Modeling informatics data to track maternal risk and care quality

对信息学数据进行建模以跟踪孕产妇风险和护理质量

• 批准号: 10522536
• 负责人: Alexander M Friedman
• 金额: $ 77.64万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-08 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10522536
• 关键词: Acute Kidney Failure; Address; Adherence; Affect; Biometry; Blood Banks; Caring; Cessation of life; Clinical; Clinical Management; Clinical Research; Clinical Trials; Complex; Complication; Country; Data; Data Analyses; Decision Analysis; Deterioration; Development; Devices; Diagnosis; Discipline of obstetrics; Eclampsia; Electronic Health Record; Emergency Situation; Event; Failure; Goals; Guidelines; Hemorrhage; Hospitalization; Hospitals; Hypertension; Hysterectomy; Individual; Infection; Informatics; Intervention; Knowledge; Leadership; Manuals; Maternal Mortality; Measures; Medical; Methodology; Modeling; Organ; Outcome; Outcome Assessment; Patient risk; Patients; Perinatal; Perinatal Epidemiology; Pharmaceutical Preparations; Phenotype; Population; Postpartum Hemorrhage; Practice Management; Procedures; Protocols documentation; Provider; Quality of Care; Radiology Specialty; Recommendation; Records; Research; Resources; Risk; Risk Estimate; Safety; Secondary to; Sepsis; Severities; Standardization; Stroke; System; Testing; Time; Variant; adverse maternal outcomes; adverse outcome; base; behavioral phenotyping; billing data; burden of illness; clinical development; clinical risk; comorbidity; data standards; design; disability; economic evaluation; evidence base; high risk; implementation science; innovation; insight; maternal morbidity; maternal risk; maternal safety; medical complication; mortality; multidisciplinary; novel; obstetric care; patient stratification; pregnancy hypertension; prevent; provider behavior; quality assurance; racial disparity; response; risk stratification; safety assessment; safety practice; service coordination; severe maternal morbidity; simulation; standardized care; systematic review; trend

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.

抽象的 虽然孕产妇的严重发病率和死亡率在近几十年来显着增加，但尚不清楚什么 遵循高风险临床方案的学位建议安全实践，并降低了不利的风险 孕产妇的结果。降低孕产妇风险的关键策略是实施“安全束”和 统一的方案，标准化高危临床条件的护理。而标准化的临床措施 这些捆绑包中的支持是基于证据的，存在与实施，护理有关的主要知识差距 质量监视和结果评估，以评估产后出血的安全方案 怀孕，败血症和严重异常的生命体征（母体预警系统）的疾病。产科 护理涉及服务，临床医生和资源的复杂协调，领导才能受到限制 能够实时跟踪结果并实时确定高质量护理。尽管管理明确 建议，孕产妇死亡率和安全审查已经确定，经常发生护理不足 偏离建议的提供者，包括延迟识别和包括延迟标识和的系统问题 反应和医院级别的影响，在非最佳实践中归一化。学位 遵循准则，可以避免不利的结果，许多医院受到限制 他们有系统地审查护理的能力。从电子健康记录（EHR）收集的数据可能是 瞬间分析以识别危险患者以及并发症以及大型护理和管理 人群。我们的研究小组对超过40,000的研究小组对产科出血的事先研究 住院表明，调整后的围断子宫切除术的调整赔率减少了一半 实施出血安全束。该提案的总体假设是EHR数据 可以可靠地确定与未能在母亲的情况下营救有关的临床管理因素 紧急情况，例如：（i）严重的高血压，（ii）产科出血，（iii）败血症，（iv）坦率地异常 产妇生命体征（母体早期警告系统）。未能救援被定义为无法防止 潜在疾病或 医疗的并发症。我们将分析捆绑建议和估计的学位护理。 不遵循指南时未能营救的风险。我们将利用EHR数据的丰富性来 提供者的行为表征，风险将患者分层。我们的研究小组包括信息学专业知识，临床 研究，围产期流行病学，决策分析和生物统计学。来自八家医院的EHR数据 研究联盟将进行分析。我们将表征临床管理，成果和护理质量 严重的高血压，产科出血，败血症和坦率的生命体征。这些分析的数据 将用于许多模拟，以告知开发临床试验和干预措施。