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The intended use for the iBed™ Wireless (with iBed™ Awareness) is to assist clinical staff to monitor bed parameters on specific Stryker beds. The desired bed parameters will be set by clinicians at the bedside. The iBed™ Wireless software is intended to be used only with specifically enabled Stryker beds that have been verified and validated with the iBed™ Wireless software, and is not intended to provide bed status information for non-Stryker beds. The iBed™ Wireless software is not intended to communicate any patient status information, nor to permanently store any type of data. The iBed™ Wireless with iBed™ Awareness System is not intended to provide automated treatment decisions or as a substitute for professional healthcare judgment. The iBed™ Wireless with iBed™ Awareness System is not a replacement or substitute for vital signs monitoring or alert equipment. All patient medical diagnosis and treatment are to be performed under direct supervision and oversight of an appropriate health care professional.

The iBed™ Awareness is used to monitor hospital bed status and to assist the Healthcare Provider in providing patient care. The system is integrated into Stryker hospital bed to monitor bed information such as: iBed™ Awareness status, bed exit status, siderail status, bed brake status, fowler angle, and weight on bed, for example. The Healthcare Professional can set the alerting function (audible and lights) to activate if bed status has changed. The iBed™ Wireless device is a tool that facilitates the wireless transmission of the bed status data using a wireless hardware and software device to the hospital server. The data can be captured by hospital data collection systems (developed and provided by Third Parties; not part of this submission). Through the use of the third party software, the data may be displayed at user-defined locations, such as nursing stations.

This document describes the regulatory submission for the iBed™ Wireless with iBed™ Awareness system, a device that monitors hospital bed status. The provided text, however, does not contain specific acceptance criteria or a detailed study proving the device meets those criteria in the typical format of clinical performance evaluation (e.g., sensitivity, specificity, accuracy against a recognized gold standard).

Instead, the document focuses on demonstrating substantial equivalence to predicate devices and adherence to existing safety and performance standards for similar medical devices.

Here's an analysis based on the provided text, addressing your points where information is available:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria (e.g., minimum accuracy rates for bed status detection) or specific performance metrics in a tabulated format. Instead, it states that the device "meets its functional, performance, safety and efficacy specifications and requirements."

The "performance" is generally described as compliance with established international standards for medical devices and software, rather than a clinical performance study with specific outcomes.

2. Sample Size Used for the Test Set and Data Provenance

The document does not mention a specific "test set" in the context of a clinical performance study with a defined sample size (e.g., number of patients, number of bed events). The testing described is primarily related to product verification and validation, adherence to safety standards, and electromagnetic compatibility.

• Test Set Sample Size: Not specified for a clinical performance study.
• Data Provenance: Not specified. The testing seems to be internal verification and validation by Stryker Medical.

3. Number of Experts Used to Establish Ground Truth and Qualifications of Experts

This information is not provided. The document does not describe a study involving human experts establishing ground truth for performance evaluation of the device's monitoring capabilities.

4. Adjudication Method for the Test Set

Not applicable, as a study involving expert adjudication for a test set (e.g., clinical images, event logs) is not described.

5. Multi Reader Multi Case (MRMC) Comparative Effectiveness Study

No MRMC comparative effectiveness study is mentioned. The device's purpose is to monitor bed parameters, not to assist human readers in interpretation or diagnosis. Therefore, the concept of "human readers improve with AI vs without AI assistance" does not apply to this device.

6. Standalone (Algorithm Only Without Human-in-the-Loop) Performance

Yes, the testing described appears to be for the standalone performance of the device's hardware and software. The text states:

• "Software testing and hardware testing of each component and of the final device have been conducted extensively."
• "The extensive performance testing that has been conducted on the individual components and on the finished system demonstrate that the iBed™ Wireless with iBed™ Awareness are safe and effective, and perform as well or better than the predicate devices."

This implies that the system itself was tested for its ability to accurately detect and transmit bed status information without direct human intervention in the detection process. The device's intended use is to "assist clinical staff to monitor bed parameters," meaning the device provides data, and staff then act upon it.

7. Type of Ground Truth Used

The document does not explicitly define the "ground truth" used for testing the iBed™ Wireless system. Given the nature of the device (monitoring bed parameters like siderail status, brake status, fowler angle, and weight on bed), the ground truth would likely be established through:

• Direct physical observation/measurement: For mechanical parameters like siderail position or brake engagement, this would involve physically checking and comparing to the device's report.
• Engineered test conditions: For parameters like weight, a known weight placed on the bed would serve as ground truth for calibration and accuracy testing.
• Internal reference systems: For angle measurements, an independent reference sensor could be used.

However, the document does not detail these ground truth establishment methods.

8. Sample Size for the Training Set

No training set is mentioned. This device appears to be rule-based or sensor-based, rather than employing machine learning algorithms that would require a "training set" in the conventional sense. The "software testing" refers to verification and validation of its programmed logic and functionality.

9. How Ground Truth for the Training Set Was Established

Not applicable, as no training set is mentioned for this device.

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