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AirStrip RPM is software capable of displaying physiologic and other patient information. This information is generated by other medical devices and patient information system, and not by AirStrip RPM. AirStrip RPM captures this information from these other systems and displays it for clinicians.

AirStrip RPM is intended to be used by clinicians for the following purposes:

• By using a cellular telephone or other device on which AirStrip RPM is installed, to review physiologic data of a patient when the clinician is not at the hospital
• To view the near real-time waveforms remotely
• To remotely review other standard or critical near real-time patient data from the monitored system
• To provide a request for remote consultation regarding a patient's waveform or other data

The AirStrip RPM software can display the following the physiologic data captured by other medical devices:

• ECG Waveform
• Blood Pressure Waveform
• 02 Waveform
• CO2 Waveform
• Heart Rate
• Respiratory Rate
• Oxygen Saturation
• Intracranial Pressure
• Central Venous Pressure
• Pulmonary Capillary Wedge Pressure
• Cardiac Index
• Cardiac Output
• Cerebral Perfusion Pressure
• Urine Output
• Urine/Stool Mix Output
• Systolic Blood Pressure Invasive
• Mean Arterial Pressure Invasive
• Diastolic Blood Pressure Invasive
• Systolic Blood Pressure Cuff
• Mean Arterial Pressure Cuff
• Diastolic Blood Pressure Cuff
• Vasoactive Infusions
• Antiarrhythmics
• Sedation
• Paralytics
• Laboratory Data including
• Blood Gas
• Chemistry
• Hematology
• Coagulation
• Allergies
• Medications

AirStrip RPM is software that runs on devices capable of running Apple iPhone OS. It interfaces with third-party centralized monitoring systems that in turn gather data from patient monitors and other devices in the hospital. AirStrip RPM gives health care providers the ability to view near-real-time patient physiological data remotely.

The provided 510(k) summary for the AirStrip RPM device does not include acceptance criteria or the specifics of a study proving the device meets acceptance criteria in the format typically found in clinical validation studies.

Instead, the document focuses on demonstrating substantial equivalence to predicate devices through technical and functional comparisons, and describes internal verification and non-clinical testing.

Here's an analysis based on the information provided, addressing the requested points where possible, and noting where information is absent:

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

The document does not explicitly define acceptance criteria in terms of numerical thresholds for performance metrics (e.g., accuracy, sensitivity, specificity).

However, it states: "AirStrip Technologies conducted verification testing of all data points listed. In this test, known datasets were gathered from Cerner iBus and displayed on AirStrip RPM. These datasets included all data points proposed in AirStrip RPM; all monitored data, all manual data, including patient demographics. In all cases, AirStrip RPM was able to display data points with complete accuracy."

Based on this, we can infer the following:

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

• Sample Size for Test Set: Not specified. The document uses the general term "known datasets" without indicating the number of datasets or cases.
• Data Provenance: The "known datasets" were "gathered from Cerner iBus." While Cerner iBus is an FDA-cleared system, the origin (e.g., country, specific hospital, patient demographics) of the data within these datasets is not provided.
• Retrospective or Prospective: Not explicitly stated, but "known datasets were gathered" suggests a retrospective approach using pre-existing data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

This information is not provided. The document states "known datasets were gathered from Cerner iBus," implying that the data from the Cerner iBus itself served as the ground truth, rather than requiring independent expert adjudication of the same data.

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

This information is not provided. Given that the Cerner iBus data was considered the "known dataset," no explicit adjudication method for establishing ground truth for the test set is described.

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, a multi-reader multi-case comparative effectiveness study was not done. The device (AirStrip RPM) is a secure remote viewing software, not an AI-powered diagnostic or assistive tool designed to improve human reader performance in interpreting medical images or data. Its primary function is to display data from other systems.

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

The verification testing described ("AirStrip Technologies conducted verification testing of all data points listed... In all cases, AirStrip RPM was able to display data points with complete accuracy") represents a standalone performance assessment of the device's ability to accurately display data. It's a test of the algorithm's output fidelity compared to the known input, without involving human interpretation of the AirStrip RPM output for diagnostic purposes.

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

The ground truth used was the data as provided by the FDA-cleared Cerner iBus system. The AirStrip RPM's performance was evaluated by directly comparing its displayed data against these "known datasets" from the iBus system.

8. The sample size for the training set

This information is not applicable/provided. AirStrip RPM is a data display and communication software, not a machine learning or AI algorithm that requires a "training set" in the conventional sense. The software's functionality is based on established communication protocols and display logic.

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

This information is not applicable/provided for the same reason as point 8. The device's functionality does not involve a training set with associated ground truth for learning purposes.

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