Improving Pediatric Intensive Care Unit Discharge Timeliness of Infants with Bronchiolitis Using Clinical Decision Support

Author:

Martin Blake12,Mulhern Brendan34,Majors Melissa4,Rolison Elise4,McCombs Tiffany14,Smith Grant34,Fisher Colin34,Diaz Elizabeth4,Downen Dana4,Brittan Mark34

Affiliation:

1. Department of Pediatrics, Section of Critical Care, University of Colorado School of Medicine, Aurora, Colorado, United States

2. Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, Colorado, United States

3. Department of Pediatrics, Section of Hospital Medicine, University of Colorado School of Medicine, Aurora, Colorado, United States

4. Department of Pediatrics, Children's Hospital Colorado, Aurora, Colorado, United States

Abstract

Abstract Background Identifying children ready for transfer out of the pediatric intensive care unit (PICU) is an area that may benefit from clinical decision support (CDS). We previously implemented a quality improvement (QI) initiative to accelerate the transfer evaluation of non–medically complex PICU patients with viral bronchiolitis receiving floor-appropriate respiratory support. Objectives Design a CDS tool adaptation of this QI initiative to further accelerate transfer evaluation of appropriate patients. Methods The original initiative focused on identifying for transfer evaluation otherwise healthy children admitted to the PICU with bronchiolitis who had been receiving floor-appropriate levels of respiratory support for at least 6 hours. However, this initiative required that clinicians manually track the respiratory support of qualifying patients. We designed an electronic health record (EHR)–based CDS tool to automate identification of transfer-ready candidates. The tool parses EHR data to identify children meeting prior QI initiative criteria and alerts clinicians to assess transfer readiness once a child has been receiving floor-appropriate respiratory support for 6 hours. We compared time from reaching floor-appropriate support to placement of the transfer order (“time-to-transfer”), PICU length of stay (LOS), and hospital LOS between patients admitted prior to our QI initiative (December 1, 2018–October 19, 2019, “pre-QI phase”), during the initiative but before CDS tool implementation (October 20, 2019–February 7, 2022, “QI phase”), and after CDS implementation (February 8–November 11, 2022, “CDS phase”). Results CDS-phase patients (n = 131) had a shorter median time-to-transfer of 5.23 (interquartile range [IQR], 3.38–10.0) hours compared with QI-phase patients (n = 304) at 5.93 (IQR, 4.23–12.2) hours (p = 0.04). PICU and hospital LOS values decreased from the pre-QI (n = 150) to QI phase. Though LOS reductions were sustained during the CDS phase, further reductions from QI to CDS phase were not statistically significant. Conclusion An EHR-based CDS adaptation of a prior QI initiative facilitated timely identification of PICU patients with bronchiolitis ready for transfer evaluation. Such tools might allow PICU clinicians to focus on other high-acuity tasks while accelerating transfer evaluation of appropriate patients.

Publisher

Georg Thieme Verlag KG

Subject

Health Information Management,Computer Science Applications,Health Informatics

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