In this issue of BMJ Quality & Safety, Schnipper et al evaluate the implementation of a multifaceted medication reconciliation intervention at six hospitals using the MARQUIS medication reconciliation implementation toolkit.1 The planned intervention included the following elements: hiring or reallocating new staff to obtain medication histories, performing both admission and discharge medication reconciliation, improving access to preadmission medication sources, introducing policy, training staff on obtaining medication histories and patient counselling, implementing a gold standard medication reconciliation process including targeting of high-risk patients, improving healthcare information technology and utilising social marketing and community engagement. The study had many methodological strengths, including independent observers for outcome verification, clinical assessment of medication discrepancies, pragmatic implementation in both community and teaching hospitals, mentored implementation and a large randomly selected patient sample with controls and temporal trending. The main result was that potentially harmful discrepancies did not decrease over time beyond baseline...
Alerts have become a routine part of our daily lives—from the apps on our phones to an increasing number of ‘wearables’ (eg, fitness trackers) and household devices. Within healthcare, frontline clinicians have become all too familiar with a barrage of alerts and alarms from electronic medical records and medical devices.
Somewhat less familiar to most clinicians, however, are the alerts received by institutions from regulators and other regional or national bodies monitoring healthcare performance. After the Bristol inquiry in 2001 in the UK,1 research showed that given the available data Bristol could have been detected as an outlier and that it was not simply a matter of the low volume of cases.2 3 Had the cumulative excess mortality been monitored using these routinely collected data, then an alarm could have given for Bristol after the publication of the 1991 Cardiac Surgical Register and...
Unintentional discrepancies across care settings are a common form of medication error and can contribute to patient harm. Medication reconciliation can reduce discrepancies; however, effective implementation in real-world settings is challenging.Methods
We conducted a pragmatic quality improvement (QI) study at five US hospitals, two of which included concurrent controls. The intervention consisted of local implementation of medication reconciliation best practices, utilising an evidence-based toolkit with 11 intervention components. Trained QI mentors conducted monthly site phone calls and two site visits during the intervention, which lasted from December 2011 through June 2014. The primary outcome was number of potentially harmful unintentional medication discrepancies per patient; secondary outcome was total discrepancies regardless of potential for harm. Time series analysis used multivariable Poisson regression.Results
Across five sites, 1648 patients were sampled: 613 during baseline and 1035 during the implementation period. Overall, potentially harmful discrepancies did not decrease over time beyond baseline temporal trends, adjusted incidence rate ratio (IRR) 0.97 per month (95% CI 0.86 to 1.08), p=0.53. The intervention was associated with a reduction in total medication discrepancies, IRR 0.92 per month (95% CI 0.87 to 0.97), p=0.002. Of the four sites that implemented interventions, three had reductions in potentially harmful discrepancies. The fourth site, which implemented interventions and installed a new electronic health record (EHR), saw an increase in discrepancies, as did the fifth site, which did not implement any interventions but also installed a new EHR.Conclusions
Mentored implementation of a multifaceted medication reconciliation QI initiative was associated with a reduction in total, but not potentially harmful, medication discrepancies. The effect of EHR implementation on medication discrepancies warrants further study.Trial registration number
To investigate the association between alerts from a national hospital mortality surveillance system and subsequent trends in relative risk of mortality.Background
There is increasing interest in performance monitoring in the NHS. Since 2007, Imperial College London has generated monthly mortality alerts, based on statistical process control charts and using routinely collected hospital administrative data, for all English acute NHS hospital trusts. The impact of this system has not yet been studied.Methods
We investigated alerts sent to Acute National Health Service hospital trusts in England in 2011–2013. We examined risk-adjusted mortality (relative risk) for all monitored diagnosis and procedure groups at a hospital trust level for 12 months prior to an alert and 23 months post alert. We used an interrupted time series design with a 9-month lag to estimate a trend prior to a mortality alert and the change in trend after, using generalised estimating equations.Results
On average there was a 5% monthly increase in relative risk of mortality during the 12 months prior to an alert (95% CI 4% to 5%). Mortality risk fell, on average by 61% (95% CI 56% to 65%), during the 9-month period immediately following an alert, then levelled to a slow decline, reaching on average the level of expected mortality within 18 months of the alert.Conclusions
Our results suggest an association between an alert notification and a reduction in the risk of mortality, although with less lag time than expected. It is difficult to determine any causal association. A proportion of alerts may be triggered by random variation alone and subsequent falls could simply reflect regression to the mean. Findings could also indicate that some hospitals are monitoring their own mortality statistics or other performance information, taking action prior to alert notification.
