Journal of hospital medicine : an official publication of the Society of Hospital Medicine
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Multicenter Study
Development and evaluation of best practice advisory alert for patient eligibility in a hospital-at-home program: A multicenter retrospective study.
Hospital-at-home (HaH) is a growing model of care that has been shown to improve patient outcomes, satisfaction, and cost-effectiveness. However, selecting appropriate patients for HaH is challenging, often requiring burdensome manual screening by clinicians. To facilitate HaH enrollment, electronic health record (EHR) tools such as best practice advisories (BPAs) can be used to alert providers of potential HaH candidates. ⋯ During the study period, 8962 notifications were triggered for 2847 patients. Providers opted to refer 711 (11.4%) of the total notifications linked to 324 unique patients. After review by the ACH clinical team, 31 of the 324 referrals (9.6%) met clinical and social criteria and were transferred to ACH. In multivariable analysis, Wisconsin nurses, physician assistants, and in-training personnel had lower odds of referring the patients to ACH when compared to attending physicians.
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Comparative Study Observational Study
Multiuser immersive virtual reality simulation for interprofessional sepsis recognition and management.
Sepsis is a leading cause of pediatric mortality. While there has been significant effort toward improving adherence to evidence-based care, gaps remain. Immersive multiuser virtual reality (MUVR) simulation may be an approach to enhance provider clinical competency and situation awareness for sepsis. ⋯ Our novel MUVR simulation demonstrated significant differences in sepsis recognition between experienced and novice participants. This validity evidence along with the data on the simulation's acceptability supports expanded use in training and assessment.
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Multisystem inflammatory syndrome in children (MIS-C) is a rare but serious complication of severe acute respiratory syndrome coronavirus 2 infection. Features of MIS-C overlap with those of Kawasaki disease (KD). ⋯ A diagnostic prediction model utilizing admission information provides excellent discrimination between MIS-C and KD. This model may be useful for diagnosis of MIS-C but requires external validation.
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Automated text messaging is a promising approach to monitor patients after hospital discharge and avert readmissions; however, it is not known to what extent patients would engage with this type of program and whether engagement may vary based on patients' characteristics. Using data from a 30-day postdischarge texting program at a large university hospital, we examined engagement over time (operationalized as response rate to text messages) and patient characteristics associated with engagement. ⋯ Patients who were male (p < .05), were Black/African American (p < .001), had lower health literacy (p < .01), or had not recently logged into the patient portal (p < .001), all had lower response rates. Results support closer examinations of patient engagement in hospital-based texting programs and who is positioned to benefit.
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Patients with limited English proficiency (LEP) may have worse health outcomes and differences in processes of care. Language status may particularly affect situations that depend on communication, such as symptom management or end-of-life (EOL) care. ⋯ LEP was not associated with differences in the amount of opioids received for patients whose EOL management involved standardized order sets for symptom management at our hospital.