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The Medley System with Bar Code Module is intended for use in today's growing professional healthcare environment for facilities that utilize infusion devices for the delivery of fluids, medications, blood and blood products.

The Medley System with Bar Code Module is intended to provide trained healthcare caregivers a way to automate the programming of infusion parameters, thereby decreasing the amount of manual steps necessary to enter infusion data. All data entry and validation of infusion parameters is performed by the trained healthcare professional according to a physician's order.

The Medley Bar Code Module functions as part of the Medley System and can operate as a fifth module (when all four infusion and/or monitoring positions are in use). The Medley Bar Code Module attaches to and receives its power from the Medley System consistent with the modular design. The Bar Code Module does not directly interface with the Medley System; all communications are routed using a separate and dedicated communications interface channel resident in the Medley System.

Through the dedicated communications channel, the Medley Bar Code Module exchanges information between itself and other sources, such as the patient wristband, medication or device packaging, Medley, or MMS System components such as the ALARIS Server and Hospital Information Management Systems. As with the Medley System with MMS, there is an information protocol for proprietary data exchange and a method to verify the integrity of the information sent or received prior to programming a Medley System infusion module. The software incorporates error checking to ensure the correct acquisition, exchange, and integrity of acquired bar coded data.

All data entry and verification of infusion parameters using the Medley System with Bar Code Module is performed by trained healthcare professionals prior to administration of medication(s). The Medley Bar Code Module does not affect the operation of the Medlev System. All Medley System functionality and performance specifications remain unchanged.

An attachable/detachable handheld scanner will be provided as an accessory to the Medley System with Bar Code Modulc as alternative way to acquire bar code data.

The provided text is a 510(k) premarket notification for a medical device (ALARIS Medical Systems® Medley™ System with Bar Code Module) from 2004. This type of document focuses on demonstrating substantial equivalence to predicate devices, rather than presenting detailed acceptance criteria and performance studies in the way you've outlined for AI/ML-based devices.

Therefore, many of the specific details you requested (like effect size of AI assistance, sample size for AI training sets, expert compensation, etc.) are not available in this type of regulatory submission for this device. The regulatory pathway for this device predates the widespread use of sophisticated AI/ML in medical devices, and the focus is on hardware and software functionality for automating infusion parameter entry.

However, I can extract the available information and indicate where details are not provided.

Here's a summary based on the provided text:

Acceptance Criteria and Study Information (ALARIS Medical Systems® Medley™ System with Bar Code Module)

This 510(k) submission primarily focuses on demonstrating substantial equivalence to predicate devices for the Medley™ System with Bar Code Module. The performance data presented indicates that the device meets specified requirements to establish this equivalence, rather than providing detailed clinical study results with explicit numerical acceptance criteria and a detailed study protocol for AI/ML performance.

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Sample Size: Not specified in the provided document. The document refers to "Performance Data" but does not detail the nature or size of any test sets used.
• Data Provenance: Not specified. This type of submission generally doesn't detail the origin of data beyond internal testing in this context.

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

• This concept of "ground truth" and expert adjudication is typically relevant for interpretative AI/ML devices. For this infusion pump module, the "ground truth" would likely be based on engineering specifications and direct comparison of barcoded data to expected values.
• The document mentions "trained healthcare professionals" for data entry and validation during use, but not for establishing ground truth in a formal test set for the device's bar code module performance.

4. Adjudication method for the test set:

• Not applicable/addressed in this document. Adjudication methods like 2+1 or 3+1 are used for expert consensus on image interpretation or complex clinical assessments, which is not the primary function of this device.

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

• Not applicable. This device is an infusion pump module with a bar code reader, designed to automate data entry. It does not employ AI in the manner that would necessitate an MRMC study comparing human readers with and without AI assistance for interpretation. Its function is to reduce manual steps for entering infusion data, not to assist in diagnostic interpretation.

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

• The device's core function is to acquire and process barcoded data. The performance of the bar code module itself (reading accuracy, error checking) would inherently be a standalone test. However, the overall intended use always involves a "trained healthcare professional" for validation. The document states: "All data entry and verification of infusion parameters using the Medley System with Bar Code Module is performed by trained healthcare professionals prior to administration of medication(s)." Therefore, while the component can be tested standalone for its technical performance, its clinical use is human-in-the-loop.

7. The type of ground truth used:

• For the bar code module, the ground truth would typically be the known, correct data encoded in the bar codes being scanned. The "error checking" software would verify that the acquired data matches this known correct information.

8. The sample size for the training set:

• Not applicable/specified. This device is not described as using machine learning that requires a "training set" in the modern sense. Its software likely relies on programmed logic and error checking rather than learned patterns.

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

• Not applicable. As no training set is indicated, this question is not relevant to the information provided for this device.

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