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Care Pal (Model no. CPW-10X) is indicated for use in non-clinical settings to collect and transmit historical medical information to healthcare professionals to help support effective management of their patients.

The product is not intended to provide automated treatment decisions nor for use as a substitute for a health care professional's judgement.

The Care Pal ("CP") remote patient monitoring system is for use in non-clinical settings as an accessory device to collect and transmit historical patient information to healthcare providers. It is intended to be used in combination with a variety of external devices. The CP remote patient monitoring system serves as the remote communication link between compatible external devices and the compatible healthcare facility at another location. The product is not intended to provide automated treatment decisions nor for use as a substitute for a health care professional's judgment.

The CP appliance contains software that can be activated to function with specific medical devices (including blood glucose meter, blood pressure and weight scale). The CP appliance with device connectivity retrieves data from a specific medical device and transmits to a remote healthcare provider using standard digital communication technologies. The CP appliance is not used directly on the patient, and poses no significant risk to the patient or other people within the patient's home.

Care Pal provides interfaces to the following connecting peripheral devices and back end server as well a. BT

Selected device (Brand/Model): weight scale, A&D/UC-321PBT

• b. USB
  Selected device (Brand/Model): glucose meter, Johnson & Johnson LifeScan / OneTouch Ultra II (K053529)

c. RS-232 (Serial Port) Selected device (Brand/Model): blood pressure meter, A&D UA-787PC (K012013)

d. Internet (Ethernet/wireless) connection to backend server

The provided text describes the 510(k) summary for the "Care Pal" device (Model no. CPW-10X). However, it does not contain specific acceptance criteria for device performance or a detailed study demonstrating how the device meets those criteria in the way typically expected for an AI/ML medical device submission (e.g., accuracy, sensitivity, specificity studies).

Instead, this document focuses on substantial equivalence to a predicate device based on intended use, technological characteristics, and conformance to safety and EMC standards.

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

1. Table of Acceptance Criteria and Reported Device Performance:

• The document primarily references general performance summaries related to operating specifications, safety, and EMC requirements.
• It states that the device conforms to applicable standards, including IEC 60601-1 and IEC 60601-1-2 requirements. These are general safety and electromagnetic compatibility standards for medical electrical equipment.
• It also states that "bench testing contained in this submission demonstrate that any differences in their technological characteristics do not raise any new questions of safety or effectiveness." However, specific acceptance criteria for this bench testing (e.g., data transfer accuracy percentage, successful connection rate) and the detailed results are not provided in this summary.

Without the actual study report, it's impossible to create a precise table. Based on the summary, a conceptual table would look like this:

Missing Information (Crucial for an AI/ML device, but this isn't one):

• Specific quantitative performance metrics like accuracy, sensitivity, specificity, or F1-score.
• Thresholds for passing these metrics.

2. Sample size used for the test set and the data provenance:

• Not applicable / Not specified. This device is a data management/transmission system, not an AI/ML algorithm that predicts or diagnoses based on a test dataset. The "bench testing" mentioned would focus on the device's ability to connect and transmit data reliably, not on a "test set" of patient data for diagnostic evaluation.
• The document mentions "historical medical information" being transmitted, but this refers to the type of data the device handles, not a dataset used for performance evaluation of the device itself.

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

• Not applicable / Not specified. As a data transmission device, ground truth for diagnostic or prognostic purposes is not established by experts for its functional evaluation. The "ground truth" for this device would likely be the accurate transfer of data from the peripheral device to the server, which would be verified through technical means rather than expert clinical review.

4. Adjudication method for the test set:

• Not applicable / Not specified. No clinical test set requiring expert adjudication is described in this summary.

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:

• Not applicable. This device is not an AI algorithm designed to assist human readers or perform diagnostic tasks. It's a data transmission system.

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

• Not applicable. This is not an algorithm, but a hardware and software system for data collection and transmission. Its "standalone" performance would relate to its ability to perform its transmission function independently, which would be covered under bench testing and EMC/safety compliance.

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

• Not applicable / Not specified in detail. For this type of device, ground truth would relate to the successful and accurate transmission of data. For instance, if a glucose meter reads "120 mg/dL," the ground truth for the Care Pal's performance would be that "120 mg/dL" is accurately received by the remote server. This is typically verified through technical validation, not clinical ground truth like pathology or expert consensus.

8. The sample size for the training set:

• Not applicable. This device is not an AI/ML algorithm that requires a training set.

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

• Not applicable. This device is not an AI/ML algorithm that requires a training set.

In summary:

The provided document describes a "Care Pal" device which is a data management/transmission system, not an AI/ML diagnostic or therapeutic device. Therefore, it does not contain the types of performance data (e.g., accuracy, sensitivity, specificity, expert ground truth, sample sizes for training/test sets) that are typically associated with AI/ML device approval. The "study" mentioned is "bench testing" and compliance with general safety and EMC standards, aimed at demonstrating substantial equivalence to a predicate device rather than statistical performance on a clinical dataset.

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