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The Ascom Mobile Monitoring Gateway (MMG) is intended to interface with the GE Healthcare patient monitoring network and the Ascom Messaging System, in order to provide a secondary means of automated visual and/or audible annunciating and displaying of patient alarm information to healthcare professionals, via display devices.

The MMG does not alter the behaviour of the monitoring system. Neither is it intended to replace or alter the primary alarm function on the patient monitor. The MMG is not intended to be used for diagnostic purposes.

The MMG is intended for use by professional clinical personnel and relies on proper use and operation of both the communication infrastructure in place at the healthcare facility and the display devices used.

The MMG software is installed on specified hardware located in a computer hall or similar, where the MMG can't come into physical contact with patients.

The Ascom Mobile Monitoring Gateway (MMG) is an on-site messaging integration solution which forwards patient monitor status and alarm information to the user via display devices provided by Ascom or third-party mobile device companies. Users receive interactive, time-critical information from patient monitoring devices directly via their display devices as text, alarms or data. The Ascom MMG allows users to be aware of their patients' status and alarm conditions when they are away from the patient and patient monitoring svstem.

The Ascom MMG connects to the information sources through wired Ethernet connections which are part of the customer's infrastructure. The Ascom MMG software acquires patient data from patient monitoring devices. The user configures the Ascom MMG to determine which information, including alarm notifications, is delivered to which users. The Ascom MMG then formats the data for wireless delivery to the display devices.

All messaging activities are recorded in the Ascom Unite Connectivity Manager providing real-time activity logging for audit trail records and reporting. The Ascom MMG hardware consists of small, embedded network appliances, and application-specific software. The Ascom MMG delivers near real-time text messaging alerts and information to text-capable display devices.

The Ascom Mobile Monitoring Gateway operates on a wireless communications system platform. The communication platform uses DECT (Digital Enhanced Cordless Telecommunications), WiFi and Paging technology. The Ascom MMG combined with a wireless communication system, is part of an Ascom end-to-end solution designed to provide all the components necessary to optimize work flow, including display devices, gateways and device management.

Here's an analysis of the acceptance criteria and study information for the Ascom Mobile Monitoring Gateway (MMG) based on the provided text:

Important Note: The provided 510(k) summary for K111215 focuses on demonstrating substantial equivalence to a predicate device and outlines various compliance tests (software, electrical safety, EMC, performance bench testing). It does not present specific clinical performance acceptance criteria in the format typically seen for diagnostic AI/ML devices (e.g., sensitivity, specificity, AUC thresholds) nor does it describe a comparative study with human readers or a standalone algorithm performance study. The device is described as a "Network and Communication Middleware" for relaying alarm information, rather than a diagnostic tool.

Acceptance Criteria and Reported Device Performance

Given the nature of the device (a communication gateway, not a diagnostic tool), the "acceptance criteria" are framed around compliance with regulations and specifications, rather than clinical performance metrics often associated with AI/ML solutions.

Study Details (Based on available information)

1. Sample size used for the test set and the data provenance:

   • The document describes "Performance Testing - Bench" and "Software Testing" but does not specify a sample size for a test set in terms of patient data or clinical cases. The testing appears to be centered on system functionality, interoperability, and adherence to technical standards, rather than clinical data samples.
   • Data provenance: Not applicable in the context of a clinical test set. The testing would have involved simulated or live system interactions and data flow.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Not applicable. The device is a communication gateway, not performing diagnostic interpretations. The "ground truth" would be about accurate transmission and display of patient monitor data/alarms, which would be verified through technical testing against expected system behavior, not expert clinical interpretation.

3. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

   • None applicable. Adjudication methods like 2+1 or 3+1 are used for establishing ground truth in clinical imaging or diagnostic studies. This is a technical system. Test results would likely be pass/fail based on predetermined functional specifications.

4. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

   • No MRMC study was done. This device is not an AI/ML diagnostic tool intended for human reader assistance. Its purpose is to relay information, not to interpret or assist in interpretation.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

   • Not applicable in the AI/ML diagnostic sense. While the "algorithm" (software) of the MMG operates standalone to process and forward information, the performance testing described is functional and system-level, ensuring it correctly handles data and alarms as per its design, rather than a standalone clinical performance evaluation against a diagnostic ground truth.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

   • Functional Ground Truth/Predetermined Specifications: The ground truth for this device's testing would be its predetermined functional specifications, communication protocols, and expected system behavior (e.g., an alarm from the patient monitor should accurately trigger and display on the receiving device within a specified timeframe and format).

7. The sample size for the training set:

   • Not applicable. The MMG is a software and hardware integration solution, not an AI/ML model that would require a "training set" in the context of machine learning.

8. How the ground truth for the training set was established:

   • Not applicable. No training set for an AI/ML model.

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