OBJECTIVES: Individuals who prefer to communicate about health care in a language other than English (LOE) experience poorer quality medical care and challenges when communicating with health care providers. The objective of this study was to elucidate how caregivers who prefer an LOE perceive communication with their physicians on an inpatient general pediatrics service.
METHODS: Caregivers of patients admitted to the general pediatrics service at our urban freestanding children's hospital whose preferred language for medical care was Spanish, Arabic, Brazilian Portuguese, or Mandarin were eligible for this qualitative study. Semistructured interviews using video interpreter services were conducted to explore the participants' experiences communicating with their physicians. Interview transcripts were analyzed using conventional content analysis.
RESULTS: We interviewed 15 participants representing 7 countries of origin and 4 non-English languages: Spanish, Arabic, Brazilian Portuguese, and Mandarin. Three main domains emerged, including: (1) use of interpreter services, (2) overall communication experience with physician providers, and (3) suggestions for improvement in physician communication. Salient themes included early identification of the need for an interpreter is essential and physicians' use of body language in combination with an interpreter enhances successful communication.
CONCLUSIONS: This project fills a gap in existing literature by describing the perspectives of caregivers who prefer an LOE, including those speaking languages other than Spanish, because they communicate with inpatient pediatricians. In addition to ensuring appropriate use of interpreter services, physicians can focus on using plain language and gestures during encounters, helping to facilitate communication and provide culturally competent care for this population.
Ongoing management of monitor alarms is important for reducing alarm fatigue among clinicians (e.g., nurses, physicians). Strategies to enhance clinician engagement in active alarm management in pediatric acute care have not been well explored. Access to alarm summary metrics may enhance clinician engagement. To lay the foundation for intervention development, we sought to identify functional specifications for formulating, packaging, and delivering alarm metrics to clinicians. Our team of clinician scientists and human factors engineers conducted focus groups with clinicians from medical-surgical inpatient units in a children's hospital. We inductively coded transcripts, developed codes into themes, and grouped themes into "current state" and "future state." We conducted five focus groups with 13 clinicians (eight registered nurses and five doctors of medicine). In the current state, information exchanged among team members about alarm burden is initiated by nurses on an ad hoc basis. For a future state, clinicians identified ways in which alarm metrics could help them manage alarms and described specific information, such as alarm trends, benchmarks, and contextual data, that would support decision-making. We developed four recommendations for future strategies to enhance clinicians' active management of patient alarms: (1) formulate alarm metrics for clinicians by categorizing alarm rates by type and summarizing alarm trends over time, (2) package alarm metrics with contextual patient data to facilitate clinicians' sensemaking, (3) deliver alarm metrics in a forum that facilitates interprofessional discussion, and (4) provide clinician education to establish a shared mental model about alarm fatigue and evidence-based alarm-reduction strategies.
Ongoing management of monitor alarms is important for reducing alarm fatigue among clinicians (e.g., nurses, physicians). Strategies to enhance clinician engagement in active alarm management in pediatric acute care have not been well explored. Access to alarm summary metrics may enhance clinician engagement. To lay the foundation for intervention development, we sought to identify functional specifications for formulating, packaging, and delivering alarm metrics to clinicians. Our team of clinician scientists and human factors engineers conducted focus groups with clinicians from medical-surgical inpatient units in a children's hospital. We inductively coded transcripts, developed codes into themes, and grouped themes into "current state" and "future state." We conducted five focus groups with 13 clinicians (eight registered nurses and five doctors of medicine). In the current state, information exchanged among team members about alarm burden is initiated by nurses on an ad hoc basis. For a future state, clinicians identified ways in which alarm metrics could help them manage alarms and described specific information, such as alarm trends, benchmarks, and contextual data, that would support decision-making. We developed four recommendations for future strategies to enhance clinicians' active management of patient alarms: (1) formulate alarm metrics for clinicians by categorizing alarm rates by type and summarizing alarm trends over time, (2) package alarm metrics with contextual patient data to facilitate clinicians' sensemaking, (3) deliver alarm metrics in a forum that facilitates interprofessional discussion, and (4) provide clinician education to establish a shared mental model about alarm fatigue and evidence-based alarm-reduction strategies.
