Healthcare contributes nearly 5% of global greenhouse gas (GHG) emissions, along with significant waste, air pollution and water use.1 The production, transport and use of pharmaceuticals, chemicals, medical devices and medical supplies, as well as testing and procedures involved with healthcare delivery, carry a substantial environmental footprint.2 Given that climate change is the defining health challenge of this century, health systems have a moral and professional responsibility not only to provide high-quality care and ensure the best possible patient outcomes but also to minimise environmental harm and protect future generations. Environmentally sustainable healthcare is consistent with high-quality care, especially when framed in terms of stewardship,3 reducing low-value care and waste and improving efficiency and resilience. Additionally, interventions to achieve sustainable healthcare and reduce pollution must ensure that high-quality care is maintained. The study by Spoyalo et al4 is a fine example...

Thirty years ago, Sue Sheridan welcomed her first child, Cal, into the world. At 16 hours of age, a clinician observed that he was jaundiced and entered this assessment into his medical record. But Sheridan didn’t know that. She sensed something was wrong and asked repeatedly about her concerns. She was pegged as a ‘nervous mother’ and reassured. Upon discharge, the nurse’s note again described neonatal jaundice ‘from head to toe’, signalling the need for monitoring and testing. Sheridan was not informed of the potential seriousness of this condition or what symptoms to look for. On day 3, a paediatrician assessed a limp and lethargic infant. On day 4, Sheridan—still concerned—returned to the hospital. There, the bilirubin level returned at 34.6 mg/dL. Cal incurred severe brain damage from kernicterus, diagnosed months later.

Today, Sheridan—and nearly all parents in the USA—would have access to their child’s electronic medical record, including...

Background

Children represent fewer than 10% of emergency medical services (EMS) encounters in the USA. We evaluated whether agency-level paediatric volume is associated with the quality of prehospital care provided.

Methods

We conducted a retrospective analysis of 7104 agencies that contributed data consistently to the 2022–2023 National Emergency Medical Services Information System database, including children (<18 years) from an out-of-hospital EMS encounter. We assessed outcomes based on adherence to paediatric-specific quality benchmarks using mixed-effects models.

Results

We identified 3 403 925 paediatric encounters (median age 10 years; IQR 3–15). The annual paediatric volumes serviced by the study agencies per year ranged from 0.5 to 62 443. Six measures had a positive association with EMS volume, one measure had a negative association with EMS volume and four measures had no association with EMS volume. Higher volumes were associated with beta agonist administration for asthma/wheeze (adjusted OR (aOR) 1.08 per twofold increase in volume, 95% CI 1.06 to 1.11), epinephrine for anaphylaxis (aOR 1.09, 95% CI 1.05 to 1.08), vital signs assessment in trauma (aOR 1.05, 95% CI 1.04 to 1.07), benzodiazepines for status epilepticus (aOR 1.21, 95% CI 1.17 to 1.25), oxygen or positive pressure ventilation for hypoxia (aOR 1.06, 95% CI 1.04 to 1.09) and naloxone for opioid overdose (aOR 1.08, 95% CI 1.02 to 1.14). Higher paediatric volume was negatively associated with improvement of pain status in trauma (aOR 0.96, 95% CI 0.95 to 0.97). Paediatric volume was not associated with management of hypoglycaemia (aOR 1.01, 95% CI 0.97 to 1.06) or hypotension (aOR 0.98, 95% CI 0.92 to 1.04), or analgesia (0.99, 95% CI 0.97 to 1.01) and pain assessment (aOR 1.01, 95% CI 0.99 to 1.04) in trauma.

Conclusion

Higher paediatric volume EMS agencies had better adherence to some paediatric care quality measures but showed no association or an inverse association with others. Efforts to improve prehospital paediatric care quality should pay special attention to low-volume agencies.

Background

Guideline concordance is associated with improved patient outcomes. Accurately quantifying the concordance between provided care and guideline recommendations offers valuable insights into the alignment of care with established guidelines and supports proactive approaches for improving the quality of care. Traditional models for calculating guideline concordance are effective in assessing clinical performance via cohort averages. However, these models fail at the individual patient level by not accounting for past clinical activities and their timing, which may give a distorted impression of the actual alignment between guideline recommendations and received care.

Objectives

To develop a model for evaluating guideline concordance that provides accurate concordance scores at the individual patient level.

