Suppr超能文献

一项旨在提高精神科医生对烟草治疗指南依从性的行为经济学干预措施:一项提供者随机研究方案。

A behavioral economic intervention to increase psychiatrist adherence to tobacco treatment guidelines: a provider-randomized study protocol.

作者信息

Rogers Erin S, Wysota Christina, Prochaska Judith J, Tenner Craig, Dognin Joanna, Wang Binhuan, Sherman Scott E

机构信息

NYU School of Medicine, Department of Population Health, 180 Madison Avenue, New York, NY 10016.

Stanford University, Department of Medicine, Stanford Prevention Research Center, 1265 Welch Road St, Stanford, California 94305.

出版信息

Implement Sci Commun. 2020;1. doi: 10.1186/s43058-020-00011-x. Epub 2020 Feb 25.

Abstract

BACKGROUND

People with a psychiatric diagnosis smoke at high rates, yet are rarely treated for tobacco use. Health care systems often use a 'no treatment' default for tobacco, such that providers must actively choose (opt-in) to treat their patients who express interest in quitting. Default bias theory suggests that opt-in systems may reinforce the status quo to not treat tobacco use in psychiatry. We aim to conduct a pilot study testing an opt-out system for implementing a 3A's (ask, advise, assist) tobacco treatment model in outpatient psychiatry.

METHODS

We will use a mixed-methods, cluster-randomized study design. We will implement a tobacco use clinical reminder for outpatient psychiatrists at the VA New York Harbor Healthcare System. Psychiatrists (N = 20) will be randomized 1:1 to one of two groups: (1) Opt-In Treatment Approach: Psychiatrists will receive a reminder that encourages them to offer cessation medications and referral to cessation counseling; or (2) Opt-Out Treatment Approach: Psychiatrists will receive a clinical reminder that includes a standing cessation medication order and a referral to cessation counseling that will automatically generate unless the provider cancels. Prior to implementation of the reminders, we will hold a 1-hour training on tobacco treatment for psychiatrists in both arms. We will use VA administrative data to calculate the study's primary outcomes: 1) the percent of smokers prescribed a cessation medication and 2) the percent of smokers referred to counseling. During the intervention period, we will also conduct post-visit surveys with a cluster sample of 400 patients (20 per psychiatrist) to assess psychiatrist fidelity to the 3 A's approach and patient perceptions of the opt-out system. At six months, we will survey the clustered patient sample again to evaluate the study's secondary outcomes: 1) patient use of cessation treatment in the prior 6 months and 2) self-reported 7-day abstinence at 6 months. At the end of the intervention period, we will conduct semi-structured interviews with 12-14 psychiatrists asking about their perceptions of the opt-out approach.

DISCUSSION

This study will produce important data on the potential of opt-out systems to overcome barriers in implementing tobacco use treatment in outpatient psychiatry.

TRIAL REGISTRATION

Clinicaltrials.gov Identifier NCT04071795 (registered August 28, 2019). https://www.clinicaltrials.gov/ct2/show/NCT04071795.

摘要

背景

患有精神疾病诊断的人群吸烟率很高,但很少接受烟草使用治疗。医疗保健系统通常对烟草使用采用“不治疗”的默认方式,即医疗服务提供者必须主动选择(选择加入)来治疗那些表达戒烟意愿的患者。默认偏差理论表明,选择加入系统可能会强化在精神病学中不治疗烟草使用的现状。我们旨在开展一项试点研究,测试一种选择退出系统,以在门诊精神病学中实施3A(询问、建议、协助)烟草治疗模式。

方法

我们将采用混合方法、整群随机研究设计。我们将在纽约港退伍军人事务医疗保健系统为门诊精神科医生实施烟草使用临床提醒。精神科医生(N = 20)将按1:1随机分为两组:(1)选择加入治疗方法:精神科医生将收到一条提醒,鼓励他们提供戒烟药物并转介至戒烟咨询;或(2)选择退出治疗方法:精神科医生将收到一条临床提醒,其中包括一份长期戒烟药物处方和一份转介至戒烟咨询的内容,除非医疗服务提供者取消,否则将自动生成。在实施提醒之前,我们将为两组的精神科医生举办一次为期1小时的烟草治疗培训。我们将使用退伍军人事务部的行政数据来计算研究的主要结果:1)开具戒烟药物的吸烟者百分比和2)转介至咨询的吸烟者百分比。在干预期间,我们还将对400名患者的整群样本(每位精神科医生20名)进行访后调查,以评估精神科医生对3A方法的忠诚度以及患者对选择退出系统的看法。在六个月时,我们将再次对整群患者样本进行调查,以评估研究的次要结果:1)患者在过去6个月内使用戒烟治疗的情况以及2)在6个月时自我报告的7天戒烟情况。在干预期结束时,我们将对12 - 14名精神科医生进行半结构化访谈,询问他们对选择退出方法的看法。

讨论

本研究将产生关于选择退出系统在克服门诊精神病学中实施烟草使用治疗障碍方面潜力的重要数据。

试验注册

Clinicaltrials.gov标识符NCT04071795(于2019年8月28日注册)。https://www.clinicaltrials.gov/ct2/show/NCT04071795

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fee/7427874/0e3fd55c6b92/43058_2020_11_Fig1_HTML.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验