(39 days)
The Health Care System Software is an optional software accessory for use with the following models with data management capabilities: a) Clever Chek blood glucose meters, b) Clever Chek blood glucose plus blood pressure monitors, and c) Clever blood pressure monitors. When use with one of these meters, Health Care System Software transfers data from the device's memory into a computer for enhanced data management.
The Health Care System Software is intended for use in home and clinical settings as an aid for users and their health care professionals to review, analyze and evaluate the historical test results to support health management effectively.
The Health Care System Software is an optional software accessory for use with the following models with data management capabilities: a) blood glucose meters, b) blood glucose plus blood pressure monitors, and c) blood pressure monitors. When use with one of these devices, Health Care System Software transfers data from the device's memory into a computer for enhanced data management.
Here's a breakdown of the acceptance criteria and study information for the Health Care System Software (K110948), based on the provided text:
Important Note: The provided 510(k) summary (K110948) focuses on demonstrating substantial equivalence rather than detailed performance studies with specific statistical metrics typical for AI-powered diagnostic devices. The device is a data management software, not a diagnostic AI. Therefore, many standard AI/ML acceptance criteria (e.g., sensitivity, specificity, AUROC) and the sophisticated study designs to prove them are not applicable or detailed in this document. The "performance studies" mentioned are primarily about software and hardware validation to ensure data transfer integrity and functionality.
Acceptance Criteria and Reported Device Performance
| Acceptance Criteria Category | Specific Criteria | Reported Device Performance |
|---|---|---|
| Functional Equivalence | Have the same intended use and intended users as the predicate device. | The device "has the same intended use and intended users" as the predicate. |
| Data Presentation | Have the same data presentation as the predicate device. | The device "has the same data presentation" as the predicate. |
| Programming Language | Utilize the same programming language as the predicate device. | The device "has same programming language" as the predicate. |
| Data Integrity | Data transferred from the device cannot be changed or modified in any way. | "data transferred from the device cannot be changed or modified in any way." |
| Added Features Functionality | The added features (temperature data tabular display, graph display, printer setting) function as intended. | Not explicitly detailed with specific metrics, but "Results demonstrate that the system meets its intended use." Implies these features work. |
| Hardware Validation | Successful data transfer through the cable. | "validation of hardware (data transfer through the cable)" was performed and met intended use. |
| Software Validation | Software functions as intended for data management, review, analysis, and evaluation. | "software validation" was performed and met intended use. |
Study Details
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Sample size used for the test set and the data provenance:
- Sample Size: Not specified. The document states "Testing of Health Care System Software included validation of hardware (data transfer through the cable) and software validation." No specific number of data points, transfers, or users are mentioned for the test set.
- Data Provenance: Not specified. It's unclear if the testing involved real-world patient data or simulated data, where it originated (e.g., country), or if it was retrospective or prospective. Given the nature of a software accessory for data management, it likely involved simulated or controlled data transfer scenarios rather than clinical patient data analysis for diagnostic accuracy.
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Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Number of Experts: Not applicable/Not specified. This device is a data management software accessory, not an AI diagnostic tool that requires expert-established ground truth for medical conditions. The "ground truth" here would relate to the correctness of data transfer and display, which is typically validated through engineering and software testing.
- Qualifications of Experts: Not applicable/Not specified.
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Adjudication method for the test set:
- Adjudication Method: Not applicable/Not specified. As it's software validation for data management rather than diagnostic interpretation, multi-reader adjudication methods are not relevant.
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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:
- MRMC Study: No, an MRMC comparative effectiveness study was not done.
- Effect Size: Not applicable. The device is a data management software, not an AI designed to assist human readers in making diagnostic decisions. Its purpose is to facilitate review and analysis of historical test results, not to interpret them or improve diagnostic accuracy directly.
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If a standalone (i.e. algorithm only without human-in-the loop performance) was done:
- Standalone Performance: Yes, in a sense, the "Performance Studies" section describes validation of the software and hardware working independently to transfer and manage data. The device's primary function is its standalone capability to capture and organize data from connected meters. It does not perform diagnostic tasks that would typically involve "human-in-the-loop" interaction in the context of AI.
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The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- Type of Ground Truth: For this type of device, the "ground truth" would be established by the expected behavior of the software and hardware according to its specifications. This means:
- Correct Data Transfer: Verification that data points (e.g., blood glucose readings, blood pressure) are transferred accurately and completely from the meter to the computer.
- Correct Data Display: Verification that the transferred data is displayed correctly (e.g., tabular format, graph format for temperature) as per the software design.
- Data Integrity: Confirmation that transferred data remains unmodified.
- This is typically validated through software testing and quality assurance protocols comparing actual output against expected output based on source data. It does not involve medical ground truths like pathology reports or expert consensus on a diagnosis.
- Type of Ground Truth: For this type of device, the "ground truth" would be established by the expected behavior of the software and hardware according to its specifications. This means:
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The sample size for the training set:
- Sample Size: Not applicable/Not specified. This device is a traditional software application for data management, not a machine learning or AI model that requires a "training set" in the conventional sense. The software is developed based on programming logic and specifications, not trained on data.
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How the ground truth for the training set was established:
- Ground Truth Establishment: Not applicable. As there is no training set for an AI/ML model, there is no ground truth established for a training set.
§ 862.1345 Glucose test system.
(a)
Identification. A glucose test system is a device intended to measure glucose quantitatively in blood and other body fluids. Glucose measurements are used in the diagnosis and treatment of carbohydrate metabolism disorders including diabetes mellitus, neonatal hypoglycemia, and idiopathic hypoglycemia, and of pancreatic islet cell carcinoma.(b)
Classification. Class II (special controls). The device, when it is solely intended for use as a drink to test glucose tolerance, is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 862.9.