Equity is one of the six core healthcare quality domains in ‘Crossing the Quality Chasm’, published by the Institute of Medicine in 2001.1 While substantial quality measurement and improvement work has focused on improving safety, patient-centredness, timeliness, efficiency and efficacy (the other five domains), far less has focused on health equity measurement and improvement. This is in part due to limited adoption of standardised definitions of racial and ethnicities and therefore limited availability of high-quality data on race and ethnicity.2 Having accurate data is a key first step in addressing health inequities, since what is measured influences what is done.3 There are substantial efforts to improve these data availability and quality by healthcare systems, nationally and internationally.2 Currently, adequate efforts require several steps: the decision to collect data, ensuring the quality of data being collected, and reconciliation of race and ethnicity...

Within healthcare services worldwide, there is a continual emphasis on innovation, including the development, evaluation and improvement of new and existing healthcare interventions and services to improve patient outcomes. In addition to evaluating efficacy, it is also important to evaluate how innovations are used in ‘real-world’ settings. A key part of this is process evaluation: understanding how interventions and services are implemented and engaged with. For example, recent Medical Research Council guidance on researching the effectiveness of complex interventions highlights the importance of measuring implementation and context, including the measurement of ‘fidelity’.1

‘Fidelity’ has been proposed to have five related domains, including fidelity of design, training, delivery (whether intervention components, as outlined in the intervention protocol, are delivered as planned), receipt (whether participants understand and are able to perform required skills) and enactment (whether participants use skills in daily life).2 Both receipt and enactment have...

The seminal report To Err is Human focused on a wide range of serious patient safety concerns; diagnostic error was mentioned only in passing.1 Very little data were available on the magnitude of harm related to diagnostic errors at that time, except for a back-of-the-napkin estimate that diagnostic error could be responsible for 40 000–80 000 in-hospital deaths annually.2 The problem finally received its due 15 years later, when the National Academy of Medicine asserted that "... most of us will experience at least one diagnostic error in our lifetime, sometimes with devastating consequences".3

In this context, the paper by Newman-Toker et al in this issue of BMJ Quality & Safety is a welcome contribution, presenting an extensively researched set of estimates that proposes that harm may be an order-of-magnitude larger.4 The paper is the third part of a larger study,...

Background

Emerging 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.

Methods

In 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.

Results

Racial 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.

Conclusions

The 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.

Background

In many quality improvement (QI) and other complex interventions, assessing the fidelity with which participants ‘enact’ intervention activities (ie, implement them as intended) is underexplored. Adapting the evaluative approach used in objective structured clinical examinations, we aimed to develop and validate a practical approach to assessing fidelity enactment—the Overall Fidelity Enactment Scale for Complex Interventions (OFES-CI).

Methods

We developed the OFES-CI to evaluate enactment of the SCOPE QI intervention, which teaches nursing home teams to use plan-do-study-act (PDSA) cycles. The OFES-CI was piloted and revised early in SCOPE with good inter-rater reliability, so we proceeded with a single rater. An intraclass correlation coefficient (ICC) was used to assess inter-rater reliability. For 27 SCOPE teams, we used ICC to compare two methods for assessing fidelity enactment: (1) OFES-CI ratings provided by one of five trained experts who observed structured 6 min PDSA progress presentations made at the end of SCOPE, (2) average rating of two coders’ deductive content analysis of qualitative process evaluation data collected during the final 3 months of SCOPE (our gold standard).

Results

Using Cicchetti’s classification, inter-rater reliability between two coders who derived the gold standard enactment score was ‘excellent’ (ICC=0.93, 95% CI=0.85 to 0.97). Inter-rater reliability between the OFES-CI and the gold standard was good (ICC=0.71, 95% CI=0.46 to 0.86), after removing one team where open-text comments were discrepant with the rating. Rater feedback suggests the OFES-CI has strong face validity and positive implementation qualities (acceptability, easy to use, low training requirements).

Conclusions

The OFES-CI provides a promising novel approach for assessing fidelity enactment in QI and other complex interventions. It demonstrates good reliability against our gold standard assessment approach and addresses the practicality problem in fidelity assessment by virtue of its suitable implementation qualities. Steps for adapting the OFES-CI to other complex interventions are offered.

Background

Diagnostic errors cause substantial preventable harms worldwide, but rigorous estimates for total burden are lacking. We previously estimated diagnostic error and serious harm rates for key dangerous diseases in major disease categories and validated plausible ranges using clinical experts.

Objective

We sought to estimate the annual US burden of serious misdiagnosis-related harms (permanent morbidity, mortality) by combining prior results with rigorous estimates of disease incidence.

