Literature DB >> 19893857

Alert system for inappropriate prescriptions relating to patients' clinical condition.

Y Matsumura1, T Yamaguchi, H Hasegawa, K Yoshihara, Q Zhang, T Mineno, H Takeda.   

Abstract

OBJECTIVES: Because information of contraindication and careful indication of medication is vast, there have been numerous cases of prescribing medication inappropriately. Our goal is to have a clinical decision support system (CDSS) combined with a computerized physician order entry (CPOE) to aid physicians in prescribing medication appropriately. In this study we developed an alert system for evaluating renal function and checking doses of medication according to the patient's renal function. In addition, we developed functions of extracting target problems from the raw data and verifying if contraindicated medication has being prescribed.
METHODS: This system scrutinizes data handled in the CPOE system. It picks up the data needed to ascertain problems and the data of medication entered from the order entry system. First we made an alert system for renal dysfunction. Creatinine clearance (Ccr) of a patient was calculated by the estimate equation of Cockcroft and Gault. If a patient data fulfills the condition of impaired renal function, the alert message is sent to the database. The alert system also checks the dosage of each medication according to a patient's renal function. When the dosage is over-prescribed, an alert is sent. Next, we made an alert system targeting contraindication for liver diseases, renal diseases and diabetes mellitus. The criteria of these problems were set in the knowledge base. If a patient's data meets the criteria, that fact is stored in the problem database. The system also keeps a prescription check master and checks whether the patient has a problem which is a contraindication of the prescribed medication. If a problem exists, an alert is sent to the alert message database. The alert-presenting module is a web system. After accepting patients' ID indicated by a user, the system searches the alerts concerning the patients from the database and constructs pages presenting the alert message.
RESULTS: We compared the period during which the contraindicated medication was prescribed before and after the alert system was put into operation. Of the patients with renal dysfunction who were prescribed the contraindicated medication, 24% had their medication discontinued before the alert system was put into operation. In contrast, the rate significantly increased to 54% after the alert system began to function.
CONCLUSION: We developed an alert system for inappropriate prescriptions for each patient's clinical condition. The alerts generated by this system were effective for discontinuing contraindicated medication.

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Year:  2009        PMID: 19893857     DOI: 10.3414/ME9244

Source DB:  PubMed          Journal:  Methods Inf Med        ISSN: 0026-1270            Impact factor:   2.176


  8 in total

1.  Drug prescribing in patients with renal impairment optimized by a computer-based, semi-automated system.

Authors:  Ana Such Díaz; Javier Saez de la Fuente; Laura Esteva; Ana María Alañón Pardo; Nélida Barrueco; Concepción Esteban; Ismael Escobar Rodríguez
Journal:  Int J Clin Pharm       Date:  2013-12

Review 2.  Computerized decision support systems: improving patient safety in nephrology.

Authors:  Jamison Chang; Claudio Ronco; Mitchell H Rosner
Journal:  Nat Rev Nephrol       Date:  2011-04-19       Impact factor: 28.314

Review 3.  Inappropriate prescribing: a systematic overview of published assessment tools.

Authors:  Carole P Kaufmann; Regina Tremp; Kurt E Hersberger; Markus L Lampert
Journal:  Eur J Clin Pharmacol       Date:  2013-09-10       Impact factor: 2.953

4.  The Effect of Laboratory Test-Based Clinical Decision Support Tools on Medication Errors and Adverse Drug Events: A Laboratory Medicine Best Practices Systematic Review.

Authors:  Nedra S Whitehead; Laurina Williams; Sreelatha Meleth; Sara Kennedy; Nneka Ubaka-Blackmoore; Michael Kanter; Kevin J O'Leary; David Classen; Brian Jackson; Daniel R Murphy; James Nichols; David Stockwell; Thomas Lorey; Paul Epner; Jennifer Taylor; Mark L Graber
Journal:  J Appl Lab Med       Date:  2019-03-11

5.  Early recognition of multiple sclerosis using natural language processing of the electronic health record.

Authors:  Herbert S Chase; Lindsey R Mitrani; Gabriel G Lu; Dominick J Fulgieri
Journal:  BMC Med Inform Decis Mak       Date:  2017-02-28       Impact factor: 2.796

Review 6.  Scoping review exploring the impact of digital systems on processes and outcomes in the care management of acute kidney injury and progress towards establishing learning healthcare systems.

Authors:  Clair Ka Tze Chew; Helen Hogan; Yogini Jani
Journal:  BMJ Health Care Inform       Date:  2021-07

Review 7.  Systematic review of clinical decision support interventions with potential for inpatient cost reduction.

Authors:  Christopher L Fillmore; Bruce E Bray; Kensaku Kawamoto
Journal:  BMC Med Inform Decis Mak       Date:  2013-12-17       Impact factor: 2.796

8.  Medication-related problems in older people in Catalonia: A real-world data study.

Authors:  Amelia Troncoso-Mariño; Tomás López-Jiménez; Albert Roso-Llorach; Noemí Villén; Ester Amado-Guirado; Marina Guisado-Clavero; Sergio Fernández-Bertolin; Mariona Pons Vigues; Quintí Foguet-Boreu; Concepción Violán
Journal:  Pharmacoepidemiol Drug Saf       Date:  2020-11-26       Impact factor: 2.890

  8 in total

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