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The Philips CompuRecord® Peri-Operative Anesthesiology Information System Software is a computer-based system which collects, processes, and records data directly from anesthesiological monitors which themselves are attached to patients in the operating room environment.

CompuRecord is generally indicated in the peri-operative environment when the anesthetist decides to generate a paper and electronic record of the administration of anesthesia to a patient, perform a pre-operative assessment, and documentation of {chart} nursing care in the PACU.

The Philips CompuRecord® Peri-Operative Anesthesia Information System is a computer-based system which collects, processes, and records data directly from anesthesiological monitors which themselves are attached to patients in the operating room environment. The modification is primarily a software based change that updates the operating system and allows network use and web access.

The Philips CompuRecord® Peri-Operative Anesthesia Information System is a software modification that updates the operating system and allows network use and web access. This device was cleared through the 510(k) pathway, indicating that it was found to be substantially equivalent to a previously cleared predicate device (K854213). For devices cleared via substantial equivalence, the primary assessment is whether the new device is as safe and effective as the predicate, rather than establishing new, specific performance metrics through detailed clinical studies in the same way a novel device might.

Here's an analysis of the provided information concerning the acceptance criteria and study, structured to address your specific points:

Since this is a software modification to an existing device, the "acceptance criteria" and "device performance" are focused on maintaining the safety and effectiveness established for the predicate device, especially regarding the new software's functionality.

1. Table of Acceptance Criteria and Reported Device Performance

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

The provided text does not specify a sample size for a test set in terms of patient data or clinical cases. The testing described is "system level tests, integration tests, and safety testing from risk analysis." This suggests a focus on software validation and verification, rather than clinical performance evaluation on a patient dataset. Therefore, there is no information on data provenance (country of origin, retrospective/prospective).

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

This information is not provided in the document. Given the nature of the testing (system, integration, safety), it's unlikely that "ground truth" in a clinical diagnostic sense (like expert consensus on medical images) was established for an external test set. The "ground truth" for software validation would be adherence to functional requirements and specifications.

4. Adjudication Method for the Test Set

This information is not provided. As explained above, the testing described does not suggest a need for a clinical adjudication method like 2+1 or 3+1.

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

An MRMC study was not conducted or described in the provided summary. The submission focuses on substantial equivalence through verification testing, not comparative clinical effectiveness with human readers.

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

The device itself is a "computer-based system which collects, processes, and records data" to assist an anesthetist. Its functionality is inherently integrated with human use (the anesthetist decides to generate records, performs assessments). The verification testing would have assessed the software's ability to perform these functions accurately and reliably, which could be considered its "standalone" performance within its intended use context. However, there isn't a separate study reported that isolates the algorithm's performance from human interaction in a way typical of diagnostic AI.

7. Type of Ground Truth Used

The "ground truth" for this device's validation appears to be the pre-defined specifications and requirements for the predicate device, as well as the software's ability to correctly implement the updated operating system, network, and web access functionality. This is a technical (engineering/software) ground truth rather than a clinical ground truth (e.g., pathology, outcomes data, expert consensus on a diagnosis).

8. Sample Size for the Training Set

This information is not applicable/not provided. The Philips CompuRecord is described as an information system for collecting, processing, and recording data, implying a rule-based or deterministic system rather than a machine learning (AI) system that requires a "training set" in the context of deep learning or similar algorithms. The term "training set" typically refers to data used to train a predictive model.

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

This information is not applicable/not provided because, as noted above, the device does not appear to be an AI/machine learning system that would have a "training set" with associated ground truth established in the usual sense. Its validation focuses on functional correctness and adherence to specifications.

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