine if the staff on a particular unit can accept responsibility for additional patients.

Informed by understanding of scientific conclusions linking staffing and patient outcomes in comparable settings, the self-governing and administrative teams of the future may use internally generated data to support decisions related to staffing adequacy and effectiveness. As clinicians and administrators in clinical settings gain greater access to real-time data that enable them to explore links between structure, process, and outcomes, increasingly sophisticated tools such as virtual dashboards are promising.

There are a great many questions in this field that are still unanswered. There is a clear need to investigate processes of care that are specific to nursing that are associated with safer patient care as well as safer, more efficient interdisciplinary team functioning.

Data issues a lack of measures and of data sources are a major barrier to work on care delivery. Future research must tackle the black box of nursing practice by acknowledging the complexity of nursing assessment, planning, intervention, and evaluation. Addressing variance in the quality of patient care performed by nurses is key to interpreting inconsistencies in the nurse staffing literature and perhaps at the heart of efforts to improve patient care outcomes.

Ultimately, it is a priority for future research to explicate links between structure, process, and outcome in nursing practice and patient care. As indicated before, study of models of care using non-RN staff in acute care, of the impacts of high levels of staffing on health-promoting nursing interventions and nurse-sensitive outcomes, and of staffing and outcomes in understudied specialties in acute care and in nonacute care settings is vital.

From a research tradition in which nurse staffing factors were primarily background variables, the study of nurse staffing and patient outcomes has emerged as a legitimate and strategically crucial field of inquiry. However, despite significant growth in the number and sophistication of studies responding to public policy and provider demand for these findings, results have been inconsistent. This chapter highlights the methodologic challenges inherent in this area of inquiry and explicates how the diversity in measures and units of analyses confound literature synthesis.

In the face of myriad pressures to adopt a position for or against mandated nurse-to-patient ratios, the state of the young science does not permit precision in prescribing safe ratios. In fact, it may be concluded that further research is crucial to tease out the nuances in the staffing-outcomes equation. Until then, selected better practices have been noted, with the potential to contribute to pragmatic efforts to improve patient care quality and safety in hospitals.

The literature on nurse staffing and patient safety is rapidly evolving, very heterogeneous in terms of measures and methods, and equivocal in terms of many of its conclusions regarding specific measures. Our aim was to describe broad trends in this literature, and to this end, we based our work on four systematic, integrated reviews that contained detailed search criteria and clearly-articulated inclusion criteria and provided detailed syntheses of findings.

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Background For several decades, health services researchers have reported associations between nurse staffing and the outcomes of hospital care. Identifying Nurse-Sensitive Outcomes 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.

A Framework Relating Nurse Staffing to Patient Care Quality and Safety Figure 2 illustrates a set of conceptual relationships between the key variables in this review, including influences on staffing levels and factors influencing outcomes. Staffing levels are set by administrators and are affected by forces that include budgetary considerations and features of local nurse labor markets.

Administrative practices result in a structure of the nursing staff of an agency nature of supervision and staff or staff hours assigned to different subunits in a facility. These practices also affect the mix and characteristics of the nurse workforce, the model of care used in assigning staff and in providing care, and a wide range of workplace environments that affect how nurses practice. Other characteristics of the workplace environments noted in the literature included the physical environment, communication systems and collaboration, information systems, and relevant support services.

The vulnerabilities of patients for adverse events varies and changes over the course of a hospital stay or episode of care. The quality of nursing care relates to the appropriate execution of assessments and interventions intended to optimize patient outcomes and prevent adverse events. For example, the extent to which nurses assess the risk for falls in hospital patients upon admission, implement evidence-based fall-prevention protocols, and sustain such preventive interventions could each be developed into measures of nursing care quality.

The quality of nursing care also includes attention to safety issues, for example, the accuracy of medication administration. Safety outcomes include rates of errors in care as well as potentially preventable complications in at-risk patients. Safe practices that avoid errors and foreseeable complications of care can be thought of as either a basic element of or a precondition for delivering high-quality care, but are generally thought of as only one component of quality.

Clinical outcomes endpoints of importance vary from patient to patient or by clinical population and include mortality, length of stay, self-care ability, adherence to treatment plans, and maintenance or improvement in functional status. Serious errors or complications often lead to poor clinical outcomes. So far, very few positive clinical outcomes have been studied by staffing-outcomes researchers, probably because of limited measures and data sources.