Methods

Cross-sectional analysis of US-based nationally representative observational data. We estimated annual incident vascular events and infections from 21.5 million (M) sampled US hospital discharges (2012–2014). Annual new cancers were taken from US-based registries (2014). Years were selected for coding consistency with prior literature. Disease-specific incidences for 15 major vascular events, infections and cancers (‘Big Three’ categories) were multiplied by literature-based rates to derive diagnostic errors and serious harms. We calculated uncertainty estimates using Monte Carlo simulations. Validity checks included sensitivity analyses and comparison with prior published estimates.

Results

Annual US incidence was 6.0 M vascular events, 6.2 M infections and 1.5 M cancers. Per ‘Big Three’ dangerous disease case, weighted mean error and serious harm rates were 11.1% and 4.4%, respectively. Extrapolating to all diseases (including non-‘Big Three’ dangerous disease categories), we estimated total serious harms annually in the USA to be 795 000 (plausible range 598 000–1 023 000). Sensitivity analyses using more conservative assumptions estimated 549 000 serious harms. Results were compatible with setting-specific serious harm estimates from inpatient, emergency department and ambulatory care. The 15 dangerous diseases accounted for 50.7% of total serious harms and the top 5 (stroke, sepsis, pneumonia, venous thromboembolism and lung cancer) accounted for 38.7%.

Conclusion

An estimated 795 000 Americans become permanently disabled or die annually across care settings because dangerous diseases are misdiagnosed. Just 15 diseases account for about half of all serious harms, so the problem may be more tractable than previously imagined.

Machine learning (ML) solutions are increasingly entering healthcare. They are complex, sociotechnical systems that include data inputs, ML models, technical infrastructure and human interactions. They have promise for improving care across a wide range of clinical applications but if poorly implemented, they may disrupt clinical workflows, exacerbate inequities in care and harm patients. Many aspects of ML solutions are similar to other digital technologies, which have well-established approaches to implementation. However, ML applications present distinct implementation challenges, given that their predictions are often complex and difficult to understand, they can be influenced by biases in the data sets used to develop them, and their impacts on human behaviour are poorly understood. This manuscript summarises the current state of knowledge about implementing ML solutions in clinical care and offers practical guidance for implementation. We propose three overarching questions for potential users to consider when deploying ML solutions in clinical care: (1) Is a clinical or operational problem likely to be addressed by an ML solution? (2) How can an ML solution be evaluated to determine its readiness for deployment? (3) How can an ML solution be deployed and maintained optimally? The Quality Improvement community has an essential role to play in ensuring that ML solutions are translated into clinical practice safely, effectively, and ethically.

Studying near-miss errors is essential to preventing errors from reaching patients. When an error is committed, it may be intercepted (near-miss) or it will reach the patient; estimates of the proportion that reach the patient vary widely. To better understand this relationship, we conducted a retrospective cohort study using two objective measures to identify wrong-patient imaging order errors involving radiation, estimating the proportion of errors that are intercepted and those that reach the patient. This study was conducted at a large integrated healthcare system using data from 1 January to 31 December 2019. The study used two outcome measures of wrong-patient orders: (1) wrong-patient orders that led to misadministration of radiation reported to the New York Patient Occurrence Reporting and Tracking System (NYPORTS) (misadministration events); and (2) wrong-patient orders identified by the Wrong-Patient Retract-and-Reorder (RAR) measure, a measure identifying orders placed for a patient, retracted and rapidly reordered by the same clinician on a different patient (near-miss events). All imaging orders that involved radiation were extracted retrospectively from the healthcare system data warehouse. Among 293 039 total eligible orders, 151 were wrong-patient orders (3 misadministration events, 148 near-miss events), for an overall rate of 51.5 per 100 000 imaging orders involving radiation placed on the wrong patient. Of all wrong-patient imaging order errors, 2% reached the patient, translating to 50 near-miss events for every 1 error that reached the patient. This proportion provides a more accurate and reliable estimate and reinforces the utility of systematic measure of near-miss errors as an outcome for preventative interventions.

Healthcare quality and safety span multiple topics across the spectrum of academic and clinical disciplines. Keeping abreast of the rapidly growing body of work can be challenging. In this series, we provide succinct summaries of selected relevant studies published in the last several months. Some articles will focus on a particular theme, whereas others will highlight unique publications from high-impact medical journals.

Key points

A randomised controlled trial showed that a communication coach improved cardiologists’ ability to respond to patients with empathy, elicit questions and facilitate enhanced conversational flow. Cardiologists reported that a communication coach helped their clinical practice. JAMA Intern Med; 1 June 2023

In a randomised controlled trial conducted across multiple hospital sites, a written communication tool provided to clinicians significantly improved documentation of goals-of-care discussions in the electronic medical record, with a more substantial impact on patients in racial or...