Pain Assessment & Intervention Needs (PAIN)

描述(由申请人提供)：该应用程序将使用从电子健康记录(EHR)和药物管理系统创建的大型数据集，使用二次数据分析来检查学术医疗中心的急性手术后疼痛管理实践。这项研究的主要目的是通过假设检验和数据挖掘来分析疼痛管理。目前的疼痛管理实践是根据美国疼痛协会发布的反映如何治疗疼痛的最佳循证知识的标准进行的，但实施仍然缓慢。疼痛仍然是手术后的一个重要问题，导致患者焦虑的程度可能超过对手术结果不成功的恐惧。改善疼痛管理的挑战之一是通过文件审计监控护士的行为(即评估、干预和重新评估)。检查电子护理文档可以提供发现知识和改进实践的方法的机会。在这项研究中，对疼痛治疗指南的遵守将在电子病历中进行衡量，主要是通过患者护理的护理文档，包括药物管理和护理流程表。范德比尔特大学医学中心作为实施临床应用信息系统的先驱，例如条形码用药管理图表和护理文件流程图，可以衡量所有接受护理的大量人口中循证标准的实施水平，而不分年龄、性别或种族。我们计划使用数据分析来评估住院患者急性疼痛治疗的流行率、严重性、治疗方法和有效性。疼痛缓解将是主要结果。我们将分析疼痛评估、干预和重新评估疼痛严重程度与预定(与需要时)药物、多模式药物和行为干预之间的时间关联。这项研究将使用回归分析来检验实施的疼痛标准对患者在住院期间经历的疼痛水平的结果。疼痛评分(在0-10的标准范围内)将根据重新评估的时间、重新评估的次数、阿片类药物的使用、非阿片类止痛剂的使用、非药物治疗以及共同使用的治疗数量(干预强度)进行预测。此外，还将进行数据挖掘，以提取隐含的、以前未知的和有用的信息，以加强疼痛管理。这项研究的长期目标是确定将这些电子数据用于持续监测的可行性，这些监测可以通过可能的增量干预来改善绩效，类似于护士领导的急性疼痛服务扩展由一些卫生保健机构实施的快速反应小组。 公共卫生相关性：项目描述：尽管出现了急性术后疼痛的管理指南和标准，但未缓解的疼痛仍然存在，推动了住院费用的增加。这项工作将对电子健康记录进行二次数据分析，包括护理疼痛评估和治疗(包括药理学和非药理学)。我们还将对其他隐含的、以前未知的有用信息进行数据挖掘，例如应该触发或升级急性疼痛管理干预警报的情况。

DESCRIPTION (provided by applicant): This application will use secondary data analysis to examine acute post surgical pain management practice in an academic medical center using large datasets created from electronic health records (EHR) and medication administration systems. The primary aim of the study is to analyze pain management by both hypothesis tests & data mining. Current pain management practice is informed with standards disseminated by the American Pain Society reflecting the best evidence-based knowledge of how to treat pain, yet implementation remains slow. Pain remains an important post operative issue, resulting in a level of patient anxiety that may surpass fear of a unsuccessful surgical outcome. One of the challenges in improving pain management is the surveillance of nurse behaviors (i.e., assessment, intervention, and re-assessment) through documentation audits. Examination of electronic nursing documentation may present opportunities to discover knowledge and ways to improve practice. In this study, adherence to pain treatment guidelines will be measured in the electronic medical record, primarily via nursing documentation of patient care, including medication administration and nursing flow sheets. Vanderbilt University Medical Center, as a pioneer in implementation of clinical application information systems, e.g. bar code medication administration charting and nursing documentation flow sheet charting, can measure the implementation levels of evidence based standards in large populations of all recipients of care regardless of age, gender or race. We plan to use the data analysis to estimate the prevalence, severity, treatment, and effectiveness of therapies to manage acute pain in hospitalized patients. Pain relief will be the primary outcome. We will analyze the temporal association of pain assessment, intervention, and reassessment to severity of pain scores in relation to scheduled (versus as needed) medications, multimodal medications, and behavioral interventions. The study will use a regression analysis to examine the outcome of implemented pain standards on the level of pain experienced by the patient during hospitalization. Pain scores (on the standard scale of 0-10) will be predicted by time to reassessment, number of reassessments, opioid administration, non-opioid analgesic administration, non-pharmacological therapies, and the number of therapies administered together (intensity of interventions). In addition, data mining will be performed to extract implicit, previously unknown, and useful information for enhancing pain management. The long term goal of this study is to determine feasibility of using this electronic data for ongoing surveillance that could be monitored to improve performance using a possible incremental intervention similar the nurse led acute pain service extension of rapid response teams implemented by some health care institutions. PUBLIC HEALTH RELEVANCE: Project Narrative: Despite the emergence of guidelines and standard for management of acute post operative pain, unrelieved pain persists, driving increased costs of hospitalization. This work will perform a secondary data analysis of electronic health records, including nursing pain assessments and therapies (both pharmacologic and non-pharmacologic). We will also data mine for other implicit, previously unknown, useful information such as conditions that should trigger or escalate an alert for an acute pain management intervention.