11 research outputs found
Automated detection of medication administration errors in neonatal intensive care
AbstractObjectiveTo improve neonatal patient safety through automated detection of medication administration errors (MAEs) in high alert medications including narcotics, vasoactive medication, intravenous fluids, parenteral nutrition, and insulin using the electronic health record (EHR); to evaluate rates of MAEs in neonatal care; and to compare the performance of computerized algorithms to traditional incident reporting for error detection.MethodsWe developed novel computerized algorithms to identify MAEs within the EHR of all neonatal patients treated in a level four neonatal intensive care unit (NICU) in 2011 and 2012. We evaluated the rates and types of MAEs identified by the automated algorithms and compared their performance to incident reporting. Performance was evaluated by physician chart review.ResultsIn the combined 2011 and 2012 NICU data sets, the automated algorithms identified MAEs at the following rates: fentanyl, 0.4% (4 errors/1005 fentanyl administration records); morphine, 0.3% (11/4009); dobutamine, 0 (0/10); and milrinone, 0.3% (5/1925). We found higher MAE rates for other vasoactive medications including: dopamine, 11.6% (5/43); epinephrine, 10.0% (289/2890); and vasopressin, 12.8% (54/421). Fluid administration error rates were similar: intravenous fluids, 3.2% (273/8567); parenteral nutrition, 3.2% (649/20124); and lipid administration, 1.3% (203/15227). We also found 13 insulin administration errors with a resulting rate of 2.9% (13/456). MAE rates were higher for medications that were adjusted frequently and fluids administered concurrently. The algorithms identified many previously unidentified errors, demonstrating significantly better sensitivity (82% vs. 5%) and precision (70% vs. 50%) than incident reporting for error recognition.ConclusionsAutomated detection of medication administration errors through the EHR is feasible and performs better than currently used incident reporting systems. Automated algorithms may be useful for real-time error identification and mitigation
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Electronic Health Record Based Algorithm to Identify Patients with Autism Spectrum Disorder
Objective: Cohort selection is challenging for large-scale electronic health record (EHR) analyses, as International Classification of Diseases 9th edition (ICD-9) diagnostic codes are notoriously unreliable disease predictors. Our objective was to develop, evaluate, and validate an automated algorithm for determining an Autism Spectrum Disorder (ASD) patient cohort from EHR. We demonstrate its utility via the largest investigation to date of the co-occurrence patterns of medical comorbidities in ASD. Methods: We extracted ICD-9 codes and concepts derived from the clinical notes. A gold standard patient set was labeled by clinicians at Boston Children’s Hospital (BCH) (N = 150) and Cincinnati Children’s Hospital and Medical Center (CCHMC) (N = 152). Two algorithms were created: (1) rule-based implementing the ASD criteria from Diagnostic and Statistical Manual of Mental Diseases 4th edition, (2) predictive classifier. The positive predictive values (PPV) achieved by these algorithms were compared to an ICD-9 code baseline. We clustered the patients based on grouped ICD-9 codes and evaluated subgroups. Results: The rule-based algorithm produced the best PPV: (a) BCH: 0.885 vs. 0.273 (baseline); (b) CCHMC: 0.840 vs. 0.645 (baseline); (c) combined: 0.864 vs. 0.460 (baseline). A validation at Children’s Hospital of Philadelphia yielded 0.848 (PPV). Clustering analyses of comorbidities on the three-site large cohort (N = 20,658 ASD patients) identified psychiatric, developmental, and seizure disorder clusters. Conclusions: In a large cross-institutional cohort, co-occurrence patterns of comorbidities in ASDs provide further hypothetical evidence for distinct courses in ASD. The proposed automated algorithms for cohort selection open avenues for other large-scale EHR studies and individualized treatment of ASD
ASD Rule-based Algorithm.
<p>ASD–Autism Spectrum Disorder; EHR–Electronic Health Records; ICD-9 –International Classification of Diseases 9<sup>th</sup> edition; DSM IV–Diagnostic and Statistical Manual of Mental Diseases 4<sup>th</sup> edition; PDD-NOS–pervasive developmental disorder not otherwise specified; sections 3a., 3b., 3c. refer to DSM IV ASD classification for Autism, Asperger’s and PDD-NOS, respectively</p
ASD Algorithm Project Overview.
<p>ASD–Autism Spectrum Disorder; ICD-9 –International Classification of Diseases 9<sup>th</sup> edition; DSM IV–Diagnostic and Statistical Manual of Mental Diseases 4<sup>th</sup> edition; ML—machine learning</p
Baseline Results (ICD-9 codes) on Test Set.
<p>Baseline Results (ICD-9 codes) on Test Set.</p
Comparison of relative prevalence of primary co-morbidity categories for clusters of NLP rule-based patients for BCH, and CCHMC and VUMC.
<p>BCH–Boston Children’s Hospital; CCHMC–Cincinnati Children’s Hospital and Medical Center; VUMC–Vanderbilt University Medical Center.</p
Dimensionality reduction using the t-SNE algorithm on PheWAS codes.
<p>Colors label clusters from the k-means algorithm. The clusters are labeled according to the comorbidity category with the highest relative prevalence for that cluster—duplicate labels appear when there is more than one cluster dominated by the same category. (t-distributed Stochastic Neighbor Embedding—t-SNE, Phenotype Wide Association Study–PheWAS, BCH–Boston Children’s Hospital; CCHMC–Cincinnati’s Children’s Hospital and Medical Center; VUMC–Vanderbilt University Medical Center, Deve.–Developmental Disorders, Seiz.–Seizure Disorders, Psych.—Psychological Disorders).</p