Search Results
Found 2 results
510(k) Data Aggregation
(49 days)
The CHM Device is a remote monitoring software solution intended to collect and store biometric data from physiological measurement devices intended for use in the home. The CHM Device also allows for the automated transmission of the biometric data to a remote secure server via an existing mobile telecommunications and/or internet infrastructure.
The stored biometric data is accessible by clinicians for analysis and intervention. Patients can also review the stored biometric data and receive educational and motivational content from clinicians.
The CHM Device can be used as a standalone device or in conjunction with supported patient monitoring devices, such as a glucometer, weight scale, pulse oximeter, and blood pressure monitor.
The CHM Device is not intended for use in surgical rooms, intensive care units, intermediate or step-down units or emergency vehicles. It is not interpretive, nor is it intended for diagnosis or as a replacement for the oversight of healthcare professionals. It does not provide real-time or emergency monitoring.
The CHM is a software platform for the collection and display of biometric data, primarily from externally supported patient monitoring devices, both to the patient and to the clinician. The CHM Device may also be used as a standalone device. The CHM Device uses existing Internet and telecommunications architecture (cell phones and computers) for the automated transmission of medical data to a remote secure server from where it can be viewed remotely by clinicians and patients for the purposes of storage and basic analysis. The CHM Device also provides educational and motivationalities allowing the clinician to send tasks, recommendations, surveys, and educational and motivational messages to patients.
Here's an analysis of the provided text regarding the acceptance criteria and supporting study for the Verizon Wireless Converged Health Management Device:
1. Table of Acceptance Criteria and Reported Device Performance:
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Ensure changes to the software have not introduced new faults. | Regression testing was performed and demonstrated that changes to the software did not introduce new faults. |
| Ensure that a change to one part of the software does not affect other parts of the software. | Usability testing was performed and demonstrated that changes to one part of the software did not affect other parts. |
| Ensure that biometric data was transferred accurately from the Telcare and Genesis Health glucometers to the server infrastructure and into the CHM platform. | Additional verification and validation activities were performed and demonstrated accurate transfer of biometric data from Telcare and Genesis Health glucometers to the server infrastructure and into the CHM platform. |
| All verification and validation activities, as required by the risk analysis, were performed. | All verification and validation activities, as required by the risk analysis, were performed. |
2. Sample Sizes Used for the Test Set and Data Provenance:
The document states that "regression and usability testing was performed" and "additional verification and verification activities were performed." However, specific sample sizes for a 'test set' (e.g., number of data points, number of users, number of transfers) are not explicitly mentioned.
The data provenance (country of origin of the data, retrospective or prospective) is not mentioned in the provided text.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of those Experts:
The document does not mention using experts to establish ground truth in the context of the performance data. The testing appears to be focused on software functionality and data transfer accuracy against design specifications rather than against expert interpretations of medical conditions.
4. Adjudication Method for the Test Set:
An adjudication method (e.g., 2+1, 3+1, none) is not applicable or mentioned given the nature of the testing described, which focuses on software functionality and data transfer accuracy rather than diagnostic performance requiring expert consensus.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance:
A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. The device is a "remote monitoring software solution" and not an interpretive or diagnostic AI. It collects and stores biometric data for clinicians to analyze. The document explicitly states: "It is not interpretive, nor is it intended for diagnosis or as a replacement for the oversight of healthcare professionals."
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done:
The performance evaluation described is a standalone algorithm-only performance assessment in the sense that the testing verified the software's ability to accurately transfer and handle data. There is no mention of human-in-the-loop performance measurement for the device's core functionality. The clinicians still analyze the data.
7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.):
The ground truth used for the testing appears to be the expected behavior and functional requirements of the software, specifically related to accurate data transfer and functionality after changes. This is not a "medical ground truth" established by experts for diagnostic purposes but rather an engineering and software validation ground truth. For instance, for data transfer accuracy, the ground truth would be the original data from the glucometers.
