<scp>SHPA</scp> Standards of Practice in Emergency Medicine Pharmacy Practice

Fitzgerald, Claire; Welch, Susan; Taylor, Simone E; Harding, Andrew M; Graudins, Linda V; Lawrence, Dona; Roman, Cristina; Curry, Elizabeth; Doran, Elizabeth; Abbott, Leonie; Morrow, Melanie; Guidone, Daniel; Heward, Sarah; Wilkinson, Lorna

doi:10.1002/jppr.1144

Cited by 9 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In developing the initial lists, investigators considered the resources produced by the Australian Commission on Quality and Safety in Health Care, specifically the classification of high-risk medicines and the Medication Risk Identification checklist of the Medication Management Plan ( Australian Commission on Quality and Safety in Healthcare, 2013 and 2022 ). Two investigators (ST and EzM) undertook a narrative review of the literature for potential variables reported in previous studies ( Claydon-Platt et al, 2012 ; Roughead et al, 2016 ; Marotti et al, 2011 ; Cornish et al, 2005 ; Galvin et al, 2013 ; Hohl et al, 2005 ; Cavin and Sen, 2005 ; Patanwala et al, 2011 ; Patanwala et al, 2012 ; Cesarz et al, 2013 ; deClifford et al, 2007 ; Kumar et al, 2011 ; Kaufmann et al, 2015 ; Saedeer et al, 2016 ; Fitzgerald et al, 2015 ). Related variables were grouped, then the four investigators worked together to come to a consensus as to the specific variables to include in the data collection tool.…”

Section: Methodsmentioning

confidence: 99%

Development of Screening Tools to Predict Medication-Related Problems Across the Continuum of Emergency Department Care: A Prospective, Multicenter Study

et al. 2022

View full text Add to dashboard Cite

Background: Medication-related problems (MRPs) occur across the continuum of emergency department (ED) care: they may contribute to ED presentation, occur in the ED/short-stay unit (SSU), at hospital admission, or shortly after discharge to the community. This project aimed to determine predictors for MRPs across the continuum of ED care and incorporate these into screening tools (one for use at ED presentation and one at ED/SSU discharge), to identify patients at greatest risk, who could be targeted by ED pharmacists.Methods: A prospective, observational, multicenter study was undertaken in nine EDs, between July 2016 and August 2017. Blocks of ten consecutive adult patients presenting at pre-specified times were identified. Within 1 week of ED discharge, a pharmacist interviewed patients and undertook a medical record review to determine a medication history, patient understanding of treatment, risk factors for MRPs and to manage the MRPs. Logistic regression was undertaken to determine predictor variables. Multivariable regression beta coefficients were used to develop a scoring system for the two screening tools.Results: Of 1,238 patients meeting all inclusion criteria, 904 were recruited. Characteristics predicting MRPs related to ED presentation were: patient self-administers regular medications (OR = 7.95, 95%CI = 3.79–16.65), carer assists with medication administration (OR = 15.46, 95%CI = 6.52–36.67), or health-professional administers (OR = 5.01, 95%CI = 1.77–14.19); medication-related ED presentation (OR = 9.95, 95%CI = 4.92–20.10); age ≥80 years (OR = 3.63, 95%CI = 1.96–6.71), or age 65–79 years (OR = 2.01, 95%CI = 1.17–3.46); potential medication adherence issue (OR = 2.27, 95%CI = 1.38–3.73); medical specialist seen in past 6-months (OR = 2.02, 95%CI = 1.42–2.85); pharmaceutical benefit/pension/concession cardholder (OR = 1.89, 95%CI = 1.28–2.78); inpatient in previous 4-weeks (OR = 1.60, 95%CI = 1.02–2.52); being male (OR = 1.48, 95%CI = 1.05–2.10); and difficulties reading labels (OR = 0.63, 95%CI = 0.40–0.99). Characteristics predicting MRPs related to ED discharge were: potential medication adherence issue (OR = 6.80, 95%CI = 3.97–11.64); stay in ED > 8 h (OR = 3.23, 95%CI = 1.47–7.78); difficulties reading labels (OR = 2.33, 95%CI = 1.30–4.16); and medication regimen changed in ED (OR = 3.91, 95%CI = 2.43–6.30). For ED presentation, the model had a C-statistic of 0.84 (95% CI 0.81–0.86) (sensitivity = 80%, specificity = 70%). For ED discharge, the model had a C-statistic of 0.78 (95% CI 0.73–0.83) (sensitivity = 82%, specificity = 57%).Conclusion: Predictors of MRPs are readily available at the bedside and may be used to screen for patients at greatest risk upon ED presentation and upon ED/SSU discharge to the community. These screening tools now require external validation and implementation studies to evaluate the impact of using such tools on patient care outcomes.

show abstract

Section: Methodsmentioning

confidence: 99%

Development of Screening Tools to Predict Medication-Related Problems Across the Continuum of Emergency Department Care: A Prospective, Multicenter Study

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In developing the initial lists, investigators considered the resources produced by the Australian Commission on Quality and Safety in Health Care, specifically the classification of highrisk medicines and the Medication Risk Identification checklist of the Medication Management Plan (Australian Commission on Quality and Safety in Healthcare, 2013 and 2022). Two investigators (ST and EzM) undertook a narrative review of the literature for potential variables reported in previous studies (Claydon-Platt et al, 2012;Roughead et al, 2016;Marotti et al, 2011;Cornish et al, 2005;Galvin et al, 2013;Hohl et al, 2005;Cavin and Sen, 2005;Patanwala et al, 2011;Patanwala et al, 2012;Cesarz et al, 2013;deClifford et al, 2007;Kumar et al, 2011;Kaufmann et al, 2015;Saedeer et al, 2016;Fitzgerald et al, 2015). Related variables were grouped, then the four investigators worked together to come to a consensus as to the specific variables to include in the data collection tool.…”

Section: Development Of the Data Collection Toolmentioning

confidence: 99%

Table1.pdf

View full text Add to dashboard Cite

“…In developing the initial lists, investigators considered the resources produced by the Australian Commission on Quality and Safety in Health Care, specifically the classification of highrisk medicines and the Medication Risk Identification checklist of the Medication Management Plan (Australian Commission on Quality and Safety in Healthcare, 2013 and 2022). Two investigators (ST and EzM) undertook a narrative review of the literature for potential variables reported in previous studies (Claydon-Platt et al, 2012;Roughead et al, 2016;Marotti et al, 2011;Cornish et al, 2005;Galvin et al, 2013;Hohl et al, 2005;Cavin and Sen, 2005;Patanwala et al, 2011;Patanwala et al, 2012;Cesarz et al, 2013;deClifford et al, 2007;Kumar et al, 2011;Kaufmann et al, 2015;Saedeer et al, 2016;Fitzgerald et al, 2015). Related variables were grouped, then the four investigators worked together to come to a consensus as to the specific variables to include in the data collection tool.…”

Section: Development Of the Data Collection Toolmentioning

confidence: 99%

DataSheet2.PDF

View full text Add to dashboard Cite

SHPA Standards of Practice in Emergency Medicine Pharmacy Practice

Cited by 9 publications

References 13 publications

Development of Screening Tools to Predict Medication-Related Problems Across the Continuum of Emergency Department Care: A Prospective, Multicenter Study

Development of Screening Tools to Predict Medication-Related Problems Across the Continuum of Emergency Department Care: A Prospective, Multicenter Study

Table1.pdf

DataSheet2.PDF

Contact Info

Product

Resources

About