State of Science on the Relationship Between Nurse Staffing and Patient Outcomes 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. Processes that are inefficient and variable, changing case mix of patients, health insurance, differences in provider education and experience, and numerous other factors contribute to the complexity of health care. The goals of measuring health care quality are to determine the effects of health care on desired outcomes and to assess the degree to which health care adheres to processes based on scientific evidence or agreed to by professional consensus and is consistent with patient preferences.

Because errors are caused by system or process failures, 5 it is important to adopt various process-improvement techniques to identify inefficiencies, ineffective care, and preventable errors to then influence changes associated with systems. Each of these techniques involves assessing performance and using findings to inform change. This chapter will discuss strategies and tools for quality improvement—including failure modes and effects analysis, Plan-Do-Study-Act, Six Sigma, Lean, and root-cause analysis—that have been used to improve the quality and safety of health care.

The rationale for measuring quality improvement is the belief that good performance reflects good-quality practice, and that comparing performance among providers and organizations will encourage better performance. In the past few years, there has been a surge in measuring and reporting the performance of health care systems and processes.

The complexity of health care systems and delivery of services, the unpredictable nature of health care, and the occupational differentiation and interdependence among clinicians and systems 16—19 make measuring quality difficult. One of the challenges in using measures in health care is the attribution variability associated with high-level cognitive reasoning, discretionary decisionmaking, problem-solving, and experiential knowledge. The Agency for Healthcare Research and Quality AHRQ , the National Quality Forum, the Joint Commission, and many other national organizations endorse the use of valid and reliable measures of quality and patient safety to improve health care.

These measures are generally developed through a process including an assessment of the scientific strength of the evidence found in peer-reviewed literature, evaluating the validity and reliability of the measures and sources of data, determining how best to use the measure e. Measures of quality and safety can track the progress of quality improvement initiatives using external benchmarks.

Benchmarking in health care is defined as the continual and collaborative discipline of measuring and comparing the results of key work processes with those of the best performers 26 in evaluating organizational performance. There are two types of benchmarking that can be used to evaluate patient safety and quality performance. Internal benchmarking is used to identify best practices within an organization, to compare best practices within the organization, and to compare current practice over time. The information and data can be plotted on a control chart with statistically derived upper and lower control limits.

However, using only internal benchmarking does not necessarily represent the best practices elsewhere. Competitive or external benchmarking involves using comparative data between organizations to judge performance and identify improvements that have proven to be successful in other organizations. More than 40 years ago, Donabedian 27 proposed measuring the quality of health care by observing its structure, processes, and outcomes. Structure measures assess the accessibility, availability, and quality of resources, such as health insurance, bed capacity of a hospital, and number of nurses with advanced training.

Process measures assess the delivery of health care services by clinicians and providers, such as using guidelines for care of diabetic patients. Outcome measures indicate the final result of health care and can be influenced by environmental and behavioral factors. Examples include mortality, patient satisfaction, and improved health status.

Twenty years later, health care leaders borrowed techniques from the work of Deming 28 in rebuilding the manufacturing businesses of post-World War II Japan. The TQM model is an organizational approach involving organizational management, teamwork, defined processes, systems thinking, and change to create an environment for improvement. This approach incorporated the view that the entire organization must be committed to quality and improvement to achieve the best results.

CQI has been used as a means to develop clinical practice 30 and is based on the principle that there is an opportunity for improvement in every process and on every occasion. CPI, an approach lead by clinicians that attempts a comprehensive understanding of the complexity of health care delivery, uses a team, determines a purpose, collects data, assesses findings, and then translates those findings into practice changes.

From these models, management and clinician commitment and involvement have been found to be essential for the successful implementation of change. Shojania and colleagues 38 developed a taxonomy of quality improvement strategies see Table 1 , which infers that the choice of the quality improvement strategy and methodology is dependent upon the nature of the quality improvement project.

Quality improvement projects and strategies differ from research: The lack of scientific health services literature has inhibited the acceptance of quality improvement methods in health care, 43 , 44 but new rigorous studies are emerging. It has been asserted that a quality improvement project can be considered more like research when it involves a change in practice, affects patients and assesses their outcomes, employs randomization or blinding, and exposes patients to additional risks or burdens—all in an effort towards generalizability.

