Literature DB >> 20186499

Electronic prescribing improves medication safety in community-based office practices.

Rainu Kaushal1, Lisa M Kern, Yolanda Barrón, Jill Quaresimo, Erika L Abramson.   

Abstract

BACKGROUND: Although electronic prescribing (e-prescribing) holds promise for preventing prescription errors in the ambulatory setting, research on its effectiveness is inconclusive.
OBJECTIVE: To assess the impact of a stand-alone e-prescribing system on the rates and types of ambulatory prescribing errors. DESIGN, PARTICIPANTS: Prospective, non-randomized study using pre-post design of 15 providers who adopted e-prescribing with concurrent controls of 15 paper-based providers from September 2005 through June 2007. INTERVENTION: Use of a commercial, stand-alone e-prescribing system with clinical decision support including dosing recommendations and checks for drug-allergy interactions, drug-drug interactions, and duplicate therapies. MAIN MEASURES: Prescribing errors were identified by a standardized prescription and chart review. KEY
RESULTS: We analyzed 3684 paper-based prescriptions at baseline and 3848 paper-based and electronic prescriptions at one year of follow-up. For e-prescribing adopters, error rates decreased nearly sevenfold, from 42.5 per 100 prescriptions (95% confidence interval (CI), 36.7-49.3) at baseline to 6.6 per 100 prescriptions (95% CI, 5.1-8.3) one year after adoption (p < 0.001). For non-adopters, error rates remained high at 37.3 per 100 prescriptions (95% CI, 27.6-50.2) at baseline and 38.4 per 100 prescriptions (95% CI, 27.4-53.9) at one year (p = 0.54). At one year, the error rate for e-prescribing adopters was significantly lower than for non-adopters (p < 0.001). Illegibility errors were very high at baseline and were completely eliminated by e-prescribing (87.6 per 100 prescriptions at baseline for e-prescribing adopters, 0 at one year).
CONCLUSIONS: Prescribing errors may occur much more frequently in community-based practices than previously reported. Our preliminary findings suggest that stand-alone e-prescribing with clinical decision support may significantly improve ambulatory medication safety. TRIAL REGISTRATION: ClinicalTrials.gov, Taconic Health Information Network and Community (THINC), NCT00225563, http://clinicaltrials.gov/ct2/show/NCT00225563?term=Kaushal&amp;rank=6 .

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Year:  2010        PMID: 20186499      PMCID: PMC2869410          DOI: 10.1007/s11606-009-1238-8

Source DB:  PubMed          Journal:  J Gen Intern Med        ISSN: 0884-8734            Impact factor:   5.128


  21 in total

1.  Using chart review to screen for medication errors and adverse drug events.

Authors:  Rainu Kaushal
Journal:  Am J Health Syst Pharm       Date:  2002-12-01       Impact factor: 2.637

2.  Outpatient prescribing errors and the impact of computerized prescribing.

Authors:  Tejal K Gandhi; Saul N Weingart; Andrew C Seger; Joshua Borus; Elisabeth Burdick; Eric G Poon; Lucian L Leape; David W Bates
Journal:  J Gen Intern Med       Date:  2005-09       Impact factor: 5.128

3.  Clinical decision support in electronic prescribing: recommendations and an action plan: report of the joint clinical decision support workgroup.

Authors:  Jonathan M Teich; Jerome A Osheroff; Eric A Pifer; Dean F Sittig; Robert A Jenders
Journal:  J Am Med Inform Assoc       Date:  2005-03-31       Impact factor: 4.497

4.  Medication errors and adverse drug events in pediatric inpatients.

Authors:  R Kaushal; D W Bates; C Landrigan; K J McKenna; M D Clapp; F Federico; D A Goldmann
Journal:  JAMA       Date:  2001-04-25       Impact factor: 56.272

5.  A method for estimating the probability of adverse drug reactions.

Authors:  C A Naranjo; U Busto; E M Sellers; P Sandor; I Ruiz; E A Roberts; E Janecek; C Domecq; D J Greenblatt
Journal:  Clin Pharmacol Ther       Date:  1981-08       Impact factor: 6.875

6.  E-prescribing and the medicare modernization act of 2003.

Authors:  Douglas S Bell; Maria A Friedman
Journal:  Health Aff (Millwood)       Date:  2005 Sep-Oct       Impact factor: 6.301

