Skip to main content
Top
Published in: International Journal of Clinical Pharmacy 5/2018

01-10-2018 | Research Article

Identification of variables influencing pharmaceutical interventions to improve medication review efficiency

Authors: Lauriane Cornuault, Victorine Mouchel, Thuy-Tan Phan Thi, Hélène Beaussier, Yvonnick Bézie, Jennifer Corny

Published in: International Journal of Clinical Pharmacy | Issue 5/2018

Login to get access

Abstract

Background Clinical pharmacists’ involvement has improved patients’ care, by suggesting therapeutic optimizations. However, budget restrictions require a prioritization of these activities to focus resources on patients more at risk of medication errors. Objective The aim of our study was to identify variables influencing the formulation of pharmaceutical to improve medication review efficiency. Setting This study was conducted in medical wards of a 643-acute beds hospital in Paris, France. Methods All hospital medical prescriptions of all patients admitted within four medical wards (cardiology, rheumatology, neurology, vascular medicine) were analyzed. The study was conducted in each ward for 2 weeks, during 4 weeks. For each patient, variables prospectively collected were: age, gender, weight, emergency admission, number of high-alert medications and of total drugs prescribed, care unit, serum creatinine. Number of pharmaceutical interventions (PIs) and their type were reported. Main outcome measures Variables influencing the number of pharmaceutical interventions during medication review were identified using simple and multiple linear regressions. Results A total of 2328 drug prescriptions (303 patients, mean age 70.6 years-old) were analyzed. Mean number of hospital drug prescriptions was 7.9. A total of 318 PIs were formulated. Most frequent PIs were drug omission (n = 88, 27.7%), overdosing (n = 69, 21.7%), and underdosing (n = 51, 16.0%). Among variables studied, age, serum creatinine level, number of high-alert medications prescribed and total number of drugs prescribed were significantly associated with the formulation of pharmaceutical interventions (adjusted R2 = 0.34). Conclusions This study identified variables (age, serum creatinine level, number of high-alert medication, number of prescribed drugs) that may help institutions/pharmacists target their reviews towards patients most likely to require pharmacist interventions.
Literature
2.
go back to reference Kuo GM, Touchette DR, Marinac JS, For the American College of Clinical Pharmacy Practice-Based Research Network Collaborative. Drug errors and related interventions reported by United States clinical pharmacists: the American College of clinical pharmacy practice-based research network medication error detection, amelioration and prevention study. Pharmacother J Hum Pharmacol Drug Ther. 2013;33:253–65.CrossRef Kuo GM, Touchette DR, Marinac JS, For the American College of Clinical Pharmacy Practice-Based Research Network Collaborative. Drug errors and related interventions reported by United States clinical pharmacists: the American College of clinical pharmacy practice-based research network medication error detection, amelioration and prevention study. Pharmacother J Hum Pharmacol Drug Ther. 2013;33:253–65.CrossRef
3.
go back to reference Bates DW, Cullen DJ, Laird N, Petersen LA, Small SD, Servi D, Laffel G, Sweitzer BJ, Shea BF, Hallisey R, et al. Incidence of adverse drug events and potential adverse drug events. Implications for prevention. ADE Prevention Study Group. JAMA. 1995;274(1):29–34.CrossRef Bates DW, Cullen DJ, Laird N, Petersen LA, Small SD, Servi D, Laffel G, Sweitzer BJ, Shea BF, Hallisey R, et al. Incidence of adverse drug events and potential adverse drug events. Implications for prevention. ADE Prevention Study Group. JAMA. 1995;274(1):29–34.CrossRef
4.
