Failure Mode, Effect and Criticality Analysis (FMECA) is a prospective method for identifying and preventing potential error risks. Using FMECA, public health medical residents calculated a Risk Priority Number (RPN), or criticality, for each possible failure mode in cancer treatment prescription and administration. Each phase of the cancer treatment process had at least one critical step identified, and actions were developed to reduce the likelihood of the error occurring and/or to increase the likelihood of the error being detected.

A missed or delayed diagnosis of stroke places patients at risk of permanent disability or death. This article describes how interdisciplinary teams used a failure modes, effects, and criticality analysis (FMECA) to create an acute stroke diagnostic process map, identify failures, and highlight existing safeguards. The FMECA process identified several steps in the diagnostic process as the most critical failures to address, including failure to screen patients for stroke soon after presentation to the Emergency Department (ED), failure to obtain an accurate history, and failure to consider a stroke diagnosis during triage.

Previous research has identified disparities in adverse events and patient safety risks for Black patients compared to White patients. In this study, researchers used a large healthcare system’s malpractice database to examine racial differences in malpractice lawsuits. Although there were no significant race differences in lawsuits, findings suggest that employees are more likely to identify potential malpractice events for White patients compared to Black patients.

In 2017, England’s National Health Service (NHS) implemented the Learning from Deaths program which requires NHS Secondary Care Trusts (NSCT) to report, investigate, and learn from potentially preventable deaths. This study focuses on what NCSTs learned during the first three years of the program, the actions taken in response and their impact, and engagement with Learning from Deaths. Trusts appear to have varied understanding and use of the term ‘learning’ and not all specified the impact their actions had on patient safety.

Medication reconciliation can help identify medication discrepancies during transitions of care. This study examined the impact of a complete medication history database to support pharmacist-led medication reconciliation and identification of medication discrepancies during the admission process for patients at one psychiatric hospital. A retrospective analysis identified 82 medication errors; 90% of these errors – primarily dosage discrepancies and omissions – could have led to patient harm if not corrected through pharmacist intervention.

Context, such as patient, clinician, location, or specialty, can affect the type and frequency of diagnostic errors. In this novel study, the diagnostic errors of a cohort of clinicians who practice in multiple locations (i.e., outpatient and emergency department) with different referral types (i.e., scheduled visit, urgent visit, emergency visit) was evaluated. Using the Revised Safer Dx instrument, researchers identified significantly more diagnostic errors in patients with scheduled visits compared to urgent or emergent referrals. The results indicate, that among clinicians in the same specialty, it may be contextual factors (i.e., referral type) that affect diagnostic errors rather than specialty.

Organizations are encouraged to proactively identify patient safety risks and learn from failures. This article describes validity testing of systems-thinking risk assessment (Net-HARMS) to identify risks associated with patient medication administration and an accident analysis method (AcciMap) to analyze a medication administration error.

Healthcare staff who are involved in a medical error often experience emotional distress. Using qualitative methods and text mining of medication error incident reports, researchers in this study identified the negative emotions experienced by healthcare staff after a medication error (e.g., fear, guilt, sadness) and perceptions regarding how superiors and colleagues effectively responded to the events (e.g., reassurance, support, and guidance).

COVID-19 changed the way care is delivered to hospitalized patients and resulted in new categories and themes in patient safety reporting. This study used machine learning to group of more than 2,000 patient safety event (PSE) reports into eight clinically relevant themes, including testing delays, diagnostic errors, pressure ulcers, and falls.

Diagnostic errors are an emerging area of patient safety research; as such, innovative methods to identify and prevent diagnostic errors are being developed. This commentary describes the development, implementation, and sustainment of a novel method of investigation. The e-Autopsy/e-Biopsy method includes dedicated patient safety staff and volunteer clinical specialists to review events and identify trends. The process is illustrated with three diagnoses: ectopic pregnancy, abdominal aortic aneurysms, and advanced colon cancer.