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The Masimo Patient Safety Net (PSN) is intended to be used as a supplemental alarm system, communicating with multiple patient monitoring devices. The PSN provides secondary display of physiological monitoring parameters. It enables the viewing and monitoring of patient physiological conditions. The PSN is used in hospitals or hospitals or hospital type environments.

The Masimo Patient Safety Net (PSN) is a supplemental alarm system for physiological monitoring devices. It is not intended to replace any part of the patient monitoring procedures already existing for the patient monitoring devices. The PSN communicates with multiple patient monitoring devices and distributes physiological monitoring information remotely. Skilled clinicians remotely receive wireless transmission of patient physiological monitoring information, from patient monitoring device(s) to their pagers. The transmitted information includes alarm information and physiological parameters.

This 510(k) summary does not contain the specific information requested about acceptance criteria and a study that proves the device meets those criteria for the Masimo Patient Safety Net. This submission focuses on establishing substantial equivalence to a predicate device rather than detailing performance testing against pre-defined acceptance criteria.

Here's a breakdown of why the requested information is absent:

• Type of Device: The Masimo Patient Safety Net is described as a "supplemental alarm system" and "system, network and communication, physiological monitors." It's essentially a system for relaying data and alarms from existing physiological monitors. It's not a diagnostic device that performs calculations or makes medical determinations based on physiological data itself.
• Regulatory Pathway: The 510(k) pathway for "substantial equivalence" often relies on demonstrating that a new device is as safe and effective as a legally marketed predicate device. This typically involves showing similar technological characteristics and performance, rather than extensive clinical studies to prove accuracy against a ground truth.

However, based on the provided text, I can infer some general information provided and what is missing:

1. Table of acceptance criteria and the reported device performance:

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

• Not provided. The submission mentions "Component Level Testing," "System Level Testing," and "Performance Testing," but it does not specify the sample size, duration, or provenance of any data used for these tests. This is a technical product, so "test set" would likely refer to system-level integration and functionality testing rather than a patient-data-based test set.

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

• Not applicable/Not provided. As this is a system for relaying data, "ground truth" in the diagnostic sense (e.g., expert consensus on an image or pathology result) is not relevant here. Ground truth would be about whether the system accurately transmits the data and alarms generated by the primary physiological monitors. No information on how this was verified by experts is given.

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

• Not applicable/Not provided. Adjudication methods are typically used in clinical studies where there's subjectivity in interpreting data (e.g., reviewing medical images). For a system that transmits data, the "ground truth" is typically the source data itself (i.e., the data from the connected physiological monitor). Verification would involve checking for fidelity of transmission, not an interpretation requiring adjudication.

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:

• No, this was not done. An MRMC study is relevant for diagnostic devices where human readers interpret medical data, and the device might assist in that interpretation (e.g., CAD systems for radiology). The Masimo Patient Safety Net is a data transmission and alarm relay system, not an interpretive or diagnostic AI. Therefore, an MRMC study is not applicable.

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

• Yes, implicitly. The "Performance Testing" and "System Level Testing" mentioned would have evaluated the system's functions (data transmission, alarm relay) independently of a human user making a clinical decision. However, no results from such testing are provided. The "system" (algorithm and hardware) is the device, and its standalone performance in transmitting data is its core function.

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

• Not explicitly stated, but inferred to be the output of the connected physiological monitors. For this type of device, the "ground truth" for its performance would be the accuracy and timeliness with which it relays the data and alarms generated by the primary, connected physiological monitoring devices. If a primary monitor shows SpO2 at 98% and generates an alarm for a low heart rate, the PSN's "ground truth" challenge is to accurately reflect that 98% and relay that alarm.

8. The sample size for the training set:

• Not applicable/Not provided. This device is described as using "application software" developed by Masimo, which suggests it might involve traditional software engineering rather than machine learning or AI that would require a "training set" of data in the common sense. If it does use machine learning (which is not indicated), no information about a training set is provided.

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

• Not applicable/Not provided. See the explanation for point 8.

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