Evaluating and Improving the Quality and Safety of Neonatal Intensive Care

The Case for Improvement

Public health and health care delivery systems in high-income nations have a great deal of which to be proud. Over the last century, they have combined to reduce infant and maternal mortality, as well as to prolong life expectancy to unsurpassed levels. During the twentieth century, infant mortality dropped from more than 100 per 1000 live births to about 5 per 1000 live births; maternal deaths dropped by 99% to about 10 per 100,000 live births.

During the first half of the twentieth century, these improvements were driven largely by advances in public health, especially access to clean water, which dramatically reduced infection-related morbidity and mortality. However, although health care delivery conferred little benefit to population-based health during the first half of the century, there have since been demonstrable contributions to added life expectancy. Neonatal intensive care is a case in point. Although relatively few interventions (i.e., heated incubator care, intravenous nutrition, oxygen) were available to support sick newborns until the 1960s, technical and scientific advances in nutrition, physiologic monitoring, mechanical ventilation, and pharmaceuticals have made it possible for infants to survive previously fatal degrees of prematurity, genetic disease, or other (or acquired?) newborn disease.

On the other hand, new medical treatments in combination with greater access and demand for care, a rise in the prevalence of chronic disease conditions, and inefficient market conditions have resulted in rising health expenditures, which have outpaced national incomes. As a consequence, in the United States, health care consumes an ever-greater proportion of gross domestic product (GDP). In 2015, national health expenditures accounted for 17.8% of GDP, which severely strains the budgets of those paying for health care: consumers, through taxes, co-pays, deductibles, insurance, and reduced wage growth; companies, through insurance; and state and federal governments, through obligations under Medicaid and Medicare.

In light of these resource constraints, a commitment to population health requires optimization of the value of each dollar expended for health care. However, research summarized in the sentinel Institute of Medicine of the National Academy of Sciences report To Err Is Human has consistently highlighted that health care delivery, including neonatal intensive care, falls short of its potential. This report as well as its successor, Crossing the Quality Chasm: A New Health System for the 21st Century , present a clear and compelling challenge to all health care professionals to improve the quality and safety of the medical care for the patients and families they serve.

Despite overwhelming evidence that deficiencies in quality and safety are widespread throughout the American health care system, many health care professionals in neonatology may feel that these problems do not apply to our clinical specialty. This is not the case. In neonatology, as in other clinical fields, opportunities for improving the quality and safety of medical care are substantial.

First, there is a large body of literature documenting tremendous variation in how medical resources are distributed, care is delivered, and outcomes are achieved. One of the best-known examples is the work by Wennberg and Fisher, who developed the Dartmouth Atlas of Healthcare . For more than 25 years they have documented large variations in the efficiency of local delivery systems that are not explained by patients’ health or preferences for care or by malpractice pressure and that do not seem to be associated with systematically better quality of care, patient outcomes, or satisfaction. In fact, this kind of variation has been shown across many settings (states, regions, cities, hospitals), many different types of patients (newborn, elderly), and many different types of care (medical, surgical, hospital, outpatient). Differences in care delivery and cost are almost entirely explained by differences in the volume of health care services received by similar patients, that is, “supply-sensitive care.” Higher-spending regions have more hospital beds, doctors, and specialists. Neonatology is no exception. The presence of this variation means that the goal of lowering costs while preserving quality is attainable and already achieved by many providers.

In the neonatal intensive care unit (NICU) setting, in addition to supply-sensitive care, dramatic variation in the processes and outcomes of care has been documented that cannot be explained by differences in case mix, suggesting differences in the quality of care delivery. In a study of over 400,000 infants with birth weights of 401 to 1500 grams born from 2005 to 2014 and cared for at 756 Vermont Oxford Network–member NICUs in the United States, the ratio of the risk-adjusted rates for mortality and major morbidities at hospitals with the highest 10% of the rates to the risk-adjusted rates for hospitals. The lowest 10% of rates in 2014 were 1.2 for mortality and severe intraventricular hemorrhage, 1.7 for chronic lung disease, 1.9 for late onset infection and necrotizing enterocolitis, and 2.0 for severe retinopathy of prematurity. This means that for several major morbidities of very low birth weight infants, NICUs in the worst decile have rates nearly twice as high as those in the best decile. For comparison, an analysis including 22 million all-payer inpatient adult admissions demonstrated a 2.1-fold difference in risk-adjusted mortality rates and an 18.3-fold difference in central venous catheter bloodstream infection rates between top and bottom decile hospitals.

