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The Well@Home System is indicated for use for patients who need home-based health care from professional clinicians. The system provides benefits for patients who need periodic remote monitoring of their vital signs (such as noninvasive blood pressure, oxygen saturation, pulse rate, temperature, respiration rate, weight, blood glucose level, heart rate, or ECG) or self-reported symptoms, who need reminders to take prescribed medications, or who need training about how to manage their illness.

The Well@Home System consists of the home-based "Well@Home Monitor" and the office-based "Well@Home Clinician Application". Using the Well@Home System, clinicians can monitor theised at home or in another remote health care facility. The use model is "daily checkups", rather them continuous monitoring.

The Well@Home Monitor collects vital signs and symptom information on a periodic basis via a patientfriendly user interface. The user interface guides the patient through a schedule of activities using voice promots, instructional diagrams and large, easy-to-read buttons. The scheduled of doutinities can'nclude reminders to take medications or to take vital signs measurements. The schedule for these activities can be customized by the clinician according to the patient's medical condition. The user interface also provides opportunities for the patient to report symbons at any time, and to review training ontent related to managing the patient's illness.

The Well@Home monitor measures noninvasive blood pressure, oxygen saturation, pulse rate, temperation | President rate, heart rate, and ECG. The Well@Home Monitor is designed to interface to Other Medical Devices (OMD's) for the purpose of gathering physiological data from those devices. The interface is through the monitor's three OMD serial ports. These serial ports are electrically isoated from each other and from the monitor's own physiological front-end. The list of supported OMD's is as follows:

• . Fairbanks Digital Scale (private labeled and device listed as a Zoe Medical accessory)
• Lifescan One Touch Ultra Blood Glucose Meter (or compatible BGM from Lifescan) .

The OMD interface mechanism is designed to be expandable. Future OMD's may include a Spirometer or a PT/INR measurement device.

The Well@Home Monitor transmits the gathered information to a clinician over the patient's existing telephone line. This information is received and stored by the Well(@Home Clinican Application.

The Well@Home Clinician Application is essentially a patient data management and record keeping program that includes an interface to the Well@Home Monitor. The Well@Home Clinician Application stores information that is gathered from multiple Well@Home Monitors and allows a clinician to review a given patient's information. It also allows the clinician to make adjustments to a patient's schedule of activities as needed. These adjustments are sent back to the patient's Well@Home Monitor over the same telephone link.

The Well@Home Clinician Application software was developed by Patient Care Technologies (PtCT) of Atlanta, Georgia, and is designed to run on standard Windows-based PC's. These PC's are typically installed at the office of a home care agency, and are referred to in this 510(k) as "Agency Servers". The Agency Servers may run other information management software developed by PtCT that is not part of the Well@Home System. The Well@Home System is product-branded to tie into PtCT's information management software product line.

Because of the remote-monitoring and spot check nature of the Well@Home System, there are no realtime "alarms" as in a hospital-style monitor. Rather, the clinician defines "alerts" that are to be generated (e.g., when a vital sign parameter exceeds set limits or when a particular symptom is reported). These alerts are part of the information package that is sent from the Well@Home Monitor to the Well@Home Clinician Application. The alerts are then visually highlighted when the clinician reviews the patient's information.

The provided text describes the Zoe Medical Well@Home System, which is a noninvasive blood pressure measurement system for remote patient monitoring. However, it does not include detailed acceptance criteria or a specific study proving the device meets those criteria in the way a modern medical device submission typically would.

Instead, the document focuses on demonstrating substantial equivalence to existing predicate devices through:

1. Technological Characteristics Comparison: Showing that the Well@Home System's design, functions, and principles of operation are similar to previously cleared devices.
2. Performance Testing: Stating that internal engineering tests were conducted to verify performance against functional requirements and to show substantial equivalence.

Given this, I will extract the information available and note where specific details (like numerical acceptance criteria, sample sizes for test sets, expert qualifications, etc.) are absent.

Acceptance Criteria and Device Performance (as inferred from the document)

Detailed Study Information (Based on Available Text):

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

   • Sample Size: Not specified. The document states "internal engineering tests" and "bench tests using simulators" were conducted, but no numerical sample sizes for these tests are provided for either human subjects or simulated data points.
   • Data Provenance: Not specified. The tests were "internal engineering tests," implying they were conducted by the manufacturer, Zoe Medical, but details on location or whether they involved human subjects are absent.
   • Retrospective or Prospective: Not specified. The nature of "internal engineering tests" and "bench tests using simulators" suggests they were likely prospective tests conducted on the device hardware and software.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience):

   • Not applicable/Not specified. The "ground truth" for the vital signs measurements appears to be established by the predicate devices themselves (Nightingale PPM and One Touch Ultra) and, for overall system functionality, by functional requirements. There is no mention of independent experts establishing ground truth for a test set in the context of clinical accuracy or interpretation that would require such expertise. "Bench tests using simulators" imply comparison against known simulator outputs.

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

   • Not applicable/Not specified. The document does not describe a clinical study design that would involve expert adjudication of results. The testing described is primarily technical and functional equivalence vs. predicate devices or simulators.

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:

   • Not applicable. This device is not an AI-assisted diagnostic tool for human readers. It is a remote monitoring system that collects and transmits vital signs and symptom data. Therefore, an MRMC study or AI-assistance effect size is not relevant to this submission.

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

   • Yes, implicitly. The "internal engineering tests" and "bench tests using simulators" for vital signs measurement evaluate the device's capability to measure data and display it correctly. For the blood glucose meter, the BGM's performance is explicitly stated as "no different when it is connected to the Well@Home Monitor as when it functions as a standalone device," indicating a standalone performance assessment of the BGM component. The Well@Home System functions as an "information transfer technology" for the BGM.

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

   • For vital signs measurement (Nightingale PPM comparison): The ground truth was presumably the known accurate outputs from physiological simulators or the validated measurements from the predicate Nightingale PPM itself.
   • For blood glucose measurement (One Touch Ultra comparison): The ground truth was the measurements provided by the standalone, predicate Lifescan One Touch Ultra Blood Glucose Meter.
   • For overall system functionality (HANC Network comparison): The ground truth was likely defined by functional requirements derived from the predicate HANC Network's capabilities.

7. The sample size for the training set:

   • Not applicable/Not specified. This document is for a medical device submission focused on substantial equivalence through hardware and software design, and functional testing. It does not describe a machine learning or AI model that would require a "training set" in the conventional sense. The software algorithms for vital signs measurement are stated to be "the same software algorithms" as the predicate Nightingale PPM, implying they were developed and validated previously, not newly trained for this specific application.

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

   • Not applicable. As there is no described training set for an AI/ML model, this question is not relevant to the provided information.

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