BACKGROUND: Many hospitals use rapid response systems (RRSs) to identify and intervene on hospitalized children at risk for deterioration.
OBJECTIVES: To describe RRS characteristics across hospitals in the Pediatric Research in Inpatient Settings (PRIS) network.
METHODS: We developed the survey through a series of prospective respondent, expert, and cognitive interviews. One institutional expert per PRIS hospital ( = 109) was asked to complete the web survey. We summarized responses using descriptive statistics with a secondary analysis of univariate associations between RRS characteristics and perceived effectiveness.
RESULTS: The response rate was 72% (79 of 109). Respondents represented diverse hospital types and were primarily physicians (97%) with leadership roles in care escalation. Many hospitals used an early warning score (77%) for identification with variable characteristics (46% automated versus 54% full or partially manual calculation; inputs included vital signs [98%], physical examination findings [88%], diagnoses [23%], medications [19%], and diagnostic tests [14%]). Few incorporated a validated prediction model (9%). Similarly, many RRSs used a rapid response team for intervention (93%) with variable team composition (respiratory therapists [94%], ICU nurses [93%], ICU providers [67%], and pharmacists [27%]). Some used the early warning score to trigger the rapid response team (50%). Only a few staffed a clinician to proactively surveil hospitalized children for risk of deterioration (18%), and these tended to be larger hospitals (annual admissions 12 000 vs 6000, = .007). Most responding experts stated their RRSs improved patient outcomes (92%).
CONCLUSIONS: RRS characteristics varied across PRIS hospitals.
IMPORTANCE: Subspecialty consultation is a frequent, consequential practice in the pediatric inpatient setting. Little is known about factors affecting consultation practices.
OBJECTIVES: To identify patient, physician, admission, and systems characteristics that are independently associated with subspecialty consultation among pediatric hospitalists at the patient-day level and to describe variation in consultation utilization among pediatric hospitalist physicians.
DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study of hospitalized children used electronic health record data from October 1, 2015, through December 31, 2020, combined with a cross-sectional physician survey completed between March 3 and April 11, 2021. The study was conducted at a freestanding quaternary children's hospital. Physician survey participants were active pediatric hospitalists. The patient cohort included children hospitalized with 1 of 15 common conditions, excluding patients with complex chronic conditions, intensive care unit stay, or 30-day readmission for the same condition. Data were analyzed from June 2021 to January 2023.
EXPOSURES: Patient (sex, age, race and ethnicity), admission (condition, insurance, year), physician (experience, anxiety due to uncertainty, gender), and systems (hospitalization day, day of week, inpatient team, and prior consultation) characteristics.
MAIN OUTCOMES AND MEASURES: The primary outcome was receipt of inpatient consultation on each patient-day. Risk-adjusted consultation rates, expressed as number of patient-days consulting per 100, were compared between physicians.
RESULTS: We evaluated 15 922 patient-days attributed to 92 surveyed physicians (68 [74%] women; 74 [80%] with ≥3 years' attending experience) caring for 7283 unique patients (3955 [54%] male patients; 3450 [47%] non-Hispanic Black and 2174 [30%] non-Hispanic White patients; median [IQR] age, 2.5 ([0.9-6.5] years). Odds of consultation were higher among patients with private insurance compared with those with Medicaid (adjusted odds ratio [aOR], 1.19 [95% CI, 1.01-1.42]; P = .04) and physicians with 0 to 2 years of experience vs those with 3 to 10 years of experience (aOR, 1.42 [95% CI, 1.08-1.88]; P = .01). Hospitalist anxiety due to uncertainty was not associated with consultation. Among patient-days with at least 1 consultation, non-Hispanic White race and ethnicity was associated with higher odds of multiple consultations vs non-Hispanic Black race and ethnicity (aOR, 2.23 [95% CI, 1.20-4.13]; P = .01). Risk-adjusted physician consultation rates were 2.1 times higher in the top quartile of consultation use (mean [SD], 9.8 [2.0] patient-days consulting per 100) compared with the bottom quartile (mean [SD], 4.7 [0.8] patient-days consulting per 100; P < .001).
