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The MetaVision Graphical Patient Information System is indicated for use in data collection, display, management, and storage in the intensive care unit. The system is used in conjunction with independent patient bedside devices and systems, connected via a network. The way the system is used for generating patient records, computation of drug and fluid dosage and research tasks is determined by the health care providers, in terms of their environment and requirements. The MetaVision application is resident on a workstation that provides for data input and patient data display -- to health care professionals. Typically, a MetaVision system comprises several workstations connected via a network system to one or more servers. Data is stored and managed by servers. The MetaVision system network can communicate with a number of remotely located patient care units.

The MetaVision Graphical ICU Patient Information System is a clinical information system utilized for data collection, display, management, and storage in the ICU environment. The ICU may be a normal adult ICU, a neonatal ICU, or other type of ICU. The system can communicate with bedside devices that are attached to a local network. The system may display patient data on one or more work stations running under the Windows NT/2000 operating system. The MetaVision system is resident on a server and one or more workstations and communicates via a network with other workstations and patient monitoring devices on the network. The MetaVision system can also communicate with a number of remotely located patient care units.

The provided document is a 510(k) summary for the MetaVision 5.0 Clinical Information System. This type of submission focuses on demonstrating substantial equivalence to a predicate device rather than providing extensive clinical performance data or acceptance criteria in the way one might find for novel or higher-risk devices.

Based on the provided text, the document does not contain the detailed information required to fill out the requested table and answer many of the questions. This is common for 510(k) submissions, especially for clinical information systems, where the substantial equivalence determination relies more on technological characteristics and intended use alignment with existing devices rather than new clinical effectiveness studies with specific performance metrics.

Here's an analysis of what can and cannot be answered based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

• Not applicable / Not provided. The document states that "Software is tested according to well-controlled procedures at MDsoft" and that "The 510(k) has demonstrated Substantial Equivalence with the predicate device." It does not provide specific performance metrics, acceptance criteria, or results from any clinical performance study. The focus is on demonstrating equivalence to the predicate device (Hewlett-Packard Carevue 9000 Clinical Information System, K992636) in terms of intended use and technological characteristics.

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

• Not provided. No information about a test set, sample size, or data provenance (country of origin, retrospective/prospective) is included.

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

• Not provided. No information about a test set or ground truth establishment by experts is included.

4. Adjudication method for the test set

• Not provided. No information about a test set or adjudication method is included.

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 performed / Not provided. The document describes a "Clinical Information System" for data collection, display, management, and storage. It is not an AI-assisted diagnostic or decision support tool that would typically undergo MRMC studies to evaluate human reader improvement. There is no mention of AI or MRMC studies.

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

• Not applicable / Not provided. This device is a "Clinical Information System" intended for use by healthcare professionals as a tool, not a standalone diagnostic algorithm. No standalone performance data is presented.

7. The type of ground truth used

• Not applicable / Not provided. Given the nature of the device as a clinical information system, the concept of "ground truth" for a diagnostic algorithm is not directly relevant in the context presented. There are no diagnostic claims requiring a defined ground truth.

8. The sample size for the training set

• Not provided. No information about a training set is included, as this is not an AI/machine learning model submission.

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

• Not applicable / Not provided. As there is no training set of an AI/machine learning model, this question is not relevant.

Summary from the provided document:

The MetaVision 5.0 Clinical Information System (K012349) is a "Medical Cathode Ray Tube Display, 21 CFR 870.2450", classified as a Cardiac Monitor (21 CFR 870.2300, Class II, Product Code MWI). It received 510(k) clearance by demonstrating substantial equivalence to the Hewlett-Packard Carevue 9000 Clinical Information System (K992636).

• Intended Use: Data collection, display, management, and storage in the intensive care unit.
• Technological Characteristics: Both MetaVision and the predicate device "employ software and computers to communicate with patient monitoring devices and other workstations to enter and display patient data."
• Testing: "Software is tested according to well-controlled procedures at MDsoft." No specific performance metrics or clinical study results are detailed in this summary.

This 510(k) summary focuses on regulatory compliance through substantial equivalence, which typically does not necessitate the kind of detailed performance studies and ground truth establishment required for AI/ML-based diagnostic devices.

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