go back to reference Santell JP, Hicks RW, McMeekin J, Cousins DD. Medication errors: experience of the United States Pharmacopeia (USP) MEDMARX reporting system. J Clin Pharmacol. 2003;43:760–7.CrossRef Santell JP, Hicks RW, McMeekin J, Cousins DD. Medication errors: experience of the United States Pharmacopeia (USP) MEDMARX reporting system. J Clin Pharmacol. 2003;43:760–7.CrossRef
5.
go back to reference Brazinha I, Fernandez-Llimos F. Barriers to the implementation of advanced clinical pharmacy services at Portuguese hospitals. Int J Clin Pharm. 2014;36(5):1031–8.CrossRef Brazinha I, Fernandez-Llimos F. Barriers to the implementation of advanced clinical pharmacy services at Portuguese hospitals. Int J Clin Pharm. 2014;36(5):1031–8.CrossRef
6.
go back to reference Minard LV, Deal H, Harrison ME, Toombs K, Neville H, Meade A. Pharmacists’ perceptions of the barriers and facilitators to the implementation of clinical pharmacy key performance indicators. PLoS One. 2016;11(4):e0152903.CrossRef Minard LV, Deal H, Harrison ME, Toombs K, Neville H, Meade A. Pharmacists’ perceptions of the barriers and facilitators to the implementation of clinical pharmacy key performance indicators. PLoS One. 2016;11(4):e0152903.CrossRef
7.
go back to reference Pfister B, Jonsson J, Gustafsson M. Drug-related problems and medication reviews among old people with dementia. BMC Pharmacol Toxicol. 2017;18:52.CrossRef Pfister B, Jonsson J, Gustafsson M. Drug-related problems and medication reviews among old people with dementia. BMC Pharmacol Toxicol. 2017;18:52.CrossRef
12.
go back to reference Vande Griend JP, Saseen JJ, Bislip D, Emsermann C, Conry C, Pace WD. Prioritization of patients for comprehensive medication review by a clinical pharmacist in family medicine. J Am Board Fam Med. 2015;28(3):418–24.CrossRef Vande Griend JP, Saseen JJ, Bislip D, Emsermann C, Conry C, Pace WD. Prioritization of patients for comprehensive medication review by a clinical pharmacist in family medicine. J Am Board Fam Med. 2015;28(3):418–24.CrossRef
13.
go back to reference Stordeur F, Khouri T, Lehrer J, Beaussier H, Bezie Y, Phan Thi TT. Prescriptions analysis: how can we target our work? Eur J Hosp Pharm. 2017;24:A101–2.CrossRef Stordeur F, Khouri T, Lehrer J, Beaussier H, Bezie Y, Phan Thi TT. Prescriptions analysis: how can we target our work? Eur J Hosp Pharm. 2017;24:A101–2.CrossRef
15.
go back to reference Vande Griend JP, Saseen JJ, Bislip D, Emsermann C, Conry C, Pace WD. Prioritization of patients for comprehensive medication review by a clinical pharmacist in family medicine. J Am Board Fam Med. 2015;28(3):418–24.CrossRef Vande Griend JP, Saseen JJ, Bislip D, Emsermann C, Conry C, Pace WD. Prioritization of patients for comprehensive medication review by a clinical pharmacist in family medicine. J Am Board Fam Med. 2015;28(3):418–24.CrossRef
16.
go back to reference Mandal K, Fraser S. The incidence of prescribing errors in an eye hospital. BMC Ophthalmol. 2005;5(1):4.CrossRef Mandal K, Fraser S. The incidence of prescribing errors in an eye hospital. BMC Ophthalmol. 2005;5(1):4.CrossRef
18.
go back to reference Motwani M, Dey D, Berman DS, Germano G, Achenbach S, Al-Mallah MH, et al. Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis. Eur Heart J. 2017;38(7):500–7.PubMed Motwani M, Dey D, Berman DS, Germano G, Achenbach S, Al-Mallah MH, et al. Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis. Eur Heart J. 2017;38(7):500–7.PubMed
Metadata
Title
Identification of variables influencing pharmaceutical interventions to improve medication review efficiency
Authors
Lauriane Cornuault
Victorine Mouchel
Thuy-Tan Phan Thi
Hélène Beaussier
Yvonnick Bézie
Jennifer Corny
Publication date
01-10-2018
Publisher
Springer International Publishing
Published in
International Journal of Clinical Pharmacy / Issue 5/2018
Print ISSN: 2210-7703
Electronic ISSN: 2210-7711
DOI
https://doi.org/10.1007/s11096-018-0668-y

Other articles of this Issue 5/2018

International Journal of Clinical Pharmacy 5/2018 Go to the issue