Despite the variation among NICUs, there has been dramatic improvement in mortality and morbidities for very low birth weight infants in the last decade. Health care–associated infection rates provide a striking example. At the 756 US NICUs in the study described above, the rate of late bacterial (including coagulase-negative staphylococcus) or fungal infection significantly decreased from 2005, when 21.9% of infants had infections, to 2014 when 10.1% of the infants had infections. Dramatically, by 2014, 98% of NICUs had achieved rates for late onset infection as low or lower than the rates achieved by the best 25% of units only a decade earlier, and 91% of NICUs had achieved rates as low or lower than the best 10% of units. Over the last decade, reduction of health care–associated infections has been at the center of national health policy efforts to improve the value of health care delivery, including aligning hospital payments with quality-of-care delivery. Widespread efforts in the neonatal community have proved successful in facilitating broad-based and sustained reductions in health care–associated infection rates and represent an encouraging blueprint for improvements in care in other areas. However, the continued wide variation indicates that opportunities for improvement in many NICUs still remain.

Another indicator for quality deficits in the neonatal intensive care setting is the widespread delivery of inappropriate care—defined as underuse, overuse, and misuse of interventions. Examples include the underuse of hand hygiene by NICU personnel, the overuse of antibiotics, and the misuse of medications because of medical errors.

We have provided an example of underuse above. A good example of an effort to address overuse and misuse is the Choosing Wisely ( www.choosingwisely.org ) effort. Medical interventions are frequently overused or misused and contribute to health care waste. Choosing Wisely, an initiative of the American Board of Internal Medicine, has challenged national medical specialty societies to identify Top Five lists of tests or procedures commonly used in their field whose necessity should be questioned and discussed with patients. The American Academy of Pediatrics Section of Neonatal Perinatal Medicine (SONPM) developed a Top Five list for newborn medicine using a process that began with a survey of neonatologists in SONPM and physicians, nurses, other NICU health professionals, and families of NICU patients at the Vermont Oxford Network Annual Quality Congress. The survey asked participants to provide from one to ten examples of tests and treatments that, in their opinion, met one or more of the following criteria: (1) evidence of lack of efficacy, (2) insufficient evidence of efficacy, or (3) unnecessary utilization of staffing or material resources. Next, a multidisciplinary expert panel of fifty-one individuals used a modified Delphi Process in which they scored each item. After the first two rounds, the items with the highest scores were subjected to a literature review and the strength and quality of the evidence were summarized using Grading of Recommendation, Assessment, Development, and Evaluation (GRADE) criteria. The panel reviewed the GRADE summaries in the final round, and the five items with the highest total points in that round were included in the newborn medicine Top Five list. The final Top Five items are shown in Box 5.1 .

Another important example of misuse are medical errors, which will be briefly discussed here. Current health care operations produce unacceptable rates of medical errors that result in patient injury or death. In To Err Is Human, the Institute of Medicine concluded that between 44,000 and 98,000 Americans die annually from hospital errors, killing more Americans than breast cancer, traffic accidents, or AIDS.

Preterm infants in the NICU are particularly vulnerable to medical errors owing to their small size, physiologic immaturity, and limited compensatory abilities. These circumstances make preterm infants particularly vulnerable to lapses in patient safety. A study at two Boston hospitals has documented that errors in the process of ordering, dispensing, or monitoring medications occurred for more than 90% of the infants cared for in the NICU. Another study, using a trigger tool in the electronic health record, identified 0.74 adverse events per patient. Fifty-six percent of these were deemed preventable. These estimates are likely only the tip of the iceberg.

