1 Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, Alberta, Canada.
2 Grey Nuns Community Hospital, Edmonton, Alberta, Canada.
J Manag Care Spec Pharm. 2017 May;23(5):566-572. doi: 10.18553/jmcp.2017.23.5.566.
Electronic medical record (EMR) screening for indicators of medication risk could improve efficiency in identifying primary care clinic patients in need of clinical pharmacist care compared with patient self-reporting.
To (a) compare the performance of an EMR medication risk assessment questionnaire (MRAQ) with a self-administered (SA) MRAQ and (b) explore each tool's ability to predict indicators of health behavior, health status, and health care utilization.
A prospective cohort study was conducted with 143 adults who attended an academic family medicine center and were taking ≥ 2 medications. All participants completed the 10-item SA-MRAQ, Morisky Medication Adherence Scale, Chew's health literacy screener, Stanford Health Distress Scale, and SF-36 overall rating of health. A blinded investigator completed the EMR-MRAQ and a chart review to ascertain 6 months of health care utilization. Outcome measures included the following: (a) scores from the 5- and 10-item SA-MRAQs and 5-item EMR-MRAQ; (b) sensitivity and specificity to determine the accuracy of the 5-item EMR versus the 5-item SA risk scores; (c) correlations between risk assessments and health behavior/status scales; and (d) area under the receiver operator curve to determine how well a high-risk score predicted health care utilization.
The 5-item SA-MRAQ, the 5-item EMR-MRAQ, and the 10-item SA-MRAQ categorized 52.9% (55/104), 69.2% (99/143), and 17.6% (18/102) of participants as high risk, respectively. For the 104 participants who completed both 5-item MRAQ tools, the EMR-MRAQ had a sensitivity of 81.8% and specificity of 49.0% in detecting a high-risk SA-MRAQ score. Both 5-item risk assessments showed weak correlations with health distress and overall health, while the 10-item SA-MRAQ additionally showed weak correlations with medication adherence. The EMR-MRAQ was most effective in predicting all-cause emergency room visits/hospitalization (c-statistic = 0.69; 95% CI=0.57-0.81) and high clinic utilization (≥ 4 visits per 6 months; c-statistic = 0.77; 95% CI = 0.69-0.85). The EMR-MRAQ had high sensitivities but low specificities for these health care utilization outcomes, respectively (82.6% and 33.3%; 88.9% and 42.7%).
This pilot study suggests that EMR-MRAQ screening has high sensitivity but low specificity in comparison with self-reporting and was able to discriminate between those who would and would not experience health care utilization outcomes. These results justify further development and validation of an automated EMR-based tool to predict patient-important consequences of medication-related problems.
This work was funded by the Canadian Society of Hospital Pharmacists Research and Education Foundation, which had no role in the analysis or interpretation of data or the decision to submit the manuscript for publication. The authors have no conflict of interests, potential or otherwise, to report. Makowsky had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design were contributed by Makowsky and Cor. Makowsky and Wong collected the data, and data interpretation was performed by Makowsky, Cor, and Wong. The manuscript was written by Makowsky and was critically reviewed for intellectual content by Makowsky, Cor, and Wong.
与患者自我报告相比,电子病历(EMR)筛选药物风险指标可以提高识别初级保健诊所患者需要临床药师护理的效率。
(a)比较电子病历药物风险评估问卷(MRAQ)与自我管理(SA)MRAQ 的性能,(b)探索每种工具预测健康行为、健康状况和医疗保健利用的能力。
对 143 名在学术家庭医学中心就诊并服用≥2 种药物的成年人进行前瞻性队列研究。所有参与者都完成了 10 项 SA-MRAQ、Morisky 药物依从性量表、Chew 健康素养筛查、斯坦福健康困扰量表和 SF-36 整体健康评分。一名经过盲法培训的调查员完成了 EMR-MRAQ 和病历回顾,以确定 6 个月的医疗保健利用情况。主要结局指标包括:(a)5 项和 10 项 SA-MRAQ 和 5 项 EMR-MRAQ 的评分;(b)确定 5 项 EMR 与 5 项 SA 风险评分的准确性的灵敏度和特异性;(c)风险评估与健康行为/状况量表之间的相关性;(d)接收者操作曲线下的面积,以确定高风险评分如何预测医疗保健利用情况。
5 项 SA-MRAQ、5 项 EMR-MRAQ 和 10 项 SA-MRAQ 分别将 52.9%(55/104)、69.2%(99/143)和 17.6%(18/102)的参与者归类为高风险。对于完成 5 项 MRAQ 工具的 104 名参与者,EMR-MRAQ 在检测高风险 SA-MRAQ 评分方面的灵敏度为 81.8%,特异性为 49.0%。两种 5 项风险评估均与健康困扰和整体健康呈弱相关,而 10 项 SA-MRAQ 还与药物依从性呈弱相关。EMR-MRAQ 在预测所有原因急诊就诊/住院(C 统计量=0.69;95%CI=0.57-0.81)和高诊所利用率(≥4 次就诊/6 个月;C 统计量=0.77;95%CI=0.69-0.85)方面最为有效。对于这些医疗保健利用结果,EMR-MRAQ 具有高敏感性和低特异性(分别为 82.6%和 33.3%;88.9%和 42.7%)。
这项初步研究表明,与自我报告相比,EMR-MRAQ 筛查具有较高的敏感性和较低的特异性,并且能够区分那些将经历和不会经历医疗保健利用结果的患者。这些结果证明了进一步开发和验证基于自动化 EMR 的工具以预测药物相关问题对患者重要影响的合理性。
这项工作得到了加拿大医院药剂师协会研究和教育基金会的资助,该基金会在数据分析或解释或提交稿件以供发表的决定方面没有任何作用。作者没有潜在的或其他利益冲突要报告。Makowsky 全面获取了研究中的所有数据,并对数据的完整性和数据分析的准确性负责。概念和设计由 Makowsky 和 Cor 提出。Makowsky 和 Wong 收集了数据,数据解释由 Makowsky、Cor 和 Wong 进行。手稿由 Makowsky 撰写,并由 Makowsky、Cor 和 Wong 进行了批判性评论,以确保内容的完整性。