Many strategies have been developed to improve recognition of, and response, to clinically deteriorating patients. This review found that simulation-based educational strategies was the most effective educational method for training staff to recognize unwell patients. However, the quality of evidence was low and additional research into simulation-based education is needed.

Misuse of prescription opioids represents a serious patient safety issue. Using commercial claims from 2014 - 2018, researchers examined the association between the 2016 CDC guidelines to reduce unsafe opioid prescribing and opioid dispensing for patients with four common chronic pain diagnoses. Findings indicate that the release of the 2016 guidelines was associated with reductions in the percentage of patients receiving opioids, average dose prescribed, percentage receiving high-dose prescriptions, number of days supplied, and the percentage of patients receiving concurrent opioid/benzodiazepine prescriptions. The authors observe that questions remain about how clinicians are tailoring opioid reductions using a patient-centered approach.

There are many challenges associated with detecting and measuring patient safety events. This meta-review provides an overview of approaches to measuring and monitoring safety in primary care. The authors suggest that instead of developing new methods for measuring and monitoring safety, researchers should focus on expanding the generalizability and comparability of existing methods, many of which are readily available, quick to administer, do not require external involvement, and are inexpensive.

The World Health Organization (WHO) released the International Classification of Diseases, 11th Revision (ICD-11) in 2018. In addition to the medical entities such as disease and injury, it contains a second component, the ICD-11 Mortality and Morbidity Statistics (MMS) linearization. The authors evaluated whether the ICD-11 MMS is appropriate for use in patient safety and quality or if a USA-specific clinical modification is necessary. 

Trauma resuscitation is a complex, specialized process with a high risk for errors. Researchers analyzed videotapes of in situ simulations to evaluate latent safety events occurring during trauma resuscitation. Themes influencing latent safety events related to physical workspace, mental model formation, equipment, unclear accountability, demands exceeding individuals’ capacity, and task-specific issues.

Despite public policies and guidelines to reduce opioid prescribing, providers continue to overprescribe these medications to children, adolescents, and young adults. In this analysis of US retail pharmacy data, 3.5% of US children and young adults were dispensed at least one opioid prescription; nearly half of those included at least one factor indicating they were high risk. Consistent with prior research, dentists and surgeons were the most frequent prescribers, writing 61% of all opiate prescriptions.

Artificial intelligence (AI) and big data have potential to impact health care improvement in a variety of ways. This commentary examines the intersection of AI, diagnosis, and inequality. The authors highlight several innovations that could reduce health disparities and diagnostic errors in at-risk patient populations if adopted.

Computerized provider order entry (CPOE) systems have the potential to reduce error, but their poor CPOE design, implementation and use can contribute to patient safety risks. In this study, researchers found that restricting the number of concurrently open electronic health records did not significantly reduce wrong patient selection errors in their hospital’s CPOE system.

The goal of the patient centered medical home (PCMH)  model is to reorganize primary care to provide team-based, coordinated, accessible health care. This study used a consensus process with input from a physician panel to examine ambulatory patient safety concerns (e.g., medication safety, diagnostic error, treatment delays, communication or coordination errors) in the context of the PCMH model and explore variability in the implementation of patient safety practices.

Research has shown that patients admitted to the hospital on the weekend may experience worse outcomes compared to those admitted on weekdays (the ‘weekend effect’). This editorial highlights the challenges to empirically evaluate the underlying mechanisms contributing to the weekend effect. The authors propose viewing the weekend effect as a proxy for staffing levels and the influence of other factors influencing outcomes for patients admitted on weekends, such as patient acuity, clinician skill-mix and access to diagnostic tests or other ancillary services.

Machine learning uses data and statistical methods to enhance risk prediction models and it has been promoted as a tool to improve healthcare safety. Using Medicaid claims data for a large cohort of White and Black pregnant females, this study evaluated approaches to reduce bias in clinical prediction algorithms for postpartum depression and mental health service utilization. The researchers found that a reweighing method in machine learning models was associated with a greater reduction in bias than excluding race from the prediction models. The authors suggest further examination of potentially biased data informing clinical prediction models and consideration of other methods to mitigate bias.

Computerized prescriber order entry (CPOE) systems embedded in electronic health systems alert clinicians to potential safety concerns such as drug-drug interactions or medication dosage errors. Results of this review indicate that alerts influenced prescriber behavior in most of the included studies. However, it is unclear whether these behavioral changes improve patient safety outcomes. Recommendations for future research include randomized controlled trials to determine which alerts maximize patient safety, while minimizing prescribers’ alert fatigue.

Communication is an essential component of safe patient care. This review of 71 studies found that communication training interventions in obstetrics can improve communication skills and behavior, particularly when combined with team training. The authors identified a lack of evidence regarding the effect of communication trainings on patient safety outcomes and suggest that future research should assess this relationship. Study findings underscore the need for adequate communication trainings to be provided to all staff and expectant mothers and their partners.

This study explored the role of machine-learning based clinical decision support (CDS) algorithms to support (rather than replace) human decision-making and the impact on diagnostic performance. This systematic review of 37 studies found limited evidence that the use of machine learning-based CDS systems contributes to improved diagnostic performance among clinicians. Interobserver agreement, user feedback, and clinician override were the most commonly reported outcomes. The authors emphasize the importance of further evaluation of human-computer interaction.

Computerized prescriber (or physician) order entry (CPOE) systems are widely used in healthcare and studies have shown a reduction in medication errors with CPOE. This study focused on whether CPOE systems improved the incidence of chemotherapy-related medication errors. The study included 11 studies in the review but only 8 studies were in the meta-analysis. The authors found that the use of CPOE was associated with an 81% reduction in chemotherapy-related medication errors, indicating that CPOE is a valuable strategy for this patient population.

In addition to being used for patient-specific clinical purposes, data within the electronic health record (EHR) may be used for other purposes including epidemiological research. Researchers in the UK developed and tested a clinical decision support system (CDSS) to evaluate changes in the types and number of observations that primary care physicians entered into the EHR during simulated patient encounters. Physicians documented more clinical observations using the CDSS compared to the standard electronic health record. The increase in documented clinical observations has the potential to improve validity of research developed from EHR data.

Health information technology (HIT) can promote patient safety in many settings. This systematic review found that HIT, such as computerized provider order entry (CPOE) systems, can improve safe prescribing practices in long-term care settings, including improved documentation and clinical processes, and fewer medication errors.

Medication errors occur frequently in ambulatory care settings. This article describes the development and testing of an ambulatory medication safety evaluation tool, which is based on an inpatient version administered by The Leapfrog Group. Pilot testing at seven clinics around the US indicates that clinics struggled in areas of advanced decision support such as drug age and drug monitoring, and that most clinics lacked EHR-based medication reconciliation functions.