Modeling informatics data to track maternal risk and care quality

摘要 虽然产妇的严重发病率和死亡率在近几十年里显著增加，但具体原因尚不清楚。 遵循针对高风险临床场景的推荐安全做法，并降低不良反应的风险 母性结局。降低产妇风险的一项关键战略是实施“安全捆绑”和 统一的协议，以标准化护理的高危临床条件。而标准化的临床措施 这些捆绑包中支持的是基于证据的，存在与实施、护理相关的主要知识差距 高血压性产后出血安全方案的质量监测和结果评估 妊娠疾病、败血症和严重异常的生命体征(产妇早期预警系统)。产科 护理涉及服务、临床医生和资源的复杂协调，而领导力在他们的 能够跟踪结果并实时识别大规模的高质量护理。尽管管理清晰 建议、孕产妇死亡率和安全审查发现，经常发生护理不足的情况。 作为偏离建议的提供商的次要问题，系统问题包括延迟识别和 反应，以及非最佳做法被正常化的医院层面的影响。在什么程度上 指南得到遵守，可以避免的不良后果尚不清楚，许多医院在 他们系统审查护理的能力。从电子健康记录(EHR)收集的数据可能是 即时分析以识别高危患者和并发症，并跟踪护理和管理 人口。我们研究组4万余名产妇对产科出血的早期电子健康记录研究 住院治疗显示，接受围产期子宫切除术的调整后优势降低了一半。 实施出血安全捆绑。这项提议的首要假设是电子病历数据 能够可靠地识别与母体环境中的抢救失败相关的临床管理因素 紧急情况，如：(一)严重高血压，(二)产科出血，(三)败血症，(四)明显异常 产妇生命体征(产妇早期预警系统)。救援失败的定义是未能防止 严重恶化临床上重要的恶化，如死亡或永久性残疾，因潜在疾病或 医疗护理的并发症。我们将分析在多大程度上遵循捆绑建议并估计 如果不遵守指导方针，就会面临救援失败的风险。我们将利用EHR数据的丰富性 确定提供者的行为特征，并对患者进行风险分层。我们的研究小组包括信息学、临床方面的专业知识 研究、围产期流行病学、决策分析和生物统计学。一年中八家医院的电子病历数据 将对研究联盟进行分析。我们将确定临床管理、结果和护理质量 严重高血压，产科出血，败血症，以及明显异常的生命体征。来自这些分析的数据 将用于一些模拟，以便为临床试验和干预措施的发展提供信息。

ABSTRACT While maternal severe morbidity and mortality increased significantly over recent decades, it is unclear to what degree recommended safety practices for high-risk clinical scenarios are followed and reduce risk for adverse maternal outcomes. A key strategy to reducing maternal risk has been implementation of `safety bundles' and uniform protocols to standardize care for high-risk clinical conditions. While the standardized clinical measures supported in these bundles are evidence based, there are major knowledge gaps related to implementation, care quality surveillance, and outcomes assessment for safety protocols for postpartum hemorrhage, hypertensive diseases of pregnancy, sepsis, and grossly abnormal vital signs (maternal early warning systems). Obstetrical care involves complex coordination of services, clinicians, and resources, and leadership are limited in their ability to track outcomes and identify high quality care in real time at scale. Despite clear management recommendations, maternal mortality and safety reviews have identified that deficiencies in care often occur secondary to providers deviating from recommendations, systems issues including delayed identification and response, and hospital-level effects where non-optimal practices are normalized. The degrees to which guidelines are followed and adverse outcomes can be averted are not known, and many hospitals are limited in their ability to systematically review care. Data collected from the electronic health record (EHR) may be instantaneously analyzed to identify at-risk patients and complications and track care and management in large populations. Prior EHR research on obstetric hemorrhage by our study group of over 40,000 delivery hospitalizations demonstrated that adjusted odds for peripartum hysterectomy decreased by half after implementation of a hemorrhage safety bundle. The overarching hypothesis of this proposal is that EHR data can reliably identify clinical-management factors associated with failure to rescue in the setting of maternal emergencies such as: (i) severe hypertension, (ii) obstetric hemorrhage, (iii) sepsis, and (iv) frankly abnormal maternal vital signs (maternal early warnings systems). Failure to rescue is defined as a failure to prevent a clinically important deterioration, such as death or permanent disability, from an underlying illness or a complication of medical care. We will analyze to what degree care follows bundle recommendations and estimate risk for failure to rescue when guidelines are not followed. We will leverage the richness of EHR data to characterize provider behavior and risk stratify patients. Our study group includes expertise in informatics, clinical research, perinatal epidemiology, decision analysis, and biostatistics. EHR data from eight hospitals in a research consortium will be analyzed. We will characterize clinical management, outcomes, and care quality for severe hypertension, obstetric hemorrhage, sepsis, and frankly abnormal vital signs. Data from these analyses will be used for a number of simulations to inform development of clinical trials and interventions.