Quality improvement projects and studies aimed at making positive changes in health care processes to effecting favorable outcomes can use the Plan-Do-Study-Act PDSA model. This is a method that has been widely used by the Institute for Healthcare Improvement for rapid cycle improvement. The purpose of PDSA quality improvement efforts is to establish a functional or causal relationship between changes in processes specifically behaviors and capabilities and outcomes.

Langley and colleagues 51 proposed three questions before using the PDSA cycles: The PDSA cycle starts with determining the nature and scope of the problem, what changes can and should be made, a plan for a specific change, who should be involved, what should be measured to understand the impact of change, and where the strategy will be targeted. Change is then implemented and data and information are collected.

Results from the implementation study are assessed and interpreted by reviewing several key measurements that indicate success or failure. Lastly, action is taken on the results by implementing the change or beginning the process again. Six Sigma, originally designed as a business strategy, involves improving, designing, and monitoring process to minimize or eliminate waste while optimizing satisfaction and increasing financial stability. This method is applicable to preanalytic and postanalytic processes a. This method is suitable for analytic processes in which the precision and accuracy can be determined by experimental procedures.

One component of Six Sigma uses a five-phased process that is structured, disciplined, and rigorous, known as the define, measure, analyze, improve, and control DMAIC approach. Next, continuous total quality performance standards are selected, performance objectives are defined, and sources of variability are defined. As the new project is implemented, data are collected to assess how well changes improved the process.

To support this analysis, validated measures are developed to determine the capability of the new process. Application of the Toyota Production System—used in the manufacturing process of Toyota cars 57 —resulted in what has become known as the Lean Production System or Lean methodology. This methodology overlaps with the Six Sigma methodology, but differs in that Lean is driven by the identification of customer needs and aims to improve processes by removing activities that are non-value-added a. Steps in the Lean methodology involve maximizing value-added activities in the best possible sequence to enable continuous operations.

Physicians, nurses, technicians, and managers are increasing the effectiveness of patient care and decreasing costs in pathology laboratories, pharmacies, 59—61 and blood banks 61 by applying the same principles used in the Toyota Production System. Two reviews of projects using Toyota Production System methods reported that health care organizations improved patient safety and the quality of health care by systematically defining the problem; using root-cause analysis; then setting goals, removing ambiguity and workarounds, and clarifying responsibilities.

When it came to processes, team members in these projects developed action plans that improved, simplified, and redesigned work processes. Root cause analysis RCA , used extensively in engineering 62 and similar to critical incident technique, 63 is a formalized investigation and problem-solving approach focused on identifying and understanding the underlying causes of an event as well as potential events that were intercepted.

The Joint Commission requires RCA to be performed in response to all sentinel events and expects, based on the results of the RCA, the organization to develop and implement an action plan consisting of improvements designed to reduce future risk of events and to monitor the effectiveness of those improvements. RCA is a technique used to identify trends and assess risk that can be used whenever human error is suspected 65 with the understanding that system, rather than individual factors, are likely the root cause of most problems.

An RCA is a reactive assessment that begins after an event, retrospectively outlining the sequence of events leading to that identified event, charting causal factors, and identifying root causes to completely examine the event. Using a qualitative process, the aim of RCA is to uncover the underlying cause s of an error by looking at enabling factors e.

Nurse Staffing and Patient Care Quality and Safety - Patient Safety and Quality - NCBI Bookshelf

Those involved in the investigation ask a series of key questions, including what happened, why it happened, what were the most proximate factors causing it to happen, why those factors occurred, and what systems and processes underlie those proximate factors. Answers to these questions help identify ineffective safety barriers and causes of problems so similar problems can be prevented in the future.

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Often, it is important to also consider events that occurred immediately prior to the event in question because other remote factors may have contributed. The final step of a traditional RCA is developing recommendations for system and process improvement s , based on the findings of the investigation.

Due the breadth of types of adverse events and the large number of root causes of errors, consideration should be given to how to differentiate system from process factors, without focusing on individual blame. The notion has been put forth that it is a truly rare event for errors to be associated with irresponsibility, personal neglect, or intention, 71 a notion supported by the IOM.

