Improving Quality by Maintaining Accurate Problem Lists in the EHR (IQ-MAPLE)

描述(申请人提供)：一份完整的患者问题清单是问题导向病历的基石。它为提供者评估患者的临床状态提供了一个有价值的工具，并在提供者之间简洁地交流这些信息。准确的问题列表推动临床决策支持工具提高质量，而准确的问题列表与更高质量的护理相关。准确的问题清单也很重要 为研究建立准确的表型并支持质量改进；然而，电子健康记录中的问题清单通常是不完整的。在这项研究中，我们建议开发和验证问题推理算法来识别患者问题列表中可能缺少的问题，并对这些算法进行随机试验，研究它们对护理质量的影响。我们称我们的方法为智商枫叶。我们的项目有三个具体目标：1)开发心、肺和血液状况的问题推理算法；2)在四个地点实施问题推理警报并优化工作流程；3)对问题推理警报进行随机对照试验，测量警报的接受率，对问题列表完整性的直接影响，以及关键的是，警报对关键临床质量指标的下游影响，包括一系列心、肺和血液疾病的过程和结果。如果成功，IQ-Maple将提高问题列表的准确性，这具有重大的下游影响：更准确的临床决策支持：大多数临床决策支持是以疾病为导向的，因此，依赖于准确的问题列表。当问题缺失时，就会错过向临床医生提供支持的机会，质量也会受到影响。更好的质量测量：今天的质量测量通常是不准确的，因为由于关于患者临床问题的信息不完整，患者被遗漏在测量之外。一个更准确的问题清单将反过来导致更准确的质量衡量。更准确的研究：临床、翻译甚至基础科学和基因组研究越来越多地使用电子病历数据，特别是在识别疾病表型患者时，通常使用问题清单。当问题缺失时，研究的准确性就会受到影响。更好的患者护理：抛开次要好处不谈，从根本上说，问题清单是组织患者护理和提供者之间沟通的工具。一份更准确的问题清单支持这些目标。智商-枫叶规则以及我们实施这些规则的最佳做法将免费提供，确保这些好处的广泛传播。

DESCRIPTION (provided by applicant): A complete patient problem list is the cornerstone of the problem-oriented medical record. It serves as a valuable tool for providers assessing a patient's clinical status and succinctly communicates this information between providers. Accurate problem lists drive clinical decision support tools that improve quality, and an accurate problem list has been associated with higher quality care. Accurate problem lists are also critical for establishing accurate phenotypes for research and supporting quality improvement; however, problem lists in electronic health records are routinely incomplete. In this study, we propose to develop and validate problem inference algorithms to identify problems potentially missing from patient problem lists, and to conduct a randomized trial of these algorithms, studying their effects on quality of care. We call our approach IQ-MAPLE. Our project has three specific aims: 1) develop problem inference algorithms for heart, lung, and blood conditions, 2) implement problem inference alerts and optimize the workflow at four sites and 3) conduct a randomized controlled trial of the problem inference alerts, measuring the acceptance rate of alerts, the direct effect on problem list completeness and, critically, downstream impact of the alerts on key clinical quality measures, including both process and outcomes across a range of heart, lung and blood conditions. If successful, IQ-MAPLE will improve problem list accuracy, which has significant downstream implications: More accurate clinical decision support: Most clinical decision support is disease-oriented and, as such, depends on an accurate problem list. When problems are missing, opportunities to provide support to the clinician are missed, and quality suffers. Better quality measurement: Quality measurement today is often inaccurate, as patients are omitted from measures due to incomplete information on their clinical problems. A more accurate problem list would, in turn, lead to more accurate quality measurement. More accurate research: Clinical, translational and even basic science and genomic research increasingly use EHR data, particularly to identify patients with disease phenotypes, generally using problem lists. When problems are missing, the accuracy of research suffers. Better patient care: Secondary benefits aside, the problem list is, fundamentally, a tool for organizing patient care and communicating among providers. A more accurate, problem list supports these goals. The IQ-MAPLE rules, and our best practices for implementing them, will be freely available, ensuring broad dissemination of these benefits.