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510(k) Data Aggregation
(95 days)
The GlucoManager Data Management Software (GDMS) is intended for use in home and clinical settings to aid people with diabetes and their health care professionals in the review, analysis and evaluation blood glucose test results to support effective diabetes management. The device is not intended to provide any diagnosis based upon patient results.
The GlucoManager Data Management Software enable users the ability to export data from the EasyMax N (KU63099) gidcose meter on computer via a RS232 Cable. The GlucoManager Data Managenient Gonward to include users in review, analysis and evaluation of historical blood glucose test results any diegencis based unon patient results.
Here's a breakdown of the acceptance criteria and study information for the GlucoManager Data Management Software based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
| Feature/Function Tested | Acceptance Criteria | Reported Device Performance/Conclusion |
|---|---|---|
| Design and Ease-of-Use | Not explicitly stated (implied user satisfaction) | Subjects demonstrated ability by usage and evaluation tests; high rating of features/functions; understanding of software and instructions. |
| Features/Functions (19 specific tests) | Not explicitly stated (implied successful operation) | Performance recorded and evaluated by study administrators (Observers), indicating successful operation. |
| User Instructions Accuracy and Followability | User instructions are accurate and easy to follow | Validated by participant feedback and observer evaluation. |
| Participant Ratings with Respect to Features/Functions | Not explicitly stated (implied satisfactory ratings) | Evaluated through a separate questionnaire, contributing to overall positive conclusion. |
| Discrepancies (Bugs/Error Messages) | No significant discrepancies found (implied) | No discrepancies (bugs/error messages) were reported as found during the evaluation. |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: 106 subjects
- Data Provenance: Not explicitly stated, but the submission is from "EPS Bio Technology Corp., Hsinchu City, Taiwan, R.O.C." and the overall submission is to the FDA for a 510(k), suggesting either data collected in Taiwan for the US market or a multinational study. The study is prospective as it's a "User acceptance Study" conducted to evaluate the software.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
- The term "ground truth" for this type of software (data management) is primarily related to its functionality and user experience, not a clinical outcome or diagnostic accuracy.
- Number of Experts: Not explicitly stated as "experts" for ground truth, but "EPS study administrators (Observers)" who were "familiar with proper use of GlucoManager Data Management Software" evaluated the performance of each user. It implies at least one, likely several, administrators.
- Qualifications of Experts: "Familiar with proper use of GlucoManager Data Management Software." No specific professional qualifications like "radiologist with 10 years of experience" are mentioned as this is a usability/functional study, not a clinical diagnostic one.
4. Adjudication Method for the Test Set
- Not explicitly stated. The study involved "observers" evaluating participant performance and participant self-reporting through questionnaires. It appears to be a direct observation and survey method rather than a multi-reader adjudication process typically seen in image analysis or diagnostic studies.
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, an MRMC comparative effectiveness study was not done. This device is a data management software for glucose meters, not an AI-powered diagnostic tool. The study focused on user acceptance and functionality.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
- No, a standalone algorithm-only performance study was not done. The primary study was a "User acceptance Study" which inherently involves human interaction ("human-in-the-loop"). The software's function is to facilitate human review and analysis, not to operate autonomously in a diagnostic capacity.
7. The Type of Ground Truth Used
- The "ground truth" for this user acceptance study was based on:
- Direct observation and evaluation of user performance by EPS study administrators.
- Participant self-reported feedback through questionnaires on features/functions, understanding of the software, and comprehension of instructions.
- Absence of reported discrepancies (bugs/error messages).
- This is primarily a functional and usability "ground truth" rather than a clinical ground truth like pathology or outcomes data.
8. The Sample Size for the Training Set
- Not applicable / not mentioned. This type of software (data management) typically does not involve a "training set" in the machine learning sense. The software is developed based on requirements and then tested for functionality and usability.
9. How the Ground Truth for the Training Set Was Established
- Not applicable. As there is no "training set" in the context of this software development and evaluation, there's no ground truth established for it. The software's design and functionality are likely based on standard software engineering practices and the requirements for managing blood glucose data.
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