Objective: To systematically quantify the prevalence, severity, and nature of preventable patient harm across a range of medical settings globally. Design: Systematic review and meta-analysis. Data sources: Medline, PubMed, PsycINFO, Cinahl and Embase, WHOLIS, Google Scholar, and SIGLE from January 2000 to January 2019. The reference lists of eligible studies and other relevant systematic reviews were also searched. Review methods: Observational studies reporting preventable patient harm in medical care. The core outcomes were the prevalence, severity, and types of preventable patient harm reported as percentages and their 95% confidence intervals. Data extraction and critical appraisal were undertaken by two reviewers working independently. Random effects meta-analysis was employed followed by univariable and multivariable meta regression. Heterogeneity was quantified by using the I ² statistic, and publication bias was evaluated. Results: Of the 7313 records identified, 70 studies involving 337 025 patients were included in the meta-analysis. The pooled prevalence for preventable patient harm was 6% (95% confidence interval 5% to 7%). A pooled proportion of 12% (9% to 15%) of preventable patient harm was severe or led to death. Incidents related to drugs (25%, 95% confidence interval 16% to 34%) and other treatments (24%, 21% to 30%) accounted for the largest proportion of preventable patient harm. Compared with general hospitals (where most evidence originated), preventable patient harm was more prevalent in advanced specialties (intensive care or surgery; regression coefficient b=0.07, 95% confidence interval 0.04 to 0.10). Conclusions: Around one in 20 patients are exposed to preventable harm in medical care. Although a focus on preventable patient harm has been encouraged by the international patient safety policy agenda, there are limited quality improvement practices specifically targeting incidents of preventable patient harm rather than overall patient harm (preventable and non-preventable). Developing and implementing evidence-based mitigation strategies specifically targeting preventable patient harm could lead to major service quality improvements in medical care which could also be more cost effective.

Nonmedical prescribing has been allowed in the United Kingdom (UK) since 1992. Its development over the past 24 years has been marked by changes in legislation, enabling the progression towards independent prescribing for nurses, pharmacists and a range of allied health professionals. Although the UK has led the way regarding the introduction of nonmedical prescribing, it is now seen in a number of other Western-European and Anglophone countries although the models of application vary widely between countries. The programme of study to become a nonmedical prescriber (NMP) within the UK is rigorous, and involves a combination of taught curricula and practice-based learning. Prescribing is a complex skill that is high risk and error prone, with many influencing factors. Literature reports regarding the impact of nonmedical prescribing are sparse, with the majority of prescribing research tending to focus instead on prescribing by doctors. The impact of nonmedical prescribing however is important to evaluate, and can be carried out from several perspectives. This review takes a brief look back at the history of nonmedical prescribing, and compares this with the international situation. It also describes the processes required to qualify as a NMP in the UK, potential influences on nonmedical prescribing and the impact of nonmedical prescribing on patient opinions and outcomes and the opinions of doctors and other healthcare professionals.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: eHealth tools are becoming increasingly popular for helping patients self-manage chronic conditions. Little research has examined the effect of ehealth tools for patient self-reporting on medication management. This review aims to determine whether ehealth tools featuring patient self-reporting of symptoms, adverse effects and drug therapy problems are effective at prompting medication changes and improving patient outcomes. Objective: The primary objective is to determine how patient self-reporting of symptoms and adverse effects via ehealth tools influences medication use and changes made to medications. Secondary objectives are to investigate how patient use of ehealth tools influences identification of adverse events, patient self-management of disease and self-efficacy, medication use behaviour, medication reconciliation and recommendations for drug changes, changes in patient signs and symptoms, health services utilization, quality of life, and patient satisfaction with health care. Methods: MEDLINE, EMBASE and CINAHL were searched from Jan 1, 2000 through to January 4, 2016. References were also searched. Title, abstract and full text review, as well as data abstraction and risk of bias assessment were performed in duplicate. Due to high heterogeneity, results were not meta-analyzed, and instead presented as a narrative synthesis. Results: 13 studies, including 10 randomized controlled trials (RCTs) and 3 single-arm cohort studies were included, from which 10 unique ehealth tools were identified. Here six of seven RCTs found improvement in patient symptoms following ehealth tool use, especially in adolescent asthma patients. Four of six RCTs reported that ehealth tools may have potential to initiate changes to medications based on patient self-reports. Two of four RCTs showed that ehealth tools may improve patient self-efficacy and self-management of chronic disease. Little or no evidence was found to support the effectiveness of ehealth tools at improving medication recommendations and reconciliation by clinicians, medication-use behaviour, health service utilization, quality of life, or patient satisfaction. eHealth tools with multifaceted functionalities and those allowing direct patient-provider communication may be more effective at improving patient self-management and self-efficacy. Conclusions: Initial evidence showing ehealth tools may improve patient symptoms and lead to medication changes is promising. Patients generally found ehealth tools useful in improving communication with health care providers. Implementation issues such as poor patient engagement and poor clinician workflow integration were identified. More high-quality research is needed to explore how ehealth tools can be used to effectively manage use of medications to improve patient outcomes.

