Analysis Of Electronic Medication Orders With Large Overdoses

Summary

Background: Users of electronic health record (EHR) systems frequently prescribe doses outside recommended dose ranges, and tend to ignore the alerts that result. Since some of these dosing errors are the result of system design flaws, analysis of large overdoses can lead to the discovery of needed system changes.

Objectives: To develop database techniques for detecting and extracting large overdose orders from our EHR. To identify and characterize users’ responses to these large overdoses. To identify possible causes of large-overdose errors and to mitigate them.

Methods: We constructed a data mart of medication-order and dosing-alert data from a quaternary pediatric hospital from June 2011 to May 2013. The data mart was used along with a test version of the EHR to explain how orders were processed and alerts were generated for large (>500%) and extreme (>10,000%) overdoses. User response was characterized by the dosing alert salience rate, which expresses the proportion of time users take corrective action.

Results: We constructed an advanced analytic framework based on workflow analysis and order simulation, and evaluated all 5,402,504 medication orders placed within the 2 year timeframe as well as 2,232,492 dose alerts associated with some of the orders. 8% of orders generated a visible alert, with ¼ of these related to overdosing. Alerts presented to trainees had higher salience rates than those presented to senior colleagues. Salience rates were low, varying between 4–10%, and were lower with larger overdoses. Extreme overdoses fell into eight causal categories, each with a system design mitigation.

Conclusions: Novel analytic systems are required to accurately understand prescriber behavior and interactions with medication-dosing CDS. We described a novel analytic system that can detect apparent large overdoses ( of these large overdoses can be mitigated by system changes. EHR design should prospectively mitigate these errors.

Citation: Kirkendall ES, Kouril M, Minich T, Spooner SA. Analysis of electronic medication orders with large overdoses: Opportunities for mitigating dosing errors. Appl Clin Inf 2014; 5: 25–45 http://dx.doi.org/10.4338/ACI-2013-08-RA-0057

Keywords

Electronic health record - electronic medical record - medical order entry system - CPOE - clinical decision support systems

• References

• 1 Caldwell NA, Power B. The pros and cons of electronic prescribing for children. Arch Dis Child 2012; 97 (02) 124-128.
• 2 Wong IC. et al. Incidence and nature of dosing errors in paediatric medications: a systematic review. Drug Saf 2004; 27 (09) 661-670.
• 3 McPhillips H. et al. Methodological challenges in describing medication dosing errors in children, in advances in patient safety: from research to implementation. (Volume 2: Concepts and Methodology), K. Henriksen, et al., Editors. 2005 Rockville (MD).
• 4 Conroy S. et al. Interventions to reduce dosing errors in children: a systematic review of the literature. Drug Saf 2007; 30 (12) 1111-1125.
• 5 Ginzburg R. et al. Effect of a weight-based prescribing method within an electronic health record on prescribing errors. Am J Health Syst Pharm 2009; 66 (22) 2037-2041.
• 6 Jani YH, Barber N, and Wong IC. Paediatric dosing errors before and after electronic prescribing. Qual Saf Health Care 2010; 19 (04) 337-340.
• 7 McPhillips HA. et al. Potential medication dosing errors in outpatient pediatrics. J Pediatr 2005; 147 (06) 761-767.
• 8 Maat B. et al. Clinical pharmacy interventions in paediatric electronic prescriptions. Arch Dis Child; 2013; 98 (03) 222-227.
• 9 Isaac TJ. et al. Overrides of medication alerts in ambulatory care. Arch Intern Med 2009; 169 (03) 305-311.
• 10 van der Sijs H. et al. Turning off frequently overridden drug alerts: limited opportunities for doing it safely. J Am Med Inform Assoc 2008; 15 (04) 439-448.
• 11 Lin CP. et al. Evaluating clinical decision support systems: Monitoring CPOE order check override rates in the Department of Veterans Affairs’ Computerized Patient Record System. J Am Med Inform Assoc 2008; 15 (05) 620-626.
• 12 del Beccaro MA. et al. Decision support alerts for medication ordering in a computerized provider order entry (CPOE) system. Applied Clinical Informatics 2010; 1 (03) 346-362.
• 13 Kirkendall ES, Spooner SA, Logan JR. Evaluating the accuracy of electronic pediatric drug dosing rules. J Am Med Inform Assoc. 2013 Jun 28. [Epub ahead of print]
• 14 Barrett JS. et al. Prescribing habits and caregiver satisfaction with resources for dosing children: rationale for more informative dosing guidance. BMC Pediatr 2011; 11: 25.
• 15 Doherty C, Mc Donnell C. Tenfold medication errors: 5 years’ experience at a university-affiliated pediatric hospital. Pediatrics 2012; 129 (05) 916-924.
• 16 Koren G, Barzilay Z, Greenwald M. Tenfold errors in administration of drug doses: a neglected iatrogenic disease in pediatrics. Pediatrics 1986; 77 (06) 848-849.
• 17 Monagle P, Studdert DM, Newall F. Infant deaths due to heparin overdose: time for a concerted action on prevention. J Paediatr Child Health 2012; 48 (05) 380-381.
• 18 Gerstle RS, Lehmann CU. Electronic prescribing systems in pediatrics: the rationale and functionality requirements. Pediatrics 2007; 119 (06) e1413-e1422.
• 19 Kim G, Lehmann C. Pediatric aspects of inpatient health information technology systems. Pediatrics 2008; 122 (06) e1287-e1296.
• 20 Spooner SA. Special requirements of electronic health record systems in pediatrics. Pediatrics 2007; 119 (03) 631-763.
• 21 ANSI.. HL7 EHR Child Health Functional Profile, Release 1. 2008 12/10/2008 [cited 2013 8/1/2013]; ANSI/HL7 EHR CHFP, R1–2008:[Available from: http://www.hl7.org/implement/standards/product_brief.cfm?product_id=15.