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Harnessing Health IT to Prevent Medication-Induced Birth Defects

利用健康信息技术预防药物引起的出生缺陷

基本信息

批准号：
7688031
负责人：
ELEANOR Bimla SCHWARZ
金额：
$ 40.03万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2007
资助国家：
美国
起止时间：
2007-09-07 至 2011-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7688031
关键词：
Harnessing Health Prevent Medication Induced

项目摘要

The Institute of Medicine has identified prevention of birth defects as one of six priorities for the nation's health because annually, 150,000 infants (1% - 3% of all U.S. births) are born with some form of physical or mental birth defect. It is estimated that each year, 12 million US women use medications that increase the risk of birth defects. With concurrent use of contraception, birth defects associated with these medications can be prevented. Unfortunately, when prescribing these medications, clinicians rarely counsel women about contraception, and approximately 6% of US pregnancies are exposed to medications that may increase risk of birth defects. As Health IT has improved the safety of medication management in some healthcare settings, we propose to develop and then rigorously evaluate ways computers may be able to help doctors counsel women about preventing birth defects caused by use of certain medications. To better understand what information about risk of medication-induced birth defects would be most useful to primary care clinicians and their patients, we will begin this project by conducting a series of focus groups with clinicians and patients seen in academic and community-based practices. Data from these discussions will help refine the two Health IT interventions we will develop. We will evaluate the impact of each of these interventions using a factorial design randomized controlled trial. In the first trial, we will compare multi-faceted decision support (intervention) to streamlined clinical alerts (control). In the second trial, we will evaluate whether collecting machine-actionable information about women?s risk of pregnancy using a networked tablet computer (intervention) is superior to the way clinicians usually collect this information (control). Over the course of 1 year, we will abstract data from the electronic medical record when study clinicians prescribe teratogenic medications (N=1500), conduct phone interviews with women (N=800) prescribed medications by participating clinicians, and survey participating clinicians (N=100) about their satisfaction with the decision support they receive. We will use this data to confirm our hypotheses that clinicians in the intervention groups will (1) prescribe fewer teratogenic medications, (2) be more likely to prescribe contraception when a teratogenic medication is prescribed, (3) have more patients report satisfaction with the counseling they received, and (4) report more satisfaction with the decision support they received. This evaluation will provide much-needed information on how Health IT can best be harnessed to prevent medication-induced birth defects nationwide. In addition, the Health IT intervention shown to be most effective will be disseminated within the University of Pittsburgh Medical Center, which provides 3 million outpatient visits each year.

医学研究所已将预防出生缺陷确定为国家的六个优先事项之一。健康，因为每年有150，000名婴儿（占美国出生人口的1% - 3%）出生时患有某种形式的身体或先天性智力缺陷据估计，每年有1200万美国妇女使用增加风险的药物。出生缺陷。如果同时使用避孕药，与这些药物相关的出生缺陷可能防止。不幸的是，当开这些药物时，临床医生很少建议女性避孕，大约6%的美国孕妇暴露于可能增加妊娠风险的药物。出生缺陷由于Health IT改善了某些医疗保健环境中药物管理的安全性，我建议开发并严格评估计算机帮助医生为女性提供咨询的方法关于预防因使用某些药物而导致的出生缺陷。为了更好地了解关于药物引起的出生缺陷的风险将是最有用的初级保健临床医生和他们的患者，我们将开始这个项目进行了一系列的重点小组与临床医生和患者看到，学术和社区实践。从这些讨论中获得的数据将有助于完善两个健康IT 我们将开发的干预措施。我们将使用一个因子来评估这些干预措施的影响。设计随机对照试验。在第一次试验中，我们将比较多方面的决策支持（干预）到精简的临床警报（控制）。在第二次审判中，我们将评估是否收集关于女人的机器可操作信息使用联网的平板电脑怀孕的风险（干预）上级临床医生通常收集此信息的方式（对照）。在1年的过程中，我们将在研究时从电子病历中提取数据临床医生开具致畸药物（N=1500），对女性进行电话访谈（N=800）由参与临床医生处方药物，并调查参与临床医生（N=100）关于他们的对他们获得的决策支持感到满意。我们将使用这些数据来证实我们的假设，干预组的临床医生将（1）开出更少的致畸药物，（2）更有可能当处方致畸药物时，处方避孕，（3）有更多的患者报告满意度与他们所接受的咨询，（4）报告更满意的决策支持，他们收到的。这项评估将提供急需的信息，说明如何最好地利用卫生信息技术来预防全国范围内药物引起的出生缺陷。此外，健康信息技术干预被证明是最有效的将在匹兹堡大学医学中心传播，该中心提供300万门诊病人，每年访问。