Modeling informatics data to track maternal risk and care quality

Active Project Grant Awarded Last action

Award ID
R01HD104943
Recipient
Awarding agency
Obligated
$4,162,244

Award facts

Completion date 84% complete
Place of performance
New York, NY 10032, USA

Award details come from USAspending.gov.

Description

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.

Transactions

Action Obligated Date
Amendment $647,919
Amendment $641,477
Amendment $642,253
Amendment $677,787
Amendment $776,404
Original award $776,404

Subawards

Subrecipient Amount Awarded
Temple University-Of The Commonwealth System Of Higher Education Project summary: Hospitals and providers need valid means of quickly identifying at-risk obstetric patients with a complication and tracking whether high… $22,604
Christiana Care Health Services Inc. University of pennsylvaniastatement of work modeling informatic's data to track maternal risk and care quality 7/1/2022- 06/30/2027KENRICK cato, PHD,… $95,726
Geisinger Clinic Project summary: Hospitals and providers need valid means of quickly identifying at-risk obstetric patients with a complication and tracking whether high… $26,314
Christiana Care Health Services Inc. University of pennsylvaniastatement of work modeling informatic's data to track maternal risk and care quality 7/1/2022- 06/30/2027KENRICK cato, PHD,… $28,488
New York-Presbyterian / Queens Project title: modeling informatic's data to track maternal risk and care qualitycontact PI: alexander friedman MD, cumcpi: sarah rossetti PHD, cumcprogram… $26,314
Rutgers, The State University Hospitals and providers need valid means of quickly identifying at-risk obstetric patients with a complication and tracking whether high quality care occurred… $24,032
Temple University-Of The Commonwealth System Of Higher Education DocuSign Envelope ID: 22A3AB58-9130-4443-9981-CC5FEA94A09E SCOPE OF WORK Project title: Modeling informatics data to track maternal risk and care quality… $21,251
Geisinger Clinic Project title: Modeling informatics data to track maternal risk and care quality Contact PI: Alexander Friedman MD, CUMC PI: Sarah Rossetti PhD, CUMC Program… $24,864
Geisinger Clinic SCOPE OF WORK Project title: Modeling informatics data to track maternal risk and care quality Contact PI: Alexander Friedman MD, CUMC PI: Sarah Rossetti PhD,… $24,864
Christiana Care Health Services Inc. University of Pennsylvania STATEMENT OF WORK “Modeling informatics data to track maternal risk and care quality� 7/1/2022- 06/30/2027 Kenrick Cato,… $94,114
A NJ Nonprofit Corporation Cooper Norcross Health 􀀾􀀵􀀨􀀰􀀤􀀬􀀱􀀧􀀨􀀵􀀃􀀲􀀩􀀃􀀷􀀫… $21,059
New York-Presbyterian / Queens Project title: Modeling informatics data to track maternal risk and care quality Contact PI: Alexander Friedman MD, CUMC PI: Sarah Rossetti PhD, CUMC Program… $8,582
Yale Univ E65768DFAAED SCOPE OF WORK Project title: Modeling informatics data to track maternal risk and care quality Contact PI: Alexander Friedman MD, CUMC PI: Sarah… $24,574
Temple University-Of The Commonwealth System Of Higher Education ô€€¶ô€€¦ô€€²ô€€³ô€€¨ô€€ƒô€€²ô€€©ô€€ƒô€€ºô€€²ô€€µô€€®ô€€ƒô€€‰ô€€ƅ $20,983
Geisinger Clinic SCOPE OF WORK Project title: Modeling informatics data to track maternal risk and care quality Contact PI: Alexander Friedman MD, CUMC PI: Sarah Rossetti PhD,… $24,495
Christiana Care Health Services Inc. SCOPE OF WORK Project title: Modeling informatics data to track maternal risk and care quality Contact PI: Alexander Friedman MD, CUMC PI: Sarah Rossetti PhD,… $26,476
The Trustees Of The University Of Pennsylvania University of Pennsylvania STATEMENT OF WORK “Modeling informatics data to track maternal risk and care quality� 7/1/2022- 06/30/2027 Kenrick Cato,… $49,972
New York-Presbyterian / Queens tle: Modeling informatics data to track maternal risk and care quality Contact PI: Alexander Friedman MD, CUMC PI: Sarah Rossetti PhD, CUMC Program… $8,582
Saint Peter's University Hospital, Inc. roject title: Modeling informatics data to track maternal risk and care quality Contact PI: Alexander Friedman MD, CUMC PI: Sarah Rossetti PhD, CUMC Program… $17,320
A NJ Nonprofit Corporation Cooper Norcross Health SCOPE OF WORK Project title: Modeling informatics data to track maternal risk and care quality Contact PI: Alexander Friedman MD, CUMC PI: Sarah Rossetti PhD,… $20,732
Rutgers The State University Of New Jersey BUDGET Attachment 1 DocuSign Envelope ID: E27143B7-0408-4008-B2E0-ECB58C8188D8 SCOPE OF WORK Project title: Modeling informatics data to track maternal risk and… $27,957
Saint Peter's University Hospital, Inc. Project title: Modeling informatics data to track maternal risk and care quality Contact PI: Alexander Friedman MD, CUMC PI: Sarah Rossetti PhD, CUMC Program… $14,697
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Quick answers

Who received award R01HD104943?

R01HD104943 was awarded to The Trustees Of Columbia University In The City Of New York by National Institute of Child Health and Human Development (National Institutes of Health) on September 8, 2022.

How much is award R01HD104943 worth?

$4,162,244 has been obligated on R01HD104943.

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