Quality “… the degree to which health services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge.” U.S. Institute of Medicine

Quality Initiative “… systematic, data-guided activities designed to bring about immediate improvements in health care delivery in particular settings.” Hastings Center

Performance Measure “… yardsticks by which all health care providers and organizations can determine how successful they are in delivering recommended care and improving patient outcomes.” U.S. Institute of Medicine

Performance Management “… a systematic process by which an organization involves its employees in improving the effectiveness of the organization and achieving the organization’s mission and strategic goals. By improving performance and quality, public health systems can save lives, cut costs, and get better results by managing performance.” Public Health Foundation

Process Improvement “… the systematic approach to closing of process or system performance gaps through streamlining and cycle time reduction, and identification and elimination of causes of below specifications quality, process variation, and non-value-adding activities.” BusinessDictionary.com

Model for Quality

Achieving quality improvement requires adoption of a 3-step model: “Data → Information → Improvement.” Quality needs to be measured. Quality data obtained from the measurement then need to be converted into meaningful information that can be compared and reported. This quality measurement must also be actionable to achieve improvements in clinical practice.

Measuring Quality

Robust quality indictors should have clinical and statistical relevance. Clinical relevance ensures that the indicators are meaningful in patient care from the standpoint of patients and clinicians. Statistical relevance ensures that the indicators have measurement properties to allow an acceptable level of accuracy and precision. These concepts are captured in the national recommendations that quality measures must meet the criteria of being valid, reliable, feasible, and usable (Table 2-2). Validity of quality measures relates to the notion that the measure is estimating the true concept of interest. Reliability relates to the notion that the measure is reproducible and provides the same result if retested. It is important that quality measures are feasible in practice. Quality measures must be useable, which means that they should be clinically meaningful. The Agency for Healthcare Research and Quality (AHRQ) has provided specific criteria to be considered when developing quality measures.

Table 2-2 PROPERTIES OF ROBUST QUALITY MEASURES

ATTRIBUTE	RELEVANCE
Validity	Indicator accurately captures the concept being measured.
Reliability	Measure is reproducible.
Feasibility	Data can be collected using paper or electronic records.
Usability	Measure is useful in clinical practice.

Quality indicators can be aimed at measuring the performance within 3 components of health care delivery: structure, process, and outcome. Structure relates to the organizational characteristics in health care delivery. Examples of organizational characteristics are the number of physicians and nurses in an acute care setting and the availability and use of systems such as electronic health records. Process related measures estimate how services are provided. Examples of a process measures are the percent of families of children with asthma who receive an asthma action plan as part of their office visit or the percent of hospitalized children who have documentation of pain assessments as part of their care. Outcome measures relate to the final health status of the child. Examples of outcome measures are risk adjusted survival in an intensive care unit setting, birth weight adjusted survival in the neonatal intensive care unit setting, and functional status of children with chronic conditions such as cystic fibrosis.

Quality data can be quantitative and qualitative. Quantitative data includes numerical data, which can be continuous (patient satisfaction scores represented as a percentage with higher numbers indicating better satisfaction), or categorical (patient satisfaction scores obtained from a survey where a Likert scale is used indicating satisfactory, unsatisfactory, good, or superior care). Data can also be qualitative in nature, which includes non-numeric data. Examples of qualitative data can include results from open-ended surveys related to the satisfaction of care in a clinic or hospital setting. It is important to be sensitive to the source and quality of data being obtained to ensure data quality.

Data measuring quality of care can be obtained from a variety of sources, which include chart reviews, patient surveys, existing administrative data sources (billing data from hospitals), disease and specialty databases, and patient registries, which track individual patients over time.

It is important to distinguish between databases and data registries. Databases are data repositories that can be as simple as a Microsoft Excel spreadsheet or relational databases using sophisticated IT platforms. Databases can provide a rich source of aggregated data for both quality measurement and research. Data registries allow tracking individual patients over time; this dynamic and longitudinal characteristic is important for population health management and quality improvement.

