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The Alaris PC Unit is the main user interface unit and power supply of the Alaris System, a modular system to be used with Alaris System modules (aka Medley System modules) intended for use in today's growing professional healthcare environment for facilities that utilize infusion and/or monitoring devices. The specific intended use for each Alaris System module is specified in its respective submission.

The Alaris System is a modular system that consists of a point-of-care unit (PC Unit) that provides the main user interface and power supply for the associated infusion and monitoring modules. The Enhanced PC Unit will include a faster processor, increased memory, and a color screen. This update is only for the Alaris PC Unit and does not require any change to the associated modules, systems, or accessories of the Alaris System.

This document describes the Alaris System with Enhanced PC Unit, which is an infusion pump. The information provided is primarily focused on demonstrating substantial equivalence to a predicate device, rather than presenting a detailed performance study with acceptance criteria in the typical sense for a novel AI/software device.

Therefore, many of the requested categories for AI/software device performance studies (like sample size for test/training sets, ground truth establishment methods, MRMC studies, or standalone performance) are not applicable here as this is a medical device hardware upgrade focusing on equivalence to an existing device.

Here's the breakdown based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not specify quantitative acceptance criteria in the way one would for an AI algorithm (e.g., "sensitivity > 90%"). Instead, it focuses on functional equivalence and meeting "specified requirements" through verification and validation activities.

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

• Sample Size for Test Set: Not applicable / Not provided. The evaluation appears to be based on engineering verification and validation activities comparing the upgraded unit to the predicate, rather than a clinical study with a "test set" of patient data.
• Data Provenance: Not applicable. This is not a data-driven device; it's a hardware upgrade.

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

• Not applicable. Ground truth in the context of expert consensus on medical images or clinical outcomes is not relevant here. The "ground truth" for this device would be its adherence to design specifications and safety/performance standards, verified through testing.

4. Adjudication method for the test set:

• Not applicable. There is no mention of an adjudication process for a "test set" in the context of clinical evaluation or expert review. Testing would likely follow a pre-defined protocol for hardware and software verification.

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 is not an AI-assisted diagnostic or therapeutic device. It is an infusion pump with an upgraded user interface and processor.

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

• Not applicable. This device is a hardware system, not an algorithm. Performance testing would be for the entire system, including its user interface.

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

• Not applicable in the typical sense. The "ground truth" for this device would be its conformance to engineering specifications, safety standards, and functional requirements, likely demonstrated through:
  • Design verification testing: Ensuring the device manufactured meets the design specifications.
  • Design validation testing: Ensuring the device meets user needs and intended use, likely involving usability and functional tests.
  • Comparison to predicate device: Demonstrating that the upgraded features (faster processor, increased memory, color screen) do not negatively impact the safety and effectiveness established by the predicate device.

8. The sample size for the training set:

• Not applicable. This is not a machine learning or AI device that requires a training set.

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

• Not applicable. As above, no training set is involved.

Summary of the Study (Implied):

The "study" or rather, the basis for substantial equivalence, was a comparison of technological characteristics of the Alaris System with Enhanced PC Unit against its predicate device (Alaris System, K950419). It involved demonstrating that the upgrades (faster processor, increased memory, color screen) did not introduce new safety or effectiveness concerns and that the device continued to meet its "specified requirements." This would typically involve:

• Engineering bench testing: To verify the performance of the new processor, memory, and display.
• Software verification and validation: To ensure the upgraded software running on the new hardware functions correctly and consistently.
• Usability testing (implied): To ensure the color screen and faster interface maintain or improve usability without introducing errors.

The document states, "The performance information provided indicates that the Alaris System with Enhanced PC Unit meets specified requirements, and is substantially equivalent to the predicate device." This indicates that adequate testing was conducted to support these claims, though the specific details of those tests are not elaborated in this summary.

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