Automated Clinician Alerting
Automated Clinician Alerting - Related Videos
10:08
Transcranial Electrical Brain Stimulation in Alert Rodents
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Cited by 11 •
2017
This protocol describes a surgical set-up for a permanent epicranial electrode socket and an implanted chest electrode in rodents. By placing a second electrode into the socket, different types of transcranial electrical brain stimulation can be delivered to the motor system in alert animals through the intact skull.
05:04
Using Simulation Models to Train Clinicians in the Use of Point-of-Care Ultrasound
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2024
The present protocol describes an ideal solution to train novices in the use of point-of-care ultrasound devices for the practical clinical skill of visually assessing distinct individual anatomical vascular conditions prior to and during an intended venous vascular cannulation using point-of-care ultrasound in a patient.
08:41
Juxtacellular Monitoring and Localization of Single Neurons within Sub-cortical Brain Structures of Alert, Head-restrained Rats
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Cited by 9 •
2015
This protocol describes the design and surgical implantation of a head-restraining mechanism to monitor neuronal activity in sub-cortical brain structures in alert rats. It delineates procedures to isolate single neurons in the juxtacellular configuration and to efficiently identify their anatomical locations.
08:06
Automated Acoustic Dispensing for the Serial Dilution of Peptide Agonists in Potency Determination Assays
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Cited by 1 •
2016
Peptide adsorption to plasticware during traditional tip-based serial dilutions can significantly impact potency determination and confound the understanding of structure-activity relationships used for lead identification and lead optimization phases of drug discovery. Here methods for automated acoustic non-contact serial dilution of peptide samples are described.
07:51
Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
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Cited by 1 •
2018
This article presents a protocol based on Hydra — a web-based system for clinical decision support that integrates a full and detailed set of functionalities and services required by physicians for complete cardiovascular analysis, risk assessment, early diagnosis, treatment, and monitoring over time.
FAQs
Automated clinician alerting can use either predefined thresholds or learned patterns to judge behavioral data. Thresholds trigger notifications when a measured action, activity pattern, or reported symptom crosses a specified boundary, whereas learned patterns identify departures from patterns the system has been designed to recognize. The choice affects how sensitivity and specificity are balanced in practice.
Sensitivity determines how readily the system detects a potentially important change, while specificity limits notifications for changes that may not require attention. Increasing sensitivity may capture more meaningful deviations but can also produce more irrelevant alerts. Increasing specificity may reduce alert volume while increasing the possibility that a meaningful behavioral change is not flagged.
Relevant inputs may include directly observed actions, activity patterns, and reported symptoms. These sources represent different aspects of behavior, allowing the system to compare different types of signals with predefined thresholds or learned patterns. The resulting notification gives clinicians a focused signal to review and can help guide follow-up when a meaningful deviation is detected.
Implementation begins by selecting the behavioral information to monitor, such as actions, activity patterns, or reported symptoms. The system then applies predefined thresholds or learned patterns, identifies a meaningful deviation, and sends a notification to a healthcare professional. Clinicians can use that signal to prioritize attention and determine whether follow-up is warranted.
It can direct limited clinical attention toward patients whose behavioral data show a potentially meaningful deviation. Rather than requiring continuous manual review of every observation or report, notifications help clinicians prioritize which cases to examine first. This may support earlier recognition of change and more targeted follow-up, particularly when the volume of patient information makes constant review difficult.
Evaluation should examine both missed changes and unnecessary notifications. A system that detects many deviations may appear sensitive but still burden clinicians if the alerts are not sufficiently specific. Conversely, a quieter system may reduce notification burden while failing to identify changes that merit assessment. These tradeoffs determine whether alerting improves prioritization and supports earlier behavioral follow-up.