Even the majority of individual factors can be addressed through education, training, and installing forcing functions that make errors difficult to commit. Errors will inevitably occur, and the times when errors occur cannot be predicted. In health care, FMEA focuses on the system of care and uses a multidisciplinary team to evaluate a process from a quality improvement perspective. This method can be used to evaluate alternative processes or procedures as well as to monitor change over time. To monitor change over time, well-defined measures are needed that can provide objective information of the effectiveness of a process.

In , the Joint Commission mandated that accredited health care providers conduct proactive risk management activities that identify and predict system weaknesses and adopt changes to minimize patient harm on one or two high-priority topics a year. In conducting a hazard analysis, it is important to list all possible and potential failure modes for each of the processes, to determine whether the failure modes warrant further action, and to list all causes for each failure mode when the decision is to proceed further.

After the hazard analysis, it is important to consider the actions needed to be taken and outcome measures to assess, including describing what will be eliminated or controlled and who will have responsibility for each new action. Fifty studies and quality improvement projects were included in this analysis. Several common themes emerged: Substantial and strong leadership support, 80—83 involvement, 81 , 84 consistent commitment to continuous quality improvement, 85 , 86 and visibility, 87 both in writing and physically, 86 were important in making significant changes.

Substantial commitment from hospital boards was also found to be necessary. Even with strong and committed leadership, some people within the organization may be hesitant to participate in quality improvement efforts because previous attempts to create change were hindered by various system factors, 93 a lack of organization-wide commitment, 94 poor organizational relationships, and ineffective communication.

Yet adopting a nonpunitive culture of change took time, 61 , 90 even to the extent that the legal department in one hospital was engaged in the process to turn the focus to systems, not individual-specific issues. The improvement process needed to engage 97 and involve all stakeholders and gain their understanding that the investment of resources in quality improvement could be recouped with efficiency gains and fewer adverse events. The successful work of these strategies was dependent upon having motivated 80 and empowered teams. There were many advantages to basing the work of the quality improvement strategies on the teamwork of multidisciplinary teams that would review data and lead change.

Team leaders and the composition of the team were also important. Team leaders that emphasized efforts offline to help build and improve relationships were found to be necessary for team success. The multidisciplinary structure of teams allowed members to identify each step from their own professional practice perspective, anticipate and overcome potential barriers, allowed the generation of diverse ideas, and allowed for good discussion and deliberations, which together ultimately promoted team building.

Teams needed to be prepared and enabled to meet the demands of the quality initiatives with ongoing education, weekly debriefings, review of problems solved and principles applied, 84 and ongoing monitoring and feedback opportunities. The influence of teamwork processes enabled those within the team to improve relationships across departments. Teamwork can have many advantages, but only a few were discussed in the reports reviewed. Teams were seen as being able to increase the scope of knowledge, improve communication across disciplines, and facilitate learning about the problem.

Group work was seen as difficult for some and time consuming, and problems arose when everyone wanted their way, 97 which delayed convergence toward a consensus on actions. Team members needed to learn how to work with a group and deal with group dynamics, confronting peers, conflict resolution, and addressing behaviors that are detrimental. As suggested by Berwick, the leaders of the quality improvement initiatives in this review found that successful initiatives needed to simplify; 96 , standardize; stratify to determine effects; improve auditory communication patterns; support communication against the authority gradient; 96 use defaults properly; automate cautiously; 96 use affordance and natural mapping e.

Simplification and standardization were found to be effective as a forcing function by decreasing reliance on individualized decisionmaking. Several initiatives standardized medication ordering and administration protocols, 78 , 87 , , , — , , — realizing improvements in patient outcomes, nurse efficiency, and effectiveness. Related to simplification and standardization is the potential benefit of using information technology to implement checks, defaults, and automation to improve quality and reduce errors, in large part to embedding forcing functions to remove the possibility of errors.

Often workflow and procedures needed to be revised to keep pace with technology. Data and information were needed to understand the root causes of errors and near errors, 99 to understand the magnitude of adverse events, to track and monitor performance, 84 , and to assess the impact of the initiatives. Using and analyzing data was viewed as critical, yet some team members and staff may have benefited from education on how to effectively analyze and display findings.

