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The Unity Network® IS Patient Viewer is intended for use under the direct supervision of a licensed healthcare practitioner. The intended use of the Unity Network® IS Patient Viewer is to provide a remote view of physiological parameter data on adult, pediatric and neonatal patients within a hospital or facility providing patient care. The Unity Network® IS Patient Viewer is NOT intended for primary monitoring but is to be used in conjunction with the bedside monitor.

The Unity Network® IS Patient Viewer is intended to provide near-real-time physiological data and graphical trends for all monitors connected to the Unity Network to secure nurse and physician personal computers (local and remote).

The Unity Network® IS Patient Viewer provides remote access to waveform, parameter data and trend data at a Web browser on a standard personal computer. The server resides on the hospital's intranet and remote access is gained through secured access to the hospital's intranet.

The data relayed from the patient monitors over the Unity® MC network includes patient name, unit and bed name, parameter data, and waveform data monitored by the bedside monitors. The user can view up to nine waveforms from the Unity® MC network as well as the parameter information in near real-time. Neither alarm messages nor parameter status messages are displayed.

The Unity Network® IS Patient Viewer system provides a secondary view of patient information, and is NOT a patient monitoring device. The clinician is instructed to always reference the primary bedside monitor before making any patient care decisions. In the event that data is not available via the Unity Network® IS Patient Viewer, the clinician is instructed to obtain the data from the primary bedside monitor.

The provided text is a 510(k) summary for the GE Medical Systems Information Technologies Unity Network® IS Patient Viewer. It details the device's intended use and the testing performed to demonstrate its safety and effectiveness. However, it does not contain specific acceptance criteria, reported device performance metrics in a table, or a detailed study description with the information requested.

The document primarily focuses on establishing substantial equivalence to a predicate device (K020661 Unity Network® IS Patient Viewer) and lists general quality assurance measures applied during development.

Here's a breakdown of the requested information based on the provided text, highlighting what is present and what is missing:

1. A table of acceptance criteria and the reported device performance

• Missing. The document states, "The results of these measurements demonstrated that the Unity Network® IS Patient Viewer is as safe, as effective, and perform as well as the predicate device." However, it does not provide any specific quantitative acceptance criteria or reported performance metrics in a table. The test summary lists various types of testing (Risk Analysis, Requirements Reviews, Design Reviews, Unit level testing, Integration testing, Final acceptance testing, Performance testing, Safety testing, Environmental testing) but doesn't elaborate on the criteria used or the outcomes of these tests in a measurable way with respect to the device's actual performance.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Missing. The document does not specify any sample sizes for a test set, nor does it provide information about data provenance (country of origin, retrospective/prospective). The "Test Summary" section lists general types of testing rather than a specific clinical or performance evaluation study with defined test sets.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Missing. Since there is no detailed description of a specific test set or a study involving human experts for ground truth establishment, this information is not present. The device is a patient data viewer, not an AI diagnostic tool that typically requires expert-established ground truth.

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

• Missing. As no specific "test set" requiring expert adjudication is described, this information is not applicable and therefore not present.

5. 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

• Not applicable / Missing. The device is described as a "secondary view of patient information" and "NOT a patient monitoring device," intended to provide a remote view of physiological parameter data. It is not an AI-assisted diagnostic tool designed to improve human reader performance. Therefore, an MRMC comparative effectiveness study in the context of AI assistance is not relevant to this device's stated function.

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

• Not applicable / Missing. The device is a system for displaying physiological data, not an algorithm performing a standalone diagnostic or analytical function. Its purpose is to provide "near-real-time physiological data and graphical trends" to human users. The concept of "standalone performance" for an algorithm in this context does not directly apply. Its performance is implicitly tied to its ability to accurately relay data from patient monitors.

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

• Missing. The concept of "ground truth" as typically applied to diagnostic or AI devices (e.g., expert consensus on images, pathology results) isn't explicitly addressed here. The device's function is to display data from existing bedside monitors. Therefore, the "ground truth" would implicitly be the data generated by the primary bedside monitors, and the device's performance would be measured by its accuracy in reflecting that data, rather than generating its own ground truth.

8. The sample size for the training set

• Not applicable / Missing. The document describes a "Computer, Information Network Server" that relays data. It does not appear to be an AI/machine learning device that would have a "training set" in the conventional sense. The testing performed (Risk Analysis, Requirements Reviews, etc.) relates to software and system validation, not machine learning model training.

9. How the ground truth for the training set was established

• Not applicable / Missing. Given that this is not an AI/machine learning device, the concept of a "training set" and establishing "ground truth" for it does not apply.

In summary: The provided 510(k) summary focuses on establishing substantial equivalence based on functional equivalence and general quality assurance measures for a patient data viewer. It does not contain the detailed performance metrics, study designs, sample sizes, or ground truth information typical for diagnostic devices or AI-powered systems. The conclusion states that the device is "as safe, as effective, and perform[s] as well as the predicate device," implying that the testing confirmed it met its functional requirements and safety standards without necessarily providing quantitative performance data.

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