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Classics and Emerging Classics

To help our readers navigate the tremendous breadth of the PSNet Collection, AHRQ PSNet editors and advisors have given the designation of “Classic” to review articles, empirical studies, government and stakeholder reports, commentaries, and books of lasting importance to the patient safety field. These items have the potential to impact how providers approach care practice and are regularly referenced in the literature. More information on the selection process.


The “Emerging Classics” designation identifies those resources that may not have met the level of a “Classic” yet due to limited citation in the published literature or in the level of impact/contribution to the environment, but these are resources which our patient safety subject matter experts believe have the potential to drive change in the field.

Popular Classics

Huang SS, Septimus E, Kleinman K, et al. N Engl J Med. 2013;368.

Healthcare associated infection is a leading cause of preventable illness and death. Methicillin-resistant Staphylococcus aureus (MRSA) is a virulent, multi-drug resistant infection increasingly seen across healthcare settings. This pragmatic,... Read More

All Classics and Emerging Classics (867)

1 - 20 of 30 Results
Emerging Classic
Coiera E. Lancet. 2018;392:2331-2332.
Artificial intelligence can improve practice by making synthesized data available in real time to inform frontline decision-making. This commentary describes factors clinicians should consider as artificial intelligence becomes more prevalent in health care and discusses how this technology can enable clinicians to focus on helping patients navigate complex care choices.
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.
Yu K-H, Kohane IS. BMJ Qual Saf. 2019;28:238-241.
Use of artificial intelligence (AI) and computer algorithms as tools to improve diagnosis have both risks and benefits. This commentary explores challenges to implementing AI systems at the front line of care in a safe manner and identifies weaknesses of advanced computing systems that influence their reliability.
Powers EM, Shiffman RN, Melnick ER, et al. J Am Med Inform Assoc. 2018;25:1556-1566.
Although hard-stop alerts can improve safety, they have been shown to result in unintended consequences such as delays in care. This systematic review suggests that while implementing hard stops can lead to improved health and process outcomes, end-user involvement is essential to inform design and appropriate workflow integration.
Wong A, Plasek JM, Montecalvo SP, et al. Pharmacotherapy. 2018;38:822-841.
Natural language processing (NLP) can efficiently analyze large narrative data sets to identify adverse events. Exploring the application of NLP to reduce medication errors, this AHRQ-funded review describes challenges associated with using NLP to extract information from clinical sources and highlights how engaging pharmacists in developing NLP systems can improve medication safety.
Gianfrancesco MA, Tamang S, Yazdany J, et al. JAMA Intern Med. 2018;178:1544-1547.
Machine learning, a type of computing that uses data and statistical methods to enable computers to progressively enhance their prediction or task performance over time, has been widely promoted as a tool to improve health care safety. This commentary describes the potential for machine learning to worsen socioeconomic disparities in health care. Disadvantaged populations are more likely to receive care in multiple health systems. Therefore, relevant data about their health may be missing in an individual health system's records, hindering performance of machine learning algorithms. Racial and ethnic minority patients may not be present in sufficient numbers for accurate prediction. The authors raise concern that implicit bias in the care that disadvantaged populations receive may influence algorithms, which will amplify this bias. They recommend inclusion of sociodemographic characteristics into algorithms, building and testing algorithms in diverse health care systems, and conducting follow-up testing to ensure that machine learning does not perpetuate or exacerbate health care disparities.
Millenson ML, Baldwin JL, Zipperer L, et al. Diagnosis (Berl). 2018;5:95-105.
Recently, several mobile health care applications have been developed and marketed directly to nonclinician consumers. Researchers reviewed the literature regarding direct-to-consumer diagnostic applications. They found wide variation in the safety of these applications and suggest that further research is needed to thoroughly assess their effectiveness.
Varghese J, Kleine M, Gessner SI, et al. J Am Med Inform Assoc. 2018;25:593-602.
This systematic review of clinical decision support on inpatient outcomes identified mostly positive effects. Clinical decision support was found to be most effective for managing blood glucose and blood transfusions and for preventing venous thromboembolism, pressure ulcers, acute kidney injury, and incipient clinical deterioration. The authors advocate for prioritizing clinical decision support for these specific conditions.
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.
Tolley CL, Slight SP, Husband AK, et al. Am J Health Syst Pharm. 2018;75:239-246.
This systematic review of clinical decision support for safe medication use found that such systems are incompletely implemented and lack standardization and integration of patient-specific factors. The authors suggest that reducing alert fatigue and employing human factors principles would enhance decision support effectiveness.
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.
Liberman AL, Newman-Toker DE. BMJ Qual Saf. 2018;27:557-566.
Patient safety measurement remains challenging. This article describes a framework to address gaps in measuring diagnostic error. The authors propose utilizing big data to develop diagnostic performance dashboards and benchmarking tools that support proactive learning and improvement strategies.
Bejnordi BE, Veta M, van Diest PJ, et al. JAMA. 2017;318:2199-2210.
Diagnostic error is a growing area of focus within patient safety. Artificial intelligence has the potential to improve the diagnostic process, both in terms of accuracy and efficiency. In this study, investigators compared the use of automated deep learning algorithms for detecting metastatic disease in stained tissue sections of lymph nodes of women with breast cancer to pathologists' diagnoses. The algorithms were developed by researchers as part of a competition and their performance was assessed on a test set of 129 slides, 49 with metastatic disease and 80 without. A panel of 11 pathologists evaluated the same slides with a 2-hour time limit and one pathologist evaluated the slides without any time constraints. The authors conclude that some of the algorithms demonstrated better diagnostic performance than the pathologists did, but they suggest that further testing in a clinical setting is warranted. An accompanying editorial discusses the potential of artificial intelligence in health care.
