Literature DB >> 9066863

Automated system for identifying potential dosage problems at a large university hospital.

S T McMullin1, R M Reichley, M G Kahn, W C Dunagan, T C Bailey.   

Abstract

A hospital's experience with an automated system for screening drug orders for potential dosage problems is described. DoseChecker was developed by the hospital pharmacy department in collaboration with a local university. Pharmacy, laboratory, and patient demographic data are transferred nightly from the hospital's mainframe system to a database server; DoseChecker uses these data and user-defined rules to (1) identify patients receiving any of 35 targeted medications, (2) evaluate the appropriateness of current dosages, and (3) generate alerts for patients potentially needing dosage adjustments. The alert reports are distributed to satellite pharmacists, who evaluate each patient's condition and make recommendations to physicians as needed. One of the system's primary purposes is to calculate creatinine clearance and verify that dosages are properly adjusted for renal function. Between May and October 1995, the system electronically screened 28,528 drug orders and detected potential dosage problems in 2859 (10%). The system recommended a lower daily dose in 1992 cases (70%) and a higher daily dose in 867 (30%). Pharmacists contacted physicians concerning 1163 (41%) of the 2859 alerts; in 868 cases (75%), the physicians agreed to adjust the dosage. The most common dosage problem identified was failure to adjust dosages on the basis of declining renal function. An automated system provided an efficient method of identifying inappropriate dosages at a large university hospital.

Entities:  

Mesh:

Year:  1997        PMID: 9066863     DOI: 10.1093/ajhp/54.5.545

Source DB:  PubMed          Journal:  Am J Health Syst Pharm        ISSN: 1079-2082            Impact factor:   2.637


  14 in total

1.  Notification of real-time clinical alerts generated by pharmacy expert systems.

Authors:  J E Miller; R M Reichley; L A McNamee; S A Steib; T C Bailey
Journal:  Proc AMIA Symp       Date:  1999

2.  Hospital prescribing errors: epidemiological assessment of predictors.

Authors:  R Fijn; P M L A Van den Bemt; M Chow; C J De Blaey; L T W De Jong-Van den Berg; J R B J Brouwers
Journal:  Br J Clin Pharmacol       Date:  2002-03       Impact factor: 4.335

3.  Deriving measures of intensive care unit antimicrobial use from computerized pharmacy data: methods, validation, and overcoming barriers.

Authors:  David N Schwartz; R Scott Evans; Bernard C Camins; Yosef M Khan; James F Lloyd; Nadine Shehab; Kurt Stevenson
Journal:  Infect Control Hosp Epidemiol       Date:  2011-05       Impact factor: 3.254

4.  Methodology and rationale for the measurement of harm with trigger tools.

Authors:  R K Resar; J D Rozich; D Classen
Journal:  Qual Saf Health Care       Date:  2003-12

Review 5.  A review of decision support systems in telecare.

Authors:  Tasos Falas; George Papadopoulos; Andreas Stafylopatis
Journal:  J Med Syst       Date:  2003-08       Impact factor: 4.460

6.  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

7.  Implementing a commercial rule base as a medication order safety net.

Authors:  Richard M Reichley; Terry L Seaton; Ervina Resetar; Scott T Micek; Karen L Scott; Victoria J Fraser; W Claiborne Dunagan; Thomas C Bailey
Journal:  J Am Med Inform Assoc       Date:  2005-03-31       Impact factor: 4.497

8.  Computerized surveillance of adverse drug events in hospital patients. 1991.

Authors:  D C Classen; S L Pestotnik; R S Evans; J P Burke
Journal:  Qual Saf Health Care       Date:  2005-06

9.  Computerized surveillance for adverse drug events in a pediatric hospital.

Authors:  Peter M Kilbridge; Laura A Noirot; Richard M Reichley; Kathleen M Berchelmann; Cortney Schneider; Kevin M Heard; Miranda Nelson; Thomas C Bailey
Journal:  J Am Med Inform Assoc       Date:  2009-06-30       Impact factor: 4.497

10.  Scaling an expert system data mart: more facilities in real-time.

Authors:  L A McNamee; B D Launsby; M E Frisse; R Lehmann; K Ebker
Journal:  Proc AMIA Symp       Date:  1998
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.