CONCLUSIONS AND RELEVANCE: In this cohort study, consultation use varied widely and was associated with patient, physician, and systems factors. These findings offer specific targets for improving value and equity in pediatric inpatient consultation.
BACKGROUND: Methods of sustaining the deimplementation of overused medical practices (i.e., practices not supported by evidence) are understudied. In pediatric hospital medicine, continuous pulse oximetry monitoring of children with the common viral respiratory illness bronchiolitis is recommended only under specific circumstances. Three national guidelines discourage its use for children who are not receiving supplemental oxygen, but guideline-discordant practice (i.e., overuse) remains prevalent. A 6-hospital pilot of educational outreach with audit and feedback resulted in immediate reductions in overuse; however, the best strategies to optimize sustainment of deimplementation success are unknown.
METHODS: The Eliminating Monitor Overuse (EMO) trial will compare two deimplementation strategies in a hybrid type III effectiveness-deimplementation trial. This longitudinal cluster-randomized design will be conducted in Pediatric Research in Inpatient Settings (PRIS) Network hospitals and will include baseline measurement, active deimplementation, and sustainment phases. After a baseline measurement period, 16-19 hospitals will be randomized to a deimplementation strategy that targets unlearning (educational outreach with audit and feedback), and the other 16-19 will be randomized to a strategy that targets unlearning and substitution (adding an EHR-integrated clinical pathway decision support tool). The primary outcome is the sustainment of deimplementation in bronchiolitis patients who are not receiving any supplemental oxygen, analyzed as a longitudinal difference-in-differences comparison of overuse rates across study arms. Secondary outcomes include equity of deimplementation and the fidelity to, and cost of, each deimplementation strategy. To understand how the deimplementation strategies work, we will test hypothesized mechanisms of routinization (clinicians developing new routines supporting practice change) and institutionalization (embedding of practice change into existing organizational systems).
DISCUSSION: The EMO trial will advance the science of deimplementation by providing new insights into the processes, mechanisms, costs, and likelihood of sustained practice change using rigorously designed deimplementation strategies. The trial will also advance care for a high-incidence, costly pediatric lung disease.
TRIAL REGISTRATION: ClinicalTrials.gov, NCT05132322 . Registered on November 10, 2021.
OBJECTIVES: Emergency transfers (ETs), deterioration events with late recognition requiring ICU interventions within 1 hour of transfer, are associated with adverse outcomes. We leveraged electronic health record (EHR) data to assess the association between ETs and outcomes. We also evaluated the association between intervention timing (urgency) and outcomes.
METHODS: We conducted a propensity-score-matched study of hospitalized children requiring ICU transfer between 2015 and 2019 at a single institution. The primary exposure was ET, automatically classified using Epic Clarity Data stored in our enterprise data warehouse endotracheal tube in lines/drains/airway flowsheet, vasopressor in medication administration record, and/or ≥60 ml/kg intravenous fluids in intake/output flowsheets recorded within 1 hour of transfer. Urgent intervention was defined as interventions within 12 hours of transfer.
RESULTS: Of 2037 index transfers, 129 (6.3%) met ET criteria. In the propensity-score-matched cohort (127 ET, 374 matched controls), ET was associated with higher in-hospital mortality (13% vs 6.1%; odds ratio, 2.47; 95% confidence interval [95% CI], 1.24-4.9, P = .01), longer ICU length of stay (subdistribution hazard ratio of ICU discharge 0.74; 95% CI, 0.61-0.91, P < .01), and longer posttransfer length of stay (SHR of hospital discharge 0.71; 95% CI, 0.56-0.90, P < .01). Increased intervention urgency was associated with increased mortality risk: 4.1% no intervention, 6.4% urgent intervention, and 10% emergent intervention.
CONCLUSIONS: An EHR measure of deterioration with late recognition is associated with increased mortality and length of stay. Mortality risk increased with intervention urgency. Leveraging EHR automation facilitates generalizability, multicenter collaboratives, and metric consistency.