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Family Healthware™ is a self-administered, interactive Web-based tool that assesses familial risk for six diseases (coronary heart disease, stroke, diabetes, and colorectal, breast, and ovarian cancer) and provides personalized recommendations for lifestyle changes and screening.

Family Healthware™ is a Web-based software tool that can be used to assess a person's familial risk for six diseases (coronary heart disease, stroke, diabetes, and colorectal, breast, and ovarian cancer). It provides users with a "prevention plan" containing personalized recommendations for lifestyle changes and screening. For each person, the tool collects data about the following:

• Health behaviors (e.g., smoking and exercise),
• Screening tests (e.g., blood cholesterol and mammography),
• Health history among his or her first- and second-degree relatives.
  One set of algorithms in the software analyzes users' family history data and assesses their familial risk for each of the six diseases. A second set of algorithms uses the data on familial risk. health behaviors, and screening results to generate personalized prevention messages.

This document is a 510(k) premarket notification for a medical device called "Family Healthware™". It focuses on demonstrating substantial equivalence to an existing predicate device rather than presenting a performance study with specific acceptance criteria and detailed device performance metrics.

Therefore, the provided text does not contain the information required to answer your request about acceptance criteria, detailed device performance, study design (sample size, data provenance, expert ground truth, adjudication, MRMC study, standalone performance), or training set details.

The document discusses the following:

• Device Name: Family Healthware™
• Regulatory Number: 21 CFR 870.2910 (Radiofrequency Physiological Signal Transmitter and Receiver)
• Regulatory Class: Class II
• Product Code: DRG
• Intended Use: A self-administered, interactive Web-based tool that assesses familial risk for six diseases (coronary heart disease, stroke, diabetes, and colorectal, breast, and ovarian cancer) and provides personalized recommendations for lifestyle changes and screening.
• Predicate Device: Wellaho Personalized Outpatient Management System (K123671)
• Basis for Substantial Equivalence: Comparison of intended use, indications for use, and technological characteristics (e.g., Internet data collection platform, proprietary software, wireless RF protocol, Wi-Fi, SSL via HTTPS for communication, patient feedback via on-screen display, encrypted patient data on servers).

The primary purpose of a 510(k) submission, as seen here, is to demonstrate that a new device is "substantially equivalent" to a legally marketed predicate device, meaning it has the same intended use and the same technological characteristics, or different technological characteristics that do not raise new questions of safety or effectiveness. This process usually does not involve a clinical performance study with the level of detail you're asking for regarding AI algorithm performance (e.g., sensitivity, specificity, AUC) and its associated validation studies. These types of detailed performance studies are more typical for novel devices or those undergoing PMA (Premarket Approval) pathways, or for AI/ML devices where specific performance claims are made and need to be validated.

In summary, the document does not provide the information to populate the requested table or answer the specific questions about device performance validation studies.

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The Wellaho™ Personalized Outpatient Management System is an accessory software application that is used by patients in non-clinical settings (e.g. home), to collect, record and transmit their physiological information to a remote secure server. Stored data is accessible by healthcare professionals for analysis and intervention using standard digital communication technologies and protocols. The Wellaho is also intended to be used in combination with a variety of external vital sign devices. The Wellaho Personalized Outpatient Management System does not measure, interpret or make any decisions on the data that it conveys. It is not intended as a replacement for the oversight of healthcare professionals nor does it provide "real-time" or emergency monitoring.

Wellaho Personalized Outpatient Management System is a software system that collects patient physiological data (such as blood pressure and blood sugar levels) for transmission to a secure central storage server, which can be accessed by health care professionals for analysis and intervention using standard digital communication technologies and protocols. These data are also available to the patient for viewing purposes and as an aid in maintaining wellness regimens.

The provided FDA 510(k) summary for the Wellaho Personalized Outpatient Management System does not include a detailed study proving the device meets specific acceptance criteria. The document focuses on demonstrating substantial equivalence to a predicate device (Vignet Tele-Health Manager, K113446) rather than presenting a performance study with defined acceptance criteria.

The device itself is described as a software system that collects, records, and transmits physiological data from patients in non-clinical settings to a remote secure server for healthcare professionals to access and analyze. Crucially, the document states: "The Wellaho Personalized Outpatient Management System does not measure, interpret or make any decisions on the data that it conveys." This indicates it's a data transmission and storage system, not a device that makes diagnoses or interpretations from the data.

Therefore, many of the questions regarding acceptance criteria, study design, ground truth, and reader studies (which are typically relevant for diagnostic or AI-driven interpretive devices) are not applicable or cannot be answered from the provided text for this specific device.

However, based on the information provided, we can infer the primary "acceptance criterion" for this type of device in the context of a 510(k) submission is substantial equivalence to a predicate device in terms of intended use, technology, and safety/effectiveness.

Here’s a breakdown based on the available information:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance:
Not applicable/Not provided. The 510(k) summary for this type of device (a data transmission system) does not typically require a "test set" in the sense of clinical data validation for diagnostic performance. The primary "test" is demonstrating functional equivalence to the predicate and adherence to cybersecurity/data handling standards, which are not detailed here.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
Not applicable/Not provided. This device does not generate interpretations or decisions that would require expert-established ground truth in a clinical validation study.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:
Not applicable/Not provided for the same reasons as above.

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. This is not an AI-driven or diagnostic support system. It is a data transmission system.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
Not applicable. The device's "performance" is in its ability to securely collect and transmit data, not in an algorithmic interpretation. The document explicitly states it "does not measure, interpret or make any decisions."

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc):
Not applicable. Ground truth for clinical outcomes or diagnoses is not relevant for this device's function as described.

8. The sample size for the training set:
Not applicable. As a data transmission and storage system, there is no "training set" in the context of machine learning or AI models.

9. How the ground truth for the training set was established:
Not applicable for the same reason as above.

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