Diagnostic Error in Pediatrics: A Narrative Review

Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives

The dawn of Artificial intelligence (AI) in healthcare stands as a milestone in medical innovation. Different medical fields are heavily involved, and pediatric emergency medicine is no exception. We conducted a narrative review structured in two parts. The first part explores the theoretical principles of AI, providing all the necessary background to feel confident with these new state-of-the-art tools. The second part presents an informative analysis of AI models in pediatric emergencies. We examined PubMed and Cochrane Library from inception up to April 2024. Key applications include triage optimization, predictive models for traumatic brain injury assessment, and computerized sepsis prediction systems. In each of these domains, AI models outperformed standard methods. The main barriers to a widespread adoption include technological challenges, but also ethical issues, age-related differences in data interpretation, and the paucity of comprehensive datasets in the pediatric context. Future feasible research directions should address the validation of models through prospective datasets with more numerous sample sizes of patients. Furthermore, our analysis shows that it is essential to tailor AI algorithms to specific medical needs. This requires a close partnership between clinicians and developers. Building a shared knowledge platform is therefore a key step.

Diagnostic odyssey of Guillain-Barré syndrome in children

BACKGROUND AND OBJECTIVES

A gap exists between difficulty in diagnosis and importance of early recognition and intervention in pediatric Guillain-Barré syndrome (GBS). Therefore, this study aimed to establish a diagnostic odyssey plot that allows "at-a-glance" overview of the diagnostic odyssey of GBS in children, including overall diagnostic delay, physician-related and patient-related diagnostic delays, and length and frequency of diagnostic errors.

METHODS

In this single-center retrospective cohort study, standardized data were obtained from children with GBS from 2003 to 2020. Overall diagnostic delay (time between symptom onset and diagnosis), physician-related diagnostic delay (time between the first medical visit and diagnosis), and patient-related diagnostic delay (time between symptom onset and the first medical visit) were analyzed.

RESULTS

The study examined a total of 21 patients (11 men, median age 4.5 years). Overall, there were 40 misdiagnoses among 17 patients, while four were diagnosed correctly at the first visit. The overall diagnostic delay was 9 days [interquartile range (IQR), 6-17 days]. Physician-related diagnostic delay, but not patient-related diagnostic delay, was correlated with the overall diagnostic delay. Patients in the late-diagnosed group were more frequently misdiagnosed during their diagnostic odyssey than patients in the other groups. Risk factors associated with diagnostic delay included delayed onset of weakness and sensory deficits, absence of swallowing problems, and misdiagnosis as orthopedic disorders or viral infections.

DISCUSSION

A unique diagnostic odyssey exists in pedaitric GBS. Several clinical risk factors were associated with the diagnostic delay.

Comparative Perspectives on Diagnostic Error Discussions Between Inpatient and Outpatient Pediatric Providers

Diagnostic error remains understudied and underaddressed despite causing significant morbidity and mortality. One barrier to addressing this issue remains provider discomfort. Survey studies have shown significantly more discomfort among providers in discussing diagnostic error compared with other forms of error. Whether the comfort in discussing diagnostic error differs depending on practice setting has not been previously studied. The objective of this study was to assess differences in provider willingness to discuss diagnostic error in the inpatient versus outpatient setting. A multicenter survey was sent out to 3881 providers between May and June 2018. This survey was designed to assess comfort level of discussing diagnostic error and looking at barriers to discussing diagnostic error. Forty-three percent versus 22% of inpatient versus outpatient providers (P = 0.004) were comfortable discussing short-term diagnostic error publicly. Similarly, 76% versus 60% of inpatient versus outpatient providers (P = 0.010) were comfortable discussing short-term diagnostic error privately. A higher percentage of inpatient (64%) compared with outpatient providers (46%) (P = 0.043) were comfortable discussing long-term diagnostic error privately. Forty percent versus 24% of inpatient versus outpatient providers (P = 0.018) were comfortable discussing long-term error publicly. No difference in barriers cited depending on practice setting. Inpatient providers are more comfortable discussing diagnostic error than their outpatient counterparts. More study is needed to determine the etiology of this discrepancy and to develop strategies to increase outpatient provider comfort.

Opportunities for Diagnostic Improvement Among Pediatric Hospital Readmissions

OBJECTIVES

Diagnostic errors, termed "missed opportunities for improving diagnosis" (MOIDs), are known sources of harm in children but have not been well characterized in pediatric hospital medicine. Our objectives were to systematically identify and describe MOIDs among general pediatric patients who experienced hospital readmission, outline improvement opportunities, and explore factors associated with increased risk of MOID.

PATIENTS AND METHODS

Our retrospective cohort study included unplanned readmissions within 15 days of discharge from a freestanding children's hospital (October 2018-September 2020). Health records from index admissions and readmissions were independently reviewed and discussed by practicing inpatient physicians to identify MOIDs using an established instrument, SaferDx. MOIDs were evaluated using a diagnostic-specific tool to identify improvement opportunities within the diagnostic process.

RESULTS

MOIDs were identified in 22 (6.3%) of 348 readmissions. Opportunities for improvement included: delay in considering the correct diagnosis (n = 11, 50%) and failure to order needed test(s) (n = 10, 45%). Patients with MOIDs were older (median age: 3.8 [interquartile range 1.5-11.2] vs 1.0 [0.3-4.9] years) than patients without MOIDs but similar in sex, primary language, race, ethnicity, and insurance type. We did not identify conditions associated with higher risk of MOID. Lower respiratory tract infections accounted for 26% of admission diagnoses but only 1 (4.5%) case of MOID.