Using a voluntary, anonymous, Internet-based error-reporting system established by the Vermont Oxford Network, Suresh and colleagues have documented a broad range of errors and near errors at 54 neonatal intensive care units. Only about half of the reported events involved medications; the remainder involved a wide variety of errors in multiple domains of care. A study from eight Dutch NICUs found that incidents concerning mechanical ventilation, blood products, intravascular lines, parenteral nutrition, and medication dosing errors pose the highest risk to patients in the NICU. Medication errors are a common problem in sick newborns owing to breakdowns in patient identification, rapid changes in physiologic maturity, body weight, and volume of distribution requiring frequent dose adjustments. Although establishing the frequency of medication errors in the NICU is difficult, published studies indicate that medication errors in the NICU are common, ranging from 13 to 91 medication errors per 100 NICU admissions. One study found that medication errors occurred in 57% of infants less than 27 weeks’ gestation age, compared with 3% reported in the care of full-term infants. In addition, NICU patients are more likely to experience a medication error than other hospital patients and to experience more harm when a medication error occurs.

Finally, our health care delivery system has not reliably delivered many interventions that are deemed to be effective. McGlynn, first in adults and later with Mangione-Smith in children, demonstrated that Americans receive guideline-recommended care only about half the time. In the newborn setting, although antenatal steroids have been demonstrated to prevent respiratory distress syndrome and intraventricular hemorrhage after preterm birth, there remains wide variation in its administration to eligible mothers.

The inability of the health care system to consistently deliver the highest quality of care results in enormous waste in human lives and valuable resources. Berwick and Hackbarth showed that “in just six categories of waste—overtreatment, failures of care coordination, failures in execution of care processes, administrative complexity, pricing failures, and fraud and abuse—the sum of the lowest available estimates exceeds 20% of total health care expenditures. The actual total may be far greater.”

In the United States, the care of preterm infants consumes about $35 billion annually (adjusted for inflation). These health expenditures directly affect families and patients. About one half of the 1.45 million American families that filed for bankruptcy in 2001 cited medical causes, even though three-fourths of them had insurance at the onset of illness. About 10% of families cited childbirth-related and congenital disorders as the principal cause.

With the recognition of the extent of the opportunities for improvement have come efforts by providers, provider networks, payers, nongovernmental organizations, and governmental agencies to improve the value of health care expenditures. At the provider network and health system level, efforts have focused on quality assurance and alignment of financial incentives. For example, quality assurance has been pursued through benchmarking of performance by provider networks such as the California Perinatal Quality Care Collaborative (CPQCC) or the Vermont Oxford Network ; other statewide neonatal collaborative organizations, including those in Massachusetts, Ohio, Tennessee, North Carolina, Florida, Wisconsin, and Michigan; or by Medicare through its Hospital Compare Program. ( http://www.medicare.gov/hospitalcompare/search.html ). Significant efforts have been made to develop, evaluate, and harmonize quality measures through organizations such as the National Quality Forum. Public reporting of quality measures and realignment of financial incentives away from a fee-for-service system toward a pay-for-quality system are becoming more widespread. More detail on these issues is provided later in this chapter.

At the hospital level, providers have engaged in quality improvement efforts to meet the expectations of this changing marketplace. Several institutions have undergone fundamental re-engineering of their health care delivery systems. Intermountain Healthcare, under the leadership of Brent James, has been a pioneer in this transformation and an example for many other health care systems.

Intermountain's transformation has been the consequence of a growing recognition that the current model of health care delivery is in many ways outmoded and has failed to incorporate many of the lessons that propelled other sectors of the economy to large gains in quality and productivity. Even today in many health care organizations, the norms for care delivery are founded on a cottage industry–based delivery model wherein the physician artisan, through training, personal competence, and professionalism, devises individualized diagnostic and treatment regimens. Although this approach may offer ideal care for some, it has limited ability to promote continual learning and improvement, because each physician may have a differing approach to a similar patient while firmly considering his or her own approach to be optimal. However, even the expert mind is fallible: (1) there is often a lack of knowledge regarding best therapy, resulting in variability of approaches and outcomes that are often not systematically tracked; (2) expert opinion has been shown to yield widely disparate results, undermining its validity and reliability; (3) humans have inherent biases that are hardwired into our brains and lead to biased judgments ; (4) the expert mind is limited to processing information on 7 ± 2 variables simultaneously, often less than what is required in complex critically ill newborns; and finally (5) the exponential expansion of medical knowledge, with a doubling time reduced to 8 years, makes it very difficult to stay up to date on the latest science. ^, *

* The line of reasoning presented here reflects teachings by Dr. Brent James at Intermountain Healthcare's Advanced Training Program.