To provide a description of the Imperial College Mortality Surveillance System and subsequent investigations by the Care Quality Commission (CQC) in National Health Service (NHS) hospitals receiving mortality alerts.Background
The mortality surveillance system has generated monthly mortality alerts since 2007, on 122 individual diagnosis and surgical procedure groups, using routinely collected hospital administrative data for all English acute NHS hospital trusts. The CQC, the English national regulator, is notified of each alert. This study describes the findings of CQC investigations of alerting trusts.Methods
We carried out (1) a descriptive analysis of alerts (2007–2016) and (2) an audit of CQC investigations in a subset of alerts (2011–2013).Results
Between April 2007 and October 2016, 860 alerts were generated and 76% (654 alerts) were sent to trusts. Alert volumes varied over time (range: 40–101). Septicaemia (except in labour) was the most commonly alerting group (11.5% alerts sent). We reviewed CQC communications in a subset of 204 alerts from 96 trusts. The CQC investigated 75% (154/204) of alerts. In 90% of these pursued alerts, trusts returned evidence of local case note reviews (140/154). These reviews found areas of care that could be improved in 69% (106/154) of alerts. In 25% (38/154) trusts considered that identified failings in care could have impacted on patient outcomes. The CQC investigations resulted in full trust action plans in 77% (118/154) of all pursued alerts.Conclusion
The mortality surveillance system has generated a large number of alerts since 2007. Quality of care problems were found in 69% of alerts with CQC investigations, and one in four trusts reported that failings in care may have an impact on patient outcomes. Identifying whether mortality alerts are the most efficient means to highlight areas of substandard care will require further investigation.
Central line associated pneumothorax (CLAP) could be a good quality of care indicator because they are objectively measured, clearly undesirable and possibly avoidable. We measured the incidence and trends of CLAP using radiograph report text search with manual review and compared them with measures using routinely collected health administrative data.Methods
For each hospitalisation to a tertiary care teaching hospital between 2002 and 2015, we searched all chest radiography reports for a central line with a sensitive computer algorithm. Screen positive reports were manually reviewed to confirm central lines. The index and subsequent chest radiography reports were screened for pneumothorax followed by manual confirmation. Diagnostic and procedural codes were used to identify CLAP in administrative data.Results
In 685 044 hospitalisations, 10 819 underwent central line insertion (1.6%) with CLAP occurring 181 times (1.7%). CLAP risk did not change over time. Codes for CLAP were inaccurate (sensitivity 13.8%, positive predictive value 6.6%). However, overall code-based CLAP risk (1.8%) was almost identical to actual values possibly because patient strata with inflated CLAP risk were balanced by more common strata having underestimated CLAP risk. Code-based methods inflated central line incidence 2.2 times and erroneously concluded that CLAP risk decreased significantly over time.Conclusions
Using valid methods, CLAP incidence was similar to those in the literature but has not changed over time. Although administrative database codes for CLAP were very inaccurate, they generated CLAP risks very similar to actual values because of offsetting errors. In contrast to those from radiograph report text search with manual review, CLAP trends decreased significantly using administrative data. Hospital CLAP risk should not be measured using administrative data.
To quantify the association between patient self-management capability measured using the Patient Activation Measure (PAM) and healthcare utilisation across a whole health economy.Results
12 270 PAM questionnaires were returned from 9348 patients. In the adjusted analyses, compared with the least activated group, highly activated patients (level 4) had the lowest rate of contact with a general practitioner (rate ratio: 0.82, 95% CI 0.79 to 0.86), emergency department attendances (rate ratio: 0.68, 95% CI 0.60 to 0.78), emergency hospital admissions (rate ratio: 0.62, 95% CI 0.51 to 0.75) and outpatient attendances (rate ratio: 0.81, 95% CI 0.74 to 0.88). These patients also had the lowest relative rate (compared with the least activated) of ‘did not attends’ at the general practitioner (rate ratio: 0.77, 95% CI 0.68 to 0.87), ‘did not attends’ at hospital outpatient appointments (rate ratio: 0.72, 95% CI 0.61 to 0.86) and self-referred attendance at emergency departments for conditions classified as minor severity (rate ratio: 0.67, 95% CI 0.55 to 0.82), a significantly shorter average length of stay for overnight elective admissions (rate ratio 0.59, 95% CI 0.37 to 0.94),and a lower likelihood of 30- day emergency readmission (rate ratio: 0.68 , 95% CI 0.39 to 1.17), though this did not reach significance.Conclusions
Self-management capability is associated with lower healthcare utilisation and less wasteful use across primary and secondary care.
