Explore the vast range of titles available.

BackgroundQuality improvement collaboratives (QIC) have proliferated internationally, but there is little empirical evidence for their effectiveness.MethodWe searched Medline, Embase, CINAHL, PsycINFO and the Cochrane Library databases from January 1995 to December 2014. Studies were included if they met the criteria for a QIC intervention and the Cochrane Effective Practice and Organisation of Care (EPOC) minimum study design characteristics for inclusion in a review. We assessed study bias using the EPOC checklist and the quality of the reported intervention using a subset of SQUIRE 1.0 standards.ResultsOf the 220 studies meeting QIC criteria, 64 met EPOC study design standards for inclusion. There were 10 cluster randomised controlled trials, 24 controlled before-after studies and 30 interrupted time series studies. QICs encompassed a broad range of clinical settings, topics and populations ranging from neonates to the elderly. Few reports fully described QIC implementation and methods, intensity of activities, degree of site engagement and important contextual factors. By care setting, an improvement was reported for one or more of the study’s primary effect measures in 83% of the studies (32/39 (82%) hospital based, 17/20 (85%) ambulatory care, 3/4 nursing home and a sole ambulance QIC). Eight studies described persistence of the intervention effect 6 months to 2 years after the end of the collaborative. Collaboratives reporting success generally addressed relatively straightforward aspects of care, had a strong evidence base and noted a clear evidence-practice gap in an accepted clinical pathway or guideline.ConclusionsQICs have been adopted widely as an approach to shared learning and improvement in healthcare. Overall, the QICs included in this review reported significant improvements in targeted clinical processes and patient outcomes. These reports are encouraging, but most be interpreted cautiously since fewer than a third met established quality and reporting criteria, and publication bias is likely.

BackgroundThe consultation process, where a clinician seeks an opinion from another clinician, is foundational in medicine. However, the effectiveness of group diagnosis has not been studied.ObjectiveTo compare individual diagnosis to group diagnosis on two dimensions: group size (n=3 or 6) and group process (interactive or artificial groups).MethodologyThirty-six internal or emergency medicine residents participated in the study. Initially, each resident worked through four written cases on their own, providing a primary diagnosis and a differential diagnosis. Next, participants formed into groups of three. Using a videoconferencing platform, they worked through four additional cases, collectively providing a single primary diagnosis and differential diagnosis. The process was repeated using a group of six with four new cases. Cases were all counterbalanced. Retrospectively, nominal (ie, artificial) groups were formed by aggregating individual participant data into subgroups of three and six and analytically computing scores. Presence of the correct diagnosis as primary diagnosis or included in the differential diagnosis, as well as the number of diagnoses mentioned, was calculated for all conditions. Means were compared using analysis of variance.ResultsFor both authentic and nominal groups, the diagnostic accuracy of group diagnosis was superior to individual for both the primary diagnosis and differential diagnosis. However, there was no improvement in diagnostic accuracy when comparing a group of three to a group of six. Interactive and nominal groups were equivalent; however, this may be an artefact of the method used to combine data.ConclusionsGroup diagnosis improves diagnostic accuracy. However, a larger group is not necessarily superior to a smaller group. In this study, interactive group discussion does not result in improved diagnostic accuracy.

BackgroundEmerging evidence has shown racial and ethnic disparities in rates of harm for hospitalised children. Previous work has also demonstrated how highly heterogeneous approaches to collection of race and ethnicity data pose challenges to population-level analyses. This work aims to both create an approach to aggregating safety data from multiple hospitals by race and ethnicity and apply the approach to the examination of potential disparities in high-frequency harm conditions.MethodsIn this cross-sectional, multicentre study, a cohort of hospitals from the Solutions for Patient Safety network with varying race and ethnicity data collection systems submitted validated central line-associated bloodstream infection (CLABSI) and unplanned extubation (UE) data stratified by patient race and ethnicity categories. Data were submitted using a crosswalk created by the study team that reconciled varying approaches to race and ethnicity data collection by participating hospitals. Harm rates for race and ethnicity categories were compared with reference values reflective of the cohort and broader children’s hospital population.ResultsRacial and ethnic disparities were identified in both harm types. Multiracial Hispanic, Combined Hispanic and Native Hawaiian or other Pacific Islander patients had CLABSI rates of 2.6–3.6 SD above reference values. For Black or African American patients, UE rates were 3.2–4.4 SD higher. Rates of both events in White patients were significantly lower than reference values.ConclusionsThe combination of harm data across hospitals with varying race and ethnicity collection systems was accomplished through iterative development of a race and ethnicity category framework. We identified racial and ethnic disparities in CLABSI and UE that can be addressed in future improvement work by identifying and modifying care delivery factors that contribute to safety disparities.