For all these reasons, a continuation of a craft-based approach to health care delivery has become untenable, and a new approach based on standardization and systematic use of clinical data and evidence rather than opinion must be pursued. Other industries have grappled with similar transitions and have provided a blueprint for improvement that is now actively pursued by many high-performing health systems. We briefly examine some of the historical foundations of these developments.

Brief History of Industrial Quality Improvement

A thorough description of the history of quality improvement is beyond the scope of this chapter. Instead, we provide a brief overview of stages of progress that are particularly salient for medicine.

Before the late nineteenth and early twentieth centuries, quality was largely conceived of as quality assurance (oversight) and the result of personal excellence and training. For example, craftsmen organized in guilds chose their trainees selectively, often based on heritage, and provided formal apprenticeships. Mastery of the craft was achieved through training, supervision, and ongoing practice. The guilds secured a livelihood for their members through a combination of trade secrecy, price collusion, and monopolistic behaviors. The quality of labor and goods was the result of limited competition, quality controls by the authorities, and peer review. This approach is still prevalent in medicine today.

With the advent of industrialization and mass production of goods, a new field called scientific management emerged, a field that combined lessons from statistics, management, and economics. The goal of this movement was to increase productivity, reduce scrap, and reduce cost. Frederick Taylor (1856–1915) was one of the main innovators during this era. A mechanic and pattern maker by training, Taylor used time and motion studies and statistical production sheets to increase efficiency and workflow. Famous for his principles, Taylor held seminars on scientific management and worked as a consultant. One of his contractors was the Ford Motor Corporation, where Taylor played an important role in the success of the famous Model T. Gains in factory efficiency substantially lowered the overall cost of each car and enabled Ford to undercut the price of other cars on the market.

Taylor's methods transferred the control of production away from frontline workers to management and engineers, but his methods proved controversial. He was quoted as saying: “Hardly a workman can be found who doesn't devote his time to studying just how slowly he can work.” This negative view of frontline workers and the drive to maximize their efficiency not infrequently resulted in social unrest. The large power differential between management and workers also tended to exclude those with the most detailed knowledge of production processes from participating in quality improvement.

The next leap in industrial management was achieved through the work of Walter Shewhart (1891–1967). An engineer, physicist, and statistician, Shewhart is known as the “father of statistical quality control.” Before Shewhart, industrial quality mostly involved inspection and removal of defective products. Working at Bell Telephone Laboratories, he investigated variation in production systems and pointed out the importance of reducing variation in manufacturing processes, a concept that also offers an important lesson for medicine. Shewhart highlighted that continual process adjustment in reaction to process nonconformance actually increases variation and degrades quality. This behavior is called “tampering” and is common in patient care. Shewhart framed the problem of process variation in terms of assignable and chance variation (Deming later called these special and common cause variation) and developed the “control chart” ( Fig. 5.1 ) to operationalize these concepts. A control chart or the related run chart, described later in the chapter, allows for simple tracking of process variation. It allows one to estimate the extent to which a process varies over time; the idea being that excessive common cause variation indicates a poorly controlled process that is not being addressed in a standardized way. It also identifies special cause variation according to a variety of patterns, including, for example, performance outside of statistical control limits. Both excessive common cause and special cause variation are indications of opportunities for quality improvement and should be further investigated and remedied. Shewhart emphasized the importance of bringing and keeping a production process in statistical control so as to predict and manage future output.

William Edwards Deming (1900–1993) is perhaps the most influential thinker on quality during the last century. Inspired by Shewhart's teachings on statistical process control, Deming realized that these principles could be applied not only to manufacturing processes but also to the management of companies.

Deming is most famous not for his work in the United States but for his contributions to Japan's economic revival after World War II. While working under General Douglas MacArthur as a census consultant to the Japanese government, he famously taught statistical process control methods to Japanese business leaders. Difficult to imagine today, Japan once had a reputation for poor-quality products. Quality control relied largely on inspection, rework, or scrapping of finished products (control of the output). This production system produced lots of waste and became a major impediment to postwar reconstruction. Japanese industrial leaders and the American Engineering Corps recognized the need to change the quality of industrial output through process control rather than output control to rebuild Japanese infrastructure and industry. In 1950, on invitation from the Japanese Union of Scientists and Engineers, Deming gave a legendary lecture series to engineers, managers, scientists, and leaders of Japanese industry. His teachings sparked an industrial revolution and are widely credited with jump-starting the Japanese postwar revival. In the United States, Deming's teachings remained largely unknown until American industry was rapidly losing market share to the Japanese competition. In 1980, he explained his ideas in an NBC broadcast “If Japan can … why can't we?”

