Design and implementation of decision support for tobacco dependence treatment in an inpatient electronic medical record: a randomized trial
Design and implementation of decision support for tobacco dependence treatment in an inpatient electronic medical record: a randomized trial
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DOI：
10.1007/s13142-017-0470-8
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发表时间：
2017-06-01
影响因子：
3.6
通讯作者：
Toll, Benjamin
中科院分区：
文献类型：
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作者：
Bernstein, Steven L.;Rosner, June;Toll, Benjamin
如果出院后继续治疗至少30天，则住院吸烟者的烟草依赖治疗会导致长期戒烟。健康信息技术可以促进出院后持续的烟草依赖治疗。描述嵌入电子健康记录（EHR）中的住院医生新决策支持工具和医嘱集的使用和影响，解决住院吸烟者的烟草依赖治疗问题。在一项群集随机试验中，254名医生被随机（1：1）分配接受或不接受决策支持工具和订单集，这些工具和订单集嵌入在Epic（麦迪逊，WI）EHR中，在单个城市的2家医院使用。当一名成年患者被允许接受医疗服务时，如果该患者在EHR中被编码为吸烟者，则会出现电子警报。对于随机分配到干预组的医生，警报与一个命令相关联，该命令设置为开烟草治疗药物，并将患者转诊到州烟草戒烟热线。此外，将“烟草使用障碍”添加到患者的问题列表中，并向患者的初级保健提供者（PCP）发送电子邮件。在控制臂中，发出警报，但无屏幕可见性。使用广义估计方程对数据进行建模。自2013年8月以来，10，939例患者出现了警报（5391例干预，5548例对照）。与对照组医生相比，干预医生更有可能订购烟草治疗药物（35%对29%，P < 0.0001），将烟草使用障碍问题列表（41%对2%，P < 0.0001），并将其转介给州吸烟者戒烟热线（30%对0%，P < 0.0001）。此外，干预医生向患者的PCP发送电子邮件4152次（99%）。在EHR中设计和实施烟草治疗的订单集和警报是可行的，可以帮助医生订购更多的烟草治疗药物，转介到州吸烟者戒烟热线，并向患者的PCP发送电子邮件。关于戒烟结果的数据尚未公布。试验注册：www. ClinicalTrials. gov（NCT01691105）。
Tobacco dependence treatment for hospitalized smokers results in long-term cessation if treatment continues at least 30 days post-discharge. Health information technology may facilitate ongoing tobacco dependence treatment after hospital discharge. To describe the use and impact of a new decision support tool and order set for inpatient physicians, addressing tobacco dependence treatment for hospitalized smokers, embedded in an electronic health record (EHR). In a cluster-randomized trial, 254 physicians were randomized (1: 1) to either receive or not receive the decision support tool and order set, which were embedded in the Epic (Madison, WI) EHR used at 2 hospitals in a single city. When an adult patient was admitted to a medical service, an electronic alert appeared if the patient was coded in the EHR as a smoker. For physicians randomized to the intervention, the alert linked to an order set to prescribe tobacco treatment medications and refer the patient to the state tobacco quitline. Additionally, "tobacco use disorder" was added to the patient's problemlist, and an e-mail was sent to the patient's primary care provider (PCP). In the control arm, an alert fired with no screen visibility. Generalized estimating equations were used to model the data. Since August 2013, the alert has appeared for 10,939 patients (5391 intervention, 5548 control). Compared to control physicians, intervention physicians were more likely to order tobacco treatment medication (35 vs. 29%, P < 0.0001), populate the problem list with tobacco use disorder (41 vs. 2%, P < 0.0001), and make a referral to the state smokers' quitline (30 vs. 0%, P < 0.0001). In addition, intervention physicians sent an e-mail to the patient's PCP 4152 (99%) times. Designing and implementing an order set and alert for tobacco treatment in an EHR is feasible and helps physicians place more orders for tobacco treatment medication, referrals to the state smokers' quitline, and e-mails to patients' PCPs. Data on cessation outcomes are pending. Trial registration: www. ClinicalTrials. gov (NCT01691105).