Evaluation of clinical rules in a standalone pharmacy based clinical decision support system for hospitalized and nursing home patients

OBJECTIVES To improve the current standalone pharmacy clinical decision support system (CDSS) by identifying and quantifying the benefits and limitations of the system. METHODS Alerts and handling of the executed clinical rules were extracted from the CDSS from the period September 2011 to December 2011. The number of executed clinical rule alerts, number of actions on alerts, and the reason why alerts were classified as not relevant were analyzed. The alerts where considered clinically relevant when the pharmacist needed to contact the physician. RESULTS The 4065 alerts have been separated into: 1137 (28.0%) new alerts, 2797 (68.8%) repeat alerts and 131 (3.2%) double alerts. When the alerts were analyzed, only 3.6% were considered clinically relevant. Reasons why alerts were considered as not to be relevant were: (a) the dosage was correct or already adjusted, (b) the drug was (temporarily) stopped and (c) the monitored laboratory value or drug dosage had already reverted to be within the reference limits. The reasons for no action were linked to three categorical limitations of the used system: 'algorithm alert criteria', 'CDSS optimization', and 'data delivery'. CONCLUSION This study highlighted a number of ways in which the CDSS could be improved. These different aspects have been identified as important for developing an efficient CDSS.

[1]  Nicolette de Keizer,et al.  The impact of computerized physician medication order entry in hospitalized patients - A systematic review , 2008, Int. J. Medical Informatics.

[2]  C. Safran,et al.  Effect of computer-based alerts on the treatment and outcomes of hospitalized patients. , 1994, Archives of internal medicine.

[3]  Shobha Phansalkar,et al.  Design of decision support interventions for medication prescribing , 2013, Int. J. Medical Informatics.

[4]  Monique W. M. Jaspers,et al.  Clinicians satisfaction with CPOE ease of use and effect on clinicians' workflow, efficiency and medication safety , 2011, Int. J. Medical Informatics.

[5]  Gregory L. Alexander,et al.  Effects of a computerized decision support system on pressure ulcers and malnutrition in nursing homes for the elderly , 2011, Int. J. Medical Informatics.

[6]  Monique W. M. Jaspers,et al.  Effects of clinical decision-support systems on practitioner performance and patient outcomes: a synthesis of high-quality systematic review findings , 2011, J. Am. Medical Informatics Assoc..

[7]  R Brian Haynes,et al.  Use of clinical decision support systems for kidney-related drug prescribing: a systematic review. , 2011, American journal of kidney diseases : the official journal of the National Kidney Foundation.

[8]  M. Kuiper,et al.  Development and validation of PRE-DELIRIC (PREdiction of DELIRium in ICu patients) delirium prediction model for intensive care patients: observational multicentre study , 2012, BMJ : British Medical Journal.

[9]  Marc Berg,et al.  Overriding of drug safety alerts in computerized physician order entry. , 2006, Journal of the American Medical Informatics Association : JAMIA.

[10]  N. Barber,et al.  What is a prescribing error? , 2000, Quality in health care : QHC.

[11]  Adam Wright,et al.  Recommended practices for computerized clinical decision support and knowledge management in community settings: a qualitative study , 2012, BMC Medical Informatics and Decision Making.

[12]  David Newby,et al.  Do computerised clinical decision support systems for prescribing change practice? A systematic review of the literature (1990-2007) , 2009, BMC health services research.

[13]  E. Roughead,et al.  Drug‐related hospital admissions: a review of Australian studies published 1988‐1996 , 1998, The Medical journal of Australia.

[14]  ELSKE AMMENWERTH,et al.  Review Paper: The Effect of Electronic Prescribing on Medication Errors and Adverse Drug Events: A Systematic Review , 2008, J. Am. Medical Informatics Assoc..

[15]  A. Egberts,et al.  Frequency of and Risk Factors for Preventable Medication-Related Hospital Admissions in the Netherlands , 2009 .

[16]  M. D. Del Beccaro,et al.  Decision Support Alerts for Medication Ordering in a Computerized Provider Order Entry (CPOE) System , 2010, Applied Clinical Informatics.

[17]  Henk-Jan Guchelaar,et al.  A Computerized Adverse Drug Event Alerting System Using Clinical Rules , 2011, Drug safety.

[18]  J. Brouwers,et al.  Clinical Relevance of Drug-Drug Interactions , 2005, Drug safety.

[19]  Shyam Visweswaran,et al.  Computerized detection of adverse drug reactions in the medical intensive care unit , 2011, Int. J. Medical Informatics.

[20]  J. Gisbert,et al.  Thiopurine-Induced Myelotoxicity in Patients With Inflammatory Bowel Disease: A Review , 2008, The American Journal of Gastroenterology.

[21]  M. Brier,et al.  Model predictive control of erythropoietin administration in the anemia of ESRD. , 2008, American journal of kidney diseases : the official journal of the National Kidney Foundation.

[22]  Willemijn L. Eppenga,et al.  Comparison of a basic and an advanced pharmacotherapy-related clinical decision support system in a hospital care setting in the Netherlands , 2012, J. Am. Medical Informatics Assoc..

[23]  A. Roach,et al.  Methods to enhance the safety of methotrexate prescribing , 2007, Journal of clinical pharmacy and therapeutics.

[24]  Ervina Resetar,et al.  Case Report: Implementing a Commercial Rule Base as a Medication Order Safety Net , 2005, J. Am. Medical Informatics Assoc..