Deming's philosophy taught that by adopting appropriate principles of management, organizations can increase quality and simultaneously reduce costs (by reducing waste, rework, staff attrition, and litigation while increasing customer loyalty). When organizations focus on quality by improving their work processes, costs will fall. Oppositely, when organizations focus on costs, then quality will fall and costs will rise. Deming developed the Plan-Do-Study-Act (PDSA) cycle, which he called the Shewhart cycle, a strategic staple in modern quality improvement. He also developed 14 Key Points in Management and 7 Deadly Diseases ( Box 5.2 ).

Deming was a strong opponent of performance appraisals, incentives, short-term thinking, and punitive benchmarking, all strategies currently proposed as “solutions” to increase the value of health care delivery. For Deming, the key to success was to practice continual improvement in the quality of products, uniformity of processes, and qualification of employees. He taught that all processes yield three parallel outcomes that must be measured: a work product (medical outcome), cost outcome, and service outcome (patient satisfaction). All processes contain built-in variation (common cause), but they are also affected by external factors (special cause). Management, not frontline workers, controls common cause variation through systems design. In opposition to Taylor's view of frontline workers, Deming cherished their contributions and felt that involving them in improving care processes would instill meaning and pride in their work.

Kiichiro Toyota, the founder of Toyota Motor Company, was among Deming's audience in 1950. Based on his lectures, Toyota developed the Toyota Production System (TPS), an integrated sociotechnical system that comprises its management philosophy and practices. The system is a precursor of the more generic “Lean Manufacturing.” At the core of TPS is elimination of waste (muda) and total focus on reliable high quality through continuous improvement (kaizen). Lean production focuses on just-in-time use of materials and optimization of production flow (mura) in response to customer demand. Toyota's success derived from its innovative production engineering, which puts quality control in the hands of the frontline workers who can stop the production line and call for help when something goes wrong. However, production engineering is only part of Toyota's success. An equally important contributor to Toyota's success has been coined the Toyota Way, which embodies a relentless focus on the needs and desires of customers.

Another variant of statistical process control originally developed by Motorola in 1985 is called Six Sigma. Six Sigma comprises a set of tools and strategies for process improvement and seeks to improve quality by minimizing defects and variability. In Six Sigma, a defect is defined as any process output that does not meet customer specifications or that could lead to creating an output that does not meet customer specifications. The term “Six Sigma process” means that if one has six standard deviations between the process mean and the nearest specification limit, only 3.4 out of 1 million outputs will fail to meet specifications. To illustrate, if a hospital achieved Six Sigma in the administration of antenatal steroids, only about three in a million preterm infants would fail to receive them. Although technically this equates to only a 4.5 sigma process, the remainder is meant to account for the fact that over the long term, processes tend to become more error prone (entropy). A one sigma process produces 69%, a two sigma process produces 31%, and a Six Sigma process produces 0.00034% defective outputs. Current evidence suggests that much of health care operates at the one to two sigma level. Six Sigma includes methodologies that lean on Deming's PDSA cycle, such as DMAIC for reengineering existing processes. DMAIC stands for define the problem; measure key aspects; analyze data for key relationships; improve the current process (using a set of specific techniques); and control the future process.

Industry has undergone fundamental changes over the last century. Scientific approaches to process improvement and management have resulted in dramatic increases in quality and productivity. Health care systems that have implemented change based on Deming's principles have seen similar benefits.

Industrial quality improvement methods have been applied to health care for more than 30 years. In Curing Health Care: New Strategies for Quality Improvement , Berwick and co-workers provided the initial evidence that the tools of modern quality improvement, with which other industries have achieved breakthroughs in performance, can help in health care as well. Since then, the core ideas of quality improvement have been adapted to the particular needs of health care and implemented by health care organizations around the world. In neonatology, multidisciplinary teams from NICUs across the United States have applied quality improvement tools and methods to address the quality and safety of medical care for newborn infants and their families.

