Integrated Clinical Prediction Rules: Bringing Evidence to Diverse Primary Care Settings

综合临床预测规则：为不同的初级保健机构提供证据

• 批准号: 9336505
• 负责人: Devin M Mann
• 金额: $ 89.8万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9336505
• 关键词: Accounting; Acute respiratory infection; Adopted; Antibiotics; Attention; Caring; Clinical; Code; Communities; Data; Decision Aid; Development; Diagnostic tests; Diffuse; Electronic Health Record; Evaluation; Funding; Generations; Goals; Health; Health Care Costs; Health care facility; Healthcare; Hybrids; Information Technology; Institution; Laboratories; Lead; Location; Measures; Organizational Culture; Patients; Pattern; Pharyngeal structure; Physical Examination; Pneumonia; Primary Health Care; Process; Provider; Randomized Controlled Trials; Recording of previous events; Reporting; Research Personnel; Resources; Site; System; Techniques; Technology; Testing; Time; United States Agency for Healthcare Research and Quality; Variant; clinical research site; contextual factors; cost; design; evidence base; health care quality; improved; innovation; invention; novel; point of care; preference; primary care setting; programs; safety net; success; theories; tool; uptake; usability

DESCRIPTION (provided by applicant): As the nation continues its efforts to contain healthcare costs and improve quality, healthcare information technology provides some of our most potent yet underutilized tools. Clinical prediction rules are frontline decision aids that combine state-of-the-art evidence with real-time patient history, physical examination, and laboratory data. While often well-validated, clinical prediction rules have been underutilized in practice. Recently, our team developed the integrated clinical prediction rule (iCPR) system, embedding CPRs within the nation's largest commercial electronic health record (EHR) system. Using this novel system, we demonstrated high rates of provider utilization and a significant reduction in antibiotic prescribing and diagnostic test ordering among suspected cases of strep throat and pneumonia at a single healthcare facility. The objective of the proposed project is to generalize this platform across diverse settings and create a toolkit for further dissemination. Building on the success of the original iCPR project, the specific aims of this proposal are to (1) integrate our previously tested and refined iCPR tool into the same commercial EHR in three different clinical settings, adapting the innovation to provider preference, culture, and local workflow rather than imposing a rigidly standardized tool, (2) identify and measure rate and variability of iCPR uptake across different settings, (3) determine iCPR impact on antibiotic prescribing and diagnostic test-ordering patterns across diverse clinical settings with a randomized controlled trial, and (4) use a well-established theory-driven implementation framework to identify facilitators and barriers to integration in each setting, and develop a toolkt for adapting and implementing the tool in diverse settings. To achieve these aims, we propose a five- year study in which we first adapt, integrate and usability-test the original iCPR at three new diverse sites. We will then conduct a two-year randomized controlled trial with a one-year post-trial open-access observation period to determine the persistence of: 1) the tool's utilizatio and 2) its impact on antibiotic- and test-ordering in patients with suspected strep throat or pneumonia. In the final year, study findings will be compiled into a toolkit so that any healthcare facility using the Epic EHR can integrate iCPR into its ambulatory workflow. The study uses several innovative and significant approaches, including: 1) adapting the nation's most widespread commercial EHR system; 2) building the new tool with "off-the-shelf" technology included in every Epic EHR package, so the innovation can be easily ported to all Epic EHR users; 3) using highly specific, well-validated clinical prediction rules as its core content; 4) guiding the integration process with highly generalizable usability testing techniques; and 5) using a hybrid RE-AIM and normalization process theory implementation evaluation framework. Together, these innovative approaches make iCPR uniquely suited to overcome longstanding barriers and integrate and disseminate evidence-based tools into the primary care workflow at the point of care in real time.

描述（由申请人提供）：随着国家继续努力控制医疗成本和提高质量，医疗信息技术提供了一些最有效但未充分利用的工具。临床预测规则是将最先进的证据与实时患者病史、体格检查和实验室数据结合起来的联合收割机。虽然临床预测规则经常得到很好的验证，但在实践中未得到充分利用。最近，我们的团队开发了集成临床预测规则（iCPR）系统，将CPR嵌入到美国最大的商业电子健康记录（EHR）系统中。使用这种新系统，我们证明了高供应商利用率和显着减少抗生素处方和诊断测试订购链球菌性咽喉炎和肺炎的疑似病例在一个单一的医疗机构。拟议项目的目标是在不同的环境中推广这一平台，并创建一个工具包供进一步传播。在最初iCPR项目成功的基础上，本提案的具体目标是（1） 将我们之前测试和改进的iCPR工具集成到三种不同临床环境中的相同商业EHR中，使创新适应提供者偏好，文化和当地工作流程，而不是强加严格的标准化工具，（2）识别和测量不同环境中iCPR摄取的速率和变异性，（3）通过随机对照试验确定iCPR对不同临床环境中抗生素处方和诊断测试排序模式的影响，以及（4）使用一个完善的理论驱动的实施框架，以确定在每种环境中整合的促进因素和障碍，并开发一个工具包，用于在不同的环境中调整和实施该工具。为了实现这些目标，我们提出了一个为期五年的研究，我们首先适应，整合和可用性测试的原始iCPR在三个新的不同的网站。然后，我们将进行一项为期两年的随机对照试验，试验后开放观察期为一年，以确定以下方面的持续性：1）该工具的使用和2）其对疑似链球菌性咽喉炎或肺炎患者的抗生素和测试顺序的影响。在最后一年，研究结果将汇编成一个工具包，以便任何医疗保健 使用Epic EHR的医疗机构可以将iCPR集成到其门诊工作流程中。该研究使用了几种创新和重要的方法，包括：1）适应全国最广泛的商业EHR系统; 2）使用每个Epic EHR包中包含的“现成”技术构建新工具，因此创新可以轻松地移植到所有Epic EHR用户; 3）使用高度特定的，经过验证的临床预测规则作为其核心内容; 4）使用高度通用的可用性测试技术指导集成过程; 5）使用混合RE-AIM和规范化过程理论实现评估框架。总之，这些创新方法使iCPR特别适合克服长期存在的障碍，并在真实的时间点将循证工具整合和传播到初级保健工作流程中。