Aim: The aim of this study was to explore how secondary care pharmacist and nurse independent prescribers clinically reason when making prescribing decisions.Background: Clinical reasoning is a central component of prescribers’ competence and professional autonomy when reaching a clinically appropriate decision. Like doctors, pharmacist and nurse independent prescribers in the UK have extensive prescribing rights, but little is known about their clinical reasoning.Design: A qualitative approach using a think‐aloud methodology and semi‐structured interviews.Methods: Eleven nurse and 10 pharmacist independent prescribers were asked to think‐aloud about validated clinical vignettes prior to interview, between March ‐ December 2015. Data were analysed using a constant‐comparative approach.Results: A strong link between clinical knowledge, grounded in previous experience and clinical reasoning was found. Despite prescribers approaching the clinical vignettes holistically, their focus varied according to professional background and job role. Nurses were more likely to describe interacting with patients, compared with pharmacists who were more focused on medical notes and laboratory results. Think‐aloud protocol analysis revealed a distinct pattern in the process undertaken to reach a clinical decision. This is presented as a decision‐making model, encompassing case familiarization, generating hypotheses, case assessment, final hypotheses and decision‐making stages, which oscillated throughout the model.Conclusion: This is the first study to explore the clinical reasoning processes of secondary care pharmacist and nurse independent prescribers. The resultant decision‐making model shows clinical reasoning as a complex and dynamic process. This model can inform the training of independent prescribers to become accurate problem solvers and continue making clinically appropriate decisions.

Objectives To survey and explore current approaches to deployment of pharmaceutical care prioritisation tools in acute hospitals in the UK. Methods A national online survey was circulated electronically to chief pharmacists of hospitals to determine if they use a prioritisation tool or process. Where such mechanisms exist, respondents were invited to participate in a semistructured telephone interview to explore the development, evaluation and application of their tool and share relevant documentation. Interviews were transcribed and thematically analysed. Results Seventy hospitals (70/130) used a tool or process to prioritise clinical pharmacy services. Thirty-six interviews were conducted, and two were excluded. The majority of tools had been developed in-house. Few hospitals had undertaken formal evaluations of their prioritisation tool. Pharmacy prioritisation tools ranged in complexity and often included a combination of pharmacy service prioritisation, such as medicines reconciliation, and a section to assign an individual patient prioritisation level. Determining the priority of a patient based on the identification of set indicators instilled confidence in pharmacists by ensuring they were not missing high-risk patients. Electronic prioritisation tools were especially useful at retrieving real-time data to prioritise workload, improving workflow and ensuring continuity in patient care. Drawbacks of using prioritisation tools included lack of tool sensitivity across certain specialties and time spent using the tool if not all information was accessible. Conclusions Prioritisation tools were seen to be useful for prioritising workload and ensuring the right patients are seen at the right time. As few hospitals had formally evaluated their tools, it is important to rigorously and systematically develop an evidence-based prioritisation tool that is both useable and acceptable. Further research to evaluate such tools would be needed to ensure it improves patient health outcomes and efficiency in pharmacy services.