The meaning of data can be better understood by using measures and benchmarks. Repeated measurements were found to be useful for monitoring progress, but only when there was a clear metric for measuring the degree of success. When multiple measures were used, along with better documentation of care, it was easier to assess the impact of the initiative on patient outcomes.

The cost of the initiative was an viewed as important factor in the potential for improvement, even when the adverse effects of current processes were considered as necessitating rapid change. It was also purported that the costs associated with change will be recouped either in return on investment or in reduced patient risk and thus reduced liability costs. Ensuring that those implementing the initiative receive education is critical. There were several examples of this. Two initiatives that targeted pain management found that educating staff on pain management guidelines and protocols for improving chronic pain assessment and management improved staff understanding, assessment and documentation, patient and family satisfaction, and pain management.

Despite the benefits afforded by the initiatives, there were many challenges that were identified in implementing the various initiatives:. Despite the aforementioned challenges, many investigators found that it was important to persevere and stay focused because introducing new processes can be difficult, 84 , but the reward of quality improvement is worth the effort. Other considerations were given to the desired objective of sustaining the changes after the implementation phase of the initiative ended.

Influential factors attributed to the success of the initiatives were effecting practice changes that could be easily used at the bedside; 82 using simple communication strategies; 88 maximizing project visibility, which could sustain the momentum for change; establishing a culture of safety; and strengthening the organizational and technological infrastructure.

Collaboratives could also be a vehicle for encouraging the use of and learning from evidence-based practice and rapid-cycle improvement as well as identifying and gaining consensus on potentially better practices. Quality tools used to define and assess problems with health care were seen as being helpful in prioritizing quality and safety problems 99 and focusing on systems, 98 not individuals. The various tools were used to address errors and growing costs 88 and to change provider practices. These are discussed as follows:.

Plan-Do-Study-Act PDSA was used by the majority of initiatives included in this analysis to implement initiatives gradually, while improving them as needed. The rapid-cycle aspect of PDSA began with piloting a single new process, followed by examining results and responding to what was learned by problem-solving and making adjustments, after which the next PDSA cycle would be initiated.

The majority of quality improvement efforts using PDSA found greater success using a series of small and rapid cycles to achieve the goals for the intervention, because implementing the initiative gradually allowed the team to make changes early in the process 80 and not get distracted or sidetracked by every detail and too many unknowns.

Failure modes and effects analysis FMEA was used to avoid events and improve or maintain the quality of care. Health failure modes and effects analysis HFMEA was used to provide a more detailed analysis of smaller processes, resulting in more specific recommendations, as well as larger processes. HFEMA was viewed as a valid tool for proactive analysis in hospitals, facilitating a very thorough analysis of vulnerabilities i.

From the improvement strategies and projects assessed in this review, several themes emerged from successful initiatives that nurses can use to guide quality improvement efforts. The strength of the following practice implications is associated with the methodological rigor and generalizability of these strategies and projects:. Given the complexity of health care, assessing quality improvement is a dynamic and challenging area.

The body of knowledge is slowly growing in this area, which could be due to the continued dilemma as to whether a quality improvement initiative is just that or whether it meets the definition of research and employs methodological rigor—even if it meets the requirements for publication. Because of the long standing importance of quality improvement, particularly driven by external sources e. With this in mind, researchers, leaders and clinicians will need to define what should be considered generalizable and publishable in the peer-reviewed literature to move the knowledge of quality improvement methods and interventions forward.

While the impact of many of the quality improvement projects included in this analysis were mentioned in terms of clinical outcomes, functional outcomes, patient satisfaction, staff satisfaction, and readiness to change, cost and utilization outcomes and measurement is important in quality improvement efforts, especially when variation occurs. There are many unanswered questions. Some key areas are offered for consideration:.

In planning quality improvement initiatives or research, researchers should use a conceptual model to guide their work, which the aforementioned quality tools can facilitate. To generalize empirical findings from quality improvement initiatives, more consideration should be given to increasing sample size by collaborating with other organizations and providers.

