Harnessing Health IT to Prevent Medication-Induced Birth Defects

医学研究所已将预防出生缺陷确定为全国六大优先事项之一 健康，因为每年有150,000名婴儿(占美国出生总数的1%-3%)出生时有某种形式的身体或 精神先天缺陷。据估计，每年有1200万美国妇女使用增加风险的药物 先天缺陷。在同时使用避孕措施的情况下，与这些药物相关的出生缺陷可能 被阻止了。不幸的是，当开出这些药物时，临床医生很少建议女性 避孕，大约6%的美国孕妇接触到可能增加患病风险的药物 先天缺陷。由于Health IT提高了某些医疗保健环境中的用药管理安全性，我们 提议开发并严格评估计算机可能能够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。为了更好地了解哪些信息 关于药物引起的出生缺陷的风险对初级保健临床医生和他们的 患者，我们将通过与临床医生和患者进行一系列焦点小组来开始这个项目 学术和社区实践。来自这些讨论的数据将有助于提炼两个健康IT 我们将制定干预措施。我们将使用阶乘来评估每种干预措施的影响 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)简化临床警报(对照)。在第二次审判中，我们将评估是否收集 关于女性的机器可操作信息？S使用联网平板电脑的怀孕风险 (干预)优于临床医生通常收集这些信息的方式(对照)。 在一年的时间里，我们将在学习时从电子病历中提取数据 临床医生开出致畸药物(N=1500)，对女性进行电话采访(N=800) 参与的临床医生的处方药物，以及参与调查的临床医生(N=100)关于他们的 对他们得到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 在开出致畸药物的时候开避孕药，(3)有更多的患者表示满意 与他们所接受的咨询有关，以及(4)他们对所得到的决策支持更满意。 这项评估将提供急需的信息，说明如何最好地利用Health IT来预防 全国范围内因药物导致的出生缺陷。此外，卫生信息技术干预被证明是最有效的 将在匹兹堡大学医疗中心内传播，该中心提供300万门诊患者 每年都会来拜访。

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.