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Background Mitigating or reducing the risk of medication harm is a global policy priority. But evidence reflecting preventable medication harm in medical care and the factors that derive this harm remain unknown. Therefore, we aimed to quantify the prevalence, severity and type of preventable medication harm across medical care settings. Methods We performed a systematic review and meta-analysis of observational studies to compare the prevalence of preventable medication harm. Searches were carried out in Medline, Cochrane library, CINAHL, Embase and PsycINFO from 2000 to 27 January 2020. Data extraction and critical appraisal was undertaken by two independent reviewers. Random-effects meta-analysis was employed followed by univariable and multivariable meta-regression. Heterogeneity was quantified using the I 2 statistic, and publication bias was evaluated. PROSPERO: CRD42020164156. Results Of the 7780 articles, 81 studies involving 285,687 patients were included. The pooled prevalence for preventable medication harm was 3% (95% confidence interval (CI) 2 to 4%, I 2  = 99%) and for overall medication harm was 9% (95% CI 7 to 11%, I 2  = 99.5%) of all patient incidence records. The highest rates of preventable medication harm were seen in elderly patient care settings (11%, 95% 7 to 15%, n  = 7), intensive care (7%, 4 to 12%, n  = 6), highly specialised or surgical care (6%, 3 to 11%, n  = 13) and emergency medicine (5%, 2 to 12%, n  = 12). The proportion of mild preventable medication harm was 39% (28 to 51%, n  = 20, I 2  = 96.4%), moderate preventable harm 40% (31 to 49%, n  = 22, I 2  = 93.6%) and clinically severe or life-threatening preventable harm 26% (15 to 37%, n  = 28, I 2  = 97%). The source of the highest prevalence rates of preventable harm were at the prescribing (58%, 42 to 73%, n  = 9, I 2  = 94%) and monitoring (47%, 21 to 73%, n  = 8, I 2  = 99%) stages of medication use. Preventable harm was greatest in medicines affecting the ‘central nervous system’ and ‘cardiovascular system’. Conclusions This is the largest meta-analysis to assess preventable medication harm. We conclude that around one in 30 patients are exposed to preventable medication harm in medical care, and more than a quarter of this harm is considered severe or life-threatening. Our results support the World Health Organisation’s push for the detection and mitigation of medication-related harm as being a top priority, whilst highlighting other key potential targets for remedial intervention that should be a priority focus for future research.

Background Long-term opioid use is associated with dependency, addiction, and serious adverse events. Although a framework to reduce inappropriate opioid prescribing exists, there is no consensus on prescribing indicators for preventable opioid-related problems in patients with chronic pain in primary care in the UK. This study aimed to identify opioid prescription scenarios for developing indicators for prescribing opioids to patients with chronic pain in primary care. Methods Scenarios of opioid prescribing indicators were identified from a literature review, guidelines, and government reports. Twenty-one indicators were identified and presented in various opioid scenarios concerning opioid-related harm and adverse effects, drug-drug interactions, and drug-disease interactions in certain disease conditions. After receiving ethics approval, two rounds of electronic Delphi panel technique surveys were conducted with 24 expert panellists from the UK (clinicians, pharmacists, and independent prescribers) from August 2020 to February 2021. Each indicator was rated on a 1–9 scale from inappropriate to appropriate. The score’s median, 30th and 70th percentiles, and disagreement index were calculated. Results The panel unanimously agreed that 15 out of the 21 opioid prescribing scenarios were inappropriate, primarily due to their potential for causing harm to patients. This consensus was reflected in the low appropriateness scores (median ranging from 1 to 3). There were no scenarios with a high consensus that prescribing was appropriate. The indicators were considered inappropriate due to drug-disease interactions ( n  = 8), drug-drug interactions ( n  = 2), adverse effects ( n  = 3), and prescribed dose and duration ( n  = 2). Examples included prescribing opioids during pregnancy, concurrently with benzodiazepines, long-term without a laxative prescription and prescribing > 120-mg morphine milligram equivalent per day or long-term duration over 3 months after surgery. Conclusions The high agreement on opioid prescribing indicators indicates that these potentially hazardous consequences are relevant and concerning to healthcare practitioners. Future research is needed to evaluate the feasibility and implementation of these indicators within primary care settings. This research will provide valuable insights and evidence to support opioid prescribing and deprescribing strategies. Moreover, the findings will be crucial in informing primary care practitioners and shaping quality outcome frameworks and other initiatives to enhance the safety and quality of care in primary care settings.

Who could disagree with the seemingly common-sense reasoning that: \"We must learn from the things that go wrong.\"? Despite major investments to improve patient safety, relatively few evaluations demonstrate convincing reductions in risk, harm, serious error or death. This disappointing trajectory of improvement from learning from errors or Safety-I as it is sometimes known has led some researchers to argue that there is more to be gained by learning from the majority of healthcare episodes: the things that go right. Based on this premise, socalled Safety-II has emerged as a new paradigm. In this commentary, we consider the ongoing value of Safety-I based approaches and explore whether now is the time to abandon learning from \"the bad\" and re-energise data collection and analysis by focusing on \"the good.\"

This chapter contains sections titled: Overview Hepatitis A Hepatitis B Influenza Pneumococcal vaccine Tetanus‐diphtheria and acellular pertussis (TdaP) Special vaccines Summary References