Modeling informatics data to track maternal risk and care quality

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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.