Literature DB >> 20584702

Comparison of methods for identifying patients at risk of medication-related harm.

Jasperien E van Doormaal1, Mirjam K Rommers, Jos G W Kosterink, Irene M Teepe-Twiss, Flora M Haaijer-Ruskamp, Peter G M Mol.   

Abstract

BACKGROUND: With the introduction of Computerised Physician Order Entry (CPOE) in routine hospital care, a great deal of effort has been put into refining Clinical Decision Support Systems (CDSS) to identify patients at risk of preventable medication-related harm.
OBJECTIVES: This study compared a CPOE with basic CDSS and 16 clinical rules with a manual pharmacist medication review to detect overdose and drug-drug interactions that actually required a change in medication.
METHODS: The study involved the review of 313 patients admitted over 5 months at an internal medicine ward where a change in medication as a result of dosing of therapeutic errors was detected by a manual medication review by a trained pharmacist. Subsequently, all these patients' medication orders (MOs) were entered into the authors' CPOE with basic CDSS. Medication orders with a safety alert indicating overdose and drug-drug interactions generated by the authors' CPOE with basic CDSS were compared with the same type of medication errors identified through manual review. The positive predictive value (PPV), sensitivity and specificity compared with manual review were determined. Second, a set of 16 clinical rules was applied to the patient and prescribing data. The overlap between the clinical rules and manual review was determined by comparing patients triggered by the clinical rule with patients with a corresponding error in the manual medication review.
RESULTS: Manual medication review identified 57 medication errors involving overdose and 143 therapeutic errors of which 46 were drug-drug interactions. The CPOE with basic CDDS generated 297 safety alerts involving overdose (PPV 0.06, sensitivity 0.32, specificity 0.92) and 365 safety alerts involving drug-drug interactions (PPV 0.12, sensitivity 0.96, specificity 0.91). The clinical rules generated 313 safety alerts identifying 39% of all the overdoses and therapeutic errors found in the manual review at which they were targeted. In 23% of the alerts generated by a clinical rule, the patients actually required a change of medication as indicated by the manual review. When CPOE with basic CDSS and the rules were combined, 66% of the overdoses and therapeutic errors were identified.
CONCLUSIONS: The authors' CPOE with basic CDSS and the clinical rules are useful early strategies for preventing medication-related harm. They could be a first step towards more advanced decision support. These computerised systems will be even more useful in daily practice, once they are further fine-tuned to decrease the number of alerts that need no clinical action.

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Year:  2010        PMID: 20584702     DOI: 10.1136/qshc.2009.033324

Source DB:  PubMed          Journal:  Qual Saf Health Care        ISSN: 1475-3898


  8 in total

1.  Identification of drug-related problems by a clinical pharmacist in addition to computerized alerts.

Authors:  Rianne J Zaal; Mark M P M Jansen; Marjolijn Duisenberg-van Essenberg; Cees C Tijssen; Jan A Roukema; Patricia M L A van den Bemt
Journal:  Int J Clin Pharm       Date:  2013-05-29

2.  A computerized adverse drug event alerting system using clinical rules: a retrospective and prospective comparison with conventional medication surveillance in the Netherlands.

Authors:  Mirjam K Rommers; Irene M Teepe-Twiss; Henk-Jan Guchelaar
Journal:  Drug Saf       Date:  2011-03-01       Impact factor: 5.606

3.  Comparison of a basic and an advanced pharmacotherapy-related clinical decision support system in a hospital care setting in the Netherlands.

Authors:  Willemijn L Eppenga; Hieronymus J Derijks; Jean M H Conemans; Walter A J J Hermens; Michel Wensing; Peter A G M De Smet
Journal:  J Am Med Inform Assoc       Date:  2011-09-02       Impact factor: 4.497

4.  A multifaceted intervention to reduce drug-related complications in surgical patients.

Authors:  Jacqueline M Bos; Patricia M L A van den Bemt; Wietske Kievit; Johan L W Pot; J Elsbeth Nagtegaal; André Wieringa; Monique M L van der Westerlaken; Gert Jan van der Wilt; Peter A G M de Smet; Cornelis Kramers
Journal:  Br J Clin Pharmacol       Date:  2016-11-10       Impact factor: 4.335

Review 5.  Drug-drug interactions and their harmful effects in hospitalised patients: a systematic review and meta-analysis.

Authors:  Wu Yi Zheng; L C Richardson; L Li; R O Day; J I Westbrook; M T Baysari
Journal:  Eur J Clin Pharmacol       Date:  2017-10-23       Impact factor: 2.953

Review 6.  Clinical decision support for drug related events: Moving towards better prevention.

Authors:  Sandra L Kane-Gill; Archita Achanta; John A Kellum; Steven M Handler
Journal:  World J Crit Care Med       Date:  2016-11-04

Review 7.  An Overview of the Current State and Perspectives of Pharmacy Robot and Medication Dispensing Technology.

Authors:  Asmaa R Alahmari; Khawlah K Alrabghi; Ibrahim M Dighriri
Journal:  Cureus       Date:  2022-08-31

8.  On the alert: future priorities for alerts in clinical decision support for computerized physician order entry identified from a European workshop.

Authors:  Jamie J Coleman; Heleen van der Sijs; Walter E Haefeli; Sarah P Slight; Sarah E McDowell; Hanna M Seidling; Birgit Eiermann; Jos Aarts; Elske Ammenwerth; Ann Slee; Robin E Ferner; Robin E Ferner; Ann Slee
Journal:  BMC Med Inform Decis Mak       Date:  2013-10-01       Impact factor: 2.796

  8 in total

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