The AHRQ PSNet Collection comprises an extensive selection of resources relevant to the patient safety community. These resources come in a variety of formats, including literature, research, tools, and Web sites. Resources are identified using the National Library of Medicine’s Medline database, various news and content aggregators, and the expertise of the AHRQ PSNet editorial and technical teams.
Missed diagnosis of stroke in emergency medicine settings is an important patient safety problem. In this study, researchers interviewed emergency medicine physicians about their perspectives on diagnostic neurology and use of clinical decision support (CDS) tools. Themes emerged related to challenges in diagnosis, neurological complaints, and challenges in diagnostic decision-making in emergency medicine, more generally. Participating physicians were enthusiastic about the possibility of involving CDS tools to improve diagnosis for non-specific neurological complaints.
Maxwell E, Amerine J, Carlton G, et al. Am J Health Syst Pharm. 2021;78:s88-s94.
Clinical decision support (CDS) tools are intended to enhance care decision and delivery processes. This single-site retrospective study evaluated whether a CDS tool can reduce discharge prescription errors for patients receiving a medication substitution at admission. Findings indicate that use of CDS did not result in a decrease in discharge prescription omissions, duplications, or inappropriate medication reconciliation.
Older adults are at increased risk of hospitalization due to COVID-19 infections. This study examined the potential severe drug-drug interactions (DDI) among hospitalized older adults taking two or more medications at admission and discharge. There was a significant increase in prescription of proton pump inhibitors and heparins from admission to discharge. Clinical decision support systems should be used to assess potential DDI with particular attention paid to the risk of bleeding complications linked to heparin-based DDIs.
Pedersen CA, Schneider PJ, Ganio MC, et al. Am J Health Syst Pharm. 2020;77:1026-1050.
This article describes results from the 2019 American Society of Health-System Pharmacists national survey regarding inpatient pharmacy practice. The authors note the increasing responsibilities placed on pharmacists and their role in addressing the opioid crisis, adopting intravenous workflow technologies, and leveraging clinical decision support tools to improve medication administration safety.
Krukas A, Franklin ES, Bonk C, et al. Patient Safety. 2020;2.
Intravenous vancomycin is an antibiotic with known medication safety risk factors. This assessment is designed to assist organizations to review clinician and organizational knowledge, medication administration activities and health information technology as a risk management strategy to minimize hazards associated with vancomycin use.
Gordon L, Grantcharov T, Rudzicz F. JAMA Surg. 2019.
Advances in technology enable real-time intraoperative data capture to prevent adverse events and improve patient safety and recovery. This commentary describes a surgical innovation that combined artificial intelligence, video technology, and clinical decision support and was designed to flag potential bleeding events in the surgical suite.
Segal G, Segev A, Brom A, et al. J Am Med Inform Assoc. 2019;26:1560-1565.
Alerts designed to prevent inappropriate prescribing of medications are frequently overridden and contribute to alert fatigue. This study describes the use of machine learning to improve the clinical relevance of medication error alerts in the inpatient setting.
McDonald EG, Wu PE, Rashidi B, et al. J Am Geriatr Soc. 2019;67:1843-1850.
This pre–post study compared patients who received medication reconciliation that was usual care at the time of hospital discharge to patients in the intervention arm who had decision support for deprescribing. Although the intervention did lead to more discontinuation of potentially inappropriate medications, there was no difference in adverse drug events between groups. The authors suggest larger studies to elucidate the potential to address medication safety using deprescribing decision support.
Reynolds TL, DeLucia PR, Esquibel KA, et al. JAMIA Open. 2019;2:49-61.
This pre–post mixed-methods implementation study examined a handheld decision support tool for nurses performing bedside administration of intravenous medications in intensive care units. Investigators found that though nurses desire decision support, the usability of the tool and fit with the critical care environment were suboptimal, leading to limited use. The authors suggest integrating mobile technology tools into existing infrastructure and developing user-informed implementation strategies.
Rodriguez-Ruiz A, Lång K, Gubern-Merida A, et al. J Natl Cancer Inst. 2019;111:916-922.
