The authors describe one institution's strategies to implement the Accreditation Council for Graduate Medical Education's (ACGME) Outcomes Project requirements while simultaneously exploring and implementing standards of quality healthcare as endorsed by the Institute of Medicine's (IOM) Crossing the Quality Chasm (2001). Of real interest, application of the authors' institution's paradigm is identical to many of the parameters for system competence as recommended in the IOM's April 2003 report, Health Professions Education: A Bridge to Quality (2003).
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1080/01421590410001730967 | DOI Listing |
Clin Dermatol
December 2024
Department of Dermatology, University of Connecticut School of Medicine, Farmington, CT, USA; Department of Dermatology, University of Florida College of Medicine, Gainesville, FL, USA. Electronic address:
The evolution of healthcare payment models has profoundly influenced clinical practices and physician decision-making. While fee-for-service (FFS) models incentivize procedural volume, systems based on Relative Value Units (RVUs) have introduced standardized metrics to compensate physicians based on care complexity and workload. As corporations increasingly own healthcare, financial incentives such as RVUs and procedural quotas raise ethical concerns.
View Article and Find Full Text PDFInt J Biol Macromol
December 2024
School of Chemistry and Chemical Engineering, Yantai University, Shandong Province, 264005, China. Electronic address:
Although the natural antibacterial agent, cinnamaldehyde, has been extensively studied in the field of food packaging, its water solubility and instability limit its further applications. The controllable responsive release can be achieved through encapsulation in responsive emulsion systems based on carboxymethyl chitosan. Herein, a pH-responsive antibacterial emulsion gel was constructed from cinnamaldehyde-loaded oil-in-water emulsion templates.
View Article and Find Full Text PDFCancer Immunol Immunother
December 2024
Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study On Anticancer Molecular Targeted Drugs, No.17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
Background: Existing biomarkers and models for predicting response to programmed cell death protein 1 monoclonal antibody in advanced squamous-cell non-small cell lung cancer (sqNSCLC) did not have enough accuracy. We used data from the ORIENT-3 study to construct artificial neural network (ANN) systems to predict the response to sintilimab for sqNSCLC.
Methods: Four ANN systems based on bulk RNA data to predict disease control (DC), immune DC (iDC), objective response (OR) and immune OR (iOR) were constructed and tested for patients with sqNSCLC treated with sintilimab.
Int J Cardiol
December 2024
Brigham and Women's Hospital Heart and Vascular Center, Boston, MA, USA; Baim Institute for Clinical Research, Boston, MA, USA. Electronic address:
Background: Patients with a history of coronary revascularization are at a higher risk for subsequent cardiovascular events and all-cause mortality. Lowering LDL-cholesterol (LDL-C) levels post-revascularization significantly reduces these risks.
Methods: This analysis compared LDL-C-lowering therapies at baseline and over time among patients with and without prior coronary revascularization in the GOULD registry (a prospective multicenter cohort study).
Neural Netw
December 2024
State Key Laboratory of IoTSC, University of Macau, Taipa, 999078, Macao Special Administrative Region of China. Electronic address:
Distributional Reinforcement Learning (RL) extends beyond estimating the expected value of future returns by modeling its entire distribution, offering greater expressiveness and capturing deeper insights of the value function. To leverage this advantage, distributional multi-agent systems based on value-decomposition techniques were proposed recently. Ideally, a distributional multi-agent system should be fully distributional, which means both the individual and global value functions should be constructed in distributional forms.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!