Methods

The newly developed ratio model incorporates past clinical activities and their timing (ie, past clinical trajectories), resulting in accurate, patient-centred concordance scores. We discuss its advantages and limitations and showcase its performance using clinical indicators for patients with type 2 diabetes mellitus.

Results

The ratio model demonstrates enhanced precision in evaluating guideline concordance at the individual level and better reflects the clinical trajectory of individual patients. While primarily designed to produce accurate individual patient scores, the model is also effective for assessing clinical performance through cohort averages. The ratio model is adaptable to diverse clinical contexts requiring regular follow-up, including chronic disease management, vaccination programmes, cancer surveillance and routine health screenings.

Conclusions

The ratio model provides accurate and patient-centred guideline concordance scores. The model’s enhanced precision at the individual level creates opportunities for research and clinical applications, including integration into clinical decision support systems.

Objectives

Many hospitals use fall prevention alarms, despite the limited evidence of effectiveness. The objectives of this study were (1) to identify, conceptualise and select strategies to deimplement fall prevention alarms and (2) to obtain feedback from key stakeholders on tailoring selected deimplementation strategies for the local hospital context.

Methods

Hospital staff working on fall prevention participated in group concept mapping (GCM) to brainstorm strategies that could be used for fall prevention alarm deimplementation, sort statements into conceptually similar categories and rate statements based on importance and current use. Hospital staff also participated in site-specific focus groups to discuss current fall prevention practices, strategies prioritised through GCM and theory-informed strategies recommended by the study team, and potential barriers/facilitators to deimplementing fall prevention alarms.

Results

90 hospital staff across 13 hospitals brainstormed, rated and sorted strategies for alarm deimplementation. Strategies that were rated as highly important but underutilised included creating/revising staff roles to support fall prevention (eg, hiring or designating mobility technicians) and revising policies and procedures to encourage tailored rather than universal fall precautions. 192 hospital staff across 22 hospitals participated in site-specific focus groups. Participants provided feedback on each strategy’s relevance for their site (eg, if site currently has a mobility technician) and local barriers or facilitators (eg, importance of having separate champions for day and night shift). Findings were used to develop a tailored implementation package for each site that included a core set of strategies (eg, external facilitation, education, audit-and-feedback, champions), a select set of site-specific strategies (eg, designating a mobility technician to support fall prevention) and guidance for how to operationalise and implement each strategy given local barriers and facilitators.

Conclusion

Findings from this study can be used to inform future programmes and policies aimed at deimplementing fall prevention alarms in hospitals.

Purpose

Misgendering of transgender and non-binary (TGNB) individuals in healthcare settings can lead to worsened mental and physical health outcomes and decreased utilisation of care. Few studies have investigated the factors that contribute to this phenomenon. The purpose of this study was to apply qualitative methods to explore sources of misgendering, its perceived impact, prevention strategies and clinician responses to accidentally misgendering a patient, as identified by TGNB patients and gender-affirming care clinicians.

Methods

Between April and June 2022, 20 semi-structured interviews were performed at an academic medical centre in Southern California. Participants were recruited via purposive sampling and included: (1) TGNB patients (n=8) recruited from an interdisciplinary gender-affirming urological practice and (2) gender-affirming care clinicians (n=12) recruited from a regional interdisciplinary Gender Health conference, three of whom identified as TGNB. Interviews were conducted in person or virtually using an open-ended topic guide, audio recorded and transcribed verbatim. Inductive thematic analysis was performed by two independent study personnel who hand-coded the transcripts.

Results

Four overarching themes were identified: (1) misgendering originates from multiple sources, (2) misgendering discourages individual access to healthcare, creates community hesitation and its perceived impact is modified by setting and intentionality, (3) building a gender-affirming healthcare system requires integration of behaviour, policy and technology and (4) clinicians respond to accidental misgendering by acknowledging, apologising, advancing and acting.

Conclusion

Our data suggest that misgendering arises from both interpersonal communication and structural factors within healthcare systems, leading to perceived harm and diminished TGNB access to health services. Any potential solution to reduce this phenomenon will require a multifaceted approach integrating behavioural, technological and institutional policy strategies with system-level implementation efforts.

Background

Access to electronic health records (EHRs) has the potential to improve the quality of care. Clinical notes, free-text entries documenting clinicians’ observations and decisions, are central to EHRs. Sharing these notes may reduce information asymmetry, enhance transparency and empower patients. However, their impact on care quality remains unclear.