BackgroundCountries with resource-poor health systems have struggled to improve access to and the quality of caesarean section (C-section; CS) for women seeking care in public health facilities. Access to C-section in Bihar State remains very low, while access has increased in many other contexts.MethodsWe used quality improvement (QI) combined with targeted resource management to test and implement changes that were designed to increase C-section delivery. We compared C-section delivery percentages after the interventions across eight intervened (QI) hospitals and between QI hospitals and the remaining 22 non-intervened (non-QI) hospitals with baseline CS <10%. We linked patterns of improvement and sustainability to theoretical drivers of improvement and timing of interventions.ResultsIn QI hospitals, C-section percentage increased from 2.9% at baseline to 5.9% in the intervention phase and 4.6% in the post intervention phase. In non-QI hospitals, we observed a small change (2.6–3.3%) during the same time period of the interventions in the QI hospitals. Addition of skilled personnel resulted in increased C-section percentage in QI hospitals (3.6–5.9%) but not non-QI hospitals (3.4–3.2%).ConclusionsC-section availability increased for a population of women giving birth following initiation of QI BTS collaborative in a low-income country public sector setting that has historically struggled to provide this service. Addition of obstetric and operating room resources alone, without interventions to support system changes, may not result in additional increase in C-section delivery. The adaptive implementation model may contribute to efforts to provide more access to C-sections in other very resource-limited settings.

BackgroundThe Child Opportunity Index (COI) is a multidimensional measure of US neighbourhood-level conditions needed for healthy development. COI is associated with healthcare delivery and outcomes. Formal quality improvement (QI) may influence the relationship between COI, quality of care and outcomes in children.ObjectiveTo assess the association between COI and paediatric sepsis care delivery and outcomes and determine if baseline disparities in care change over time among hospitals in the Improving Pediatric Sepsis Outcomes (IPSO) collaborative.MethodsRetrospective cohort study of IPSO patients probabilistically linked to the Pediatric Health Information System database from 2017 to 2021. Primary exposure was COI. We estimated differences in the proportions of patients in each COI quintile identified via standardised sepsis recognition protocols (screening tool, huddle documentation and/or order set use) and who received a bundle of recommended care (standardised sepsis recognition, plus bolus <1 hour and antibiotic <3 hours). We further assessed the timeliness of each bundle component and mortality. We evaluated changes in standardised sepsis recognition over time using generalised linear models.Results31 260 sepsis cases from 24 hospitals were included. Cross-sectional analysis over the entire study period found patients in the Very High COI quintile were most likely to be identified via standardised recognition protocols and receive IPSO’s recommended care bundle (67.7% and 46%, respectively). Over time, standardised sepsis recognition improved for all; the greatest improvements were among inpatients in the Very Low COI quintile.ConclusionDisparities exist in paediatric sepsis care delivery by COI. Over the course of the IPSO collaborative, care improved most for children in the lowest COI quintile. QI collaboratives focused on standardisation and shared learning may reduce disparities.

