Maintaining the safety and continuity of medicines at care transitions is a long-standing healthcare challenge and a global priority.1 Medication errors at hospital discharge are common and harmful: a systematic review reported a median rate of medication error and unintentional medication discrepancies of approximately 50% of adult and elderly patients, and adverse drug events affected a fifth of all discharged adult and older patients.2 Older people are particularly at risk, and more susceptible to the impact of errors, yet approximately a third to two-thirds of medication-related harm experienced by older people after discharge is considered preventable.3 Problems can often arise from care fragmentation when two or more healthcare organisations operating distinct work systems are involved in the care transition.4 Between those systems the tasks, tools and technologies are misaligned, and the resulting processes cause error and confuse patients.5

In...

Sutton et al report in this edition of BMJ Quality and Safety the findings from a qualitative process evaluation of a pilot patient-activated escalation system in four surgical wards in three English hospitals.1 The study is part of a £2.5 million National Institute for Health and Care Research grant2 testing an escalation system with the name ‘Early 3S’ (See it Early, Speak up Early and Save lives Early). In the participating wards, the system was publicised to patients through leaflets and posters. In two sites, the responder to telephone calls for help from patients and family was a nurse on the actual ward team; in one of these sites the programme was discontinued after 2 months and in the second site most calls were not picked up by the responder. In the third site, a critical care outreach team received seven calls in 8 months but identified no...

To develop and evaluate measures of patient work system factors in medication management that may be modifiable for improvement during the care transition from hospital to home among older adults.

Measures were developed and evaluated in a multisite prospective observational study of older adults (≥65 years) discharged home from medical units of two US hospitals from August 2018 to July 2019.

Patient work system factors for managing medications were assessed during hospital stays using six capacity indicators, four task indicators and three medication management practice indicators. Main outcomes were assessed at participants’ homes approximately a week after discharge for (1) Medication discrepancies between the medications taken at home and those listed in the medical record, and (2) Patient experiences with new medication regimens.

274 of the 376 recruited participants completed home assessment (72.8%). Among capacity indicators, most older adults (80.6%) managed medications during transition without a caregiver, 41.2% expressed low self-efficacy in managing medications and 18.3% were not able to complete basic medication administration tasks. Among task indicators, more than half (57.7%) had more than 10 discharge medications and most (94.7%) had medication regimen changes. Having more than 10 discharge medications, more than two medication regimen changes and low self-efficacy in medication management increased the risk of feeling overwhelmed (OR 2.63, 95% CI 1.08 to 6.38, OR 3.16, 95% CI 1.29 to 7.74 and OR 2.56, 95% CI 1.25 to 5.26, respectively). Low transportation independence, not having a home caregiver, low medication administration skills and more than 10 discharge medications increased the risk of medication discrepancies (incidence rate ratio 1.39, 95% CI 1.01 to 1.91, incidence rate ratio 1.73, 95% CI 1.13 to 2.66, incidence rate ratio 1.99, 95% CI 1.37 to 2.89 and incidence rate ratio 1.91, 95% CI 1.24 to 2.93, respectively).

Patient work system factors could be assessed before discharge with indicators for increased risk of poor patient experience and medication discrepancies during older adults’ care transition from hospital to home.

The management of acute deterioration following surgery remains highly variable. Patients and families can play an important role in identifying early signs of deterioration but effective contribution to escalation of care can be practically difficult to achieve. This paper reports the enablers and barriers to the implementation of patient-led escalation systems found during a process evaluation of a quality improvement programme Rescue for Emergency Surgery Patients Observed to uNdergo acute Deterioration (RESPOND).

The research used ethnographic methods, including over 100 hours of observations on surgical units in three English hospitals in order to understand the everyday context of care. Observations focused on the coordination of activities such as handovers and how rescue featured as part of this. We also conducted 27 interviews with a range of clinical and managerial staff and patients. We employed a thematic analysis approach, combined with a theoretically focused implementation coding framework, based on Normalisation Process Theory.

We found that organisational infrastructural support in the form of a leadership support and clinical care outreach teams with capacity were enablers in implementing the patient-led escalation system. Barriers to implementation included making changes to professional practice without discussing the value and legitimacy of operationalising patient concerns, and ensuring equity of use. We found that organisational work is needed to overcome patient fears about disrupting social and cultural norms.

This paper reveals the need for infrastructural support to facilitate the implementation of a patient-led escalation system, and leadership support to normalise the everyday process of involving patients and families in escalation. This type of system may not achieve its goals without properly understanding and addressing the concerns of both nurses and patients.