Data quality can become a significant impediment when using data from secondary sources, which can adversely impact the overall quality evaluation. Once data on the quality indicator has been collected, quality measurement can occur at 3 levels: (1) measuring quality status at 1 point in time (e.g., percent of children seen in a primary care office setting who received the recommended 2 yr immunizations); (2) tracking performance over time (e.g., change in immunization rates in the primary care office setting for children 2 yr of age); and (3) comparing performance across clinical settings after accounting for epidemiologic confounders (e.g., immunization rates for children under 2 yr of age in a primary care office setting stratified by race and socioeconomic status as compared to the rates of other practices in community and rates at national levels).

Pediatric quality measures are being developed nationally. Table 2-3 provides a list of some of the important currently endorsed pediatric national quality indicators.

Table 2-3 NATIONAL PEDIATRIC QUALITY MEASURES

NQF PDIs (2008)	NQF-ENDORSED INPATIENT MEASURES AMONG PICUs	NQF-ENDORSED CHIPRA MEASURES (2009)
Accidental puncture or laceration	PICU standardized mortality ratio	Childhood immunization status
Decubitus ulcer	PICU severity adjusted length of stay	Appropriate testing for children with pharyngitis
Foreign body left after procedure, age under 18 yr	Unplanned PICU readmission (readmissions within 24 hr after discharge/transfer from PICU)	Chlamydia screening in women
Iatrogenic pneumothorax in non-neonates	Review of unplanned PICU readmissions	Follow-up/mental illness
Pediatric heart surgery mortality	PICU pain assessment on admission	Follow-up/ADHD medication
Pediatric heart surgery volume	PICU periodic pain assessment (minimum of every 6 hr)	HEDIS CAHPS/chronic conditions
Postoperative wound dehiscence, age under 18 yr	Catheter-associated bloodstream infections	Weight assessment and counseling
Transfusion reaction, age under 18 yr

ADHD, attention-deficit/hyperactivity disorder; CAHPS, Consumer Assessment of Healthcare Providers and Systems; CHIPRA, Children’s Health Insurance Program Reauthorization Act; HEDIS, Healthcare Effectiveness Data and Information Set; NQF, National Quality Forum; PDIs, pediatric quality indicators; PICU, pediatric intensive care unit.

Comparing and Reporting Quality

There is an increasing emphasis on quality reporting in the USA. Many states have mandatory policies for reporting of quality data. This reporting may be tied to reimbursement using the policy of pay for performance (P4P). P4P implies that reimbursements by insurers to hospitals and physicians will be partially based on the quality metrics. P4P can include both incentives and disincentives. Incentives relate to additional payments for meeting certain quality thresholds. Disincentives relate to withholding certain payments for not meeting those quality thresholds. An extension of the P4P concept relates to the implementation of the policy of Never Events by the Centers for Medicare and Medicaid (CMS). CMS has identified a list of Never Events, which are specific quality events that will result in no payment for care provided to patients (e.g., wrong site surgery, catheter associated bloodstream infections, and decubitus ulcers acquired in the hospital).

Quality reporting is also being used in a voluntary manner as a business growth strategy. Leading children’s hospitals across the USA actively compete to have high ratings in national quality evaluations that are reported in publications such as Parents (formerly Child) magazine and US News & World Report. Many children’s hospitals have also developed their own websites for voluntarily reporting their quality information for greater transparency. Although greater transparency may provide a competitive advantage to institutions, the underlying goal of transparency is to improve the quality of care being delivered, and for families to be able to make informed choices in selecting hospitals and physicians for their children.

