This article reviews several common quality improvement methodologies, including the Model for Improvement, Lean, and Six Sigma. We demonstrate how these methods are based on a similar improvement science foundation. We describe the tools used to understand problems in the context of systems and the mechanisms to learn and build knowledge, using specific examples from the neonatology and pediatric literature. We conclude with a discussion on the importance of the human side of change in quality improvement, including team formation and culture.

Neurodevelopmental challenges in children born very preterm are common and not improving. This study tested the feasibility of using Evidence-based Practice to Improve Quality (EPIQ), a proven quality improvement technique that incorporates scientific evidence to target improving language abilities in very preterm populations in 10 Canadian neonatal follow-up programs. Feasibility was defined as at least 70% of sites completing four intervention cycles and 75% of cycles meeting targeted aims. Systematic reviews were reviewed and performed, an online quality improvement educational tool was developed, multidisciplinary teams that included parents were created and trained, and sites provided virtual support to implement and audit locally at least four intervention cycles of approximately 6 months in duration. Eight of ten sites implemented at least four intervention cycles. Of the 48 cycles completed, audits showed 41 (85%) met their aim. Though COVID-19 was a barrier, parent involvement, champions, and institutional support facilitated success. EPIQ is a feasible quality improvement methodology to implement family-integrated evidence-informed interventions to support language interventions in neonatal follow-up programs. Further studies are required to identify potential benefits of service outcomes, patients, and families and to evaluate sustainability.

Evidence-based Practice for Improving Quality (EPIQ) is a collaborative quality improvement method adopted by the Canadian Neonatal Network that led to decreased mortality and morbidity in very preterm neonates. The Alberta Collaborative Quality Improvement Strategies to Improve Outcomes of Moderate and Late Preterm Infants (ABC-QI) Trial aims to evaluate the impact of EPIQ collaborative quality improvement strategies in moderate and late preterm neonates in Alberta, Canada.

In a 4-year, multicentre, stepped-wedge cluster randomized trial involving 12 neonatal intensive care units (NICUs), we will collect baseline data with the current practices in the first year (all NICUs in the control arm). Four NICUs will transition to the intervention arm at the end of each year, with 1 year of follow-up after the last group transitions to the intervention arm. Neonates born at 32 + 0 to 36 + 6 weeks' gestation with primary admission to NICUs or postpartum units will be included. The intervention includes implementation of respiratory and nutritional care bundles using EPIQ strategies, including quality improvement team building, quality improvement education, bundle implementation, quality improvement mentoring and collaborative networking. The primary outcome is length of hospital stay; secondary outcomes include health care costs and short-term clinical outcomes. Neonatal intensive care unit staff will complete a survey in the first year to assess quality improvement culture in each unit, and a sample will be interviewed 1 year after implementation in each unit to evaluate the implementation process.

The ABC-QI Trial will assess whether collaborative quality improvement strategies affect length of stay in moderate and late preterm neonates. It will provide detailed population-based data to support future research, benchmarking and quality improvement.

ClinicalTrials.gov, no. NCT05231200.

The objective of the Chinese Neonatal Network (CHNN) is to provide a platform for collaborative research, outcomes evaluation and quality improvement for preterm infants with gestational age less than 32 weeks in China. The CHNN is the first national neonatal network and has the largest geographically representative cohort from neonatal intensive care units (NICUs) in China.

Individual-level data from participating NICUs will be collected using a unique database developed by the CHNN on an ongoing basis from January 2019. Data will be prospectively collected from all infants <32 weeks gestation or <1500 g birth weight at 58 participating NICUs. Infant outcomes and inter-institutional variations in outcomes will be examined and used to inform quality improvement measures aimed at improving outcomes. Information about NICU environmental and human resource factors and processes of neonatal care will also be collected and analysed for association with outcomes. Clinical studies, including randomised controlled trials will be conducted using the CHNN data platform.

This study was approved by the ethics review board of Children's Hospital of Fudan University, which was recognised by all participating hospitals. Waiver of consent were granted at all sites. Only non-identifiable patient level data will be transmitted and only aggregate data will be reported in CHNN reports and publications.

To investigate how two different strategies to form larger neonatal intensive care units (NICU) impact neonatal mortality rates.

