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eCareCoordinator and its accessories are indicated for use by patients and by care teams for collecting and reviewing patient data from patients who are capable and willing to engage in use of this software, to transmit medical and non-medical information through integrated technologies.

eCareCoordinator (eCC) is a software-only telemedicine system. eCareCoordinator (eCC) is a combination of technology and clinical programs designed to enable the support of patients in the home setting, eCC is intended to support the clinician with monitoring of remote patients. Clinicians use eCC to manage populations of ambulatory care patients, while keeping primary care physicians informed of patient status.

eCC is comprised of the following primary components:

• . eCareCoordinator (eCC): eCC is the platform supporting the Clinical User Interface. eCC is a cloud-based system, which is used to acquire patient data from home devices, as well as provide a population management triage dashboard to enable the clinician's team to prioritize and manage populations of patients.
• . eCareCompanion (eCP): eCP is a patient application element of eCareCoordinator used to engage patients in their own health. eCP is a mobile application which runs on an commercial off-the-shelf (COTS) Android tablet. Patients manually input measurements (including weight, blood pressure, pulse, blood glucose concentration, SpO2, temperature, prothrombin time (PT), coagulation ratio (INR), and transthoracic impedance) from measurement devices into the COTS tablet containing eCP. The COTS tablet wirelessly communicates with eCC to transmit the data stored by eCP to eCC.

Here's an analysis of the acceptance criteria and study information based on the provided text, focusing on the eCareCoordinator (eCC) device:

Important Note: The provided document is a 510(k) Summary, which primarily focuses on demonstrating substantial equivalence to a legally marketed predicate device, not necessarily extensive performance validation against strict clinical acceptance criteria with statistical power. Therefore, some of the requested information (like specific effect sizes for MRMC studies, detailed sample sizes for "test sets" in a diagnostic accuracy sense, or specific ground truth methodologies for training data) are typically not found in such summaries for devices of this nature. The "studies" described here are mainly for verification and validation of the software's functionality and usability.

Acceptance Criteria and Reported Device Performance

The document does not explicitly list "acceptance criteria" for diagnostic performance in the way one might expect for an AI diagnostic device (e.g., sensitivity, specificity thresholds). Instead, the performance objective is to demonstrate that the eCareCoordinator (eCC) performs "as intended" and is "substantially equivalent" to its predicate devices. The "performance data" section states: "Bench tests were executed to verify and validate eCC. Verification testing consisted of verification of the system-level requirements and verification of the sub-system level requirements. Validation testing consisted of formative usability testing and summative usability testing. The verification and validation test results confirm that eCC performs as intended."

The acceptance criteria are implicitly tied to the functional and usability requirements, ensuring the system reliably collects, transmits, and presents patient data, and facilitates patient/clinician interaction as designed.

Additional Information:

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

   • The document mentions "bench tests" and "usability testing." It does not specify a "test set" in terms of a dataset for diagnostic accuracy, nor does it provide a sample size for data points or patients used in these tests.
   • Data provenance (country of origin, retrospective/prospective) is not mentioned for validation testing. Given the nature of a telemedicine system for data aggregation, the "data" itself might be simulated or test data for functional verification, and participants for usability studies.

2. 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 device is a "telemedicine system" for data aggregation and communication, not a diagnostic AI that generates a "ground truth" to be compared against expert readings. The "ground truth" for its type of validation would likely be correct data transmission, correct display, and appropriate user interaction as per design specifications, rather than a clinical diagnosis.

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

   • Not applicable as this is not a diagnostic performance study requiring adjudication of expert readings against an AI output.

4. 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 MRMC study was conducted or mentioned. The device is not presented as an AI-assisted diagnostic tool for human readers. It's a system to facilitate data collection and communication.

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

   • The "bench tests" and "verification testing" would likely constitute a standalone evaluation of the software's functionality. However, the device's purpose inherently involves human interaction (patients entering data, clinicians reviewing it), so a complete "standalone" clinical performance evaluation without any human interaction would not be meaningful for this device type. The document states it's an "informational tool only and is not to be used as a substitute for professional judgment of healthcare providers."

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

   • For the functional aspects (data aggregation, transmission, display), the "ground truth" would be the expected system behavior and correct data handling as defined by the software requirements and design specifications.
   • For usability, the "ground truth" would be successful completion of tasks by users and positive feedback on the user experience without critical errors. This is not a clinical ground truth in the diagnostic sense.

7. The sample size for the training set:

   • Not applicable. The eCareCoordinator is described as a "software-only telemedicine system" and a "platform supporting the Clinical User Interface," and "engages patients in their own health" by having them manually input measurements. It does not appear to employ machine learning that would require a "training set" of data to develop algorithms for diagnostic or predictive purposes.

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

   • Not applicable, as there is no mention of a training set or machine learning algorithms in the traditional sense described for this device.

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