BackgroundThere is limited evidence from antimicrobial stewardship programmes in less-resourced settings. This study aimed to improve the quality of antibacterial prescriptions by mitigating overuse and promoting the use of narrow-spectrum agents in intensive care units (ICUs) in a middle-income country.MethodsWe established a quality improvement collaborative (QIC) model involving nine Argentine ICUs over 11 months with a 16-week baseline period (BP) and a 32-week implementation period (IP). Our intervention package included audits and feedback on antibacterial use, facility-specific treatment guidelines, antibacterial timeouts, pharmacy-based interventions and education. The intervention was delivered in two learning sessions with three action periods along with coaching support and basic quality improvement training.ResultsWe included 912 patients, 357 in BP and 555 in IP. The latter had higher APACHE II (17 (95% CI: 12 to 21) vs 15 (95% CI: 11 to 20), p=0.036), SOFA scores (6 (95% CI: 4 to 9) vs 5 (95% CI: 3 to 8), p=0.006), renal failure (41.6% vs 33.1%, p=0.009), sepsis (36.1% vs 31.6%, p<0.001) and septic shock (40.0% vs 33.8%, p<0.001). The days of antibacterial therapy (DOT) were similar between the groups (change in the slope from BP to IP 28.1 (95% CI: −17.4 to 73.5), p=0.2405). There were no differences in the antibacterial defined daily dose (DDD) between the groups (change in the slope from BP to IP 43.9, (95% CI: −12.3 to 100.0), p=0.1413).The rate of antibacterial de-escalation based on microbiological culture was higher during the IP (62.0% vs 45.3%, p<0.001).The infection prevention control (IPC) assessment framework was increased in eight ICUs.ConclusionImplementing an antimicrobial stewardship program in ICUs in a middle-income country via a QIC demonstrated success in improving antibacterial de-escalation based on microbiological culture results, but not on DOT or DDD. In addition, eight out of nine ICUs improved their IPC Assessment Framework Score.

Learning networks (LNs) have demonstrated success as a useful model for building a learning health system, envisioned by the National Academy of Medicine as a system in which innovation and continuous improvement are achieved through stakeholder alignment and in which both known best practices and new knowledge generation are embedded in healthcare delivery processes.1 The results-oriented LN model has become increasingly popular in paediatrics, where there is often a lack of evidence-based best practices. Paediatric LNs aim to improve outcomes and generate new knowledge by using an actor-oriented network structure composed of multiple care sites, a group of varied stakeholders (including patients, families, clinicians, researchers and health system leadership) and use of data for improvement, research and innovation.2 Establishing an effective LN requires intentional design to achieve alignment around a common goal, build standard processes and infrastructure that enable collaboration, and create a shared commons for information exchange.3 This network architecture enables LNs to study variation across sites, test ideas to improve outcomes, identify best practices from these ideas and then enhance efficient dissemination of these best practices across sites.3 4 Using this model, several paediatric LNs have reported significant and sustained improvements in outcomes, including decreased incidence of healthcare acquired conditions,5 increased rates of inflammatory bowel disease remission6 7 and reduction in mortality of infants with high-risk congenital heart disease.8 As emphasised by Britto et al, because not all improvement interventions work equally well, LNs must have methods to test ideas to determine which interventions work best.3 However, despite the importance of this observation, the mechanisms of testing or learning that occur within an LN have not been previously described. Activity at the local level is distinguished from activity at the network level in figure 1, and the ratio of activity between the two levels varies across the three pathways.Table 1 Comparison of learning and improvement framework pathways Pathway Intended scope of impact Source of idea Improvement approach Support from LN Expected output LN approval timing Design/plan phase participants Test/learn phase participants Evaluation of test/learn phase Share/spread phase participants Network-wide Many or all LN sites Ideally from evidence of improved outcome: successful incubator project, review of LN registry data and published research Rigorous approach including phases for design, testing and learning with a small cohort, spreading to network-wide improvement and sustaining improvement with defined expectations for site participation at each phase Formal support from experts in QI, data and project management including centralised area for topic-specific learning, facilitating meetings, aggregate and site-level data reporting, and communication to entire LN All LN sites expected to participate in active improvement; scientific publication expected Prior to design phase 1–4 project leaders, 6–10 subject matter experts, LN support staff 5–10 sites, 1–4 project leaders, 6–10 subject matter experts, LN support staff If collective improvement in outcomes and high reliability to processes, plan to spread to all LN sites All LN sites expected to participate in active improvement Incubator 3–5 sites Promising idea based on solid theory, often extension of successful single-site project Project leaders design multisite project using QI methodology (eg, key driver diagram) and then share best practices and learnings during regular calls. Given the significant resource investment required for a network-wide project, these projects follow a rigorous structured process for design, implementation and sustainment (table 1) and ideally are driven by strong evidence. Because network-wide projects are anticipated to result in a significant change in an outcome of interest to the LN, an idea for a network-wide project may come from review of LN registry data showing variation in outcomes or practices across LN sites, or alternatively, network-wide projects may represent ideas that previously moved through the other L&I framework pathways.

