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Paeds Casesinvestigations-procedures-and-technology

Paeds Cases · investigations-procedures-and-technology

Artificial intelligence and clinical decision support in paediatrics — OSCE

OSCE clinical-reasoning and communication station in which the candidate works through three AI and decision-support scenarios with the ward team: a deterioration score that reads low in a sick child, a drug-interaction alert burden driving an override culture, and a deep-learning retinal image flag in the nursery — and reaches a safe, accountable plan for each.

osce clinical reasoning and shared decision station4 min readVerification in progress

Target exams

MRCPCH ClinicalRACP DCERACP General Paediatrics
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Study tools

Target exams

MRCPCH ClinicalRACP DCERACP General Paediatrics
Prompt
You have 9 minutes with the night-shift ward team. A deterioration score reads low on a six-year-old who looks unwell; the team overrides more than nine in ten of their drug-interaction alerts; and a deep-learning tool has flagged a retinal image on a premature baby with no ophthalmologist on site. Work through each scenario, state the principles, and reach a safe accountable plan.

Station brief (candidate)

  • Manage the child whose deterioration score reads low but who looks unwell: state that the bedside assessment overrides the score, escalate to the higher acuity, and treat the child, because automation bias has caused missed deterioration. [2]
  • Frame the drug-interaction alert override culture as alert fatigue and a patient-safety failure, and outline the stewardship response: measure the override rate, retire or convert the lowest-value alerts to passive display, and tune the thresholds with the prescribers. [7] [8] [9]
  • Manage the deep-learning retinal image flag as a prompt to escalate rather than a diagnosis, arrange the formal ophthalmology review by telehealth where the ophthalmologist is off site, and hold the clinician over-read and accountability principle. [5] [6]
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References7ShowHide
  1. [2]Mayampurath A Development and External Validation of a Machine Learning Model for Prediction of Potential Transfer to the PICU Pediatr Crit Care Med, 2022.PMID 35446816
  2. [5]Taylor S Monitoring Disease Progression With a Quantitative Severity Scale for Retinopathy of Prematurity Using Deep Learning JAMA Ophthalmol, 2019.PMID 31268518
  3. [6]Young BK Efficacy of Smartphone-Based Telescreening for Retinopathy of Prematurity With and Without Artificial Intelligence in India JAMA Ophthalmol, 2023.PMID 37166816
  4. [7]Chaparro JD Clinical Decision Support Stewardship: Best Practices and Techniques to Monitor and Improve Interruptive Alerts Appl Clin Inform, 2022.PMID 35613913
  5. [8]Fallon A Addressing Alert Fatigue by Replacing a Burdensome Interruptive Alert with Passive Clinical Decision Support Appl Clin Inform, 2024.PMID 38086417
  6. [9]Simpao AF Optimization of drug-drug interaction alert rules in a pediatric hospital's electronic health record system using a visual analytics dashboard J Am Med Inform Assoc, 2015.PMID 25318641
  7. [10]Liu X Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension Nature Medicine, 2020.PMID 32908283
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