Quality measures may also be used for purposes of certifying individual physicians as part of the Maintenance of Certification (MOC) process. In the past, specialty and subspecialty certification in medicine, including pediatrics, was largely based on demonstrating a core fund of knowledge by being successful in an examination. No specific evidence of competency in actual practice needed to be demonstrated beyond successful completion of a training program. There continues to be significant variations in practice patterns even among physicians who are board certified, which highlighted the concept that medical knowledge is important but not sufficient for the delivery of high-quality care. Subsequently, the American Board of Medical Specialties (ABMS) including its member board, the ABP, implemented the MOC process in 2010. Within the MOC process, there is a specific requirement (Part IV of Maintenance of Certification) to demonstrate the assessment of quality of care and implementation of improvement strategies by the physician as part of recertification in pediatrics and subspecialties. Lifelong learning and the translation of learning into practice are the basis for the MOC process and for an essential competency for physicians—professionalism. There are also discussions to adopt a similar requirement for Maintenance of Licensure for physicians by state medical regulatory boards.

The Accreditation Council for Graduate Medical Education (ACGME) requires residency programs to incorporate quality improvement curriculum to ensure that systems-based practice and quality improvement are part of the overall competencies within accredited graduate medical training programs. One form of continuing medical education (CME), the Performance Improvement (PI) CME, is utilized for ongoing physician education. These initiatives require physicians to measure the quality of care they deliver to their patients, to compare their performance to peers or known benchmarks, and to work toward improving their care by leveraging quality improvement methods. This forms a feedback loop for continued learning and improvement in practice.

Prior to comparing quality measures data both within and across clinical settings, it is important to perform risk adjustment to the extent that is feasible. Risk adjustment is the statistical concept that utilizes measures of underlying severity or risk so that the outcomes can be compared in a meaningful manner. The importance of risk adjustment was highlighted in the pediatric intensive care unit (PICU) setting many years ago. The unadjusted mortality rate for large tertiary care centers was significantly higher than that for smaller hospital settings. By performing severity of illness risk adjustment it was subsequently shown that the risks in tertiary care large PICUs were higher because patients had higher levels of severity of illness. These patients were sicker than other patients, which would explain the higher mortality rate. Although this concept is now intuitive for most clinicians, the use of severity of illness models in this study allowed a mathematical estimate of patient severity using physiologic and laboratory data, which allowed for the statistical adjustment of outcomes; this permits meaningful comparisons of the outcomes of large and small critical care units. Severity of illness models and the concepts of statistical risk adjustment are most developed in pediatric critical care, but these concepts are relevant for all comparisons of outcomes in the hospital settings where sicker patients may be transferred to the larger institutions for care and therefore, would be expected to have poorer outcomes as compared to other settings with less sick patients.

Risk adjustment can be performed at 3 levels. First, patients who are sicker can be excluded from the analysis, thereby allowing the comparisons to be within homogenous groups. Although this approach is relatively simple to use, it is limited in that it would result in patient groups being excluded from the analysis. Second, risk stratification can be performed using measures of patient acuity. An example of this relates to the use of the APR-DRG (All-Patient Refined Diagnosis-Related Group) system where patients can be grouped or stratified into different severity criteria based on acuity weights. This approach may provide relatively homogenous strata within which comparisons can be performed, but it is not able to predict the overall outcomes within patient risk groups. Third, severity of illness risk adjustment relates to the use of clinical data to predict the outcomes of patient groups. An example of a clinical severity of illness risk adjustment process is the use of the Pediatric Risk of Mortality (PRISM) scoring system in the PICU setting (Chapter 61). The PRISM score, and its subsequent iterations, composed of a combination of physiologic and laboratory perimeters that are weighted on a statistical logistic scale to predict the risk of mortality within that PICU stay. By comparing the observed and expected outcomes (i.e., mortality or survival), a quantitative estimate of the performance of that PICU can be established which can then be used to compare outcomes with other PICUs (standardized mortality ratio).

Risk adjustment systems have been effectively incorporated into specialty databases. An example of such a system is the Virtual Pediatric Intensive Care Unit System (VPS), which represents the pediatric critical care database system in the USA. The VPS, comprising over 100 PICUs and cardiac PICUs across the USA as well as international PICUs, currently has over 300,000 patients within this database. The VPS database emphasizes data quality, both data validity and reliability, to ensure that the resulting data are accurate. Data validity has been established using standard data definitions with significant clinical input. Data reliability is established using inter-rater reliability to ensure that the manual data collection that involves several data collectors within pediatric institutions is consistent. The PRISM scoring system is programmed into the VPS software to allow the rapid estimation of the severity of illness of individual patients. This in turn allows risk adjustment of the various outcomes that are compared within institutions over time and across institutions for purposes of quality improvement.

