Diagnostic Errors in Pediatric Critical Care: A Systematic Review*

Cifra, Christina L.; Custer, Jason W.; Singh, Hardeep; Fackler, James C.

doi:10.1097/pcc.0000000000002735

Cited by 17 publications

(19 citation statements)

References 41 publications

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“…A systematic review in Pediatric Critical Care found that up to 67% of diagnostic errors in the pediatric critical care setting are related to system factors, while up to 30% included cognitive factors. Notably, 40% of the diagnostic errors combined cognitive and system factors [61].…”

Section: Common Errorsmentioning

confidence: 99%

Patient Safety in the Critical Care Setting: Common Risks and Review of Evidence-Based Mitigation Strategies

Arteaga¹,

Bacu²,

Franco³

2023

Contemporary Topics in Patient Safety - Volume 2

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The Intensive Care Unit (ICU) has evolved in the last 50 years. This evolution’s main drivers include equipment and software improvements, the patient safety movement, and a better pathophysiological understanding of critical illness. There is mounting pressure from accreditation agencies, governmental regulation, financial challenges, operational dynamics, staffing changes, and increased acuity affecting-ICU care delivery and impacting patient safety. There are higher than ever expectations to improve clinical outcomes after an intensive care stay, to enhance patient safety, to increase family involvement in decision making, and merge the multidisciplinary medical experience into an effective teamwork. Leadership focus is directed towards increasing diversity and inclusion in the workforce while enhancing psychological safety. This review addresses the common risks for patient safety in the intensive care setting and describes the changes in mindset and application of evidence-based mitigation strategies.

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Section: Common Errorsmentioning

confidence: 99%

Patient Safety in the Critical Care Setting: Common Risks and Review of Evidence-Based Mitigation Strategies

Arteaga¹,

Bacu²,

Franco³

2023

Contemporary Topics in Patient Safety - Volume 2

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“…MOID is thought to be frequent and associated with negative outcomes in pediatric and adult hospital settings. 10,[13][14][15] However, identifying MOID can be challenging and resourceintensive, which limits the potential for using MOID to inform systems learning and improvement.…”

Section: Introductionmentioning

confidence: 99%

“…Diagnostic errors, commonly referred to as “missed opportunities to improve diagnosis” (MOID), are defined as missed opportunities to make the correct or timely diagnosis. MOID is thought to be frequent and associated with negative outcomes in pediatric and adult hospital settings 10,13–15 . However, identifying MOID can be challenging and resource‐intensive, which limits the potential for using MOID to inform systems learning and improvement.…”

Section: Introductionmentioning

confidence: 99%

Opportunities to improve diagnosis in emergency transfers to the pediatric intensive care unit

Mehta

Congdon

Phillips

et al. 2023

Journal of Hospital Medicine

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Background: Late recognition of in-hospital deterioration is a source of preventable harm. Emergency transfers (ET), when hospitalized patients require intensive care unit (ICU) interventions within 1 h of ICU transfer, are a proximal measure of late recognition associated with increased mortality and length of stay (LOS).Objective: To apply diagnostic process improvement frameworks to identify missed opportunities for improvement in diagnosis (MOID) in ETs and evaluate their association with outcomes.Design, Settings, and Participants: A single-center retrospective cohort study of ETs, January 2015 to June 2019. ET criteria include intubation, vasopressor initiation, or ≥60 mL/kg fluid resuscitation 1 h before to 1 h after ICU transfer. The primary exposure was the presence of MOID, determined using SaferDx. Cases were screened by an ICU and non-ICU physician. Final determinations were made by an interdisciplinary group. Diagnostic process improvement opportunities were identified.Main Outcome and Measures: Primary outcomes were in-hospital mortality and posttransfer LOS, analyzed by multivariable regression adjusting for age, service, deterioration category, and pretransfer LOS.Results: MOID was identified in 37 of 129 ETs (29%, 95% confidence interval [CI] 21%-37%). Cases with MOID differed in originating service, but not demographically. Recognizing the urgency of an identified condition was the most common diagnostic process opportunity. ET cases with MOID had higher odds of mortality (odds ratio 5.5; 95% CI 1.5−20.6; p = .01) and longer posttransfer LOS (rate ratio 1.7; 95% CI 1.1-2.6; p = .02). Conclusion: MOID are common in ETs and are associated with increased mortality risk and posttransfer LOS. Diagnostic improvement strategies should be leveraged to support earlier recognition of clinical deterioration.

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“…Up to 70% of all medical decisions are based on laboratory tests [5]. Furthermore, diagnostic errors contribute to about 10% of patients' death and close to 17% of hospital complications [6]. To address these challenges, many researchers and organizations are working on introducing machine learning to diagnostic applications to improve the speed and accuracy of diagnosis which will produce results that are similar to previous applications of diagnoses as well as improve the reliability of the process [7].…”

Section: Introductionmentioning

confidence: 99%

A comparative analysis of haemoglobin variants using machine learning algorithms

Okandeji¹,

Odeyinka²,

Sogbesan³

et al. 2022

Nig. J. Tech.

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In medical sciences, to ascertain the origin of a sickness, professionals utilize their expertise and knowledge to analyze a person's symptoms and indications. These symptoms (indicators) are threshold values that health specialists use to determine the cause of the illness by comparing a specific proportion of measurements to where a healthy population would fall. Consequently, diagnostic mistakes occur as a result of inaccuracy and imprecision. This study utilizes machine learning to categorize haemoglobin variations. Specifically, the data set used in this study includes 752 complete blood count laboratory analyses of adult patients aged eighteen and above obtained from Lagos State University Teaching Hospital (LASUTH). Multiple machine learning methods were utilized for classification from which five of the methods employed were examined and assessed. Comparative analysis was done using the five algorithms (K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Naive Bayes (NB)). Contrary to work done by previous researchers, it was observed that the SVM model showed the best classification accuracy of 94.7%, with an F1-score of 94.5%, precision of 94.8%, recall of 94.7%, specificity of 97.3%, and area under curve (AUC) of 99.0%. Among the other models considered, the RF model gave the least accuracy result of 87.4%. The study shows that the support vector machine algorithm outperforms the other classifiers in terms of accuracy when predicting haemoglobin variants given the haematological parameters.

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Diagnostic Errors in Pediatric Critical Care: A Systematic Review*

Cited by 17 publications

References 41 publications

Patient Safety in the Critical Care Setting: Common Risks and Review of Evidence-Based Mitigation Strategies

Patient Safety in the Critical Care Setting: Common Risks and Review of Evidence-Based Mitigation Strategies

Opportunities to improve diagnosis in emergency transfers to the pediatric intensive care unit

A comparative analysis of haemoglobin variants using machine learning algorithms

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