Several countries have national policies and programmes requiring hospitals to use quality and safety (QS) indicators. To present an overview of these indicators, hospital-wide QS (HWQS) dashboards are designed. There is little evidence how these dashboards are developed. The challenges faced to develop these dashboards in Dutch hospitals were retrospectively studied.Methods
24 focus group interviews were conducted: 12 with hospital managers (n=25; 39.7%) and 12 support staff (n=38; 60.3%) in 12 of the largest Dutch hospitals. Open and axial codings were applied consecutively to analyse the data collected.Results
A heuristic tool for the general development process for HWQS dashboards containing five phases was identified. In phase 1, hospitals make inventories to determine the available data and focus too much on quantitative data relevant for accountability. In phase 2, hospitals develop dashboard content by translating data into meaningful indicators for different users, which is not easy due to differing demands. In phase 3, hospitals search for layouts that depict the dashboard content suited for users with different cognitive abilities and analytical skills. In phase 4, hospitals try to integrate dashboards into organisational structures to ensure that data are systematically reviewed and acted on. In phase 5, hospitals want to improve the flexibility of their dashboards to make this adaptable under differing circumstances.Conclusion
The literature on dashboards addresses the technical and content aspects of dashboards, but overlooks the organisational development process. This study shows how technical and organisational aspects are relevant in development processes.
Identifying mechanisms to improve provider compliance with quality metrics is a common goal across medical disciplines. Nudge interventions are minimally invasive strategies that can influence behavioural changes and are increasingly used within healthcare settings. We hypothesised that nudge interventions may improve provider compliance with lung-protective ventilation (LPV) strategies during general anaesthesia.Methods
We developed an audit and feedback dashboard that included information on both provider-level and department-level compliance with LPV strategies in two academic hospitals, two non-academic hospitals and two academic surgery centres affiliated with a single healthcare system. Dashboards were emailed to providers four times over the course of the 9-month study. Additionally, the default setting on anaesthesia machines for tidal volume was decreased from 700 mL to 400 mL. Data on surgical cases performed between 1 September 2016 and 31 May 2017 were examined for compliance with LPV. The impact of the interventions was assessed via pairwise logistic regression analysis corrected for multiple comparisons.Results
A total of 14 793 anaesthesia records were analysed. Absolute compliance rates increased from 59.3% to 87.8%preintervention to postintervention. Introduction of attending physician dashboards resulted in a 41% increase in the odds of compliance (OR 1.41, 95% CI 1.17 to 1.69, p=0.002). Subsequently, the addition of advanced practice provider and resident dashboards lead to an additional 93% increase in the odds of compliance (OR 1.93, 95% CI 1.52 to 2.46, p<0.001). Lastly, modifying ventilator defaults led to a 376% increase in the odds of compliance (OR 3.76, 95% CI 3.1 to 4.57, p<0.001).Conclusion
Audit and feedback tools in conjunction with default changes improve provider compliance.
In 2009, the National Patient Safety Foundation’s Lucian Leape Institute (LLI) published a paper identifying five areas of healthcare that require system-level attention and action to advance patient safety.The authors argued that to truly transform the safety of healthcare, there was a need to address medical education reform; care integration; restoring joy and meaning in work and ensuring the safety of the healthcare workforce; consumer engagement in healthcare and transparency across the continuum of care. In the ensuing years, the LLI convened a series of expert roundtables to address each concept, look at obstacles to implementation, assess potential for improvement, identify potential implementation partners and issue recommendations for action. Reports of these activities were published between 2010 and 2015. While all five areas have seen encouraging developments, multiple challenges remain. In this paper, the current members of the LLI (now based at the Institute for Healthcare Improvement) assess progress made in the USA since 2009 and identify ongoing challenges.