We need to have a better understanding of what tools work the best, either alone or in conjunction with other tools. It is likely that mixed methods, including nonresearch methods, will offer a better understanding of the complexity of quality improvement science. We also know very little about how tailoring implementation interventions contributes to process and patient outcomes, or what the most effective steps are that cross intervention strategies. Lastly, we do not know what strategies or combination of strategies work for whom and in what context, why they work in some settings or cases and not others, and what the mechanism is by which these strategies or combination of strategies work.

Whatever the acronym of the method e. Quality improvement requires five essential elements for success: To identify quality improvement efforts for potential inclusion in this systematic review, PubMed and CINAL were searched from to present. The following key words and terms were used: Findings from the projects and research included in the final analysis were grouped into common themes related to applied quality improvement.

Turn recording back on. National Center for Biotechnology Information , U. Show details Hughes RG, editor. Author Information Authors Ronda G. Background The necessity for quality and safety improvement initiatives permeates health care. Quality Improvement Strategies More than 40 years ago, Donabedian 27 proposed measuring the quality of health care by observing its structure, processes, and outcomes. Six Sigma Six Sigma, originally designed as a business strategy, involves improving, designing, and monitoring process to minimize or eliminate waste while optimizing satisfaction and increasing financial stability.

Root Cause Analysis Root cause analysis RCA , used extensively in engineering 62 and similar to critical incident technique, 63 is a formalized investigation and problem-solving approach focused on identifying and understanding the underlying causes of an event as well as potential events that were intercepted.

Failure Modes and Effects Analysis Errors will inevitably occur, and the times when errors occur cannot be predicted. Research Evidence Fifty studies and quality improvement projects were included in this analysis. Lack of time and resources made it difficult to implement the initiative well. Some physicians would notaccept the new protocol and thwarted implementation until they had confidence in the tool. Hospital leadership was not adequately engaged.

There was insufficient emphasis on importance and use of measures. The number and type of collaborative staffing was insufficient. The time required for nurses and other staff to implement the changes was underestimated. The extent to which differences in patient severity accounted for results could not be evaluated because severity of illness was not measured. Improvements associated with each individual PDSA cycle could not be evaluated.

Patient Safety and Quality: An Evidence-Based Handbook for Nurses.

The full impact on the costs of care, including fixed costs for overhead, could not be evaluated. Failure to consider the influence of factors such as fatigue, distraction, time pressures. The Hawthorne effect may have caused improvements more so than the initiative. Many factors were interrelated and correlated. There was a lack of generalizability because of small sample size.

Addressing some of the problems created others e. These are discussed as follows: Evidence-Based Practice Implications From the improvement strategies and projects assessed in this review, several themes emerged from successful initiatives that nurses can use to guide quality improvement efforts. The strength of the following practice implications is associated with the methodological rigor and generalizability of these strategies and projects: The importance of having strong leadership commitment and support cannot be overstated.

Leadership needs to empower staff, be actively involved, and continuously drive quality improvement. Without the commitment and support of senior-level leadership, even the best intended projects are at great risk of not being successful. Champions of the quality initiative and quality improvement need to be throughout the organization, but especially in leadership positions and on the team.

A culture of safety and improvement that rewards improvement and is driven to improve quality is important. The culture is needed to support a quality infrastructure that has the resources and human capital required for successfully improving quality. Due to the complexity of health care, multidisciplinary teams and strategies are essential.

Quality improvement teams and stakeholders need to understand the problem and root causes. There must be a consensus on the definition of the problem. To this end, a clearly defined and universally agreed upon metric is essential. This agreement is as crucial to the success of any improvement effort as the validity of the data itself.

Use a proven, methodologically sound approach without being distracted by the jargon used in quality improvement. The importance given to using clear models, terms, and process is critical, especially because many of the quality tools are interrelated; using only one tool will not produce successful results. Standardizing care processes and ensuring that everyone uses those standards should improve processes by making them more efficient and effective—and improve organizational and patient outcomes. Efforts to change practice and improve the quality of care can have multiple purposes , including redesigning care processes to maximize efficiency and effectiveness, improving customer satisfaction, improving patient outcomes, and improving organizational climate.

Appropriate use of technology can improve team functioning, foster collaboration, reduce human error, and improve patient safety. Continually collect and analyze data and communicate results on critical indicators across the organization. The ultimate goal of assessing and monitoring quality is to use findings to assess performance and define other areas needing improvement.