Appl Clin Inform 2014; 05(01): 25-45
DOI: 10.4338/ACI-2013-08-RA-0057
Research Article
Schattauer GmbH

Analysis Of Electronic Medication Orders With Large Overdoses

Opportunities For Mitigating Dosing Errors
E.S. Kirkendall
1  Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
2  Department of Information Services, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
3  Division of Hospital Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
4  James M. Anderson Center for Health Systems Excellence, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
,
M. Kouril
1  Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
,
T. Minich
2  Department of Information Services, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
5  Division of Pharmacy, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
,
SA. Spooner
1  Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
2  Department of Information Services, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
3  Division of Hospital Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
› Author Affiliations
Further Information

Correspondence to:

Eric Kirkendall, MD, MBI, FAAP
Assistant Professor of Clinical Pediatrics
3333 Burnet Avenue, MLC-5035
Cincinnati, Ohio 45229

Publication History

received: 02 August 2013

accepted: 17 January 2013

Publication Date:
20 December 2017 (online)

 

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


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Conflict of Interest

The authors declare that they have no conflicts of interest in the research.


Correspondence to:

Eric Kirkendall, MD, MBI, FAAP
Assistant Professor of Clinical Pediatrics
3333 Burnet Avenue, MLC-5035
Cincinnati, Ohio 45229