8. The Sample Size for the Training Set:
The document does not mention a training set because the device is a data collection and management platform, not a machine learning or AI algorithm that requires training on a dataset to learn patterns or make predictions.
9. How the Ground Truth for the Training Set Was Established:
As there is no training set, this question is not applicable.
Ask a specific question about this device
(351 days)
The CHM Device is a remote monitoring software solution intended to collect and store biometric data from physiological measurement devices intended for use in the home. The CHM Device also allows for the automated transmission of the biometric data to a remote secure server via existing mobile telecommunications and/or Internet infrastructure.
The stored biometric data is accessible by clinicians for analysis and intervention. Patients can also review the stored biometric data and receive educational content from clinicians.
The CHM Device can be used as a standalone device or in conjunction with supported patient monitoring devices, such as a glucometer, weight scale, pulse oximeter, and blood pressure monitor.
The CHM Device is not intended for use in surgical rooms, intensive care units, intermediate or stepdown units or emergency vehicles. It is not interpretive, nor is it intended for diagnosis or as a replacement for the oversight of healthcare professionals. It does not provide real-time or emergency monitoring.
The CHM Device is a software platform for the collection and display of biometric data, primarily from externally supported patient monitoring devices, both to the clinician. The CHM Device may also be used as a standalone device. The CHM Device uses existing Internet and telecommunications architecture (cellphones and computers) for the automated transmission of medical data to a remote server from where it can be viewed remotely by clinicians and patients for the purposes of storage and basic analysis. The CHM Device also provides educational and motivational functionalities allowing the clinician to send tasks, recommendations, surveys, and educational and motivational messages to patients.
The Verizon Wireless Converged Health Management Device (K122458) is a software solution for remote monitoring, not an AI/ML diagnostic tool. Therefore, many of the typical acceptance criteria and study components requested for AI/ML devices, such as performance metrics (e.g., sensitivity, specificity), ground truth establishment by experts, and MRMC studies, are not applicable.
The submission focuses on software verification and validation, demonstrating that the device functions as intended and safely transmits data.
Here's a breakdown of the relevant information from the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Software Functionality: The CHM Device performs appropriately per defined specifications. | Software verification and validation testing, including usability validation, was performed successfully, demonstrating that the CHM Device performs appropriately per defined specifications. |
| Input Requirements: The CHM Device meets all input requirements. | Software verification and validation testing... demonstrating that the CHM Device... meets all input requirements. |
| Intended Use: The CHM Device fulfills the device's intended use. | Software verification and validation testing... demonstrating that the CHM Device... fulfills the device's intended use. |
| Safety Mitigations: The CHM Device correctly incorporates all required safety mitigations. | Software verification and validation testing... demonstrating that the CHM Device... correctly incorporates all required safety mitigations. |
2. Sample size used for the test set and the data provenance
Not applicable for this type of software device. The "test set" would primarily involve internal software testing and validation rather than a clinical dataset of patient data.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
Not applicable. The ground truth for this device is its adherence to software specifications and functional requirements, which are typically established by software engineers and quality assurance personnel, not medical experts.
4. Adjudication method for the test set
Not applicable. Software testing for functionality does not typically involve an adjudication method by medical experts in the way an AI diagnostic tool would.
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. This device is a data collection and transmission platform, not an interpretive or diagnostic AI. Therefore, an MRMC study related to human reader improvement with AI assistance is not relevant.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Yes, the software verification and validation described is a standalone assessment of the algorithmic/software performance. It focuses on the device's ability to collect, store, and transmit data accurately and according to specifications, independent of human interpretation of that data.
7. The type of ground truth used
The ground truth used for this device's performance validation is its defined software specifications and functional requirements. The testing confirmed that the software behaved as designed and intended.
8. The sample size for the training set
Not applicable. This is not an AI/ML algorithm that requires a training set of data.
9. How the ground truth for the training set was established
Not applicable. As this device is not an AI/ML algorithm requiring training data, this question is not relevant.
Ask a specific question about this device
Page 1 of 1