The PSNet Collection: All Content

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.

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Improving Diagnostic Safety and Quality

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Perspective on Safety April 26, 2023

Throughout 2022, AHRQ PSNet has shared research that elucidates the complex nature of misdiagnosis and diagnostic safety. This Year in Review explores recent work in diagnostic safety and ways that greater safety may be promoted using tools developed to improve diagnostic practices.

Technology as a Tool for Improving Patient Safety

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Perspective on Safety March 29, 2023

In the past several decades, technological advances have opened new possibilities for improving patient safety. Using technology to digitize healthcare processes has the potential to increase standardization and efficiency of clinical workflows and to reduce errors and cost across all healthcare settings.¹ However, if technological approaches are designed or implemented poorly, the burden on clinicians can increase. For example, overburdened clinicians can experience alert fatigue and fail to respond to notifications. This can lead to more medical errors.

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Value and Patient Safety

March 8, 2023

Value as an element of patient safety is emerging as an approach to prioritize and evaluate improvement actions. This library highlights resources that explore the business case for cost effective, efficient and impactful efforts to reduce medical errors.

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Diagnostic Errors Case Studies

January 19, 2023

The Primary-Care Research in Diagnosis Errors (PRIDE) Learning Network was a Boston-based national effort to improve diagnostic safety. Hosted by the State of Massachusetts’ Betsy Lehman Center, it was led by the Harvard Brigham and Women’s Center for Patient Safety Research and Practice with funding from the Gordon and Betty Moore Foundation. ...

Improving diagnostic decision support through deliberate reflection: a proposal.

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Schmidt HG, Mamede S. Diagnosis (Berl). 2023;10:38-42.

Critical thinking skills can enhance diagnostic reasoning. This article discusses how deliberate reflection can improve diagnosis and how deliberate reflection can be integrated into digital decision support tools.

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Diagnostic Safety

October 10, 2022

Selected PSNet materials for a general safety audience focusing on improvements in the diagnostic process and the strategies that support them to prevent diagnostic errors from harming patients.

Emergency medicine physicians' perspectives on diagnostic accuracy in neurology: a qualitative study.

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Liberman AL, Cheng NT, Friedman BW, et al. Diagnosis (Berl). 2022;9:225-235.

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.

Coming up for Err – Missed Diagnosis in a Patient with Recurrent Pneumothorax

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WebM&M Case August 25, 2021

A 31-year-old woman presented to the ED with worsening shortness of breath and was unexpectedly found to have a moderate-sized left pneumothorax, which was treated via a thoracostomy tube. After additional work-up and computed tomography (CT) imaging, she was told that she had some blebs and mild emphysema, but was discharged without any specific follow-up instructions except to see her primary care physician.

Streamlining care in crisis: rapid creation and implementation of a digital support tool for COVID-19.

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Stark N, Kerrissey M, Grade M, et al. West J Emerg Med. 2020;21:1095-1101.

This article describes the development and implementation of a digital tool to centralize and standardize COVID-19-related resources for use in the emergency department (ED). Clinician feedback suggests confirms that the tool has affected their management of COVID-19 patients. The tool was found to be easily adaptable to accommodate rapidly evolving guidance and enable organizational capacity for improvisation and resiliency.

Making Healthcare Safer III.

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Holmes A, Long A, Wyant B, et al. Rockville, MD: Agency for Healthcare Research and Quality; March 2020. AHRQ Publication No. 20-0029-EF.

This newly issued follow up to the seminal AHRQ Making Health Care Safer report (first published in 2001 and updated in 2013 critically examines the evidence supporting 47 separate patient safety practices chosen for the high-impact harms they address. It includes diagnostic errors, failure to rescue, sepsis, infections due to multi-drug resistant organisms, adverse drug events and nursing-sensitive conditions. The report discusses the evidence on cross-cutting safety practices, including safety culture, teamwork and team training, clinical decision support, patient and family engagement, cultural competency, staff education and training, and monitoring, audit and feedback. The report provides recommendations for clinicians and decision-makers on effective patient safety practices.

Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists.

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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.

Decision support tools, systems, and artificial intelligence in cardiac imaging.

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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.

Evaluating a mobile application for improving clinical laboratory test ordering and diagnosis.

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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.

Balancing innovation and safety when integrating digital tools into health care.

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Auerbach AD, Neinstein A, Khanna R. Ann Intern Med. 2018;168:733-734.

Digital tools have the potential to improve diagnosis, patient self-care, and patient–clinician communication. This commentary argues that digital tools that alter diagnosis or treatment require examination to ensure safety. The authors provide recommendations such as involving experts in evaluating the tools, engaging information technologists, and continuous local review and assessment to identify and address risks associated with use of such tools in practice.

Symptom–Disease Pair Analysis of Diagnostic Error (SPADE): a conceptual framework and methodological approach for unearthing misdiagnosis-related harms using big data.

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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.

Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer.

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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.

Sharing the process of diagnostic decision making.

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Brush JE, Brophy JM. JAMA Intern Med. 2017;177:1245-1246.

Improving diagnosis has been recently promoted as a core area in patient safety. This commentary discusses how physicians can utilize cognitive decision making and likelihood ratios to address uncertainty and augment diagnosis. The authors suggest that clinicians learn to assess their diagnostic practices and skills to improve their performance.

Identifying hospitalized patients at risk for harm: a comparison of nurse perceptions vs. electronic risk assessment tool scores.

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Stafos A, Stark S, Barbay K, et al. Am J Nurs. 2017;117:26-31.

This study compared nurses' identification of patients at risk for harm to an electronic predictive model and found that nurses more commonly identified psychological or social risks as relevant to harm. The nurses did not identify some patients whom the predictive model deemed high risk in cases where the risk had been incorporated into the plan of care. The authors suggest that nurse perceptions could inform more accurate predictive models, though neither approach was tested against an actual safety outcome.

Deep learning is a black box, but health care won't mind.

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Brouillette M.

Artificial intelligence can support diagnostic decision-making. This magazine article reports on the use of algorithms to identify dermatologic cancers and highlights progress toward achieving success with these tools.

A learning health care system using computer-aided diagnosis.

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Cahan A, Cimino JJ. J Med Internet Res. 2017;19:e54.

Although advanced computing can assist in diagnosis, these systems are not routinely utilized. This commentary suggests a framework to develop diagnostic support technologies that capture physician knowledge to enhance diagnostic safety. The authors encourage drawing from crowdsourced data to guide improvements at a system level to address future practice and educational needs.

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