Aim

To assess the impact of sharing clinical notes online with patients on the domains of quality as defined by the Institute of Medicine (ie, patient-centredness, effectiveness, efficiency, safety, timeliness and equity).

Methodology

A systematic review was conducted with no time limit, using CINAHL, Cochrane, OVID Embase, HMIC, Medline/PubMed and PsycINFO. A narrative synthesis method was employed to extract the study characteristics, and reported outcomes were organised using the six IOM quality domains. The risk of bias of included studies was assessed using the Risk of Bias in Non-randomised Studies of Interventions (ROBINS-I) tool.

Results

Nineteen studies involving 203 152 participants met inclusion criteria. Outcomes included patient-centredness (n=16), patient safety (n=14), equity (n=6), efficiency (n=4), timeliness (n=0) and effectiveness (n=0). Patient-centredness studies reported high satisfaction (n=6), increased engagement (n=11) and stronger patient–provider trust (n=7). Patient safety studies noted improvements in medication adherence (n=4) and note accuracy (n=5), alongside privacy concerns (n=5). Equity studies found benefits for minority (n=3) and less-educated patients (n=2), with one reporting equitable outcomes (n=1). No significant changes in efficiency were observed (n=4).

Discussion

Online sharing of clinical notes with patients positively impacted self-reported patient-centredness and patient safety, particularly benefiting underserved populations. However, privacy concerns must be effectively addressed, and robust safeguarding is essential to mitigate confidentiality issues. Further research is needed to evaluate the long-term impact on timeliness, effectiveness and efficiency of care.

Background

Intraoperative hypothermia can lead to adverse clinical outcomes and avoidable financial and environmental costs. Environmentally preferable warming practices have been identified, including using reusable resistive blankets, extending the life cycle of forced air warming (FAW) garments and minimising flannel blanket use. This study integrates existing environmental data with best practices and quality improvement methodology to develop an optimised patient warming pathway (OPWP). This pathway was adapted to our local context, implemented and evaluated.

Methods

The OPWP was developed using a scoping review, prior environmental impact assessment and root cause analysis. It was tailored to the workflows, patient population and warming practices at a tertiary care hospital and implemented using a multifaceted approach encompassing nine PDSA (Plan-Do-Study-Act) cycles. Major interventions included expanding pre-warming criteria to meet best practice guidelines, preserving the FAW Flex Gown, staff education and training, behaviourally informed strategies, gamification and policy development. Pre-intervention and post-intervention audits assessed environmental and financial savings, incidence of hypothermia and patient-reported outcomes (PROs).

Results

The OPWP recommends preferential use of the resistive blanket for intraoperative warming, preservation of the Flex Gown for postoperative use when warming with FAW and minimising flannel blanket use. A modified pathway was implemented using FAW with preservation of a single Flex Gown throughout the perioperative journey. From pre-intervention (N=51) to post-intervention (N=64), flannel blanket use decreased from an average of 6 to 3 per patient (p<0.01). Active warming increased from 55% to 80% (p=0.04) preoperatively and from 0% to 55% (p<0.01) postoperatively. There was no significant change in the incidence of hypothermia (18% to 15%, p=0.77) and PROs remained favourable. Implementation of this pathway could lead to annual environmental savings of 940 339 kg of carbon dioxide equivalents and cost savings of $C117 978.

Conclusions

This study demonstrates the successful implementation of an evidence-based and environmentally sustainable perioperative warming pathway to achieve low-carbon, high-quality patient care.

Artificial intelligence (AI) has great potential to assist healthcare staff and organisations in maintaining and improving the quality and safety of healthcare1 in the face of workforce shortages, rising service demand and escalating costs. Despite hundreds of regulator-approved AI-enabled tools internationally, relatively few feature in routine clinical care,2 in part due to inattention to how AI tools integrate into sociotechnical healthcare environments.3 In this Viewpoint, based on our experience as AI implementation researchers, we discuss what we see as seven key barriers to the adoption of AI in healthcare and offer some solutions.

AI literacy and engagementUnderdeveloped professional skills and consumer understanding

AI will never be adopted at scale unless health professionals better understand AI and its limitations, acquire competencies in co-designing, co-evaluating and effectively using AI tools, undertake continual vigilance of AI tool performance and avoid over-reliance on AI with...

