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AlertWatch:OR is intended for use by clinicians for secondary monitoring of patients within operating rooms. AlertWatch:OR combines data from networked physiologic monitors, anesthesia information management systems and patient medical records and displays them in one place. AlertWatch:OR can only be used with both physiological monitors and AIMS versions that have been validated by AlertWatch. Once alerted, you must refer to the primary monitor or device before making a clinical decision.

AlertWatch:OR is also intended for use by supervising anesthesiologists outside of operating rooms. Once alerted, the supervising anesthesiologist must contact the clinician inside the operating room or must return to the operating room before making a clinical decision. Once either clinician is alerted, they must refer to the primary monitor or device before making a clinical decision.

AlertWatch:OR is a display and secondary alert system used by the anesthesiology staff residents. CRNA's, and attending anesthesiologists - to monitor patients in operating rooms. The purpose of the program is to synthesize a wide range of patient data and inform clinicians of potential problems that might lead to immediate or long-term complications. Once alerted, the clinician is instructed to refer to the primary monitoring device before making a clinical decision. AlertWatch:OR should only be connected to AIMS systems and physiologic monitors that have been validated for use with AlertWatch:OR. AlertWatch, LLC performs the validation for each installation site.

The purpose of this 510(k) is for marketing clearance of AlertWatch:OR 2.50 which includes minor modifications to some display views, user features, indicators and alerts as well as compatibility with the iPad and the iPhone.

The provided text is a 510(k) summary for the medical device AlertWatch:OR, focusing on its substantial equivalence to a previously cleared device. The "Performance Data" section discusses a human factors study and the process for establishing default limits and thresholds, but it does not describe an in-depth study with quantitative acceptance criteria for device performance in detecting or alerting.

Specifically, the document does not contain a table of acceptance criteria and reported device performance in terms of diagnostic metrics (e.g., sensitivity, specificity, accuracy). It focuses more on usability and the validation of alert thresholds as part of its performance data.

Therefore, many of the requested details about a study proving the device meets acceptance criteria are not present in this document.

Here's a breakdown of what can be extracted and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance

Not available in the provided text. The document refers to the validation of default limits and thresholds and a human factors study, but not quantitative performance criteria for the device's alerting function.

2. Sample Size for Test Set and Data Provenance

• Human Factors Study: The text mentions a "comprehensive human factors study" for the iPhone version. It does not specify the sample size of users or the provenance of the data used in this study (e.g., retrospective/prospective, country of origin).
• Default Limits and Thresholds: The validation of these limits involved "Review of References," an "Expert Committee" (anesthesia physicians at the University of Michigan Health System), and "External Experts" (four anesthesiology experts). This is not a "test set" in the traditional sense of evaluating device performance on patient data, but rather a process for establishing system parameters.

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

• Human Factors Study: Not specified.
• Default Limits and Thresholds:
  • Expert Committee: An unspecified number of "anesthesia physicians at the University of Michigan Health System." Their specific qualifications beyond being "anesthesia physicians" are not detailed (e.g., years of experience).
  • External Experts: Four "anesthesiology experts." Specific qualifications are not detailed.

4. Adjudication Method for the Test Set

Not applicable as a traditional "test set" with adjudicated ground truth for diagnostic performance is not described. The expert involvement for default limits was for "opinion and confirmation" and "final review," not for adjudicating individual cases on a test set.

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

No MRMC comparative effectiveness study is mentioned. The document primarily discusses changes and validation of an existing secondary monitoring system, not its comparative effectiveness against human readers.

6. Standalone Performance

The device is described as a "secondary monitoring" and "secondary alert system." The Indications for Use explicitly state: "Once alerted, you must refer to the primary monitor or device before making a clinical decision." This indicates it's designed to assist human clinicians, not to operate autonomously as a standalone diagnostic tool. Therefore, a standalone performance study in the sense of demonstrating diagnostic accuracy independent of a human is not directly applicable or discussed for decision-making. The "Performance Data" section addresses usability and the clinical validity of its alert thresholds rather than standalone diagnostic performance metrics.

7. Type of Ground Truth Used

• Human Factors Study: Ground truth would relate to user task completion and usability issues, not clinical diagnosis.
• Default Limits and Thresholds: Based on "Review of References" (published studies), "Expert Committee" opinion/confirmation, and "External Experts" review. This is expert consensus/opinion based on clinical knowledge and literature rather than pathology or outcomes data on a specific patient cohort for device performance evaluation.

8. Sample Size for the Training Set

No training set is mentioned in the context of machine learning. This device appears to be a rule-based or threshold-based system rather than one that employs machine learning requiring a training set.

9. How Ground Truth for the Training Set Was Established

Not applicable, as no training set for machine learning is described.

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