Automated detection of medication administration errors in neonatal intensive care

OBJECTIVE To 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. METHODS We 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. RESULTS In 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. CONCLUSIONS Automated 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.

[1]  R S Evans,et al.  Computerized surveillance of adverse drug events in hospital patients* , 2005, Quality and Safety in Health Care.

[2]  Marlene R. Miller,et al.  NICU medication errors: identifying a risk profile for medication errors in the neonatal intensive care unit , 2010, Journal of Perinatology.

[3]  Christoph U. Lehmann,et al.  Computer based medication error reporting: insights and implications , 2006, Quality and Safety in Health Care.

[4]  Claudia R Campbell,et al.  A comparison of hospital adverse events identified by three widely used detection methods. , 2009, International journal for quality in health care : journal of the International Society for Quality in Health Care.

[5]  P. Barach,et al.  Reporting and preventing medical mishaps: lessons from non-medical near miss reporting systems , 2000, BMJ : British Medical Journal.

[6]  Louise Deléger,et al.  Evaluating the impact of pre-annotation on annotation speed and potential bias: natural language processing gold standard development for clinical named entity recognition in clinical trial announcements , 2013, J. Am. Medical Informatics Assoc..

[7]  Paul J. Ranauro,et al.  Electronic medical record: research tool for pancreatic cancer? , 2014, The Journal of surgical research.

[8]  Louise Deléger,et al.  A sequence labeling approach to link medications and their attributes in clinical notes and clinical trial announcements for information extraction , 2012, J. Am. Medical Informatics Assoc..

[9]  S. Tardieu,et al.  Iatrogenic events in admitted neonates: a prospective cohort study , 2008, The Lancet.

[10]  Philip V. Ogren,et al.  Knowtator: A Protégé plug-in for annotated corpus construction , 2006, NAACL.

[11]  George Hripcsak,et al.  Detecting adverse events for patient safety research: a review of current methodologies , 2003, J. Biomed. Informatics.

[12]  Antoinette Nelson,et al.  Developing and implementing a standardized process for global trigger tool application across a large health system. , 2013, Joint Commission journal on quality and patient safety.

[13]  A. Potts,et al.  Computerized physician order entry and medication errors in a pediatric critical care unit. , 2004, Pediatrics.

[14]  Jared Cash,et al.  Standard Drug Concentrations and Smart-Pump Technology Reduce Continuous-Medication-Infusion Errors in Pediatric Patients , 2005, Pediatrics.

[15]  Carol A. Keohane,et al.  Integrating incident data from five reporting systems to assess patient safety: making sense of the elephant. , 2010, Joint Commission journal on quality and patient safety.

[16]  P. Conway,et al.  Identifying causes of adverse events detected by an automated trigger tool through in-depth analysis , 2010, Quality and Safety in Health Care.

[17]  Keith Marsolo,et al.  Building Gold Standard Corpora for Medical Natural Language Processing Tasks , 2012, AMIA.

[18]  B. Franklin,et al.  The incidence and nature of prescribing and medication administration errors in paediatric inpatients , 2010, Archives of Disease in Childhood.

[19]  Michael S. Leonard.  Patient safety and quality improvement: medical errors and adverse events. , 2010, Pediatrics in review.

[20]  J. Gray,et al.  Medication errors in the neonatal intensive care unit: special patients, unique issues , 2004, Archives of Disease in Childhood - Fetal and Neonatal Edition.

[21]  Alireza Kazemi,et al.  The Effect of Computerized Physician Order Entry and Decision Support System on Medication Errors in the Neonatal Ward: Experiences from an Iranian Teaching Hospital , 2011, Journal of Medical Systems.

[22]  T. Vaughn,et al.  Reporting of medication errors by pediatric nurses. , 2004, Journal of pediatric nursing.

[23]  Monica M. Horvath,et al.  Reevaluating the Safety Profile of Pediatrics: A Comparison of Computerized Adverse Drug Event Surveillance and Voluntary Reporting in the Pediatric Environment , 2008, Pediatrics.

[24]  Mark Steedman,et al.  Example Selection for Bootstrapping Statistical Parsers , 2003, NAACL.

[25]  C. Longhurst,et al.  Decrease in Hospital-wide Mortality Rate After Implementation of a Commercially Sold Computerized Physician Order Entry System , 2010, Pediatrics.

[26]  L. F. Nazarian.  Pediatrics in Review : The Next Quarter Century , 2005 .

[27]  K. Shojania.  The elephant of patient safety: what you see depends on how you look. , 2010, Joint Commission journal on quality and patient safety.

[28]  W. King,et al.  The effect of computerized physician order entry on medication errors and adverse drug events in pediatric inpatients. , 2003, Pediatrics.

[29]  Bruce M Greenwald,et al.  Incidence and impact of adverse drug events in pediatric inpatients. , 2003, Archives of pediatrics & adolescent medicine.

[30]  G. Kadmon,et al.  Computerized Order Entry With Limited Decision Support to Prevent Prescription Errors in a PICU , 2009, Pediatrics.

[31]  V. Vaidya,et al.  Computerized orders with standardized concentrations decrease dispensing errors of continuous infusion medications for pediatrics. , 2010, The journal of pediatric pharmacology and therapeutics : JPPT : the official journal of PPAG.

[32]  Eric S. Kirkendall,et al.  Phenotyping for patient safety: algorithm development for electronic health record based automated adverse event and medical error detection in neonatal intensive care , 2014, Journal of the American Medical Informatics Association : JAMIA.

[33]  G Koren,et al.  Tenfold errors in administration of drug doses: a neglected iatrogenic disease in pediatrics. , 1986, Pediatrics.

[34]  Brian Jacobs,et al.  Electronic medical record, error detection, and error reduction: A pediatric critical care perspective , 2007, Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies.

[35]  K. Chappell,et al.  Potential tenfold drug overdoses on a neonatal unit , 2004, Archives of Disease in Childhood - Fetal and Neonatal Edition.

[36]  A. Wall,et al.  Book ReviewTo Err is Human: building a safer health system Kohn L T Corrigan J M Donaldson M S Washington DC USA: Institute of Medicine/National Academy Press ISBN 0 309 06837 1 $34.95 , 2000 .

[37]  Praveen Kumar,et al.  Medication use in the neonatal intensive care unit: current patterns and off-label use of parenteral medications. , 2008, The Journal of pediatrics.

[38]  Eric S. Kirkendall,et al.  Measuring Adverse Events and Levels of Harm in Pediatric Inpatients With the Global Trigger Tool , 2012, Pediatrics.

[39]  David W Bates,et al.  Comparison of methods for detecting medication errors in 36 hospitals and skilled-nursing facilities. , 2002, American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists.

[40]  I. Solti,et al.  Developing and evaluating an automated appendicitis risk stratification algorithm for pediatric patients in the emergency department , 2013, Journal of the American Medical Informatics Association : JAMIA.

[41]  D. Bates,et al.  Medication errors and adverse drug events in pediatric inpatients. , 2001, JAMA.

[42]  L. Kohn,et al.  To Err Is Human : Building a Safer Health System , 2007 .

[43]  Marlene R. Miller,et al.  Medication errors in paediatric care: a systematic review of epidemiology and an evaluation of evidence supporting reduction strategy recommendations , 2007, Quality and Safety in Health Care.