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The Constant Care LILAH Home Health Monitoring System is a non-emergency, delayed, remote patient monitoring device available by prescription. The Constant Care LILAH Home Health Monitoring System is designed to be used in the home of a patient or healthcare setting to provide remote monitoring for maintenance of chronic disease. The Constant Care LILAH Home Health Monitoring System provides guidance in operating medical sensor devices, reminders for medication compliance and connectivity to healthcare professionals through text messaging and realtime video conferencing technology.

The Constant Care LILAH Home Health Monitoring System connects to commercially available wireless and wired medical devices such as glucose meters, weight scales, blood pressure monitors, electronic thermometers, peak-flow meters and pulse oximeters. The Constant Care LILAH Home Health Monitoring System stores and displays the information on a computer screen and transmits the information to the Caretaker Portal secure host server using connectivity including, but not limited to, FCC approved Wi-Fi, Cellular Wireless, Internet, or Ethernet.

Healthcare professionals and remote caregivers can review the transmitted information unilizing the Caretaker Portal and set thresholds to trigger non-emergency alerts based on specific thresholds being exceeded.

The Constant Care LILAH Home Health Monitoring System is not intended for diagnosis or as a substitute for medical care, and it is not intended to provide real time data. The data is made available to the patients when timecritical care is not required. The device is contraindicated for patients requiring direct medical supervision or emergency intervention. The system is intended for patients who are willing and capable of managing its use, Judgment and experience by a caregiver or by a family member are required to check and interpret the information delivered.

The Constant Care LILAH Home Health Monitoring System - Multi-User Version is a software device consisting of two components: patient-side software to be installed on hardware compatible with an Asus EeeTop PC ET1611PUT 15.6 inch TouchScreen Atom D425 All-in-One PC, or an Acer ICONIA Tab W500-C52G03iss 10.1' LED Tablet PC for use in performing patient monitoring, and sending data to the Caretaker Portal secure host server; and caregiver-side software for receiving patient-side data and transmitting that data to healthcare professionals and remote caregivers through the Caretaker Portal secure host server.

The patient-side software component is offered in two different versions. The single-user version, designated as "LILAH Version 1.1," is designed to be used by one individual, and is streamlined for simplicity of use. The multi-user version, designated as "LILAH Multi-User," allows for multiple individuals to be monitored through a single application. Changes covered by this clearance are applicable to both the LILAH Multi-User and LILAH Version 1.1 versions of the patient-side software component.

The Constant Care LILAH Home Health Monitoring System - Multi-User Version is a remote patient monitoring device available with or without prescription designed to be used in the home or healthcare setting of patients undergoing remote monitoring for maintenance of chronic disease. The Constant Care LILAH Home Health Monitoring System - Multi-User Version can be used to monitor and track a single patient or multiple patients. The Constant Care LILAH Home Health Monitoring System - Multi-User Version provides guidance in operating medical sensor devices, reminders for medication compliance and connectivity to healthcare professionals through text messaging and real-time video conferencing technology.

Here's an analysis of the provided text regarding the Constant Care LILAH Home Health Monitoring System:

This 510(k) summary does not contain the information requested about acceptance criteria and a study proving the device meets them.

The document is a 510(k) submission, which is a premarket notification for medical devices. The primary purpose of a 510(k) is to demonstrate substantial equivalence to a legally marketed predicate device, not necessarily to provide detailed performance study results against specific acceptance criteria.

The key statement regarding performance is: "Results of software validation and verification activities establish the device as safe and effective for its intended use, which is comparable to other predicate devices." This is a general statement and does not provide specifics about the studies or acceptance criteria.

Therefore, most of the requested information cannot be extracted from this document. Here's a breakdown of what can be inferred or is explicitly stated:

1. Table of Acceptance Criteria and Reported Device Performance

Not available in this document. The document states that "software validation and verification activities establish the device as safe and effective," but it does not specify acceptance criteria or report specific performance metrics from those activities.

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

Not available in this document. No details about a specific test set, its size, or data provenance (e.g., country of origin, retrospective/prospective) are provided.

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

Not available in this document. Since no specific test set or ground truth establishment is described, this information is not present.

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

Not available in this document. No information on adjudication methods for a test set is provided.

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/Not available in this document. The Constant Care LILAH system is a remote patient monitoring system that provides data, reminders, and communication tools. It is not an AI-driven diagnostic or interpretative tool that would typically involve human "readers" improving performance with AI assistance in the way a MRMC study would measure for imaging or pathology. The document does not describe any such study.

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

Not available in this document. The device is described as a system integrating patient data, providing guidance, and connecting to healthcare professionals. Its function isn't primarily a standalone algorithmic interpretation in the way, for example, an AI-powered diagnostic algorithm would be evaluated. The "software validation and verification activities" likely focused on the software's functionality, data integrity, and compliance with its intended use, rather than a standalone performance metric against a ground truth.

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

Not available in this document. No specific ground truth type is mentioned as no detailed performance study is described. The "validation and verification activities" would typically involve testing the system's functions (e.g., data transmission, alarm triggering, user interface functionality) against design specifications and user requirements.

8. The sample size for the training set

Not applicable/Not available in this document. This device is a software system for monitoring, not a machine learning or AI model that requires a "training set" in the conventional sense of supervised learning. Its "training" would be its software development and testing phases.

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

Not applicable/Not available in this document. As with point 8, the concept of a "training set ground truth" is not relevant for this type of device based on the information provided.

In summary: The provided 510(k) summary focuses on demonstrating substantial equivalence to a predicate device and broadly states that "software validation and verification activities establish the device as safe and effective." It does not delve into the detailed performance studies, acceptance criteria, sample sizes, ground truth establishment, or expert evaluations that would be expected for a device with an AI/ML component performing diagnostic or interpretative tasks.

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