Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital Department of Medicine, Boston, Massachusetts, USA
Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA.
BMJ Qual Saf. 2023 Aug;32(8):457-469. doi: 10.1136/bmjqs-2022-014806. Epub 2023 Mar 22.
The second Multicenter Medication Reconciliation Quality Improvement Study demonstrated a marked reduction in medication discrepancies per patient. The aim of the current analysis was to determine the association of patient exposure to each system-level intervention and receipt of each patient-level intervention on these results.
This study was conducted at 17 North American Hospitals, the study period was 18 months per site, and sites typically adopted interventions after 2-5 months of preintervention data collection. We conducted an on-treatment analysis (ie, an evaluation of outcomes based on patient exposure) of system-level interventions, both at the category level and at the individual component level, based on monthly surveys of implementation site leads at each site (response rate 65%). We then conducted a similar analysis of patient-level interventions, as determined by study pharmacist review of documented activities in the medical record. We analysed the association of each intervention on the adjusted number of medication discrepancies per patient in admission and discharge orders, based on a random sample of up to 22 patients per month per site, using mixed-effects Poisson regression with hospital site as a random effect. We then used a generalised linear mixed-effects model (GLMM) decision tree to determine which patient-level interventions explained the most variance in discrepancy rates.
Among 4947 patients, patient exposure to seven of the eight system-level component categories was associated with modest but significant reductions in discrepancy rates (adjusted rate ratios (ARR) 0.75-0.97), as were 15 of the 17 individual system-level intervention components, including hiring, reallocating and training personnel to take a best possible medication history (BPMH) and training personnel to perform discharge medication reconciliation and patient counselling. Receipt of five of seven patient-level interventions was independently associated with large reductions in discrepancy rates, including receipt of a BPMH in the emergency department (ED) by a trained clinician (ARR 0.40, 95% CI 0.37 to 0.43), admission medication reconciliation by a trained clinician (ARR 0.57, 95% CI 0.50 to 0.64) and discharge medication reconciliation by a trained clinician (ARR 0.64, 95% CI 0.57 to 0.73). In GLMM decision tree analyses, patients who received both a BPMH in the ED and discharge medication reconciliation by a trained clinician experienced the lowest discrepancy rates (0.08 per medication per patient).
Patient-level interventions most associated with reductions in discrepancies were receipt of a BPMH of admitted patients in the ED and admission and discharge medication reconciliation by a trained clinician. System-level interventions were associated with modest reduction in discrepancies for the average patient but are likely important to support patient-level interventions and may reach more patients. These findings can be used to help hospitals and health systems prioritise interventions to improve medication safety during care transitions.
第二项多中心药物重整质量改进研究表明,每位患者的药物差异明显减少。本分析的目的是确定患者接触每个系统水平干预措施和接受每个患者水平干预措施对这些结果的关联。
这项研究在北美 17 家医院进行,每个地点的研究期为 18 个月,各地点通常在预干预数据收集后 2-5 个月内采用干预措施。我们对系统水平干预措施进行了治疗分析(即在基于患者接触的情况下评估结果),既进行了类别水平的分析,也进行了个别组成部分水平的分析,基于每个地点实施地点负责人的每月调查(响应率为 65%)。然后,我们根据研究药剂师对病历中记录的活动的审查,对患者水平干预措施进行了类似的分析。我们基于每个地点每月最多 22 名患者的随机样本,使用混合效应泊松回归分析了每个干预措施对入院和出院医嘱中每例患者药物差异数量的调整后影响,采用医院地点作为随机效应。然后,我们使用广义线性混合效应模型(GLMM)决策树来确定哪些患者水平干预措施解释了差异率变化的最大差异。
在 4947 名患者中,有 8 种系统水平成分类别的 7 种患者接触与差异率的适度但显著降低有关(调整后的比率比(ARR)0.75-0.97),17 种系统水平干预措施中的 15 种也与差异率的降低有关,包括雇用、重新分配和培训人员以获得最佳药物史(BPMH)以及培训人员进行出院药物重整和患者咨询。接受七种患者水平干预措施中的五种与差异率的大幅降低独立相关,包括接受经过培训的临床医生在急诊科(ED)进行的 BPMH(ARR 0.40,95%CI 0.37 至 0.43)、接受经过培训的临床医生进行的入院药物重整(ARR 0.57,95%CI 0.50 至 0.64)和接受经过培训的临床医生进行的出院药物重整(ARR 0.64,95%CI 0.57 至 0.73)。在 GLMM 决策树分析中,接受 ED 中接受培训的临床医生进行的 BPMH 和出院药物重整的患者的差异率最低(每位患者每例药物 0.08)。
与差异减少最相关的患者水平干预措施是接受 ED 中入院患者的 BPMH 和经过培训的临床医生进行的入院和出院药物重整。系统水平干预措施与每位患者的差异适度减少有关,但可能对支持患者水平干预措施很重要,并且可能会覆盖更多患者。这些发现可用于帮助医院和卫生系统确定干预措施,以改善护理过渡期间的药物安全性。