An additional challenge for effective QI is that many health care providers have not been actively engaged in quality improvement efforts or exposed to the lessons from industrial engineering. Successful adaptation of these methods to the NICU setting requires recognition that health care is a process and that quality improvement requires process management. Albeit, a focus on care delivery processes, although necessary, is not sufficient. Rather, similar to the way that Deming expanded Shewhart's focus on process control to include organizational management, medical process management needs to be accompanied by efforts to optimize the organizational environment in ways that promote continual learning, teamwork, and adherence to the mission of serving patients and families.

Several theoretical frameworks exist to further our understanding of the complex interplay between organizational environment, process, and outcomes.

Definition and Conceptual Frameworks for Quality

Translation of Frameworks into Action

Frontline application of quality improvement efforts draws from the various theoretical frameworks presented in the preceding section. In the following, a few approaches are highlighted that are commonly applied in neonatology. For practitioners, the practical application of quality improvement work may be more effective if supported by a formal approach, as provided by Dr. Deming (integration and standardization as practiced by Intermountain Healthcare), Lean, Six Sigma, or the Model for Improvement, advocated by the Institute for Healthcare Improvement. Each of these frameworks makes use of various management tools to assist with project planning and execution, including team organization, process mapping, problem identification, problem resolution, task prioritization, and execution. We briefly mention several of these in the section titled Quality and Safety Applied . However, a full description of these tools is beyond the scope of this chapter. For more detail on these, refer to the extant literature.

Model for Improvement

The Model for Improvement has been widely adopted as a strategy for collaborative quality improvement in the neonatal community. It is based on The Improvement Guide , by Langley and Nolan, and identifies four strategic elements of successful process improvement : (1) specific and measurable aims, (2) measures of improvement that are tracked over time, (3) strategic changes that will result in the desired improvement, and (4) a series of PDSA cycles during which teams learn how to apply crucial change ideas. These principles correspond to three questions asked of collaborative teams ( Fig. 5.2 ).

The first question, which refers to the aim(s) of the project, is “What are we trying to accomplish?” In other words, what are the main outcomes that the team is trying to change or improve? There are several concerns when considering the aim of a prospective project. Are there historical and/or peer benchmark data to suggest that there is a problem in the local NICU? Is there a variation in the outcome among NICUs to suggest that variations in practices can lead to differences in outcomes? Are there evidence-based practices that have been shown to improve the outcome of interest? The aim should be well defined and realistic. It should also be quantifiable and encompass a defined time period.

One helpful framework in crafting a well-designed aim statement is the “SMART Aim” approach ( Box 5.3 ). An example of a suboptimal aim statement may be, “Reduce infection rates in our NICU.” Although a worthy goal, that statement is vague, and it will be difficult to assess if it has been accomplished. In contrast, the following aim statement is more specific and follows the SMART Aim approach: “Reduce by 50% the number of central line–associated bloodstream infections as defined by the Centers for Disease Control in infants born at 24 to 30 weeks’ gestational age in the NICU by using an insertion and maintenance bundle over a 12-month time period.”

Box 5.3

SMART Aim Statement

Specific	What exactly are we going to do for whom? Should specify target population, setting, and actions. Uses verbs such as train, increase, decrease.
Measurable	Is it quantifiable and can we measure it? Source and mechanism for collecting data are identified, and collection of data is feasible. Baseline data are available to document change.
Achievable	Can it be done (time frame and resources)? Project is feasible with available resources and appropriate in scope. A more time-limited aim may make it easier to collect relevant clinical and cost data.
Relevant	Will this objective have an effect on the desired mission/broader goal? May be informed by literature review, best practices, or your theory of change.
Time Bound	What is the time frame for change? When will this be accomplished? Indicate a specified and reasonable time frame in the aim statement. Take into consideration the environment, scope of change, and how it fits into the overall work plan.