7.  Effect of computerized physician order entry and a team intervention on prevention of serious medication errors.

Authors:  D W Bates; L L Leape; D J Cullen; N Laird; L A Petersen; J M Teich; E Burdick; M Hickey; S Kleefield; B Shea; M Vander Vliet; D L Seger
Journal:  JAMA       Date:  1998-10-21       Impact factor: 56.272

8.  Recommendations for comparing electronic prescribing systems: results of an expert consensus process.

Authors:  Douglas S Bell; Richard S Marken; Robin C Meili; C Jason Wang; Mayde Rosen; Robert H Brook
Journal:  Health Aff (Millwood)       Date:  2004 Jan-Jun       Impact factor: 6.301

9.  Perceptions of standards-based electronic prescribing systems as implemented in outpatient primary care: a physician survey.

Authors:  C Jason Wang; Mihir H Patel; Anthony J Schueth; Melissa Bradley; Shinyi Wu; Jesse C Crosson; Peter A Glassman; Douglas S Bell
Journal:  J Am Med Inform Assoc       Date:  2009-04-23       Impact factor: 4.497

10.  The effect of automated alerts on provider ordering behavior in an outpatient setting.

Authors:  Andrew W Steele; Sheri Eisert; Joel Witter; Pat Lyons; Michael A Jones; Patricia Gabow; Eduardo Ortiz
Journal:  PLoS Med       Date:  2005-09-06       Impact factor: 11.069

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  59 in total

1.  Ambulatory prescribing errors among community-based providers in two states.

Authors:  Erika L Abramson; David W Bates; Chelsea Jenter; Lynn A Volk; Yolanda Barrón; Jill Quaresimo; Andrew C Seger; Elisabeth Burdick; Steven Simon; Rainu Kaushal
Journal:  J Am Med Inform Assoc       Date:  2011-12-01       Impact factor: 4.497

2.  Prescribers' expectations and barriers to electronic prescribing of controlled substances.

Authors:  Cindy Parks Thomas; Meelee Kim; Ann McDonald; Peter Kreiner; Stephen J Kelleher; Michael B Blackman; Peter N Kaufman; Grant M Carrow
Journal:  J Am Med Inform Assoc       Date:  2011-09-21       Impact factor: 4.497

3.  Commentary on the federal government's role in influencing e-prescribing use and research.

Authors:  Olufunmilola K Odukoya; Michelle A Chui
Journal:  Perspect Health Inf Manag       Date:  2012-04-01

4.  Retail pharmacy staff perceptions of design strengths and weaknesses of electronic prescribing.

Authors:  Olufunmilola Odukoya; Michelle A Chui
Journal:  J Am Med Inform Assoc       Date:  2012-06-29       Impact factor: 4.497

5.  Safe Implementation of Computerized Provider Order Entry for Adult Oncology.

Authors:  D B Martin; D Kaemingk; D Frieze; P Hendrie; T H Payne
Journal:  Appl Clin Inform       Date:  2015-10-28       Impact factor: 2.342

6.  Automatic Errors: A Case Series on the Errors Inherent in Electronic Prescribing.

Authors:  Laura M Lourenco; Adam Bursua; Vicki L Groo
Journal:  J Gen Intern Med       Date:  2016-02-16       Impact factor: 5.128

Review 7.  Precision diagnosis: a view of the clinical decision support systems (CDSS) landscape through the lens of critical care.

Authors:  Arnaud Belard; Timothy Buchman; Jonathan Forsberg; Benjamin K Potter; Christopher J Dente; Allan Kirk; Eric Elster
Journal:  J Clin Monit Comput       Date:  2016-02-22       Impact factor: 2.502

8.  Transitioning between electronic health records: effects on ambulatory prescribing safety.

Authors:  Erika L Abramson; Sameer Malhotra; Karen Fischer; Alison Edwards; Elizabeth R Pfoh; S Nena Osorio; Adam Cheriff; Rainu Kaushal
Journal:  J Gen Intern Med       Date:  2011-04-16       Impact factor: 5.128

9.  The Triangle Model for evaluating the effect of health information technology on healthcare quality and safety.

Authors:  Jessica S Ancker; Lisa M Kern; Erika Abramson; Rainu Kaushal
Journal:  J Am Med Inform Assoc       Date:  2011-08-20       Impact factor: 4.497

Review 10.  E-prescribing: a focused review and new approach to addressing safety in pharmacies and primary care.

Authors:  Olufunmilola K Odukoya; Michelle A Chui
Journal:  Res Social Adm Pharm       Date:  2012-10-11
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