The presence of powerful computers in the pockets of most patients should transform how we practise medicine. Yet changes in practice to date have remained modest and occurred only gradually.1 Applications of new technologies often only emerge once the underlying technologies have become ubiquitous and long passed the period of counting as new. As one writer put it: ‘Communication tools don’t get socially interesting until they get technologically boring.’2 Once a technology reaches the stage of being taken for granted, it becomes easier to harness it for new functions and activities.
Cellphones with short message service (SMS) or text messaging first appeared 25 years ago,3 and by 2010, users were sending 6.1 trillion texts per year globally.4 This tool had obvious applications for communication between physicians or to advise about critical lab results, where information could be displayed directly rather necessitating...
Many countries and health systems are struggling with growing healthcare expenditures in general, and those for prescription drugs in particular. The USA is no exception, with pharmaceutical spending per capita above that of any other Organisation for Economic Co-operation and Development country.1 Cost-sharing through copayments or coinsurance is a common approach to attempt to manage drug spending and utilisation. However, although cost-sharing reduces potentially unnecessary medication use,2 it also reduces adherence to needed medications. Reduced adherence can lead to long-term health detriments and to offsetting costs.3–5 The effects of cost-sharing may be exacerbated among low-income individuals.6 7
Most previous studies of the impact of cost-sharing on adherence have focused on patients who have filled a prescription at least once. In contrast, there are fewer studies focused on adherence to filling the initial prescription. In this issue,...
Monitoring blood pressure at 72 hours and 7–10 days post partum in women with hypertensive disorders is recommended to decrease morbidity. However, there are no recommendations as to how to achieve this.Objective
To compare the effectiveness of text-based blood pressure monitoring to in-person visits for women with hypertensive disorders of pregnancy in the immediate postpartum period.Methods
Randomised clinical trial among 206 postpartum women with pregnancy-related hypertension diagnosed during the delivery admission between August 2016 and January 2017. Women were randomised to 2 weeks of text-based surveillance using a home blood pressure cuff and previously tested automated platform or usual care blood pressure check at their prenatal clinic 4–6 days following discharge. The primary study outcome was a single recorded blood pressure in the first 10 days post partum. The ability to meet American Congress of Obstetricians and Gynecologists (ACOG) guidelines, defined as having a blood pressure recorded on postpartum days 3–4 and 7–10 was evaluated in the text message group. The study was powered to detect a 1.4-fold increase in a single recorded blood pressure using text messaging. All outcomes were analysed as intention to treat.Results
206 women were randomised (103 in each arm). Baseline characteristics were similar. There was a statistically significant increase in a single blood pressure obtained in the texting group in the first 10 days post partum as compared with the office group (92.2% vs 43.7%; adjusted OR 58.2 (16.2–208.1), p<0.001). Eighty-four per cent of patients undergoing text-based surveillance met ACOG criteria for blood pressures at both recommended points.Conclusions
Text-based monitoring is more effective in obtaining blood pressures and meeting current clinical guidelines in the immediate postdischarge period in women with pregnancy-related hypertension compared with traditional office-based follow-up.Trial registration number
Copayment policies aim to reduce the burden of medication expenditure but may affect adherence and generate inequities in access to healthcare. The objective was to evaluate the impact of two copayment measures on initial medication non-adherence (IMNA) in several medication groups and by income level.Design
A population-based study was conducted using real-world evidence.Setting
Primary care in Catalonia (Spain) where two separate copayment measures (fixed copayment and coinsurance) were introduced between 2011 and 2013.Participant
Every patient with a new prescription issued between 2011 and 2014 (3 million patients and 10 million prescriptions).Outcomes
IMNA was estimated throughout dispensing and invoicing information. Changes in IMNA prevalence after the introduction of copayment policies (immediate level change and trend changes) were estimated through segmented logistic regression. The regression models were stratified by economic status and medication groups.Results
Before changes to copayment policies, IMNA prevalence remained stable. The introduction of a fixed copayment was followed by a statistically significant increase in IMNA in poor population, low/middle-income pensioners and low-income non-pensioners (OR from 1.047 to 1.370). In high-income populations, there was a large statistically non-significant increase. IMNA decreased in the low-income population after suspension of the fixed copayment and the introduction of a coinsurance policy that granted this population free access to medications (OR=0.676). Penicillins were least affected while analgesics were affected to the greatest extent. IMNA to medications for chronic conditions increased in low/middle-income pensioners.Conclusion
Even nominal charge fixed copayment may generate inequities in access to health services. An anticipation effect and expenses associated with IMNA may have generated short-term costs. A reduction in copayment can protect from non-adherence and have positive, long-term effects. Copayment scenarios could have considerable long-term consequences for health and costs due to increased IMNA in medication for chronic physical conditions.