Improving Quality

Quality Improvement (QI) is a rapidly growing science. There are currently 4 techniques available for QI.

Model for Improvement

The Model for Improvement can be implemented using a framework of rapid cycle improvement also known as the Plan-Do-Study-Act (PDSA) cycle (Fig. 2-1). The PDSA cycle is typically aimed at testing small changes and then studying the results to plan and implement the next cycle of change (i.e., multiple PDSA cycles build on previous learnings from PDSAs). Valuable information can be obtained from PDSA cycles that are successful and those that are not, to help plan the next iteration of the PDSA cycle. The PDSA cycle specifically requires that improvements be data driven. This is important because many clinicians attempt to make changes for improvement in their practice but do not emphasize the importance of data collection.

Figure 2-1 The Plan-Do-Study-Act Cycle.

(From Langley GJ: The improvement guide: a practical guide to enhancing organizational performance, San Francisco, 1996, Jossey-Bass. © 1996 by Gerald J. Langley, Kevin M. Nolan, Thomas W. Nolan, L. Norman, and Lloyd P. Provost. Reprinted with permission of John Wiley & Sons, Inc.)

The Model for Improvement has been successfully used in the Vermont Oxford Network (VON) to achieve improvements in care in the neonatal intensive care unit (NICU) setting. The VON is a global network of collaborating NICUs involved in several studies that have favorably impacted the care of newborns. An example of a successful VON quality improvement effort is a project aimed at reducing rates of chronic lung disease in extremely low birthweight infants. Clinical teams participating in this improvement effort used special reports from the VON database, reviewed the available evidence with content faculty experts, and then identified improvement goals. The teams received quality improvement training through conference calls and emails for a period of 1 yr. This effort resulted in a 37% increase in early surfactant administration for preterm infants achieving a high degree of quality improvement.

Another example of a successful quality improvement collaborative using the Improvement Model relates to the reduction of catheter-associated bloodstream infections in the PICU setting. Similar to the VON experience, this effort included a group of PICUs that collaborated to impact a serious preventable problem in the PICU—Catheter-Associated Bloodstream Infections (CA-BSI). National content experts and local PICU quality champions monitored and provided performance data at the local level in an almost real-time basis to ensure continued learning and improvement. The engagement of the entire PICU team—physicians, trainees, nurses, respiratory therapists, and others created a culture of quality and accountability. There was a strong emphasis on team learning across the participating institutions. This national collaborative sponsored by the National Association of Children’s Hospitals and Related Institutions (NACHRI) and the ABP has resulted in a significant measurable reduction in catheter associated bloodstream infection rates across PICUs in the USA and is now in its next iteration of the PDSA cycle.

Six Sigma

Six Sigma relates to the reduction in undesirable variation in processes (Fig. 2-2). Every process has some level of inherent variation built into it. There are 2 types of variations in a process. Random variation relates to the variation that is inherent in the process simply due to the fact that the process is being performed by humans. A physician completing a history and physical for a patient more than once may have a slightly different process each time, even though it is the same patient and the same physician. Random variation in processes is acceptable. In contrast, special cause variation relates to nonrandom variation that can adversely affect a process; when tracking infection rates in a nursery, a sudden increase in the infection rates may be secondary to poor handwashing techniques by a new health care provider in the system. This would represent a special cause variation (i.e., once this practice is improved, the infection rates will likely go back to the baseline level). Six Sigma attempts to provide a structured approach to unwanted variations in health care processes (Fig. 2-3). Six Sigma approaches have been successfully used in health care to improve processes in both the clinical and nonclinical settings.