Language barriers between healthcare professionals and their patients remain a persistent challenge. Patients with limited proficiency in the primary language of the country where they receive care face higher risks of adverse events, misdiagnosis and unplanned readmissions.1 2 In linguistically diverse countries, services often fall short of meeting the needs of patients who speak minority languages. This leads to inequities in care across inpatient, outpatient and emergency settings. While concern for language discordance in healthcare is by no means a novel development, guidelines have rarely progressed beyond recommending implementation of professional interpreter services.3 In-person interpretation is generally considered the gold standard for addressing language barriers during direct care delivery4 5 and is in line with regulatory and ethical standards.6 Other modalities of interpretation, including telephone and video, are alternative options, although the feasibility of providing timely written...

Introduction

Machine translation of patient-specific information could mitigate language barriers if sufficiently accurate and non-harmful and may be particularly useful in healthcare encounters when professional translators are not readily available. We evaluated the translation accuracy and potential for harm of ChatGPT-4 and Google Translate in translating from English to Spanish, Chinese and Russian.

Methods

We used ChatGPT-4 and Google Translate to translate 50 sets (316 sentences) of deidentified, patient-specific, clinician free-text emergency department instructions into Spanish, Chinese and Russian. These were then back-translated into English by professional translators and double-coded by physicians for accuracy and potential for clinical harm.

Results

At the sentence level, we found that both tools were ≥90% accurate in translating English to Spanish (accuracy: GPT 97%, Google Translate 96%) and English to Chinese (accuracy: GPT 95%; Google Translate 90%); neither tool performed as well in translating English to Russian (accuracy: GPT 89%; Google Translate 80%). At the instruction set level, 16%, 24% and 56% of Spanish, Chinese and Russian GPT-translated instruction sets contained at least one inaccuracy. For Google Translate, 24%, 56% and 66% of Spanish, Chinese and Russian translations contained at least one inaccuracy. The potential for harm due to inaccurate translations was ≤1% for both tools in all languages at the sentence level and ≤6% at the instruction set level. GPT was significantly more accurate than Google Translate in Chinese and Russian at the sentence level; the potential for harm was similar.

Conclusion

These results support the potential of machine translation tools to mitigate gaps in translation services for low-stakes written communication from English to Spanish, while also strengthening the case for caution and for professional oversight in non-low-risk communication. Further research is needed to evaluate machine translation for other languages and more technical content.

Background

Healthcare patient safety investigations inappropriately focus on individual culpability and the target of recommendations is often on the behaviours of individuals, rather than addressing latent failures of the system. The aim of this study was to explore whether outcome bias might provide some explanation for this. Outcome bias occurs when the ultimate outcome of a past event is given excessive weight, in comparison to other information, when judging the preceding actions or decisions.

Methods

We conducted a survey in which participants were each presented with three incident scenarios, followed by the findings of an investigation. The scenarios remained the same, but the patient outcome was manipulated. Participants were recruited via social media and we examined three groups (general public, healthcare staff and experts) and those with previous incident involvement. Participants were asked about staff responsibility, avoidability, importance of investigating and to select up to five recommendations to prevent recurrence. Summary statistics and multilevel modelling were used to examine the association between patient outcome and the above measures.

Results

212 participants completed the online survey. Worsening patient outcome was associated with increased judgements of staff responsibility for causing the incident as well as greater motivation to investigate. More participants selected punitive recommendations when patient outcome was worse. While avoidability did not appear to be associated with patient outcome, ratings were high suggesting participants always considered incidents to be highly avoidable. Those with patient safety expertise demonstrated these associations but to a lesser extent, when compared with other participants. We discuss important comparisons between the participant groups as well as those with previous incident involvement, as victim or staff member.

Interpretation

Outcome bias has a significant impact on judgements following incidents and investigations and may contribute to the continued focus on individual culpability and individual focused recommendations observed following investigations.

Background

Natural hazards, such as earthquakes, pose a significant risk to both the public and healthcare professionals, jeopardising patient safety due to the disruption of healthcare systems and services. This study aimed to explore the lived experiences of healthcare professionals concerning patient safety during natural hazards, specifically earthquakes.

Methods

Employing a descriptive phenomenological approach, the study followed the Consolidated Criteria for Reporting Qualitative Research guidelines. 23 participants, including doctors, nurses and paramedics, were interviewed using purposive sampling. Data were gathered through semistructured interviews, which were audio recorded and transcribed. Ethical approval was obtained, and Colaizzi’s method was used for data analysis, with findings validated through researcher consensus and participant feedback.