Intravenous medication administration has traditionally been regarded as error prone, with high potential for harm. A recent US multisite study revealed few potentially harmful errors despite a high overall error rate. However, there is limited evidence about infusion practices in England and how they relate to prevalence and types of error.Objectives
To determine the prevalence, types and severity of errors and discrepancies in infusion administration in English hospitals, and to explore sources of variation, including the contribution of smart pumps.Methods
We conducted an observational point prevalence study of intravenous infusions in 16 National Health Service hospital trusts. Observers compared each infusion against the medication order and local policy. Deviations were classified as errors or discrepancies based on their potential for patient harm. Contextual issues and reasons for deviations were explored qualitatively during observer debriefs.Results
Data were collected from 1326 patients and 2008 infusions. Errors were observed in 231 infusions (11.5%, 95% CI 10.2% to 13.0%). Discrepancies were observed in 1065 infusions (53.0%, 95% CI 50.8% to 55.2%). Twenty-three errors (1.1% of all infusions) were considered potentially harmful; none were judged likely to prolong hospital stay or result in long-term harm. Types and prevalence of errors and discrepancies varied widely among trusts, as did local policies. Deviations from medication orders and local policies were sometimes made for efficiency or patient need. Smart pumps, as currently implemented, had little effect, with similar error rates observed in infusions delivered with and without a smart pump (10.3% vs 10.8%, p=0.8).Conclusion
Errors and discrepancies are relatively common in everyday infusion administrations but most have low potential for patient harm. Better understanding of performance variability to strategically manage risk may be a more helpful tactic than striving to eliminate all deviations.
Medication non-adherence in ambulatory care has received substantial attention in the literature, but less so as it affects acute care. Accordingly, we aimed to estimate the frequency with which non-adherence to medication contributes to hospital admissions.Methods
We searched the Cochrane Library, EMBASE, Cumulative Index to Nursing and Allied Health Literature, International Pharmaceutical Abstracts and PubMed (until December 2017) to identify prospective observational studies that examined prevalence rates of hospital admissions associated with medication non-adherence. A quality assessment was performed using an expanded Crombie checklist. Data extraction covered patterns, circumstances, and patient and other key characteristics of non-adherence. Pooled estimates were obtained using a random-effect model.Results
Of 24 included studies, 8 were undertaken in North America, 7 from Europe, 6 from Asia and 3 from Australia. Most studies (79%) were rated as low risk of bias. All but three studies used combination measures to detect non-adherence, but approaches to assess preventability varied considerably. Across the studies, there was high heterogeneity among prevalence estimates (2=548, df 23, p<0.001, I2=95.8%). The median prevalence rate of hospital admissions associated with non-adherence was 4.29% (IQR 3.22%–7.49%), with prevalence rates ranging from 0.72% to 10.79%. By definition, almost all of these admissions were considered preventable. The underlying causes contributing to these admissions included medication cost and side effects, and non-adherence most often involved cardiovascular medicines.Conclusions
Hospital admissions associated with non-adherence to medication are a common problem. This systematic review highlights important targets for intervention. Greater attention could be focused on adherence to medication during the hospital stay as part of an enhanced medication reconciliation process. Standardisation in study methods and definitions is needed to allow future comparisons among settings; future studies should also encompass emerging economies.
