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Medication Safety Officers Society 3671 Members Strong A society of healthcare professionals dedicated to improving medication safety in healthcare organizations |
The widely known United Nations sustainable development goals indicate that good quality healthcare should be available to all those who need it.1 Unfortunately, the availability of good care tends to vary inversely with the actual need for it. This is not a new issue and had already been dubbed ‘the inverse care law’ as far back as 1971.2 In response, those working in healthcare strived to make changes, aiming to provide equitable access to high-quality healthcare to all patients. Despite this, differences between groups persist. Even in populations with good healthcare coverage, certain subgroups attend screening less,3 experience lower availability of care4 and have ongoing unmet needs.5 To address this, the concept of health equity was introduced, with multiple recent landmark articles stressing its importance.6 7 BMJ Quality and Safety has also embraced this concept,...
Early intervention for unmet needs is essential to improve health. Clear inequalities in healthcare use and outcomes exist. The Children and Young People’s Health Partnership (CYPHP) model of care uses population health management methods to (1) identify and proactively reach children with asthma, eczema and constipation (tracer conditions); (2) engage these families, with CYPHP, by sending invitations to complete an online biopsychosocial Healthcheck Questionnaire; and (3) offer early intervention care to those children found to have unmet health needs. We aimed to understand this model’s effectiveness to improve equitable access to care.
MethodsWe used primary care and CYPHP service-linked records and applied the same methods as the CYPHP’s population health management process to identify children aged <16 years with a tracer condition between 1 April 2018 and 30 August 2020, those who engaged by completing a Healthcheck and those who received early intervention care. We applied multiple imputation with multilevel logistic regression, clustered by general practitioner (GP) practice, to investigate the association of deprivation and ethnicity, with children’s engagement and receiving care.
ResultsAmong 129 412 children, registered with 70 GP practices, 15% (19 773) had a tracer condition and 24% (4719) engaged with CYPHP’s population health management system. Children in the most deprived, compared with least deprived communities, had 26% lower odds of engagement (OR 0.74; 95% CI 0.62 to 0.87). Children of Asian or black ethnicity had 31% lower odds of engaging, compared with white children (0.69 (0.59 to 0.81) and 0.69 (0.62 to 0.76), respectively). However, once engaged with the population health management system, black children had 43% higher odds of receiving care, compared with white children (1.43 (1.15 to 1.78)), and children from the most compared with least deprived communities had 50% higher odds of receiving care (1.50 (1.01 to 2.22)).
ConclusionDetection of unmet needs is possible using population health management methods and increases access to care for children from priority populations with the highest needs. Further health system strengthening is needed to improve engagement and enhance proportionate universalist access to healthcare.
Trial registration numberClinicalTrials.gov Registry (NCT03461848).
Large-scale improvement programmes are a frequent response to quality and safety problems in health systems globally, but have mixed impact. The extent to which they meet criteria for programme quality, particularly in relation to transparency of reporting and evaluation, is unclear.
AimTo identify large-scale improvement programmes focused on intrapartum care implemented in English National Health Service maternity services in the period 2010–2023, and to conduct a structured quality assessment.
MethodsWe drew on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidance to inform the design and reporting of our study. We identified relevant programmes using multiple search strategies of grey literature, research databases and other sources. Programmes that met a prespecified definition of improvement programme, that focused on intrapartum care and that had a retrievable evaluation report were subject to structured assessment using selected features of programme quality.
ResultsWe identified 1434 records via databases and other sources. 14 major initiatives in English maternity services could not be quality assessed due to lack of a retrievable evaluation report. Quality assessment of the 15 improvement programmes meeting our criteria for assessment found highly variable quality and reporting. Programme specification was variable and mostly low quality. Only eight reported the evidence base for their interventions. Description of implementation support was poor and none reported customisation for challenged services. None reported reduction of inequalities as an explicit goal. Only seven made use of explicit patient and public involvement practices, and only six explicitly used published theories/models/frameworks to guide implementation. Programmes varied in their reporting of the planning, scope and design of evaluation, with weak designs evident.
ConclusionsPoor transparency of reporting and weak or absent evaluation undermine large-scale improvement programmes by limiting learning and accountability. This review indicates important targets for improving quality in large-scale programmes.
Antiplatelet therapy (APT) can substantially reduce the risk of further vascular events in individuals with established atherosclerotic cardiovascular disease (ASCVD). However, knowledge regarding the extent and determinants of APT use is limited.
ObjectivesEstimate the extent and identify patient groups at risk of suboptimal APT use at different stages of the treatment pathway.
MethodsRetrospective cohort study using linked NHS Scotland administrative data of all adults hospitalised for an acute ASCVD event (n=150 728) from 2009 to 2017. Proportions of patients initiating, adhering to, discontinuing and re-initiating APT were calculated overall and separately for myocardial infarction (MI), ischaemic stroke and peripheral arterial disease (PAD). Multivariable logistic regression and Cox proportional hazards models were used to assess the contribution of patient characteristics in initiating and discontinuing APT.
ResultsOf patients hospitalised with ASCVD, 84% initiated APT: 94% following an MI, 83% following an ischaemic stroke and 68% following a PAD event. Characteristics associated with lower odds of initiation included female sex (22% less likely than men), age below 50 years or above 70 years (aged <50 years 26% less likely, and aged 70–79, 80–89 and ≥90 years 21%, 39% and 51% less likely, respectively, than those aged 60–69 years) and history of mental health-related hospitalisation (45% less likely). Of all APT-treated individuals, 22% discontinued treatment. Characteristics associated with discontinuation were similar to those related to non-initiation.
