Journal of hospital medicine : an official publication of the Society of Hospital Medicine
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Hospitalists have long been involved in optimizing perioperative care for medically complex patients. In 2015, the Society of Hospital Medicine organized the Perioperative Care Work Group to summarize this experience and to develop a framework for providing optimal perioperative care. ⋯ The Perioperative Care Matrix for Inpatient Surgeries provides an essential collaborative framework hospitalists can use to develop and continually improve perioperative care programs. Journal of Hospital Medicine 2017;12:277-282.
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The diagnosis of sepsis requires that objective criteria be met with a corresponding subjective suspicion of infection. We conducted a study to characterize the agreement between different providers' suspicion of infection and the correlation with patient outcomes using prospective data from a general medicine ward. Registered nurse (RN) suspicion of infection was collected every 12 hours and compared with medical doctor or advanced practice professional (MD/APP) suspicion, defined as an existing order for antibiotics or a new order for blood or urine cultures within the 12 hours before nursing screen time. ⋯ Progression to severe sepsis or shock was highest when both providers suspected infection in a SIRS-positive patient (17.7%), was substantially reduced with single-provider suspicion (6.0%), and was lowest when neither provider suspected infection (1.5%) (P < 0.001). Provider disagreement regarding suspected infection is common, with RNs suspecting infection more often, suggesting that a collaborative model for sepsis detection may improve timing and accuracy. Journal of Hospital Medicine 2017;12:256-258.
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Observational Study
Predicting 30-Day Pneumonia Readmissions Using Electronic Health Record Data.
Readmissions after hospitalization for pneumonia are common, but the few risk-prediction models have poor to modest predictive ability. Data routinely collected in the electronic health record (EHR) may improve prediction. ⋯ EHR data collected from the entire hospitalization can accurately predict readmission risk among patients hospitalized for pneumonia. This approach outperforms a first-day pneumonia-specific model, the Centers for Medicare and Medicaid Services pneumonia model, and 2 commonly used pneumonia severity of illness scores. Journal of Hospital Medicine 2017;12:209-216.
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Venous thromboembolism (VTE) risk scores assist providers in determining the relative benefit of prophylaxis for individual patients. While automated risk calculation using simpler electronic health record (EHR) data is feasible, it lacks clinical nuance and may be less predictive. Automated calculation of the Padua Prediction Score (PPS), requiring more complex input such as recent medical events and clinical status, may save providers time and increase risk score use. ⋯ Automated calculation of VTE risk had similar ability to predict hospital-acquired VTE as manual calculation despite differences in how often specific scoring criteria were considered present by the 2 methods. Journal of Hospital Medicine 2017;12: 231- 237.