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

Abstract

BACKGROUND: Identifying children ready for transfer out of the pediatric ICU (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 LOS, and hospital LOS between patients admitted prior to our QI initiative (12/1/2018-10/19/2019, “pre-QI-phase”), during the initiative but before CDS tool implementation (10/20/2019-2/7/2022, “QI-phase”), and after CDS implementation (2/8/2022-11/11/2022, “CDS-phase”). RESULTS: CDS-phase patients (n=131) had a shorter median time-to-transfer of 5.23 (IQR 3.38-10.0) hours compared to QI-phase patients (n=304) at 5.93 (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. CONCLUSIONS: 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.

Between‐hospital variation in clinical decision support availability for common inpatient pediatric conditions: Results of a national Pediatric Research in Inpatient Settings (PRĪS) Network survey

“…11 CDS usage in a variety of settings has been associated with improved patient care processes or outcomes for these diagnoses. [12][13][14][15][16][17][18][19] The survey included EHR vendor-agnostic operational definitions of each CDS tool type that appeared when the respondent hovered over the corresponding response option to aid in their understanding. Upon receipt of the survey invitation, site leads were given the option to forward the invitation to a colleague more familiar with CDS at their site.…”

“…Diagnoses were chosen based upon epidemiologic research identifying the most frequently encountered conditions in inpatient pediatric hospital medicine 11 . CDS usage in a variety of settings has been associated with improved patient care processes or outcomes for these diagnoses 12–19 . The survey included EHR vendor‐agnostic operational definitions of each CDS tool type that appeared when the respondent hovered over the corresponding response option to aid in their understanding.…”

Usability Testing of Situation Awareness Clinical Decision Support in the Intensive Care Unit

“…CDS solutions that enhance human activities and improve people-based work processes are an important focus, leading to improved outcomes. 14,28 While examples of effective, automated CDS tools in the PICU setting exist, 29,30 the implementation and perceived benefits of currently available automated PICU CDS tools are limited. 14 It is crucial to note that these limitations extend beyond the PICU, as predictive algorithms and advanced CDS tools often fall short in successful implementation and effectively improving patient outcomes.…”