Search Results

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.

Ask a specific question about this device

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 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 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 Alert Watch:OR. Alert Watch, Inc. performs the validation for each installation site.

Here's a breakdown of the acceptance criteria and the study details for the AlertWatch:OR device, based on the provided document:

The document does not explicitly state formal acceptance criteria with specific performance metrics (e.g., sensitivity, specificity, accuracy thresholds). Instead, the performance testing section describes verification and validation activities designed to ensure the product works as designed, meets its stated requirements, and is clinically useful.

1. Table of Acceptance Criteria and Reported Device Performance

As specific numerical acceptance criteria (e.g., sensitivity > X%, specificity > Y%) are not provided, the table below reflects the described performance testing outcomes.

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

• Sample Size for Verification (Step 4: Historical Data): "a set of cases from actual patients" - The exact number of cases is not specified.
• Data Provenance: "data from the EMR" and "a set of cases from actual patients." The document does not specify the country of origin, nor explicitly whether it was retrospective or prospective, though "historical data" strongly implies retrospective data.

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

The document does not specify the number of experts used or their qualifications for establishing ground truth, and it does not explicitly describe a ground truth establishment process involving experts in the traditional sense (e.g., for diagnostic accuracy). The "clinical accuracy" review of the software requirements specification implies expert involvement, but details are missing.

4. Adjudication Method for the Test Set

The document does not describe an adjudication method (such as 2+1, 3+1) for the test set.

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

A multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly mentioned or described. The study focused on the device's functionality and utility rather than a direct comparison of human readers with and without AI assistance to quantify improvement.

6. Standalone (Algorithm Only) Performance Study

The verification steps, particularly "Verify the analysis output" and "Verify the data display" using "constructed data," and "Verify the product with historical data," indicate that the algorithm's performance was evaluated in a standalone manner (without human-in-the-loop) to ensure it performs "as designed" and produces "desired output/display." However, these are functional verifications rather than a typical standalone diagnostic performance study with metrics like sensitivity, specificity, or PPV/NPV.

7. Type of Ground Truth Used

The ground truth for the performance testing appears to be established by:

• "Desired output" based on the "Software Requirements Specification" for constructed data tests (functional verification).
• "Works as designed" when tested with "a set of cases from actual patients" (implies comparison to expected system behavior based on its design, rather than a clinical outcome or expert diagnosis acting as a gold standard).
• "Clinical utility... by analyzing case outcomes" suggests that real-world patient outcomes were used to assess the value of the alerts generated. This hints at an outcome-based ground truth for the validation of clinical utility, but details are scarce.

8. Sample Size for the Training Set

The document does not specify the sample size for a training set. The descriptions of verification and validation do not refer to machine learning model training. The device seems to operate based on "rules/algorithms in the Software Requirements Specification" rather than a trained AI model.

9. How the Ground Truth for the Training Set Was Established

As there's no mention of a dedicated training set or a machine learning model requiring such, this information is not applicable based on the provided text. The device likely relies on predefined rules and algorithms.

Ask a specific question about this device