Deprescribing (medication dose reduction or cessation) is an integral component of appropriate prescribing. The extent to which deprescribing recommendations are included in clinical practice guidelines is unclear. This scoping review aimed to identify guidelines that contain deprescribing recommendations, qualitatively explore the content and format of deprescribing recommendations and estimate the proportion of guidelines that contain deprescribing recommendations.

Bibliographic databases and Google were searched for guidelines published in English from January 2012 to November 2022. Guideline registries were searched from January 2017 to February 2023. Two reviewers independently screened records from databases and Google for guidelines containing one or more deprescribing recommendations. A 10% sample of the guideline registries was screened to identify eligible guidelines and estimate the proportion of guidelines containing a deprescribing recommendation. Guideline and recommendation characteristics were extracted and language features of deprescribing recommendations including content, form, complexity and readability were examined using a conventional content analysis and the SHeLL Health Literacy Editor tool.

80 guidelines containing 316 deprescribing recommendations were included. Deprescribing recommendations had substantial variability in their format and terminology. Most guidelines contained recommendations regarding for who (75%, n=60), what (99%, n=89) and when or why (91%, n=73) to deprescribe, however, fewer guidelines (58%, n=46) contained detailed guidance on how to deprescribe. Approximately 29% of guidelines identified from the registries sample (n=14/49) contained one or more deprescribing recommendations.

Deprescribing recommendations are increasingly being incorporated into guidelines, however, many guidelines do not contain clear and actionable recommendations on how to deprescribe which may limit effective implementation in clinical practice. A co-designed template or best practice guide, containing information on aspects of deprescribing recommendations that are essential or preferred by end-users should be developed and employed.

osf.io/fbex4.

Severe mental illness (SMI) incorporates schizophrenia, bipolar disorder, non-organic psychosis, personality disorder or any other severe and enduring mental health illness. Medication, particularly antipsychotics and mood stabilisers are the main treatment options. Medication optimisation is a hallmark of medication safety, characterised by the use of collaborative, person-centred approaches. There is very little published research describing medication optimisation with people living with SMI.

Published literature and two stakeholder groups were employed to answer: What works for whom and in what circumstances to optimise medication use with people living with SMI in the community?

A five-stage realist review was co-conducted with a lived experience group of individuals living with SMI and a practitioner group caring for individuals with SMI. An initial programme theory was developed. A formal literature search was conducted across eight bibliographic databases, and literature were screened for relevance to programme theory refinement. In total 60 papers contributed to the review. 42 papers were from the original database search with 18 papers identified from additional database searches and citation searches conducted based on stakeholder recommendations.

Our programme theory represents a continuum from a service user’s initial diagnosis of SMI to therapeutic alliance development with practitioners, followed by mutual exchange of information, shared decision-making and medication optimisation. Accompanying the programme theory are 11 context-mechanism-outcome configurations that propose evidence-informed contextual factors and mechanisms that either facilitate or impede medication optimisation. Two mid-range theories highlighted in this review are supported decision-making and trust formation.

Supported decision-making and trust are foundational to overcoming stigma and establishing ‘safety’ and comfort between service users and practitioners. Avenues for future research include the influence of stigma and equity across cultural and ethnic groups with individuals with SMI; and use of trained supports, such as peer support workers.

CRD42021280980.

Identifying high and poorly performing organisations is common practice in healthcare. Often this is done within a frequentist inferential framework where statistical techniques are used that acknowledge that observed performance is an imperfect measure of underlying quality. Various methods are employed for this purpose, but the influence of chance on the degree of misclassification is often underappreciated. Using simulations, we show that the distribution of underlying performance of organisations flagged as the worst performers, using current best practices, was highly dependent on the reliability of the performance measure. When reliability was low, flagged organisations were likely to have an underlying performance that was near the population average. Reliability needs to reach at least 0.7 for 50% of flagged organisations to be correctly flagged and 0.9 to nearly eliminate incorrectly flagging organisations close to the overall mean. We conclude that despite their widespread use, techniques for identifying the best and worst performing organisations do not necessarily identify truly good and bad performers and even with the best techniques, reliable data are required.

Healthcare regulators are having trouble keeping up. There is always a lag between regulators getting on top of things and fast-paced changes in health systems. Care is continuously becoming more complex.1 Rapid technological shifts (eg, new-generation drugs, artificial intelligence (AI) and advances in genomics) are accelerating. This confers new opportunities for better care, but it also implies new risks which need to be regulated differently. Yet the current paradigm is largely predicated on regulators mainly inspecting and investigating harmful events in retrospect, responding after they occur.2 This is despite developments and innovations in proactive inspection methods, and more collaborative approaches.

Another problem is that regulators often argue for system-based approaches to adverse events, but then often act by sanctioning individuals—in part because this is what they are empowered to do. The regulatory logic is: assemble objective evidence and assess this against compliance to the standard...