ConclusionsAPT use remains suboptimal for the secondary prevention of ASCVD, particularly among women and older patients, and following ischaemic stroke and PAD hospitalisations.
To estimate the number and burden of medication errors associated with prescription information transfer within the National Health Service (NHS) in England and the impact of implementing an interoperable prescription information system (a single digital prescribing record shared across NHS settings) in reducing these errors.
MethodsWe constructed a probabilistic mathematical model. We estimated the number of transition medication errors that would be undetected by standard medicines reconciliation, based on published literature, and scaled this up based on the annual number of hospital admissions. We used published literature to estimate the proportion of errors that lead to harm and applied this to the number of errors to estimate the associated burden (healthcare resource use and deaths). Finally, we used reported effect sizes for electronic prescription information sharing interventions to estimate the impact of implementing an interoperable prescription information system on number of errors and resulting harm.
ResultsAnnually, around 1.8 million (95% credibility interval (CrI) 1.3 to 2.6 million) medication errors were estimated to occur at hospital transitions in England, affecting approximately 380 000 (95% CrI 260 397 to 539 876) patient episodes. Harm from these errors affects around 31 500 (95% CrI 22 407 to 42 906) patients, with 36 500 (95% CrI 25 093 to 52 019) additional bed days of inpatient care (costing around £17.8 million (95% CrI £12.4 to £24.9 million)) and >40 (95% CrI 9 to 146) deaths. Assuming the implementation of an interoperable prescription information system could reduce errors by 10% and 50%, there could be 180 000–913 000 fewer errors, 3000–15 800 fewer people who experience harm and 4–22 lives saved annually.
ConclusionsAn interoperable prescription information system could provide major benefits for patient safety. Likely additional benefits include healthcare professional time saved, improved patient experience and care quality, quicker discharge and enhanced cross-organisational medicines optimisation. Our findings provide vital safety and economic evidence for the case to adopt interoperable prescription information systems.
The objective of this review was to develop a taxonomy of pressures experienced by health services and an accompanying taxonomy of strategies for adapting in response to these pressures. The taxonomies were developed from a review of observational studies directly assessing care delivered in a variety of clinical environments.
DesignIn the first phase, a scoping review of the relevant literature was conducted. In the second phase, pressures and strategies were systematically coded from the included papers, and categorised.
Data sourcesElectronic databases (MEDLINE, Embase, CINAHL, PsycInfo and Scopus) and reference lists from recent reviews of the resilient healthcare literature.
Eligibility criteriaStudies were included from the resilient healthcare literature, which used descriptive methodologies to directly assess a clinical environment. The studies were required to contain strategies for managing under pressure.
Results5402 potential articles were identified with 17 papers meeting the inclusion criteria. The principal source of pressure described in the studies was the demand for care exceeding capacity (ie, the resources available), which in turn led to difficult working conditions and problems with system functioning. Strategies for responding to pressures were categorised into anticipatory and on-the-day adaptations. Anticipatory strategies included strategies for increasing resources, controlling demand and plans for managing the workload (efficiency strategies, forward planning, monitoring and co-ordination strategies and staff support initiatives). On-the-day adaptations were categorised into: flexing the use of existing resources, prioritising demand and adapting ways of working (leadership, teamwork and communication strategies).
ConclusionsThe review has culminated in an empirically based taxonomy of pressures and an accompanying taxonomy of strategies for adapting in response to these pressures. The taxonomies could help clinicians and managers to optimise how they respond to pressures and may be used as the basis for training programmes and future research evaluating the impact of different strategies.
The capabilities of artificial intelligence (AI) have accelerated over the past year, and they are beginning to impact healthcare in a significant way. Could this new technology help address issues that have been difficult and recalcitrant problems for quality and safety for decades? While we are early in the journey, it is clear that we are in the midst of a fundamental shift in AI capabilities. It is also clear these capabilities have direct applicability to healthcare and to improving quality and patient safety, even as they introduce new complexities and risks. Previously, AI focused on one task at a time: for example, telling whether a picture was of a cat or a dog, or whether a retinal photograph showed diabetic retinopathy or not. Foundation models (and their close relatives, generative AI and large language models) represent an important change: they are able to handle many different kinds of problems without additional datasets or training. This review serves as a primer on foundation models’ underpinnings, upsides, risks and unknowns—and how these new capabilities may help improve healthcare quality and patient safety.
To date, most safety and quality improvement efforts to mitigate harm have focused on the single diagnosis for which the patient was admitted to the hospital. Most often, the objective has been to ensure patients receive the appropriate evidence-based therapies for their diagnosis using guidelines, checklists, learning from defect tools1 or other interventions. However, people often have multiple morbidities and the interactions between them may increase their risk of harm when hospitalised.
Approximately half of all Americans have a chronic disease.2 In addition, an estimated 100 million disability-adjusted life years were added between 2000 and 2019 from a global rise in diabetes, ischaemic heart disease and several other non-communicable diseases.3 However, healthcare has paid less attention to mitigating significant risks of harm from the chronic diseases or disabilities patients have when admitted for another health reason. For example, 63% of hospitalised patients with Parkinson’s...
