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

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.

Overdose and take-home naloxone in emergency settings: A pilot study examining feasibility of delivering brief interventions addressing overdose prevention with 'take-home naloxone' in emergency departments.

OBJECTIVE: Although most unintentional opioid deaths in Australia are attributed to pharmaceutical opioids, take-home naloxone (THN) programmes have to date predominantly targeted people using illicit opioids in drug treatment and harm reduction settings. We sought to examine the feasibility of delivering THN brief interventions (THN-BIs) with intranasal naloxone in EDs. METHODS: This pilot feasibility study was conducted across three major metropolitan EDs in Sydney and Melbourne. ED staff were surveyed about their perspectives regarding THN before completing a 30-min training programme in THN-BI delivery. Patients presenting with opioid overdose or considered high risk for future overdose were eligible to receive the THN-BI. Staff survey responses were compared between hospitals and provider types using one-way analysis of variances. Patient demographic and clinical characteristics were extracted from medical records and compared between hospitals and overdose type using Fisher's exact test and one-way analysis of variances. RESULTS: One hundred and twenty-two ED staff completed the survey. One hundred and ten (90.2%) agreed that EDs should provide THN-BIs, whereas 23 (19.2%) identified time constraints and 17 (12.9%) felt uncomfortable discussing overdose with patients. Fifty-seven patients received the THN-BI, with the majority (n = 50, 87.7%) having presented following opioid overdose. The median age was 44 years and 40 (71.4%) were men. Two-thirds of the overdoses (n = 31, 66.0%) were attributed to heroin with one-third (n = 16, 34%) being attributed to pharmaceutical opioids. CONCLUSIONS: ED-based delivery of THN-BIs can reach a wide range of individuals at-risk of overdose. The present study supports the feasibility of THN interventions in EDs and underscores the importance of addressing implementation barriers including staff training.

Comprehensive identification of medication-related problems occurring prior to, during and after emergency department presentation: An Australian multicentre, prospective, observational study.

OBJECTIVE: Patients present to EDs with various medication-related problems (MRPs). MRPs are also associated with ED care, occurring during ED presentation or shortly afterwards. The aim of the present study is to describe the prevalence and nature of MRPs that occur prior to, during or shortly after leaving ED. METHODS: We undertook a prospective, observational study in nine Australian EDs. Blocks of 10 consecutive adult patients who were not seen by a pharmacist in ED and who presented at pre-specified times were identified. Within 1 week of ED discharge, a pharmacist interviewed patients and undertook a medical record review to determine their medication history, patients' understanding of treatment, potential MRP risk factors and manage any identified MRPs. RESULTS: A total of 904 patients were recruited: 14.8% aged ≥80 years, 18.9% taking more than eight regular medications; 581 MRPs were identified; 287 (49.4%, 95% confidence interval [CI] 45.3-53.5%) of moderate-high significance. Most highly significant MRPs involved high-risk medications, particularly strong opioids, insulin and anti-coagulants. The most common types of MRPs were prescribing errors (46.8%), patient adherence/knowledge issues (31.2%) and adverse drug reactions (7.4%). Of all patients, 381 (42.1%, 95% CI 38.9-45.5%) had at least one MRP; 31.4% (95% CI 28.4-34.6%) had MRPs that could be identified or managed by screening at ED presentation and 12.4% (95% CI 10.4-14.8%) had MRPs that could be identified or managed by screening at ED discharge. CONCLUSIONS: Patients experienced a range of MRPs throughout the ED continuum of care. From these data, screening tools will be developed so that ED clinicians may identify patients at greatest risk of MRPs.

Measuring the relationship between interruptions, multitasking and prescribing errors in an emergency department: a study protocol.

INTRODUCTION: Interruptions and multitasking are frequent in clinical settings, and have been shown in the cognitive psychology literature to affect performance, increasing the risk of error. However, comparatively less is known about their impact on errors in clinical work. This study will assess the relationship between prescribing errors, interruptions and multitasking in an emergency department (ED) using direct observations and chart review. METHODS AND ANALYSIS: The study will be conducted in an ED of a 440-bed teaching hospital in Sydney, Australia. Doctors will be shadowed at proximity by observers for 2 h time intervals while they are working on day shift (between 0800 and 1800). Time stamped data on tasks, interruptions and multitasking will be recorded on a handheld computer using the validated Work Observation Method by Activity Timing (WOMBAT) tool. The prompts leading to interruptions and multitasking will also be recorded. When doctors prescribe medication, type of chart and chart sections written on, along with the patient's medical record number (MRN) will be recorded. A clinical pharmacist will access patient records and assess the medication orders for prescribing errors. The prescribing error rate will be calculated per prescribing task and is defined as the number of errors divided by the number of medication orders written during the prescribing task. The association between prescribing error rates, and rates of prompts, interruptions and multitasking will be assessed using statistical modelling. ETHICS AND DISSEMINATION: Ethics approval has been obtained from the hospital research ethics committee. Eligible doctors will be provided with written information sheets and written consent will be obtained if they agree to participate. Doctor details and MRNs will be kept separate from the data on prescribing errors, and will not appear in the final data set for analysis. Study results will be disseminated in publications and feedback to the ED.