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The intended use of Critical Alert CommonPath Enterprise is to provide an interface with clinical systems to forward information associated to the particular event to the designated display device(s).

For medical, near real time alarms, Critical Alert CommonPath Enterprise is intended to serve as a parallel, redundant, forwarding mechanism to inform healthcare professionals of particular medical related events. Critical Alert CommonPath Enterprise does not alter the behavior of the primary medical devices and associated alarm annunciations. The display device provides a visual, and/or audible and/or vibrating mechanism upon receipt of the alert.

Critical Alert CommonPath Enterprise is intended for use as a secondary alert. It does not replace the alarm function on the primary device.

Critical Alert CommonPath Enterprise (CommonPath) is a software application installed on a Windows server environment capable of acquiring alarms, events, and parameters from clinical systems and intelligently forwarding this information as notifications to designated display devices provided by third-party mobile device companies.

Critical Alert CommonPath Enterprise is intended for use as a secondary alarm; it does not replace the alarm function on the primary device.

Users receive interactive, time-critical information from clinical systems directly via their display devices as text (visual) or alarms (audible) or data. Received attributes related to the presentation of alerts include text and tones (beeps) in addition to and in coordination with event priorities. CommonPath allows users to be aware of their patients' status and alarm conditions when they are away from the patient and patient monitoring system.

CommonPath connects to the information sources through wired ethernet connections which are part of the customer's infrastructure and acquires patient data from clinical systems. The user configures CommonPath to determine which information, including alarm notifications, is delivered to which users. CommonPath then formats the data for wireless delivery to the display devices through a messaging server.

The provided text describes a 510(k) premarket notification for a medical device called "Critical Alert CommonPath Enterprise." This documentation is primarily a regulatory submission aiming to demonstrate substantial equivalence to a legally marketed predicate device rather than presenting a performance study with detailed acceptance criteria and the results of a clinical trial or large-scale validation study in the way one might for an AI/ML diagnostic device.

The "acceptance criteria" and "device performance" in this context refer to the device meeting its specified functional requirements and regulatory standards rather than statistical performance metrics of an AI algorithm (e.g., sensitivity, specificity, AUC).

Here's an analysis based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implied by the device's intended use and the regulatory standards it aims to comply with. The "performance" is stated as compliance with these standards and internal requirements.

2. Sample Size Used for the Test Set and Data Provenance

This document does not describe a "test set" in the context of an AI/ML model where a specific number of cases are evaluated for accuracy. Instead, the testing appears to be functional and performance testing of a software system. The "sample size" would refer to the various configurations, alarm types, and system loads tested to ensure functionality and performance. Specific numbers of test scenarios or user types are not provided.

• Data Provenance: Not applicable in the context of clinical data for AI/ML validation. The "data" here are system events, alarms, and configurations generated or simulated during software testing. There is no mention of country of origin or retrospective/prospective clinical data for evaluation.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of those Experts

Not applicable. This is not an AI diagnostic device requiring expert interpretation for ground truth establishment. The ground truth for this device is its defined functional specifications and compliance with regulatory standards.

4. Adjudication Method for the Test Set

Not applicable. There's no subjective interpretation requiring adjudication.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

Not applicable. This device is a communication and alarm forwarding system, not an image-based diagnostic AI. It does not involve human readers interpreting cases with or without AI assistance.

6. Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

The device is inherently a "standalone" software system in that it processes and forwards information automatically. Its performance is evaluated on its ability to correctly interface, process, and forward alarms based on predefined rules, not on diagnostic accuracy of an AI algorithm. So, while it's "algorithm only" in its core function, it's not "standalone" in the typical AI diagnostic sense where it would produce a diagnostic output without human review. Its function is to inform humans.

7. The Type of Ground Truth Used

The "ground truth" for this device's validation is its functional specification, software requirements, and compliance with relevant medical device standards (e.g., IEC 60601-1-8 for alarm systems, IEC 62366-1 for usability). The testing verifies that the device adheres to these defined specifications and performance benchmarks (e.g., processing speed, number of supported connections, correct routing of alarms), and that these meet or exceed the predicate device's capabilities.

8. The Sample Size for the Training Set

Not applicable. This is not an AI/ML device that requires a "training set" of data to learn patterns or make predictions. It is a communication system with programmed logic and rules.

9. How the Ground Truth for the Training Set Was Established

Not applicable, as there is no training set for an AI/ML model. The "ground truth" for its development would be its functional design specifications and clinical needs defined by engineers and medical professionals involved in its design.

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