Cross-sectional study modeling admission volumes and mortality rates of 177,086 VLBW infants aggregated into 862 NICUs. Cumulative 3-year data was abstracted from Vermont Oxford Network. The model simulated a reduction in number of NICUs by stepwise exclusion using either admission volume (VOL) or quality (QUAL) cut-offs. After randomly redirecting infants of excluded to remaining NICUs resulting system mortality rates were calculated with and without adjusting for effects of experience levels (EL) using published data to reflect effects of different team-to-patient exposure.

The quality-based strategy is more effective in reducing mortality; while VOL alone was not able to reduce system mortality, QUAL already achieved a 5% improvement after reducing 8% of NICUs and redirecting 6% of infants. Including "EL", a 5% improvement of mortality was achieved by reducing 77% (VOL) vs. 7% (QUAL) of NICUs and redirecting 54% (VOL) vs. 5% (QUAL) of VLBW infants, respectively.

While a critical number of admissions is needed to maintain skills this study emphasizes the importance of including quality parameters to restructure neonatal care. The findings can be generalized to other medical fields.

Drug therapy is a powerful tool to improve outcome, but there is an urgent need to improve pharmacotherapy in neonates through tailored prevention and management of adverse drug reactions (ADRs). At present, infants commonly receive off-label drugs, at dosages extrapolated from those in children or adults. Besides the lack of labelling, inappropriate formulations, (poly)pharmacy, immature organ function and multiple illnesses further raise the risk for ADRs in neonates and infants. Pharmacovigilance to improve the prevention and management of ADRs needs to be tailored to neonates and infants. We illustrate this using prevention strategies for drug prescription and administration errors (e.g. formulation, bedside manipulation, access), detection through laboratory signalling or clinical outlier data (e.g. reference laboratory values, overall high morbidity), assessment through algorithm scoring (e.g. Naranjo or population specific), as well as understanding of the developmental toxicology (e.g. covariates, developmental pharmacology) to avoid re-occurrence and for development of guidelines. Such tailored strategies need collaborative initiatives to combine the knowledge and expertise of different disciplines, but hold promise to become a very effective tool to improve pharmacotherapy and reduce ADRs in infants.

The International Network for Evaluating Outcomes in Neonates (iNeo) is a collaboration of population-based national neonatal networks including Australia and New Zealand, Canada, Israel, Japan, Spain, Sweden, Switzerland, and the UK. The aim of iNeo is to provide a platform for comparative evaluation of outcomes of very preterm and very low birth weight neonates at the national, site, and individual level to generate evidence for improvement of outcomes in these infants.

Individual-level data from each iNeo network will be used for comparative analysis of neonatal outcomes between networks. Variations in outcomes will be identified and disseminated to generate hypotheses regarding factors impacting outcome variation. Detailed information on physical and environmental factors, human and resource factors, and processes of care will be collected from network sites, and tested for association with neonatal outcomes. Subsequently, changes in identified practices that may influence the variations in outcomes will be implemented and evaluated using quality improvement methods.

The evidence obtained using the iNeo platform will enable clinical teams from member networks to identify, implement, and evaluate practice and service provision changes aimed at improving the care and outcomes of very low birth weight and very preterm infants within their respective countries. The knowledge generated will be available worldwide with a likely global impact.

Neonatal intensive care unit networks that encompass regions, states, and even entire countries offer the perfect platform for implementing continuous quality improvement initiatives to advance the health care provided to vulnerable neonates. Through cycles of identification and implementation of best available evidence, benchmarking, and feedback of outcomes, combined with mutual collaborative learning through a network of providers, the performance of health care systems and neonatal outcomes can be improved. We use examples of successful neonatal networks from across North America to explore continuous quality improvement in the neonatal intensive care unit, including the rationale for the formation of neonatal networks, the role of networks in continuous quality improvement, quality improvement methods and outcomes, and barriers to and facilitators of quality improvement.

Retinopathy of prematurity (ROP) programs require collaboration between neonatologists, ophthalmologists, nurses, and allied health personnel, together with parents. The concept of a ROP program will vary according to the setting. However, in every situation there should be 2 main aspects: primary prevention of ROP through better overall care, and secondary prevention through case detection (often called screening), treatment, and follow-up. ROP programs will have different content and emphasis according to whether the setting is in an economically advanced or developing country.