Jamie J Coleman1, James Hodson, Robin E Ferner. 1. University Hospital Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Birmingham, UK. j.j.coleman@bham.ac.uk
Abstract
INTRODUCTION: : Electronic decision support can reduce medication errors, and dose-range checking is one element of that support. OBJECTIVE: : The aim of this study was to design an approach to setting upper dose warning limits in electronic prescribing systems where there are historical data on dosing. METHOD: : We used historical data on 56 drug-form combinations for which over 100 prescriptions had been issued between 1 June 2009 and 31 May 2010 in a bespoke electronic prescribing system at University Hospital Birmingham, UK. First, two experts derived dose limits for each drug-form combination, then the drugs were randomly divided into a training set and a test set. A variation of the 'Nearest Rank' approach to estimate statistical limits was used to derive the percentile with the optimal sensitivity and specificity. RESULTS: : For the 28 drug-form combinations in the test set, the 86th percentile of dose gave a mean sensitivity of 95.3% and a mean specificity of 97.9% for warning limits, representing the highest reasonable dose; the 96th percentile gave a mean sensitivity of 90.2% and mean specificity of 99.5% for disallow limits, beyond which no dose should be prescribed. CONCLUSIONS: : Dosing decision support within electronic prescribing systems can be derived by statistical analysis of historical prescription data. We advocate a combined theoretical and statistical derivation of dose checking rules in order to ensure that prescribers are alerted appropriately to potentially toxic doses.
INTRODUCTION: : Electronic decision support can reduce medication errors, and dose-range checking is one element of that support. OBJECTIVE: : The aim of this study was to design an approach to setting upper dose warning limits in electronic prescribing systems where there are historical data on dosing. METHOD: : We used historical data on 56 drug-form combinations for which over 100 prescriptions had been issued between 1 June 2009 and 31 May 2010 in a bespoke electronic prescribing system at University Hospital Birmingham, UK. First, two experts derived dose limits for each drug-form combination, then the drugs were randomly divided into a training set and a test set. A variation of the 'Nearest Rank' approach to estimate statistical limits was used to derive the percentile with the optimal sensitivity and specificity. RESULTS: : For the 28 drug-form combinations in the test set, the 86th percentile of dose gave a mean sensitivity of 95.3% and a mean specificity of 97.9% for warning limits, representing the highest reasonable dose; the 96th percentile gave a mean sensitivity of 90.2% and mean specificity of 99.5% for disallow limits, beyond which no dose should be prescribed. CONCLUSIONS: : Dosing decision support within electronic prescribing systems can be derived by statistical analysis of historical prescription data. We advocate a combined theoretical and statistical derivation of dose checking rules in order to ensure that prescribers are alerted appropriately to potentially toxic doses.
Authors: Jamie J Coleman; Karla Hemming; Peter G Nightingale; Ian R Clark; Mary Dixon-Woods; Robin E Ferner; Richard J Lilford Journal: J R Soc Med Date: 2011-05 Impact factor: 5.344
Authors: Allie D Woods; David P Mulherin; Allen J Flynn; James G Stevenson; Christopher R Zimmerman; Bruce W Chaffee Journal: J Am Med Inform Assoc Date: 2013-11-19 Impact factor: 4.497
Authors: Adrian Wong; Christine Rehr; Diane L Seger; Mary G Amato; Patrick E Beeler; Sarah P Slight; Adam Wright; David W Bates Journal: Drug Saf Date: 2019-04 Impact factor: 5.606