Results

Nine overarching themes emerged, such as the emotional toll of communication breakdowns, struggles with patient identification, stress due to resource scarcity, operational chaos, ethical dilemmas and psychological impacts on both patients and staff. The study found that these factors collectively influenced patient safety during the earthquake.

Conclusion

The emotional strain caused by communication failures, patient identification issues and resource shortages compounded the challenges of providing safe care during the earthquake. Strengthening disaster preparedness through improved communication systems, resource management, psychological support, interagency coordination and regular realistic disaster drills is essential for safeguarding patient safety in future disasters.

Importance

Patients with poor glycaemic control have a high risk for major cardiovascular events. Improving glycaemic monitoring in patients with diabetes can improve morbidity and mortality.

Objective

To assess the effectiveness of a patient portal message in prompting patients with poorly controlled diabetes without a recent glycated haemoglobin (HbA1c) result to have their HbA1c repeated.

Design

A pragmatic, randomised clinical trial.

Setting

A large academic health system consisting of over 350 ambulatory practices.

Participants

Patients who had an HbA1c greater than 10% who had not had a repeat HbA1c in the prior 6 months.

Exposures

A single electronic health record (EHR)-based patient portal message to prompt patients to have a repeat HbA1c test versus usual care.

Main outcomes

The primary outcome was a follow-up HbA1c test result within 90 days of randomisation.

Results

The study included 2573 patients with a mean (SD) HbA1c of 11.2%. Among 1317 patients in the intervention group, 24.2% had follow-up HbA1c tests completed within 90 days, versus 21.1% of 1256 patients in the control group (p=0.07). Patients in the intervention group were more likely to log into the patient portal within 60 days as compared with the control group (61.2% vs 52.3%, p<0.001).

Conclusions

Among patients with poorly controlled diabetes and no recent HbA1c result, a brief patient portal message did not significantly increase follow-up testing but did increase patient engagement with the patient portal. Automated patient messages could be considered as a part of multipronged efforts to involve patients in their diabetes care.

Objective

Overconfidence is an important source of medical error. This review analyses experimental studies of confidence in medical diagnosis to identify factors affecting clinicians’ confidence in their diagnoses and how confidence impacts patient care.

Method

A scoping review of medical and psychological literature was conducted. Articles were categorised according to methodology and clinical specialty. Findings were analysed thematically. Our review methodology adheres to the JBI’s Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist.

Data sources

We searched SCOPUS, MEDLINE, PsycINFO and Global Health. We then performed citation tracking within these papers’ references to identify additional articles.

Eligibility criteria

Papers were included if they reported quantitative results from an empirical study in which participants reported their confidence or certainty during a diagnostic decision. Studies comprised several medical subdisciplines.

Results

77 articles met the inclusion criteria. Across these articles, confidence was not found to be well-calibrated to true diagnostic accuracy regardless of clinician experience. We organised articles under two main themes: the determinants of confidence and the uses of confidence during the patient’s care pathway. Confidence is found to be affected by several factors, including case complexity, early diagnostic differentials and the healthcare environment. Factors that affect confidence, but not accuracy, demonstrate how the two can become decoupled, resulting in overconfidence/underconfidence. Confidence is found to affect patient testing, medication administration and referral rates, among other clinical actions.

Conclusions

Improving the calibration of confidence should be a priority for medical education and clinical practice (eg, via decision aids). We propose a theoretical model of factors that affect diagnostic confidence/certainty. Such a model can inform future work on how appropriate diagnostic confidence can be prompted and communicated among clinicians.

Statistical process control (SPC) charts provide a natural approach to analysing time series data for healthcare quality improvement (QI) initiatives. A problem arising in practice is that having established baseline control limits, there is no accepted objective and transparent approach to deciding when to establish new control limits for a given chart. We present the Stable Shift Algorithm, a new algorithm to aid analysts by identifying when control limits should be re-established, partitioning a control chart of time series data into distinct time periods. The algorithm aims to achieve this while (1) using only the theory of SPC, familiar to many QI practitioners, (2) avoiding re-establishing limits prematurely and (3) remaining flexible to choice of basic parameters of typical control chart use in QI. This is achieved through the commonly used shift rule of control charts, applied to establish whether shifts warrant new control limits or not. We conducted a simulation study to evaluate the effectiveness of the algorithm in achieving its aims, and a case study demonstrating application of the algorithm to 557 time series of accident and emergency care measures for providers in England and Scotland. Simulation results show that the algorithm avoids premature re-establishment of limits more often than simply re-establishing at every shift rule break. Application of the algorithm to the accident and emergency measures demonstrates this is not achieved at the cost of excessive additional rule breaks that might indicate control limits do not represent the underlying process. The Stable Shift Algorithm offers a potentially highly valuable tool for QI practitioners and researchers undertaking SPC analyses, providing an automated, consistent and rigorous approach facilitating large-scale analyses.

