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The DataCaptor™ System is indicated for use in data collection and clinical information management either directly or through networks with independent bedside devices. DataCaptor™ is not intended for monitoring purposes, nor is the software intended to control any of the clinical devices (independent bedside devices / information systems) to which it is connected.

The DataCaptor™ System consists of DataCaptor™ Connectivity Software a data acquisition system designed to retrieve and deliver near-real-time data from multiple vendors' bedside medical devices and send it to clinical or hospital information systems in HL7 standard format; Capsule Neuron™ with Docking Station (for high acuity environments) or Mini-Dock (for low acuity environments), a bedside hardware platform for device connectivity, and the Datack (1017 Terminal Server, a Serial-to-Ethernet concentrator for the medical environment that connects RS-232 equipped bedside medical devices to the hospital network for safe transmission of the clinical or hospital information system.

This appears to be a 510(k) summary for a medical device called the DataCaptor™ System. It's important to note that a 510(k) summary focuses on demonstrating "substantial equivalence" to a predicate device, rather than proving novel clinical effectiveness through extensive performance studies like those required for a PMA (Premarket Approval).

Therefore, the type of detailed "study that proves the device meets the acceptance criteria" as typically understood for AI/ML devices (with metrics, sensitivity/specificity, reader studies, etc.) is not present in this document. This document primarily describes design changes and verification/validation activities to ensure the modified device performs comparably to its predicate.

Here's an analysis of the provided text in response to your questions:

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

The document states: "Design verification and validation activities to pre-determined pass/fail criteria were based on results of risk an alres va confirm system performance, functionality and reliability to be commensurate to the predicate device."

However, no specific quantitative acceptance criteria or reported device performance data (e.g., in terms of accuracy, precision, or error rates for data collection) are provided in a table format in this public summary. The focus is on demonstrating that the modified DataCaptor™ System performs commensurately to the predicate device. This is typical for a Special 510(k), which deals with modifications to a previously cleared device.

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

The document mentions "Design verification and validation activities," implying testing was conducted. However, no specific sample sizes for a test set, data provenance (country of origin), or whether the data was retrospective or prospective are mentioned. This level of detail is typically not included in a 510(k) summary, as the emphasis is on confirming that the system itself functions as intended, not necessarily on its performance with a specific dataset of clinical outcomes.

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)

Not applicable. This device is a data collection system, not an AI/ML diagnostic or assistive tool that would require expert-established ground truth on clinical images or patient conditions. Its "performance" refers to its ability to accurately retrieve and transmit data from medical devices. The verification and validation activities would focus on the technical integrity of data transfer, not on clinical interpretations.

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

Not applicable. As above, the device's function does not involve clinical 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 device is a data connectivity solution, not an AI-powered assistive tool for human readers. Therefore, an MRMC study is irrelevant to its function.

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

The DataCaptor™ system is described as "a data acquisition system designed to retrieve and deliver near-real-time data from multiple vendors' bedside medical devices and send it to clinical or hospital information systems." Its performance is inherent in its ability to correctly acquire and transmit data. While it operates "standalone" in capturing data, its "performance" is about technical accuracy and reliability of data transfer, not about a standalone clinical interpretation task. The document states it is "not intended for monitoring purposes, nor is the software intended to control any of the clinical devices." This reinforces that its performance is not a clinical "standalone" assessment in the way AI diagnostics are evaluated.

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

For a data collection system, the "ground truth" would likely involve:

• Verification of data integrity: Comparing the data captured by DataCaptor™ to the raw data output directly from the bedside medical devices (e.g., via a controlled test environment or direct measurement).
• Verification of data transmission: Ensuring the data is correctly formatted and transmitted to the clinical information systems (HL7 standard).
• System functionality testing: Ensuring all software and hardware components operate as designed.

No specific methodology for establishing this "ground truth" (e.g., expert consensus) is outlined in the summary, as it's a technical verification process rather than a clinical one.

8. The sample size for the training set

The DataCaptor™ System is a software and hardware system for data collection, not an AI/ML model that would be "trained" on a dataset in the typical sense. Therefore, there is no "training set" or corresponding sample size.

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

Not applicable, as there is no training set for this type of device.

Summary of what can be gleaned about "acceptance criteria" and "study" from this document:

• Acceptance Criteria (Implied): The central acceptance criterion for this Special 510(k) was that the modified DataCaptor™ System would continue to demonstrate "substantial equivalence" to its predicate device. This means its performance, functionality, and reliability should be "commensurate" with the previously cleared device, and it should meet its intended use of data collection and clinical information management without being used for monitoring or control of devices.
• Study (Verification/Validation Activities): The "study" mentioned is not a clinical trial or performance study in the sense of comparing diagnostic accuracy. Instead, it refers to:
  • Software verification and validation: Ensuring the software changes meet specifications and function correctly.
  • Environmental and safety testing: For hardware changes.
  • System validation: Confirming the overall system performance, functionality, and reliability.
    These activities were designed to ensure the device met "pre-determined pass/fail criteria" derived from a risk analysis, confirming its equivalence to the predicate. Specific details of these technical tests (e.g., number of test cases, types of data simulated) are not provided in this public summary.

In essence, this 510(k) summary focuses on demonstrating that modifications to an existing device do not alter its safety or effectiveness, rather than performing a new clinical efficacy study.

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