The second question, corresponding to the measures of the project, is “How will we know that a change is an improvement?” As a team embarks on a quality improvement project, one can consider three main groups of measurements: outcomes, processes, and balancing measures. The outcome measure is typically a clinical outcome, such as nosocomial infection or bronchopulmonary dysplasia. A process measure related to reduction of nosocomial infection could be the use of an insertion checklist. In some instances, a process measure can serve as the main outcome of a quality improvement project, when that process measure has been clearly linked to a benefit in clinical outcomes. Examples are a focus on improving the rate of antenatal steroid administration for premature birth or on increasing breast milk provision for premature infants. A balancing measure for increased breast milk provision for premature infants may be growth velocity, which may have a negative correlation. The idea behind a balancing measure is to ensure that the quality improvement intervention does not generate unwanted secondary effects.

The third question, which corresponds to the strategic changes that will lead to improvement, is “What changes can we make that will result in improvement?” These changes should be evidence based, but must also take into account local cultural and organizational characteristics. Plsek has coined the term “potentially better practices” rather than “better” or “best” practices to stress that no change idea is truly better or best until it has been adapted, implemented, and tested in the local context in which it will be applied. This is consistent with the ideas surrounding “context” described by Pawson and Batalden, as noted in the previous section on frameworks for quality. The PDSA cycles in the Model for Improvement can be conceptualized as an application of the scientific method (see Fig. 5.2 ). Each PDSA cycle is a test of a hypothesis of how a specific change will affect the system.

Integrated Quality Management

Another method for quality improvement in health care focuses on the elimination of process variation. This approach derives from Deming's work and has been adapted with success for health care by several organizations. For example, Dr. Brent James, former Chief Quality Officer and Executive Director, Institute for Health Care Delivery Research, Intermountain Healthcare, helped refocus the health systems quality improvement efforts on process standardization, active management, and ongoing organizational learning. Work units identify high-value clinical processes and set up multidisciplinary provider teams to develop evidence-based detailed care pathways (shared baselines) to execute these processes in a consistent, reliable manner. The name shared baseline , rather than protocol or guideline, highlights its development and ongoing refinement by the local work unit. Control rests with frontline care providers, not managers. The purpose of these shared baselines is to minimize the variation that is driven by providers and not accounted for by patient need. Providers are encouraged to deviate from the shared baseline as warranted by the needs of individual patients. This approach allows providers to bring their expertise to bear on the clinical situations that truly warrant an alternative treatment approach. It also effectively ameliorates concerns regarding “cookbook” medicine, given an explicit expectation that no guideline perfectly fits any patient. On the contrary, providers are asked to assess situations where they have deviated from the shared baseline. This information is then used to modify the shared baseline when appropriate. Overall, the use of shared baselines makes the workflow more predictable, efficient, safer for patients, and less stressful for nursing staff.

Similar to the Model for Improvement, the modified Deming approach uses key measures to track cornerstones of the process, clinical outcomes, service outcomes (such as patient satisfaction), and cost. The shared baseline is eventually integrated into the electronic medical record and managed according to results. The key to promote organizational learning and standardization is tracking of process deviations and systems to modify the shared baselines according to ongoing provider input. To facilitate the success of local projects, strategic staff from clinical work units receive training and technical support for their improvement work from a centralized quality improvement institute. Institutional commitment is foundational to achieving excellence in care delivery in the NICU. Neonatal providers often embark on grass root QI efforts that may not align with hospital leadership priorities. Close communication and support is necessary to assure that efforts are properly prioritized, aligned with frontline and leadership needs, and executed. Sufficient resources for QI management are critical for their staff to carry out “two jobs,” their primary work duties, as well as efforts to do their jobs better.

Lean

Lean methodology has become increasingly popular with health care administrators and providers. Lean focuses on reducing process waste and improving process flow. The seven wastes on focus with Lean are overproduction, inventory, waiting, transportation, defects, staff movement, and unnecessary processing. Lean has an intense focus on customer, patients, and families, and encourages users to examine their process steps and eliminate those that do not provide value to customers. A focus on flow attempts to optimize the use of time and resources.

These approaches and others are guiding practical and theoretical improvement work. Although meaningful in their differences, their common thread lies in the centrality of managing work processes while optimizing the care delivery environment. Organizational goals for high-quality care delivery and patient safety are undergirded by strategies that encourage continuous monitoring of processes and outcomes, facilitate continuous learning, and minimize unwarranted waste and process variation.