Oral Concurrent Session 9 - Quality
Oral Concurrent Sessions
Nandini Raghuraman, MD, MSCI (she/her/hers)
Associate Professor
Washington University School of Medicine
Washington University School of Medicine, Missouri, United States
Megan L. Lawlor, MD (she/her/hers)
Asst Prof of Ob & Gyn
Washington University School of Medicine
St. Louis, Missouri, United States
Lori M. Stevenson, MSN, RN
Barnes-Jewish Hospital
St. Louis, Missouri, United States
Steve Porter, MD
Clinical Instructor
Case Western Reserve University, University Hospitals MacDonald Women's Hospital
Cleveland, Ohio, United States
Susan Mann, MD
Assistant Professor
Beth Israel Deaconess Medical Center, Harvard Medical School
Boston, Massachusetts, United States
Amanda C. Zofkie, MD
Assistant Professor
Washington University School of Medicine
St. Louis, Missouri, United States
Jeannie C. Kelly, MD, MS (she/her/hers)
Associate Professor
Washington University School of Medicine
Washington University School of Medicine, Missouri, United States
Antonina I. Frolova, MD, PhD (she/her/hers)
Assistant Professor of Ob&Gyn
Washington University School of Medicine
St. Louis, Missouri, United States
Roxane Rampersad, MD
Washington University School of Medicine
St. Louis, Missouri, United States
Timely treatment of severe hypertension (HTN) and intraamniotic infection (IAI) are key quality metrics for labor and delivery (L&D) units. We evaluated whether riskLD, a clinical decision support tool integrated within electronic health records (EHR) which issues real-time alerts to prompt timely care, reduced treatment delays.
Study Design:
We conducted an interrupted time series study over 24 months (3/2023-2/2025) on a single tertiary care L&D unit. riskLD was implemented in February 2024. We assessed monthly rates of delayed initiation of antibiotics for IAI ( >1 hour) after meeting ACOG-defined IAI criteria and delayed treatment of severe HTN ( >1hour) after two consecutive severe blood pressures. Segmented regression was used to assess trends over time. Prais–Winsten estimation was used to account for autocorrelation. Models included terms for pre-implementation trend, immediate change after riskLD implementation, and post-implementation trend.
Results:
There were 2992 and 3853 deliveries in the pre and post implementation periods, respectively. For IAI, there was a significant increasing trend in delayed treatment prior to riskLD implementation (β = +3.7% per month, p</span> = 0.01), followed by a significant immediate 32% reduction in delayed treatment after riskLD implementation (p = 0.01) that remained stable post-implementation (p=0.24). For severe HTN, there was no significant trend pre-implementation, however riskLD implementation was associated with a significant immediate 9% reduction in treatment delays (p = 0.048), which remained stable post-implementation (p=0.40). Implementation of real-time EHR alerts via riskLD was associated with immediate and durable reductions in delayed treatment of IAI and severe HTN.
Conclusion: