Previous studies have demonstrated that text message reminders can improve pediatric vaccination rates, including low income & diverse settings such as those served by federally qualified health centers. In this study, we aimed to improve compliance with routine childhood immunizations via a text message intervention in a network of urban, federally qualified health centers at a large academic medical center. We targeted parents or guardians of children aged 0-2 years who were overdue or due within 14 days for at least one routine childhood immunization without a scheduled appointment. In Round 1, two versions of a text were compared to a control (no text). In subsequent Rounds, a new text was compared to a control (no text). In each round the content, wording, and frequency of texts changed. Subjects were randomized to receive a text (treatment group(s)) or to not receive a text (control group) in each round between 2020 and 2022. The primary outcome was whether overdue vaccines had been given by 12 week follow up. The secondary outcome was appointment scheduling within the 72 hours after text messages were sent. In Round 1 (n=1203) no significant differences were found between groups in overdue vaccine administration per group or per patient at follow up, or in appointment scheduling. In Round 2 (n=251) there was no significant difference in vaccine administration per group or per patient. However, significantly more patients in the intervention group scheduled an appointment (9.1% vs. 1.7%, p=0.01). In Round 3 (n=1034), vaccine administration was significantly higher in the intervention group compared to the control overall (7.0% vs. 5.5%, 0.016) and per subject (p=0.02). Significantly more patients in the intervention group scheduled an appointment compared to the control (3.3% vs. 1.2%, p=0.02). We found that text messaging can be an effective intervention to promote health service utilization such as pediatric vaccination rates, which although improved in this study, remain low.

Natural hazards, such as earthquakes, pose a significant risk to both the public and healthcare professionals, jeopardising patient safety due to the disruption of healthcare systems and services. This study aimed to explore the lived experiences of healthcare professionals concerning patient safety during natural hazards, specifically earthquakes.

In the intensive care unit (ICU), antibiotics are often given longer than recommended in guidelines. A better understanding of the factors influencing antibiotic therapy duration is needed to develop improvement strategies to effectively address these drivers of excessive duration. This study aimed to explore the determinants of adherence to recommended antibiotic therapy durations among healthcare professionals involved in antibiotic decision-making within the ICU, focusing on multidisciplinary meetings (MDMs).

Quality improvement (QI) is aimed at improving care. Equity is one of the six domains of healthcare quality, as defined by the Institute of Medicine. If this domain is ignored, QI projects have the potential to maintain or even worsen inequalities.

Generative artificial intelligence (AI) technologies have the potential to revolutionise healthcare delivery but require classification and monitoring of patient safety risks. To address this need, we developed and evaluated a preliminary classification system for categorising generative AI patient safety errors. Our classification system is organised around two AI system stages (input and output) with specific error types by stage. We applied our classification system to two generative AI applications to assess its effectiveness in categorising safety issues: patient-facing conversational large language models (LLMs) and an ambient digital scribe (ADS) system for clinical documentation. In the LLM analysis, we identified 45 errors across 27 patient medical queries, with omission being the most common (42% of errors). Of the identified errors, 50% were categorised as low clinical significance, 25% as moderate clinical significance and 25% as high clinical significance. Similarly, in the ADS simulation, we identified 66 errors across 11 patient visits, with omission being the most common (83% of errors). Of the identified errors, 55% were categorised as low clinical significance and 45% were categorised as moderate clinical significance. These findings demonstrate the classification system's utility in categorising output errors from two different AI healthcare applications, providing a starting point for developing a robust process to better understand AI-enabled errors.

Over the past two decades, the UK has actively developed policies to enhance early cancer diagnosis, particularly for individuals with non-specific cancer symptoms. Non-specific symptom (NSS) pathways were piloted and then implemented in 2015 to address delays in referral and diagnosis. The aim of this study was to outline the functions that enable NSS teams to investigate cancer and other diagnoses for patients with NSSs.

To improve patient safety, surgeons can continually monitor the surgical outcomes of their patients. To this end, they can use statistical process control tools, which primarily originated in the manufacturing industry and are now widely used in healthcare. These tools belong to a broad family, making it challenging to identify the most suitable methodology to monitor surgical outcomes. The selected tools must balance statistical rigour with surgeon usability, enabling both statistical interpretation of trends over time and comprehensibility for the surgeons, their primary users. On one hand, the observed minus expected (O-E) chart is a simple and intuitive tool that allows surgeons without statistical expertise to view and interpret their activity; however, it may not possess the sophisticated algorithms required to accurately identify important changes in surgical performance. On the other hand, a statistically robust tool like the cumulative sum (CUSUM) method can be helpful but may be too complex for surgeons to interpret and apply in practice without proper statistical training. To address this issue, we developed a new risk-adjusted (RA) O-E CUSUM chart that aims to provide a balanced solution, integrating the visualisation strengths of a user-friendly O-E chart with the statistical interpretation capabilities of a CUSUM chart. With the RA O-E CUSUM chart, surgeons can effectively monitor patients' outcomes and identify sequences of statistically abnormal changes, indicating either deterioration or improvement in surgical outcomes. They can also quantify potentially preventable or avoidable adverse events during these sequences. Subsequently, surgical teams can try implementing changes to potentially improve their performance and enhance patient safety over time. This paper outlines the methodology for building the tool and provides a concrete example using real surgical data to demonstrate its application.

Ethnic inequities in maternity care persist in England for Black, African, Caribbean and mixed-Black heritage families, resulting in poorer care experiences and health outcomes than other minoritised ethnic groups. Co-production using an integrated care approach is crucial for reducing these disparities and improving care quality and safety. Therefore, this study aimed to understand the alignment of health and local authority professional perspectives with community needs on how to improve maternity experiences for this ethnic group within a London integrated care system (ICS).

Women residing in lower socioeconomic status (SES) areas have lower breast cancer survival but it is not clear how differences in the quality of care received contribute to these disparities. We compared adherence to clinical practice guidelines (CPG) for the diagnosis and treatment of breast cancer and subsequent breast cancer survival between women residing in lower versus higher SES areas.

Allied health professionals (AHPs) in inpatient mental health, learning disability and autism services work in cultures dominated by other professions who often poorly understand their roles. Furthermore, identified learning from safety incidents often lacks focus on AHPs and research is needed to understand how AHPs contribute to safe care in these services.

Surgical errors in ophthalmology can have devastating consequences. We developed an artificial intelligence (AI)-based surgical safety system to prevent errors in patient identification, surgical laterality and intraocular lens (IOL) selection. This study aimed to evaluate its effectiveness in real-world ophthalmic surgical settings.