Improving pain management and opioid safety through a systemwide, data driven evaluation of the CDC opioid prescribing guideline best practices and the use of Clinical Decision Support

通过对 CDC 阿片类药物处方指南最佳实践进行全系统、数据驱动的评估并使用临床决策支持，改善疼痛管理和阿片类药物安全

• 批准号: 10588405
• 负责人: Jason Hoppe
• 金额: $ 107.28万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2024-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10588405
• 关键词: Address; Agreement; Architecture; Behavior; Caring; Centers for Disease Control and Prevention (U.S.); Cessation of life; Chronic; Clinical; Clinical effectiveness; Cluster randomized trial; Common Data Element; Communities; Cost Effectiveness Analysis; Dangerousness; Data; Data Element; Data Linkages; Data Set; Development; Effectiveness; Electronic Health Record; Eligibility Determination; Evaluation; Evidence based practice; Future; Goals; Guidelines; Health system; Hospitals; Infrastructure; Institutional Review Boards; Integrated Health Care Systems; Learning; Link; Medicine; Morbidity - disease rate; National Institute of Drug Abuse; Opioid; Opioid Analgesics; Outcome; Outcome Measure; Outpatients; Pain; Pain management; Patient-Focused Outcomes; Patients; Positioning Attribute; Practical Robust Implementation and Sustainability Model; Primary Prevention; Process; Provider; Reach Effectiveness Adoption Implementation and Maintenance; Recommendation; Reporting; Research; Research Project Grants; Safety; System; Testing; Time; Training; United States National Institutes of Health; Update; Visit; Work; base; clinical decision support; contextual factors; dashboard; data infrastructure; data quality; effectiveness evaluation; effectiveness implementation study; effectiveness implementation trial; evidence based guidelines; health care service utilization; high risk; implementation barriers; implementation framework; implementation outcomes; implementation science; implementation strategy; improved; individual patient; innovation; insurance claims; mortality; non-opioid analgesic; opioid abuse; opioid epidemic; opioid exposure; opioid mortality; opioid overdose; opioid policy; opioid tapering; opioid use; overdose death; pilot test; pragmatic implementation; prescription monitoring program; prescription opioid; process evaluation; programs; prospective; provider behavior; public health priorities; randomized trial; research clinical testing; response; rural setting; suburb; success; support tools; systems research; tool; treatment as usual; user centered design

Project Summary Effectively treating pain while safely prescribing opioid pain medicines is a public health priority. The CDC opioid prescribing guidelines are an evidence-based approach to decreasing unnecessary opioid exposures, high-risk opioid use and abuse. The 2016 version produced modest improvements, but were sometimes misapplied for unsafe opioid tapering; highlighting the challenges of implementation and changing provider behavior. With guideline updates expected soon, there is a need for prospective trials to identify strategies to efficiently and effectively deliver CDC guideline recommended practices while documenting their impact on patient centered outcomes (e.g., pain control, morbidity and mortality). Clinical decision support (CDS) is a promising implementation strategy to both operationalize evidence-based practices and maximize the value of routinely collected data. We will use electronic health record (EHR) embedded CDS to modify clinical behavior toward CDC guideline-concordant recommendations. We propose a hybrid effectiveness-implementation trial using accepted implementation science frameworks PRISM (Practical, Robust Implementation and Sustainability Model) and RE-AIM (Reach, Effectiveness, Adoption, Implementation and Maintenance) to consider the multilevel contextual factors that influence implementation success. By developing a data-driven learning health system with the ability to evaluate patient outcomes through linkages to Prescription Drug Monitoring Program, insurance claims and death data, this project will evaluate: (1) The effectiveness of CDC guideline-concordant prescribing by describing the association between guideline concordant actions and patient outcomes. (2) The user-centered design of EHR based CDS to facilitate CDC guideline concordant actions and promote non-opioid approaches to pain management. (3) The evaluation of the implementation and effectiveness of CDS strategies to deliver guideline- concordant care in a pragmatic cluster randomized trial in a large, integrated health system. We will systematically assess key implementation outcomes and then evaluate the effectiveness of CDS on patient outcomes (vs usual care). This pragmatic research will address the need to link provider prescribing actions to individual patient outcomes. Using established implementation science approaches to evaluate CDS as an implementation strategy is innovative and important. Our results will provide robust data to document the effectiveness of CDC guideline concordant prescribing and evaluate an emerging, scalable implementation strategy using existing data to decrease morbidity and mortality from the opioid crisis.

项目摘要 有效治疗疼痛，同时安全地处方阿片类止痛药是公共卫生的优先事项。 CDC阿片类药物处方指南是减少不必要阿片类药物的循证方法 暴露、高风险阿片类药物使用和滥用。2016年的版本进行了适度的改进，但 有时被滥用于不安全的阿片类药物减量;强调执行方面的挑战， 改变供应商的行为。随着指南的更新，有必要进行前瞻性试验 确定有效和高效地提供CDC指南推荐实践的策略， 记录它们对以患者为中心的结果的影响（例如，疼痛控制、发病率和死亡率）。 临床决策支持（CDS）是一种很有前途的实施策略， 以证据为基础的做法，并最大限度地提高定期收集的数据的价值。我们将使用电子 健康记录（EHR）嵌入CDS，以修改临床行为，使其与CDC指南一致 建议.我们提出了一个混合的有效性，实施试验使用公认的 PRISM（Practical，Robust Implementation and Sustainability Model）实用、稳健的实施和可持续性模型 RE-AIM（Reach，Effectiveness，Adoption，Implementation and Maintenance）， 影响实施成功的多层次背景因素。通过开发数据驱动的 学习型卫生系统，能够通过与处方的联系来评估患者结果 药物监测计划、保险索赔和死亡数据，本项目将评估： (1)CDC指南一致性处方的有效性，通过描述 指导一致的行动和患者结果。 (2)以用户为中心的电子健康档案设计的基础上的CDS，以促进疾病预防控制中心的指导方针一致的行动， 促进非阿片类药物的疼痛管理方法。 (3)评估CDS战略的实施和有效性，以提供指南- 在一个大型综合卫生系统中进行的一项实用性群集随机试验中的一致性护理。我们将 系统地评估关键的实施成果，然后评估CDS对 患者结局（vs常规护理）。 这项务实的研究将解决需要链接提供者处方行动，以个人 患者结局。使用已建立的实施科学方法来评估CDS作为 执行战略是创新和重要的。我们的研究结果将为记录 CDC指南一致处方有效性，并评估一种新兴的、可扩展的 利用现有数据减少类阿片危机的发病率和死亡率的执行战略。