Figure 2-2 Improving quality by reducing variation and shifting the mean. This is illustrated in an example from the End-Stage Renal Disease Registry showing reduction in variation and shifting of the overall mean times toward the goal.

Figure 2-3 Six Sigma DMAIC method—define, measure, analyze, improve, control methodology.

LEAN

LEAN Methodology, which stems from the Toyota Production System, aims at reducing waste within a process in a system. Figure 2-4A illustrates the steps in the process of a patient coming to the emergency department. After the initial registration, the patient is seen by a nurse and then the physician. In a busy emergency department, a patient may need to wait for hours before registration is complete and the patient is placed in the examination room. This wait time is a waste from the perspective of the patient and the family; by incorporating the registration process after placing the patient in the physician examination room, time can be saved and waste minimized (Fig. 2-4B). LEAN methods have been successfully used in several outpatient and inpatient settings with resulting improvements in efficiency. LEAN principles have also been adopted as a core strategy for children’s hospitals with the goal of improving efficiencies and reducing waste.

Figure 2-4 A and B, LEAN—waste reduction.

Management Sciences

Management Sciences (MS), also known as Operations Management, stems from operations research and relates to the use of mathematical principles to maximize efficiencies within systems. MS has been successfully used in many non–health care settings such as airlines and the military. MS principles have been successful in many European health care settings to optimize efficiencies in outpatient primary care office settings, inpatient acute care hospital settings, surgical settings including operating rooms, and also for effective planning of transport and hospital expansion policies. MS principles are being explored for use in the U.S. health care system. One of the techniques for MS, Discrete Event Simulation (DES) was used at the Children’s Hospital of Wisconsin to effectively plan the expansion of the pediatric critical care services with the goal of improving quality and safety. The DES model illustrated in Figure 2-5 depicts the various steps of the process in a PICU. Patients stratified across 3 levels of severity (low, medium, high) are admitted to the PICU, are initially seen by a nurse and physician, and then stay in the PICU with ongoing care being provided by physicians and nurses, and are finally discharged from the PICU. The DES model illustrated in Figure 2-5 is a computer model developed using real estimates of numbers of patients, numbers of physicians and nurses in a PICU, and patient outcomes. DES models are created using real historical data, which allows testing the “what if” scenarios, such as the impact on patient flow and throughput by increasing the number of beds and/or changing nurse and physician staffing.

Figure 2-5 Management Sciences—Discrete Event Stimulation (DES). PICU, pediatric intensive care unit.

Another MS technique developed in Europe relates to the concept of Cognitive Mapping. Cognitive Mapping aims at measuring the soft aspects of MS as illustrated in Figure 2-6. Cognitive Mapping highlights the importance of perceptions and constructs of health care providers and how these constructs are linked in a hierarchical manner. Goals and aspirations of individual health care providers are identified by structured interviews and are mapped to strategic issues and problems, and options. By using specialized computer software, complex relationships can be identified to better understand the relationships between different constructs in a system. A DES model views patient throughput based on numbers of beds, physicians, and nurses and accounts for differences in patient mix. It does not account for many other factors such as individual unit characteristics related to culture. By interviewing health care providers, Cognitive Maps can be developed that can help to better inform decision-making.

Figure 2-6 Management Sciences—Cognitive Mapping.

Quality Improvement or Research?

Research aims at generating new knowledge. Quality improvement aims at implementing the new knowledge into practice. Whereas research aims at developing new generalizable knowledge, quality improvement aims at adopting the available evidence into practice at a local level. With the growing interest of research in the field of quality improvement and efforts to expand the generalizability of quality improvement initiatives, there can be situations in which research and quality improvement overlap. In the future, the gap between quality improvement and research will likely narrow to allow a continuum of active knowledge transfer from research into practice using quality improvement methods.