Artificial intelligence (AI) may have the capacity to improve diagnosis. Researchers found that an AI system was able to detect breast cancer using mammography with accuracy similar to that of the average of the 101 radiologists whose interpretations were included in the study.
Artificial intelligence (AI) technologies can improve the use of data in care delivery. This review recommends steps to enhance the use of AI in bedside care. The author highlights the need for clinicians to accept that AI tools will affect care processes and be trained to participate in AI integration on the front line.
Murray B, Streitz MJ, Hilliard M, et al. Clin Pediatr (Phila). 2019;58:413-416.
Pediatric medication dosing is often weight-based and therefore subject to calculation errors. This prospective study demonstrated that use of a clinical decision support tool (a dosing calculator) significantly reduced medication errors.
Wong A, Rehr C, Seger DL, et al. Drug Saf. 2019;42:573-579.
Although clinical decision support is intended to improve safety, decision support alerts often result in alert fatigue and overrides. This prospective observational study examined overrides for exceeding the maximum dose of a medication in the intensive care unit. Researchers determined that insulin was the most frequent medication for which a maximum dosage alert was overridden. In almost 90% of cases, the overrides were deemed clinically appropriate. The authors conclude that more intelligent clinical decision support for medication dosing is needed to balance safety with alert fatigue in the intensive care unit. A past PSNet perspective discussed the challenges of implementing effective medication decision support systems.
Shortliffe EH, Sepúlveda MJ. JAMA. 2018;320:2199-2200.
Clinical decision support on the front line of care harbors both potential benefits and barriers to effective care delivery. This commentary outlines system challenges such as complexity and poor communication that hinder reliable adoption and use of clinical decision support. The authors highlight the need for research and evaluation models to help bring clinical decision support safely and effectively into daily health care work.
Massalha S, Clarkin O, Thornhill R, et al. Can J Cardiol. 2018;34:827-838.
Decision support tools can help reduce diagnostic uncertainty. Discussing how artificial intelligence can be utilized to inform diagnostic decision making and improve the accuracy of cardiac image interpretation, this review suggests that use of such technology can reduce production pressure and cognitive load for imaging physicians.
Rambaran KA, Huynh HA, Zhang Z, et al. Cureus. 2018;10:e2860.
Incomplete information resources can contribute to misinformed decisions that result in patient harm. This review compared drug information resources and found problematic inconsistencies in their content. The authors suggest that providers refer to several sources for medication information to ensure safe decision making.
Meyer AND, Thompson PJ, Khanna A, et al. J Am Med Inform Assoc. 2018;25:841-847.
Clinical decision support is a widely recommended patient safety strategy. This study examined whether a mobile application created by the Centers for Disease Control and Prevention improved clinician decision-making about anticoagulation test ordering for simulated case vignettes. Each participating physician completed a series of vignettes; half used the application and half did not. When using the application, physicians demonstrated greater diagnostic accuracy and confidence, and they needed less time to complete each vignette. The authors suggest that mobile applications may be useful for providing decision support.
Yeung S, Downing L, Fei-Fei L, et al. New Engl J Med. 2018;378:1271-1273.
Artificial intelligence technologies can support diagnostic decision-making. This commentary discusses application of deep learning tools to create visual cues to track deviations in activities to flag areas of improvement. Although early in its development, the authors outline the potential of this technology in clinical care and review early efforts employed to enhance hand hygiene.
Wong A, Amato MG, Seger DL, et al. BMJ Qual Saf. 2018;27:718-724.
Clinical decision support systems in electronic health records (EHRs) aim to avert adverse events, especially medication errors. However, alerts are pervasive and often irrelevant, leading patient safety experts to question whether their modest improvement in safety outweighs the harms of alert fatigue. This study assessed provider overrides of a commercial EHR's medication alerts in intensive care units at one institution. Providers overrode most alerts, and the majority of those overrides were appropriate. Inappropriate overrides occasionally led to medication errors and did so more frequently than appropriate overrides. A recent WebM&M commentary recommends employing human factors engineering to make clinical decision support more effective.
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