Brenner SK, Kaushal R, Grinspan Z, et al. J Am Med Inform Assoc. 2016;23:1016-36.
Health information technology (IT) has had a profound impact on health care. Although health IT has led to efficiency gains and improved safety, unintended consequences remain a concern. In this systematic review, researchers analyzed 69 studies from 2001 through 2012 that examined the use of health IT in a clinical setting and its effect on safety outcomes for patients. About one-third of the studies demonstrated a positive impact of health IT on patient safety outcomes, but many of these focused on the hospital setting, involved a single institution, and looked at decision support or computerized provider order entry. The authors suggest that future studies should focus on other areas in which the impact of health IT remains understudied, such as in outpatient and long-term care settings, and they underscore the need for higher quality research. A recent WebM&M commentary described the unintended consequences of health IT.
Riches N, Panagioti M, Alam R, et al. PLoS One. 2016;11:e0148991.
Despite increasing focus on diagnostic error, it remains a controversial patient safety issue. The Institute of Medicine recently suggested that further research is needed regarding electronic tools to improve diagnosis. Differential diagnosis generators provide a list of possible diagnoses for a problem. The investigators conducted a systematic review and found that differential diagnosis generators have been shown to improve diagnostic accuracy when a clinician has an opportunity to re-review the case using the software in pre-post studies. The degree of improvement varied between studies. The effect on actual clinician behaviors—such as test ordering, clinical outcomes, and cost—is unclear. Clinicians need prospective studies in order to determine whether such tools enhance diagnosis in actual practice. A recent PSNet perspective discussed future research avenues to ensure progress in diagnostic safety.
Graber ML, Kissam S, Payne VL, et al. BMJ Qual Saf. 2012;21:535-57.
Cognitive errors by individual physicians are at the root of most diagnostic errors, combining with system failures to result in preventable patient harm. Despite a rich body of literature exploring cognitive biases that contribute to misdiagnosis, few interventions to address this problem have been formally tested. This review identified 141 articles containing 3 approaches to prevent cognitive errors: improving knowledge or experience (such as using simulation training), improving clinical decision-making skills (through metacognition and reflection), and providing cognitive assistance (such as clinical decision support). However, most of the proposed interventions have not been formally tested, and even fewer have evaluated interventions outside of training settings. This group of authors also recently published a review of system interventions to prevent diagnostic errors.
Murff HJ, FitzHenry F, Matheny ME, et al. JAMA. 2011;306:848-55.
Many adverse event identification methods cannot detect errors until well after the event has occurred, as they rely on screening administrative data or review of the entire chart after discharge. Electronic medical records (EMRs) offer several potential patient safety advantages, such as decision support for averting medication or diagnostic errors. This study, conducted in the Veterans Affairs system, reports on the successful development of algorithms for screening clinicians' notes within EMRs to detect postoperative complications. The algorithms accurately identified a range of postoperative adverse events, with a lower false negative rate than the Patient Safety Indicators. As the accompanying editorial notes, these results extend the patient safety possibilities of EMRs to potentially allow for real time identification of adverse events.
Metzger J, Welebob E, Bates DW, et al. Health Aff (Millwood). 2010;29:655-663.
Computerized provider order entry (CPOE) has provided significant safety benefits in research studies, especially when combined with clinical decision support to prevent common prescribing errors. However, CPOE's "real-world" performance has been mixed, with high-profile studies documenting a variety of unintended consequences. This AHRQ-funded study used simulated patient records to evaluate the ability of eight commercial CPOE modules to prevent medication errors. The overall results were disappointing, as CPOE failed to prevent many medication errors—including fully half of potentially fatal errors, which are considered never events. The individual CPOE products varied significantly in their ability to detect potential errors. Some hospitals did achieve superior performance, which the authors ascribe to greater experience with CPOE and implementation of more advanced decision support tools. Another recent article found that reminders within CPOE systems resulted in only small improvements in adherence to recommended care processes. Taken together, these studies imply that CPOE implementation may not result in large immediate effects on safety and quality in typical practice settings.
Matheny ME, Sequist TD, Seger AC, et al. J Am Med Inform Assoc. 2008;15:424-9.
Electronic reminders to clinicians are one of the earliest methods used to improve patient safety. In this cluster-randomized controlled trial conducted in primary care clinics, clinicians received targeted reminders within the existing electronic medical record prompting them to order laboratory tests to detect adverse medication effects. The most encouraging study result was that clinicians were generally already monitoring patients as recommended—in contrast to data from prior studies—and as a result, the reminders did not appreciably increase test ordering. Prior research has addressed barriers to effective implementation of electronic reminders.
Kaushal R, Shojania KG, Bates DW. Arch Intern Med. 2003;163:1409-16.
Computerized physician order entry (CPOE) systems hold the promise of potentially reducing medication errors, especially when coupled with clinical decision support systems (CDSS) that guide clinicians' medication ordering practices. This systematic review did find substantial reductions in potential medication errors in studies of both CPOE and CDSS systems. However, the few studies found were not adequately powered to determine the effects on adverse drug events requiring clinical intervention, and chiefly assessed the effects of "home-grown" CPOE systems. The studies also provided only limited information on implementation issues and potential unintended consequences of CPOE. Such issues will need to be addressed in order to improve the slow pace of uptake of CPOE in US hospitals.