Machine-Learning Prediction and Reducing Overdoses with EHR Nudges (mPROVEN)

美国继续与阿片类药物流行病作斗争，2020年约有69，700例阿片类药物过量死亡。健康 系统已经制定了多种干预措施，以减少病人的风险，许多侧重于减少不安全的 阿片类药物处方被视为高风险。然而，有有限的工具来确定谁是真正的 用药过量风险高，导致针对过于广泛的人群或缺少关键的干预措施 高危人群。即使可以确定那些处于危险中的人，干预措施也缺乏有效的战略， 改变临床医生的行为，而不是专注于钝工具，以减少处方，而不是降低风险。 在之前的工作中，我们开发并外部验证了机器学习算法，该算法可以识别高血压患者。 阿片类药物过量的风险，即使没有积极处方阿片类药物。另外，我们证明了行为 嵌入在电子健康记录（EHR）中的轻推警报可以与风险预测工具相结合， 改变临床医生的行为。在这个项目中，我们建议通过将 通过可扩展的EHR干预，基于机器学习的过量风险预测和行为轻推 以改善临床医生的处方行为。在大型学术卫生系统（UPMC）中，我们提出以下建议 具体目标：（1）将我们先前验证的机器学习算法纳入EHR，以预测3- 阿片类药物过量的一个月风险;（2）在EHR中进行临床医生针对性行为轻推干预的试点测试， 具有阿片类药物过量高预测风险的患者;（3）评价在研究中提供风险评分的有效性。 EHR有和没有行为推动，以提高阿片类药物处方安全性并降低过量风险。 在目标1中，我们将把我们的梯度增强机器过量预测算法应用于基于UPMC Epic的 电子病历我们将优化算法用于UPMC初级保健实践，解决模型准确性问题， 算法偏差在目标2中，我们将联合收割机结合我们的算法生成的风险评分与临床医生的轻推， EHR，使用3个阶段的试点与焦点小组，沉默测试，并在3个初级保健实践现场测试。 轻推干预将针对照顾高风险患者的临床医生，并将使用主动选择提示， 纳洛酮和阿片类药物和苯二氮卓类药物处方的合理性。在目标3中，我们将进行 在45个UPMC初级保健实践中进行的随机分组试验，分为3组：（1）常规护理;（2）EHR嵌入式 风险评分; 3）EHR嵌入式风险评分加上目标2的推动。EHR嵌入式风险评分 组将由EHR中的警报组成，该警报将患者识别为药物过量的高风险。在风险评分中 与轻推臂相结合，类似的关于高风险状态的EHR警报将与来自Aim 2的轻推一起沿着标记。 主要结局将是与药物过量风险降低相关的3种处方实践的复合结果： 纳洛酮处方，阿片类药物剂量<50 MME/天，并且没有阿片类药物/苯二氮卓类药物重叠。 我们的建议建立在我们以前的NIDA资助的工作和经验，与助推干预， NIDA的战略目标是开发和测试预防阿片类药物滥用和过量的新策略。

The US continues to grapple with an opioid epidemic, with ~69,700 opioid overdose deaths in 2020. Health systems have instituted multiple interventions to reduce patient risk, many focusing on decreasing unsafe opioid prescribing among those viewed as high-risk. However, there are limited tools to identify who is truly at high risk of overdose, leading to burdensome interventions targeting an overly broad population or missing key high-risk individuals. Even if those who are at risk can be identified, the interventions lack effective strategies to change clinician behavior, focusing instead on blunt tools to reduce prescribing rather than reduce risk. In prior work, we developed and externally validated machine-learning algorithms that identify patients at high risk of opioid overdose, even if not actively prescribed opioids. Separately, we demonstrated how behavioral nudge alerts embedded in the electronic health record (EHR) can be combined with risk prediction tools to change clinician behavior. In this project, we propose to reduce opioid overdose risk by bringing together machine-learning based overdose risk prediction and behavioral nudges through a scalable EHR intervention to improve clinician prescribing behavior. In a large academic health system (UPMC), we propose the following specific aims: (1) Incorporate our previously validated machine learning algorithm into the EHR to predict 3- month risk of opioid overdose; (2) Pilot test a clinician-targeted behavioral nudge intervention in the EHR for patients at high predicted risk for opioid overdose; (3) Evaluate the effectiveness of providing risk scores in the EHR with and without a behavioral nudge to improve opioid prescribing safety and reduce overdose risk. In Aim 1, we will apply our gradient boosting machine overdose prediction algorithm to the UPMC Epic-based EHR. We will optimize the algorithm for use in UPMC primary care practices, addressing model accuracy and algorithmic biases. In Aim 2, we will combine the risk score generated by our algorithm with clinician nudges in the EHR, using a 3-phase pilot with focus groups, silent testing, and live testing in 3 primary care practices. The nudge intervention will target clinicians caring for high-risk patients and will use active choice prompts for naloxone and accountable justifications for opioid and benzodiazepine prescribing. In Aim 3, we will conduct a cluster randomized trial in 45 UPMC primary care practices, with 3 arms: (1) usual care; (2) EHR-embedded risk score; 3) EHR-embedded risk score coupled with the nudge from Aim 2. The EHR-embedded risk score arm will consist of an alert in the EHR that identifies the patient as high risk for overdose. In the risk score coupled with nudge arm, a similar EHR alert about high-risk status will flag, along with the nudges from Aim 2. The primary outcome will be a composite of 3 prescribing practices associated with reduced risk of overdose: naloxone prescription, opioid dosage <50MME per day, and no opioid/benzodiazepine overlap. Our proposal builds on our prior NIDA-funded work and experience with nudge interventions and is aligned with NIDA’s strategic goals to develop and test novel strategies for preventing opioid misuse and overdose.