Quality and Patient Safety

Safety is an important dimension of quality, and errors in health care are a leading cause of death and injury; 3-4% of hospitalized adult patients are harmed by the care that is supposed to help them, and 7% are exposed to a serious medication error that harms or could harm them. Multiple factors contribute to errors: an increasingly complex health care system with diffuse accountability; a culture of attributing errors to individuals, which overlooks problematic systems; lack of allegiance between physicians and hospitals, which detracts from patient-centered practices; and reimbursement policies that frequently discourage safety measures.

Medical Errors in Children’s Health Care

Few epidemiologic data are available regarding medication errors in the pediatric setting, and the potential for pediatric inpatient medical errors is substantial. This may be due, in part, to the fact that children have unique clinical experiences that are prone to error. These unique risk factors or safety issues, the “4 Ds,” are developmental change, dependence on adults, different disease epidemiology, and demographic characteristics. Developmental change might refer to the unique susceptibility of neonates to infections or the need for weight-based dosing with growth. Children’s dependence on adults puts them at heightened risk for experiencing medical errors because children do not usually manage their own treatments or provide their own medical history and may not have the insight to question their own care. Different disease epidemiology refers to the unique illnesses and medical needs that predispose children to unique safety events as compared with adults (e.g., birth trauma and screening for metabolic abnormalities). Children have distinct demographic characteristics and are more likely to live in poverty than any other segment of the population.

Adverse drug events (ADEs) may occur in pediatric patients at a similar rate as in adult patients; the potential ADE rate may be 3 times higher in children. A potential ADE is one that is intercepted before causing harm. Most potential ADEs occur at the stage of drug ordering and involve incorrect dosing, anti-infective drugs, and intravenous medications. In an ambulatory setting, 13% of prescriptions for children had potential medication errors. These errors are more common for infants and toddlers, children obtaining multiple prescriptions at the same time, and prescriptions for analgesics/narcotics. Technology software does not always address issues specific to children, such as pediatric dosing calculations and age-based normal ranges. It is estimated that inpatient nonmedication errors involving children result in over $1 billion in reconciliation costs per year and are associated with significant increases in length of stay, charges, and in-hospital deaths.

Key Issues in Patient Safety

Making care safer requires the identification and control of things that could cause harm to patients. Several key concepts regarding patient safety are summarized in the following sections and are available in curriculum overviews at www.patientsafety.gov, www.npsf.org, and www.va.gov.

Implications of the U.S. Health Care Reform for Quality

Between 2009 and 2010 there has been a significant paradigm shift in health care delivery systems in the USA. The American Recovery and Reinvestment Act (ARRA) has allocated $2 billion to the Office of the National Coordinator with $600 million identified for implementation of meaningful use requirements that will be available from 2011. In March 2010 the Patient Protection and Affordable Care Act (PPACA) was implemented, which highlights reductions in government program funding and expansion for coverage of the uninsured to shift from a fee-for-service model of payment to an outcomes-based model for reimbursement. These new laws and policies related to health care reform in the USA will continue to rapidly move health care organizations, hospitals and health systems, and physicians to a greater level of accountability and an outcomes-based approach to clinical practice. Historically, a significant impediment in adopting this paradigm was the lack of easily available, meaningful outcomes data. The spread of electronic health records and implementation of the HIT strategy across the country will significantly minimize this barrier in the years ahead.

To implement health care reform, children’s hospitals and health care systems will be engaged in Accountable Care Organizations, which will require organizations to manage the care of pediatric populations (based on geographic distributions or clinical service lines) in order to receive payments from state and federal insurers. This will result in the integration of quality concepts into the core operations and strategy of health care organizations and the adoption of outcomes-based reimbursement models.