CONCLUSIONS

Standardized review of pediatric readmissions identified MOIDs and opportunities for improvement within the diagnostic process, particularly in clinician decision-making. We identified conditions with low incidence of MOID. Further work is needed to better understand pediatric populations at highest risk for MOID.

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

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≥ $\ge \phantom{\rule{}{0ex}}$ 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.

Diagnostic consistency between admission and discharge of pediatric cases in a tertiary teaching hospital in China

BACKGROUND

Patient-centered, high-quality health care relies on accurate and timely diagnosis. Diagnosis is a complex, error-prone process. Prevention of errors involves understanding the cause of errors. This study investigated diagnostic discordance between admission and discharge in pediatric cases.

METHODS

We retrospectively reviewed the electronic medical records of 5381 pediatric inpatients during 2017-2018 in a tertiary teaching hospital. We analyzed diagnostic consistency by comparing the first 4 digits of admission and discharge ICD-10 codes of the cases and classified them as concordant for "complete and partial match" or discordant for "no match".

RESULTS

Diagnostic discordance was observed in 49.2% with the highest prevalence in infections of the nervous and respiratory systems (Ps < 0.001). Multiple (multivariable) logistic regression analysis predicted a lower risk of diagnostic discordance with older children (aOR, 95%CI: 0.94, 0.93-0.96) and a higher risk with infectious diseases (aOR, 95%CI: 1.49, 1.33-1.66) and admission by resident and attending pediatricians (aOR, 95%CI: 1.41, 1.30-1.54). Discordant cases had a higher rate of antibiotic prescription (OR, 95%CI: 2.09, 1.87-2.33), a longer duration of antibiotic use (P = 0.02), a longer length of hospital stay (P < 0.001), and higher medical expenses (P < 0.001).

CONCLUSIONS

This study denotes a considerably high rate of discordance between admission and discharge diagnoses with an associated higher and longer prescription of antibiotics, a longer length of stay, and higher medical expenses among Chinese pediatric inpatient cases. Infectious diseases were identified as high-risk clinical conditions for discordance. Considering potential diagnostic and coding errors, departmental investigation of preventable diagnostic discordance is suggested for quality health care and preventing potential medicolegal consequences.

Developing a machine learning model to detect diagnostic uncertainty in clinical documentation

BACKGROUND AND OBJECTIVE

Diagnostic uncertainty, when unrecognized or poorly communicated, can result in diagnostic error. However, diagnostic uncertainty is challenging to study due to a lack of validated identification methods. This study aims to identify distinct linguistic patterns associated with diagnostic uncertainty in clinical documentation.

DESIGN, SETTING AND PARTICIPANTS

This case-control study compares the clinical documentation of hospitalized children who received a novel uncertain diagnosis (UD) diagnosis label during their admission to a set of matched controls. Linguistic analyses identified potential linguistic indicators (i.e., words or phrases) of diagnostic uncertainty that were then manually reviewed by a linguist and clinical experts to identify those most relevant to diagnostic uncertainty. A natural language processing program categorized medical terminology into semantic types (i.e., sign or symptom), from which we identified a subset of these semantic types that both categorized reliably and were relevant to diagnostic uncertainty. Finally, a competitive machine learning modeling strategy utilizing the linguistic indicators and semantic types compared different predictive models for identifying diagnostic uncertainty.

RESULTS

Our cohort included 242 UD-labeled patients and 932 matched controls with a combination of 3070 clinical notes. The best-performing model was a random forest, utilizing a combination of linguistic indicators and semantic types, yielding a sensitivity of 89.4% and a positive predictive value of 96.7%.

CONCLUSION

Expert labeling, natural language processing, and machine learning methods combined with human validation resulted in highly predictive models to detect diagnostic uncertainty in clinical documentation and represent a promising approach to detecting, studying, and ultimately mitigating diagnostic uncertainty in clinical practice.

Diagnosis Code and Health Care Utilization Patterns Associated With Diagnostic Uncertainty

BACKGROUND AND OBJECTIVES

Diagnostic uncertainty is challenging to identify and study in clinical practice. This study compares differences in diagnosis code and health care utilization between a unique cohort of hospitalized children with uncertain diagnoses (UD) and matched controls.

PATIENTS AND METHODS

This case-control study was conducted at Cincinnati Children's Hospital Medical Center. Cases were defined as patients admitted to the pediatric hospital medicine service and having UDs during their hospitalization. Control patients were matched on age strata, biological sex, and time of year. Outcomes included type of diagnosis codes used (ie, disease- or nondisease-based) and change in code from admission to discharge. Differences in diagnosis codes were evaluated using conditional logistic regression. Health care utilization outcomes included hospital length of stay (LOS), hospital transfer, consulting service utilization, rapid response team activations, escalation to intensive care, and 30-day health care reutilization. Differences in health care utilization were assessed using bivariate statistics.

RESULTS

Our final cohort included 240 UD cases and 911 matched controls. Compared with matched controls, UD cases were 8 times more likely to receive a nondisease-based diagnosis code (odds ratio [OR], 8.0; 95% confidence interval [CI], 5.7-11.2) and 2.5 times more likely to have a change in their primary International Classification of Disease, 10th revision, diagnosis code between admission and discharge (OR, 2.5; 95% CI, 1.9-3.4). UD cases had a longer average LOS and higher transfer rates to our main hospital campus, consulting service use, and 30-day readmission rates.

CONCLUSIONS

Hospitalized children with UDs have meaningfully different patterns of diagnosis code use and increased health care utilization compared with matched controls.