Introduction

The role of quality and safety professionals and leaders in realising the potential and managing the risks of artificial intelligence (AI) tools has not been well defined. We suggest these leaders focus on three areas: using quality, safety and implementation sciences to increase the likelihood of beneficial AI adoption; using AI to enhance and support the methods of quality and safety management; and serving as experts and champions for AI tool use that promotes health and equity (figure 1).

Background and approach

We used 90-day research and development cycles1 to examine AI topics and the role of quality and safety leaders with respect to AI integration, including applying AI tools for quality improvement (QI) use cases,2 the broad application of AI in quality management systems, the implications of AI for patient safety3 and the use of AI...

Despite decades of progress in global child health, neonatal mortality remains high, accounting for nearly half of all under-five deaths worldwide.1 Most of these deaths occur in low- and middle-income countries and are preventable with timely, high-quality care for small and sick newborns.2 The WHO has called for every newborn to receive essential, high-impact interventions,3 yet the challenge lies not only in knowing what works, but in implementing those interventions at scale, with quality, and within real-world health systems. Quality improvement (QI) and implementation science (IS) offer complementary strategies to address this challenge. QI focuses on local, iterative problem solving to adapt and improve evidence-based or locally generated care processes,4 5 while IS provides structured, theory-driven methods to promote their uptake and sustainability.6 7 Yet too often, these fields operate independently rather than in a...

The number of complaints received by healthcare organisations from patients and families is on an upward trajectory.1 For example, in 2023–2024, the NHS in England received 241 922 complaints,2 an increase of 5% on the previous year and 37% since 2013–2014. Moreover, while relatively few NHS patient encounters result in a formal complaint (approximately 0.4%), just 9% of patients who report poor healthcare experiences actually submit one.3

Although the motivation for complainants can vary—for instance, some patients seek redress, and others want resolution of ongoing problems—they nearly always request organisational learning.4 Furthermore, while complaints can be incorrect or ill-intentioned, leading to concerns about their validity,5 the collective scale of the information they provide is hard to dismiss. They are, in effect, a massive rolling compendium of ethnographies from patients and families at the sharp end of treatment delivery, revealing perceived...

Background

Improving small and sick newborn care (SSNC) is crucial in resource-limited settings. Newborn Essential Solutions and Technologies (NEST360), a multicountry alliance, aims to reduce newborn mortality through evidence-based interventions. NEST360 developed a multipronged approach to improving quality. We use implementation research (IR) to describe this approach and report emerging implementation outcomes.

Methods

The implementation research logic model (IRLM) was applied to link contextual factors, implementation strategies, mechanisms and implementation outcomes, capturing successes and challenges of the improving quality approach. Data sources included programme data, peer-reviewed publications and team input. Contextual factors were organised by the NEST360-UNICEF SSNC implementation toolkit. Strategies were grouped by the Expert Recommendations for Implementation Change list, and implementation outcomes were measured using Proctor’s implementation outcomes.

Results

We developed an IRLM to describe the implementation of NEST360’s improving quality model. This IRLM included 33 contextual factors; 42% were barriers, 42% were facilitators, and 15% were both a barrier and facilitator. Additionally, we identified 10 implementation strategies that NEST360 used. The logic model also describes the connections between the contextual factors, the strategies that address them, and the preliminary implementation outcomes. Examples of the outcomes measured include Reach with 100% of units logging into the NEST360-Implementation Tracker (NEST-IT) at least once (October 2023 to March 2024), Adoption with 100% of units conducting a quality improvement (QI) project (April 2024 to June 2024), and Feasibility with 93% of units reporting NEST-IT data in their QI project documentation (April 2024 to June 2024). Finally, this study identified sustainability strategies as a critical need.

Conclusions

Integrating IR and QI enhances SSNC in resource-limited settings. Addressing barriers, leveraging facilitators and using structured IR frameworks advanced QI efforts, thereby improving intervention reach, adoption and feasibility while building scalable systems for high-quality healthcare.