Information Technology and Quality Improvement

The underlying goal of the health information technology (HIT) movement is to improve quality and safety. HIT includes electronic health records, personal health records, and health information exchange. The purpose of a well-functioning electronic health record is to allow collection and storage of patient data in an electronic form, to allow this information to be provided to clinicians and health care providers, to have the ability to allow clinicians to enter patient care orders through the computerized physician order entry (CPOE), and to have the infrastructure to provide clinical decision support, which will improve physician decision-making at the level of individual patients. Personal health records will allow patients and families to be more actively engaged in managing their own health by monitoring their clinical progress and laboratory information, and also be able to communicate with their physicians for appointments, obtaining medications, and getting their questions answered. Appropriate, timely, and seamless sharing of patient information across physician networks and health care organizations is critical to quality care and to achieve the full vision of a medical home for children. Health information exchange would allow the sharing of health care information in an electronic format to facilitate the appropriate connections between providers and health care organizations within a community or region. Despite the potential benefits of HIT, there are significant cost and time barriers that remain for adoption of HIT. The entire field of HIT as a mechanism to improve quality is likely to continue to be in the forefront of the quality journey for physicians and health care organizations for the next several years.

Despite the emphasis on HIT and data, it is important to understand that data does not lead to improvement in itself. Improvement is an affirmative choice and requires translating data (measurement) into clinically relevant information (data that has context and relevance) that is actionable for quality improvement.

A Comprehensive Model for Quality Improvement—Experience from the American Academy of Pediatrics (AAP)

The AAP has adopted quality as part of its strategic direction, supporting a wide range of strategic priorities. The broad-based AAP effort for quality improvement emphasizes developing evidence-based recommendations for pediatric care and linking efforts toward education, testing, and spread in a comprehensive manner. The Education in Quality Improvement for Pediatric Practice (EQIPP) represents a web-based educational resource related to quality improvement for pediatricians. The EQIPP has several quality improvement models in place that can provide opportunities for education to implement quality improvement into practice. Available EQIPP modules include nutrition, asthma, gastroesophageal reflux and gastroesophageal reflux disease, immunizations, Bright Futures, and the Medical Home. The Quality Improvement Innovation Network (QuIIN) represents the testing arm for new quality measures and techniques in practice. The QuIIN represents a group of pediatric practices around the country that allow the real-time field testing of new innovations in quality improvement. The QuIIN currently includes nearly 200 practices in almost every state in the USA, and in several other countries. The Chapter Alliance in Quality Improvement (CAQI) represents the spread arm for implementation of quality improvement. CAQI includes the AAP chapters from across the USA where quality improvement efforts can be put into practice and spread. An ongoing example of this collaboration is an initiative to help spread best practices for asthma care for all children.

In addition to the AAP, there are other national organizations that are actively working with physicians and hospitals to provide resources for quality improvement in health care for children (Table 2-4).

Table 2-4 NATIONAL ORGANIZATIONS INVOLVED IN PEDIATRIC QUALITY IMPROVEMENT (QI)

ORGANIZATION	ROLE	ACTIVITIES
American Academy of Pediatrics (AAP)	Representing over 60,000 pediatricians and pediatric subspecialists worldwide	Resources for QI to improve health for all children, best practices, advocacy, policy, research and practice, and medical home
American Board of Pediatrics (ABP)	Certifying board for pediatrics and pediatric subspecialties	Certification policies and resources for activities such as Maintenance of Certification (MOC)
National Association of Children’s Hospitals and Related Organizations (NACHRI)	Association of children’s hospitals and pediatric institutions	Databases, QI collaboratives, and policy
Child Health Corporation of America (CHCA)	Association of children’s hospitals	Databases and QI collaboratives
Institute for Healthcare Improvement (IHI)	QI organization for adult and pediatric care	QI collaboratives, QI educational workshops and materials
National Initiative for Child Health Quality (NICHQ)	QI organization for pediatric care	QI training, improvement networks
Joint Commission	Hospital accreditation organization	Unannounced surveys to evaluate quality of care in hospitals
National Committee for Quality Assurance (NCQA)	QI organization	Healthcare Effectiveness Data and Information Set (HEDIS) and quality measures for improvement
National Quality Forum (NQF)	Multidisciplinary group including health care providers, purchases, consumers, and accrediting bodies	Endorsing national quality measures, convening expert groups, and setting national priorities
American Medical Association (AMA)	Physician member association	Physician Consortium for Performance Improvement (PCPI)—physician-led initiative