Literature DB >> 28971492

Drug-related problems identified during medication review before and after the introduction of a clinical decision support system.

S Verdoorn1,2, H F Kwint2, P Hoogland3, J Gussekloo4,5, M L Bouvy1,2.   

Abstract

WHAT IS KNOWN AND
OBJECTIVE: To facilitate the identification of drug-related problems (DRPs) during medication review, several tools have been developed. Explicit criteria, like Beers criteria or STOPP (Screening Tool of Older Peoples' Prescriptions) and START (Screening Tool to Alert doctors to Right Treatment) criteria, can easily be integrated into a clinical decision support system (CDSS). The aim of this study was to investigate the effect of adding a CDSS to medication review software on identifying and solving DRPs in daily pharmacy practice.
METHODS: Pre- to post-analysis of clinical medication reviews (CMRs) performed by 121 pharmacies in 2012 and 2013, before and after the introduction of CDSS into medication review software. Mean number of DRPs per patient, type of DRPs and their resolution rates were compared in the pharmacies pre- and post-CDSS using paired t tests. RESULTS AND DISCUSSION: In total, 9151 DRPs were identified in 3100 patients pre-CDSS and 15 268 DRPs were identified in 4303 patients post-CDSS. The mean number of identified DRPs per patient (aggregated per pharmacy) was higher after the introduction of CDSS (3.2 vs 3.6 P < .01). The resolution rate was lower post-CDSS (50% vs 44%; P < .01), which overall resulted in 1.6 resolved DRPs per patient in both groups (P = .93). After the introduction of CDSS, 41% of DRPs were detected by the CDSS. The resolution rate of DRPs generated by CDSS was lower than of DRPs identified without the help of CDSS (29% vs 55%; P < .01). The two most prevalent DRP types were "Overtreatment" and "Suboptimal therapy" in both groups. The prevalence of "Overtreatment" was equal in both groups (mean DRPs per patient: 0.84 vs 0.77; P = .22), and "Suboptimal therapy" was more frequently identified post-CDSS (mean DRPs per patient: 0.54 vs 1.1; P < .01). WHAT IS NEW AND
CONCLUSION: The introduction of CDSS to medication review software generated additional DRPs with a lower resolution rate. Structural assessment including a patient interview elicited the most relevant DRPs. Further development of CDSS with more specific alerts is needed to be clinical relevant.
© 2017 John Wiley & Sons Ltd.

Entities:  

Keywords:  computerised decision support; elderly; medication; pharmacist consultation; pharmacy practice

Mesh:

Year:  2017        PMID: 28971492     DOI: 10.1111/jcpt.12637

Source DB:  PubMed          Journal:  J Clin Pharm Ther        ISSN: 0269-4727            Impact factor:   2.512


  7 in total

1.  End-users feedback and perceptions associated with the implementation of a clinical-rule based Check of Medication Appropriateness service.

Authors:  Charlotte Quintens; Willy E Peetermans; Lorenz Van der Linden; Peter Declercq; Bart Van den Bosch; Isabel Spriet
Journal:  BMC Med Inform Decis Mak       Date:  2022-07-05       Impact factor: 3.298

2.  Effectiveness of medication review on the number of drug-related problems in patients visiting the outpatient cardiology clinic: A randomized controlled trial.

Authors:  Victor Johan Bernard Huiskes; Cornelia Helena Maria van den Ende; Martine Kruijtbosch; Hendrik Tinus Ensing; Marieke Meijs; Veronique Maria Mathea Meijs; David Marinus Burger; Bartholomeus Johannes Fredericus van den Bemt
Journal:  Br J Clin Pharmacol       Date:  2019-12-03       Impact factor: 4.335

3.  A user-centered evaluation of medication therapy management alerts for community pharmacists: Recommendations to improve usability and usefulness.

Authors:  Margie E Snyder; Omolola A Adeoye-Olatunde; Stephanie A Gernant; Julie DiIulio; Heather A Jaynes; William R Doucette; Alissa L Russ-Jara
Journal:  Res Social Adm Pharm       Date:  2020-11-04

4.  Prevalence of adverse drug reactions in the primary care setting: A systematic review and meta-analysis.

Authors:  Widya N Insani; Cate Whittlesea; Hassan Alwafi; Kenneth K C Man; Sarah Chapman; Li Wei
Journal:  PLoS One       Date:  2021-05-26       Impact factor: 3.240

5.  Incidence, types and acceptability of pharmaceutical interventions about drug related problems in a general hospital: an open prospective cohort.

Authors:  Valdjane Saldanha; Rand Randall Martins; Sara Iasmin Vieira Cunha Lima; Ivonete Batista de Araujo; Antonio Gouveia Oliveira
Journal:  BMJ Open       Date:  2020-04-23       Impact factor: 2.692

6.  Intervention protocol: OPtimising thERapy to prevent avoidable hospital Admission in the Multi-morbid elderly (OPERAM): a structured medication review with support of a computerised decision support system.

Authors:  Erin K Crowley; Bastiaan T G M Sallevelt; Corlina J A Huibers; Kevin D Murphy; Marco Spruit; Zhengru Shen; Benoît Boland; Anne Spinewine; Olivia Dalleur; Elisavet Moutzouri; Axel Löwe; Martin Feller; Nathalie Schwab; Luise Adam; Ingeborg Wilting; Wilma Knol; Nicolas Rodondi; Stephen Byrne; Denis O'Mahony
Journal:  BMC Health Serv Res       Date:  2020-03-17       Impact factor: 2.655

7.  A Clinical Decision Support System for Sleep Staging Tasks With Explanations From Artificial Intelligence: User-Centered Design and Evaluation Study.

Authors:  Jeonghwan Hwang; Taeheon Lee; Honggu Lee; Seonjeong Byun
Journal:  J Med Internet Res       Date:  2022-01-19       Impact factor: 5.428

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.