Articles: cations.
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Prior studies have shown worse outcomes in patients with cardiogenic shock (CS) who have reduced left ventricular ejection fraction (LVEF), but the association between other transthoracic echocardiogram (TTE) findings and mortality in CS patients remains uncertain. We hypothesized that Doppler TTE measurements would outperform LVEF for risk stratification. ⋯ Early comprehensive Doppler TTE can provide important prognostic insights in CS patients, highlighting its potential utility in clinical practice. The LVOT VTI, reflecting forward flow, is an important measurement to obtain on bedside TTE.
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Anesthesia and analgesia · Mar 2022
Understanding the Economic Impact of an Essential Service: Applying Time-Driven Activity-Based Costing to the Hospital Airway Response Team.
As the United States moves toward value-based care metrics, it will become essential for anesthesia groups nationwide to understand the costs of their services. Time-driven activity-based costing (TDABC) estimates the amount of time it takes to perform a clinical activity by dividing complex tasks into process steps and mapping each step and has historically been used to estimate the costs of various health care services. TDABC is a tool that can be adapted for variable staffing models and the volume of service provided. Anesthesia departments often provide staffing for airway response teams (ART). The economic implications of staffing ART have not been well described. We present a TDABC model for ART activation in a tertiary-care center to estimate the cost incurred by an anesthesiology department to staff an ART. ⋯ Our analysis of ART-activation pages suggests that while the revenue generated may cover the cost of staffing the team during ART activations, it does not cover consumable equipment costs. Additionally, the current fee-for-service model relies on the team being able to perform other clinical duties in addition to covering the airway pager and would be impossible to capture using traditional top-down costing methods. By using TDABC, anesthesia groups can demonstrate how certain services, such as ART, are not fully covered by current reimbursement models and how to negotiate for subsidy agreements.As the transition from traditional fee-for-service payments to value-based care models continues in the United States, improving the understanding and communication of medical care costs will be essential. In the United States, it is common for anesthesia groups to receive direct revenue from hospitals to preserve financial viability, and therefore, knowledge of true cost is essential regardless of payer model.1 With traditional payment models, what is billable and nonbillable may not reflect either the need for or the cost of providing the service. As anesthesia departments navigate the transition of care from volume to value, actual costs will be essential to understand for negotiations with hospitals for support when services are nonbillable, when revenue from payers does not cover anesthesia costs, and when calculating the appropriate share for anesthesia departments when bundled payments are distributed.
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Statistically significant positive results are more likely to be published than negative or insignificant outcomes. This phenomenon, also termed publication bias, can skew the interpretation of meta-analyses. The widespread presence of publication bias in the biomedical literature has led to the development of various statistical approaches, such as the visual inspection of funnel plots, Begg test, and Egger test, to assess and account for it. ⋯ Taken together, these results indicate that publication bias remains largely unaccounted for in neurosurgical meta-analyses.
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Anasthesiol Intensivmed Notfallmed Schmerzther · Mar 2022
[Artificial Intelligence: Challenges and Applications in Intensive Care Medicine].
The high workload in intensive care medicine arises from the exponential growth of medical knowledge, the flood of data generated by the permanent and intensive monitoring of intensive care patients, and the documentation burden. Artificial intelligence (AI) is predicted to have a great impact on ICU work in the near future as it will be applicable in many areas of critical care medicine. These applications include documentation through speech recognition, predictions for decision support, algorithms for parameter optimisation and the development of personalised intensive care medicine. ⋯ Speech recognition has the potential to reduce this documentation burden. It is not yet precise enough to be usable in the clinic. The application of AI in medicine, with the help of large data sets, promises to identify diagnoses more quickly, develop individualised, precise treatments, support therapeutic decisions, use resources with maximum effectiveness and thus optimise the patient experience in the near future.