Search

Purpose: The effect of communication between referring and accepting clinicians during patient transitions to the pediatric intensive care unit (PICU) on diagnostic quality is largely unknown. This pilot study aims to determine the feasibility of using focused ethnography to understand the relationship between referral communication and the diagnostic process for critically ill children., Materials and Methods: We conducted focused ethnography in an academic tertiary referral PICU by directly observing the referral and admission of 3 non-electively admitted children 0-17 years old. We also conducted 21 semi-structured interviews of their parents and admitting PICU staff (intensivists, fellows/residents, medical students, nurses, and respiratory therapists) and reviewed their medical records post-discharge., Results: Performing focused ethnography in a busy PICU is feasible. We identified three areas for additional exploration: (1) how information transfer affects the PICU diagnostic process; (2) how uncertainty in patient assessment affects the decision to transfer to the PICU; and (3) how the PICU team's expectations are influenced by referral communication., Conclusions: Focused ethnography in the PICU is feasible to investigate relationships between clinician referral communication and the diagnostic process for critically ill children., Competing Interests: Declaration of Competing Interest Dr. Cifra is supported by the Agency for Healthcare Research and Quality (AHRQ) through a K08 grant (HS026965) and an internal start-up grant from the University of Iowa Carver College of Medicine Department of Pediatrics. Dr. Singh is funded in part by the Houston VA Health Services Research and Development (HSR&D) Center for Innovations in Quality, Effectiveness, and Safety (CIN13-413), the VA HSR&D Service (CRE17-127) and the Presidential Early Career Award for Scientists and Engineers (USA 14–274), the VA National Center for Patient Safety, AHRQ (R01HS27363), and the Gordon and Betty Moore Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the AHRQ. Dr. Reisinger is supported by an NIH Clinical and Translational Science Award (UL1TR002537) through the University of Iowa's Institute for Clinical and Translational Science. The remaining authors have no declarations of interest., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Purpose: To create a simplified critical illness severity scoring system with high prediction accuracy for 30-day mortality using only commonly available variables., Materials and Methods: This is a retrospective cohort study of ICU admissions 2010-2015 in 306 ICUs in 117 Veterans Affairs (VA) hospitals. We randomly divided our cohort into a training dataset (75%) and a validation dataset (25%). We created a critical illness severity scoring system (CISSS) using age, comorbidities, heart rate, mean arterial blood pressure, temperature, respiratory rate, hematocrit, white blood cell count, creatinine, sodium, glucose, albumin, bilirubin, bicarbonate, use of invasive mechanical ventilation, and whether the admission was surgical or not. We validated the performance of CISSS to predict 30-day mortality internally., Results: After excluding 31,743 re-admissions, we divided our sample (n = 534,001) into a training (n = 400,613) and a validation dataset (n = 133,388). In the training dataset, the area under the curve (AUC) of CISSS was 0.847(95%CI = 0.845-0.850). In the validation dataset, the AUC was 0.848 (95%CI = 0.844-0.852), the standardized mortality ratio (SMR) was 1.00 (95%CI = 0.98-1.02), and Brier's score for 30-day mortality was 0.058 (95%CI = 0.057-0.059). CISSS calibration was acceptable., Conclusions: CISSS has very good performance and requires only commonly used variables that can be easily extracted by electronic health records., Competing Interests: Declaration of Competing Interest Drs. Fortis, Beck, O'Shea, and Reisinger's institutions received funding from VA Office of Rural Health and Center for Access & Delivery Research & Evaluation (CADRE), Award # CIN-13-412, Department of Veterans Affairs Health Services Research and Development Program. Drs. Beck, O'Shea, Goto, Kaboli, Reisinger, and Sarrazin disclosed government work. Dr. Fortis has received grants from American Thoracic Society and Fisher &Paykel. Dr. Schmidt received funding from McGraw-Hill, Springer-Link, and UpToDate. Dr. Nair disclosed that he does not have any potential conflicts of interest., (Published by Elsevier Inc.)

Purpose: To evaluate the performance of an automated computerized ICU severity scoring derived from the APACHE III., Materials and Methods: Within a retrospective cohort of patients admitted to Veterans Health Administration ICUs between 2009 and 2015, we created an automated illness severity score(modified APACHE or mAPACHE), that we extracted from the electronic health records, using the same scoring as the APACHE III excluding the Glasgow Coma Scale, urine output, arterial blood gas components of APACHE III. We assessed the mAPACHE discrimination by using the area under the curve(AUC), and calibration by using the Hosmer-Lemeshow test and calculating the difference between observed and expected mortality across equal-sized risk deciles for death., Results: The ICU and 30-day mortality was 5.07% of 7.82%, respectively (n = 490,955 patients). The AUC of mAPACHE for ICU and 30-day mortality was 0.771 and 0.786, respectively. The Hosmer-Lemeshow test was significant for both ICU and 30-day mortality (p < .001). The absolute difference between observed and expected mortality did not exceed ±1.53% across equal-sized deciles of risk for death. The AUC for ICU mortality was >0.7 in all admission diagnosis categories except in endocrine, respiratory, and sepsis. The AUC for 30-day mortality was >0.7 in every category., Conclusion: mAPACHE has adequate performance to predict mortality., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Purpose: We conducted an evaluation to identify factors related to intensive care unit (ICU) staff acceptance of a telemedicine ICU (Tele-ICU) program in preimplementation and postimplementation phases., Methods: Individual or group semistructured interviews and site observations were conducted with staff from the Veterans Affairs Midwest Health Care Network Tele-ICU and affiliated ICUs. A qualitative content analysis of preimplementation and postimplementation transcripts and field notes was undertaken to identify themes positively and negatively influencing Tele-ICU acceptance., Results: Telemedicine ICU training, Tele-ICU understanding, perceived need, and organizational factors emerged as influencing acceptance of the Tele-ICU before implementation. After implementation, Tele-ICU understanding, impact on work systems, perceived usefulness, and relationships were factors influencing acceptance and utilization. Barriers to implementation included confusion about how to use the Tele-ICU, disruptions to communication and workflows, unmet expectations, and discomfort with being monitored. Facilitators included positive experiences, discovery of new benefits, and recognition of Tele-ICU staff as complementing bedside care., Conclusions: Telemedicine ICU implementation is complex. Time and resources should be allocated for local coordination, continuous needs assessment for Tele-ICU support, staff training, developing interpersonal relationships, and systems design and evaluation. Such efforts are likely to be rewarded with more rapid staff acceptance of this new technology., (Published by Elsevier Inc.)