BackgroundSurveys are widely used in healthcare to gather knowledge and information about services provided. There is a recognised gap between survey findings and their impact on practice, particularly for standardised surveys conducted at the national or organisational level. Findings are more likely to be acted on where there is a culture and infrastructure supportive of quality improvement (QI), but little is known about the experiences of local QI teams designing and using surveys in practice.ObjectiveTo understand the experiences of QI teams designing and using surveys within a national QI collaborative, including perceived value and challenges.MethodsUsing an interactive research approach, 14 semistructured interviews were conducted with members of the Cystic Fibrosis Lung Transplant Transition Learning and Leadership Collaborative. Data were analysed through multiple rounds of coding and inductive thematic analysis.ResultsCollaborative participants viewed surveys positively as an improvement tool. The design and use of surveys was a team-based effort, embedded within the structure of the collaborative. Surveys illuminated local, microsystem and mesosystem data and provided patient and staff insights. As one step in the QI journey, surveys helped shape the direction of local QI work, resulting in positive changes in areas such as working relationships, patient interactions, staff education and work processes.Challenges experienced included: response rates and survey design, inability to act on findings, issues of sensitivity and anonymity, expertise to design surveys, time requirements, and survey fatigue.ConclusionsSurveys played a crucial role in driving QI efforts, leading to impactful changes in practice. Used within a supportive collaborative context, surveys became an essential tool for ongoing learning and improvement, highlighting the distinct needs of surveys used in QI compared with research.

Evaluation of learning collaboratives (LC) needs to account for not just outcomes and context, but also the mechanisms participating teams use to implement and normalise new practices. Normalisation process theory (NPT) mechanisms—coherence, cognitive participation, collective action and reflexive monitoring—were used to do a constant comparison coding of transcripts of weekly calls between team coaches and mentors during a 9-month LC to implement team-based primary care in 13 health centres. Both the positive and negative (eg, lack of coherence) use of normalising mechanisms, as well as when they occurred over time, were noted. Findings suggest that normalising mechanisms are not linear, but work concurrently in real time, in a recursive fashion and in negative and positive ways. Clarity of purpose (coherence) became clearer as teams met regularly, and optimised team relational work and commitment to using a shared quality improvement process (cognitive participation). Similarly, the concurrence of cognitive participation and collective action likely refined each other. It took 3–4 months for most teams to establish sufficient coherence and cognitive participation, and to access actionable data. Nine months was not enough time for some teams to both implement and reflexively monitor change using data. A separate analysis indicated that prominent topics of discussion were interactions within the team, its relationship with the larger organisation, and difficulties accessing data and determining its reliability. Teams which experience sufficient positive aspects of normalising mechanisms are able to tolerate the unevenness and negative aspects of normalising change to succeed.

ObjectiveSurveys are a commonly used tool in quality improvement (QI) projects, but little is known about the standards to which they are designed and applied. We aimed to investigate the quality of surveys used within a QI collaborative, and to characterise the common errors made in survey design.MethodsFive reviewers (two research methodology and QI, three clinical and QI experts) independently assessed 20 surveys, comprising 250 survey items, that were developed in a North American cystic fibrosis lung transplant transition collaborative. Content Validity Index (CVI) scores were calculated for each survey. Reviewer consensus discussions decided an overall quality assessment for each survey and survey item (analysed using descriptive statistics) and explored the rationale for scoring (using qualitative thematic analysis).Results3/20 surveys scored as high quality (CVI >80%). 19% (n=47) of survey items were recommended by the reviewers, with 35% (n=87) requiring improvements, and 46% (n=116) not recommended. Quality assessment criteria were agreed upon. Types of common errors identified included the ethics and appropriateness of questions and survey format; usefulness of survey items to inform learning or lead to action, and methodological issues with survey questions, survey response options; and overall survey design.ConclusionSurvey development is a task that requires careful consideration, time and expertise. QI teams should consider whether a survey is the most appropriate form for capturing information during the improvement process. There is a need to educate and support QI teams to adhere to good practice and avoid common errors, thereby increasing the value of surveys for evaluation and QI. The methodology, quality assessment criteria and common errors described in this paper can provide a useful resource for this purpose.