Many intensive care (ICU) survivors experience early unplanned hospital readmission, but the reasons and potential prevention strategies are poorly understood. We aimed to understand contributors to readmissions from the patient/carer perspective.Methods
This is a mixed methods study with qualitative data taking precedence. Fifty-eight ICU survivors and carers who experienced early unplanned rehospitalisation were interviewed. Thematic analysis was used to identify factors contributing to readmissions, and supplemented with questionnaire data measuring patient comorbidity and carer strain, and importance rating scales for factors that contribute to readmissions in other patient groups. Data were integrated iteratively to identify patterns, which were discussed in five focus groups with different patients/carers who also experienced readmissions. Major patterns and contexts in which unplanned early rehospitalisation occurred in ICU survivors were described.Results
Interviews suggested 10 themes comprising patient-level and system-level issues. Integration with questionnaire data, pattern exploration and discussion at focus groups suggested two major readmission contexts. A ‘complex health and psychosocial needs’ context occurred in patients with multimorbidity and polypharmacy, who frequently also had significant psychological problems, mobility issues, problems with specialist aids/equipment and fragile social support. These patients typically described inadequate preparation for hospital discharge, poor communication between secondary/primary care, and inadequate support with psychological care, medications and goal setting. This complex multidimensional situation contrasted markedly with the alternative ‘medically unavoidable’ readmission context. In these patients medical issues/complications primarily resulted in hospital readmission, and the other issues were absent or not considered important.Conclusions
Although some readmissions are medically unavoidable, for many ICU survivors complex health and psychosocial issues contribute concurrently to early rehospitalisation. Care pathways that anticipate and institute anticipatory multifaceted support for these patients merit further development and evaluation.
Little is known about patient/family comfort voicing care concerns in real time, especially in the intensive care unit (ICU) where stakes are high and time is compressed. Experts advocate patient and family engagement in safety, which will require that patients/families be able to voice concerns. Data on patient/family attitudes and experiences regarding speaking up are sparse, and mostly include reporting events retrospectively, rather than pre-emptively, to try to prevent harm. We aimed to (1) assess patient/family comfort speaking up about common ICU concerns; (2) identify patient/family-perceived barriers to speaking up; and (3) explore factors associated with patient/family comfort speaking up.Methods
In collaboration with patients/families, we developed a survey to evaluate speaking up attitudes and behaviours. We surveyed current ICU families in person at an urban US academic medical centre, supplemented with a larger national internet sample of individuals with prior ICU experience.Results
105/125 (84%) of current families and 1050 internet panel participants with ICU history completed the surveys. Among the current ICU families, 50%–70% expressed hesitancy to voice concerns about possible mistakes, mismatched care goals, confusing/conflicting information and inadequate hand hygiene. Results among prior ICU participants were similar. Half of all respondents reported at least one barrier to voicing concerns, most commonly not wanting to be a ‘troublemaker’, ‘team is too busy’ or ‘I don’t know how’. Older, female participants and those with personal or family employment in healthcare were more likely to report comfort speaking up.Conclusion
Speaking up may be challenging for ICU patients/families. Patient/family education about how to speak up and assurance that raising concerns will not create ‘trouble’ may help promote open discussions about care concerns and possible errors in the ICU.
The US National Academy of Sciences has called for the development of a Learning Healthcare System in which patients and clinicians work together to choose care, based on best evidence, and to drive discovery as a natural outgrowth of every clinical encounter to ensure innovation, quality and value at the point of care. However, the vision of a Learning Healthcare System has remained largely aspirational. Over the last 13 years, researchers, clinicians and families, with support from our paediatric medical centre, have designed, developed and implemented a network organisational model to achieve the Learning Healthcare System vision. The network framework aligns participants around a common goal of improving health outcomes, transparency of outcome measures and a flexible and adaptive collaborative learning system. Team collaboration is promoted by using standardised processes, protocols and policies, including communication policies, data sharing, privacy protection and regulatory compliance. Learning methods include collaborative quality improvement using a modified Breakthrough Series approach and statistical process control methods. Participants observe their own results and learn from the experience of others. A common repository (a ‘commons’) is used to share resources that are created by participants. Standardised technology approaches reduce the burden of data entry, facilitate care and result in data useful for research and learning. We describe how this organisational framework has been replicated in four conditions, resulting in substantial improvements in outcomes, at scale across a variety of conditions.
An enduring challenge for the improvement of healthcare quality is variation in the success of quality improvement (QI) interventions when implemented across settings.1 This is particularly true in the field of healthcare-associated infection (HAI) prevention. Some of the brightest success stories in QI have emerged from large-scale efforts to reduce HAIs such as central venous catheter-related bloodstream infections (CRBSIs)2 or catheter-associated urinary tract infections.3 The light dims, however, when efforts to export these interventions to other settings fail to meaningfully improve outcomes.4 5
To make sense of this phenomenon, attention must be paid to the social, organisational, economic, and cultural factors that may shape the observed associations between interventions and their outcomes.1 6–8 These factors are components of context, which is a key modifier of the impact of QI interventions.