Driving Forces: Traditional specialty consults are resource intensive and may be delayed or omitted if the treating physician does not recognize the need for specialty advice. Targeted automatic e-consults (TACos) address these limitations by prospectively identifying patients using the electronic health record (EHR) and presenting pertinent information on a dashboard, enabling consultants to provide a virtual consult with written recommendations. The TACo model may improve value by facilitating more expert input without a proportional increase in cost.
Building A Taco: Through our experience developing a TACo program, we have identified four key steps. First, identify appropriate conditions that have support from the health system and from frontline clinicians. Second, design the digital infrastructure, including lists and dashboards. Third, create a funding plan to support the consultant's time, either through internal grants, external grants, e-consult billing codes, or some combination of the three. Fourth, pilot on a select number of services, iterate, and scale.
Challenges: Funding for TACos has been a major barrier to adoption. Fortunately, new e-consult billing codes may make it possible to recoup as least part of the program's cost. Technological hurdles also exist, particularly in building real-time lists within the EHR to prospectively identify patients based on complex criteria.
Next Steps: We look for this model to gain popularity as evidence of clinical and operational benefit mounts. We anticipate reimbursement policies may be updated to support this type of consult. Finally, we expect machine learning to play a role in identifying patients and providing recommendations in the future.
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http://dx.doi.org/10.1016/j.jcjq.2021.10.007 | DOI Listing |
Stat Med
February 2025
U.S. Food and Drug Administration, Silver Spring, Maryland.
The recent U.S. Food and Drug Administration guidance on complex innovative trial designs acknowledges the use of Bayesian strategies to incorporate historical information based on clinical expertise and data similarity.
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Beijing Aerospace Automatic Control Institute, Beijing 100854, China.
The traditional method is capable of detecting and tracking stationary and slow-moving targets in a sea surface environment. However, the signal focusing capability of such a method could be greatly reduced especially for those variable-speed targets. To solve this problem, a novel tracking algorithm combining range envelope alignment and azimuth phase filtering is proposed.
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Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816-8005, USA.
Recognizing targets in infra-red images is an important problem for defense and security applications. A deployed network must not only recognize the known classes, but it must also reject any new or objects without confusing them to be one of the known classes. Our goal is to enhance the ability of existing (or pretrained) classifiers to detect and reject unknown classes.
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School of Biomedical Engineering, Tsinghua University, Shuang Qing Road, Beijing 100084, China.
Mastoidectomy is critical in acoustic neuroma surgery, where precise planning of the bone milling area is essential for surgical navigation. The complexity of representing the irregular volumetric area and the presence of high-risk structures (e.g.
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School of Automation, Beijing Institute of Technology, Beijing 100081, China.
Existing autonomous driving systems face challenges in accurately capturing drivers' cognitive states, often resulting in decisions misaligned with drivers' intentions. To address this limitation, this study introduces a pioneering human-centric spatial cognition detecting system based on drivers' electroencephalogram (EEG) signals. Unlike conventional EEG-based systems that focus on intention recognition or hazard perception, the proposed system can further extract drivers' spatial cognition across two dimensions: relative distance and relative orientation.
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