The Health-e-Connect System is intended for use in the home and clinical settings by people with diabetes and healthcare providers as an aid in the review, analysis and evaluation of historical glucose test results and associated usage data in support of an effective diabetes management program.
Device Story
Health-e-Connect System (HeC) is an internet-based diabetes management tool. It collects historical blood glucose data from legally marketed glucose meters via a PC-based application (HeC Programmer) and transmits this data over existing internet connections to a web-based server. The system stores and displays this data for review by patients, healthcare providers, and relatives. It facilitates retrospective communication and remote monitoring through notifications; it does not provide real-time alerts or alarms. Healthcare providers use the web-based interface to analyze patient glucose trends, which supports clinical decision-making regarding diabetes management programs. The system is intended for use in both home and clinical environments.
Clinical Evidence
No clinical trials were performed. Evidence consists of bench testing and usability studies. Bench testing verified accurate data transfer (glucose values, timestamps, serial numbers, model IDs, and control solution flags) from 10 different glucose meter models. Usability studies included 22 lay-users for electronic upload and 28 participants for manual data entry; participants reported the system was easy to operate. A readability study confirmed a Flesch-Kincaid Grade Level of 7.5 for the user manual.
Technological Characteristics
Software-only data management system; operates on personal computers via internet; uses encrypted communications. No patient-contacting materials. Performs data collection, storage, and retrospective analysis. Connectivity via standard internet connection to web-based servers. No physical, electrical, or sterility specifications.
Indications for Use
Indicated for use in home and clinical settings by people with diabetes and healthcare providers as an aid in the review, analysis, and evaluation of historical glucose test results and associated usage data to support diabetes management programs.
Regulatory Classification
Identification
A glucose test system is a device intended to measure glucose quantitatively in blood and other body fluids. Glucose measurements are used in the diagnosis and treatment of carbohydrate metabolism disorders including diabetes mellitus, neonatal hypoglycemia, and idiopathic hypoglycemia, and of pancreatic islet cell carcinoma.
Special Controls
*Classification.* Class II (special controls). The device, when it is solely intended for use as a drink to test glucose tolerance, is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 862.9.
Predicate Devices
Electronic House Call System (k090801)
Submission Summary (Full Text)
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# 510(k) SUBSTANTIAL EQUIVALENCE DETERMINATION DECISION SUMMARY INSTRUMENT ONLY TEMPLATE
A. 510(k) Number:
k102063
B. Purpose for Submission:
A new 510(k) for a diabetes management software accessory to cleared blood glucose meters
C. Manufacturer and Instrument Name:
Health-e-Connect System
D. Type of Test or Tests Performed:
Diabetes data management system
E. System Descriptions:
1. Device Description:
The ALRT Health-e-Connect System (HeC) is an internet based blood glucose monitoring system that allows healthcare providers and patients the opportunity to review, analyze and evaluate the efficacy of a diabetes management program.
The Health-e-Connect System is comprised of a home based application, legally marketed blood glucose meters (FreeStyle Freedom, FreeStyle Feedom Lite, FreeStyle Lite, Precision Xtra, Breeze2, Contour, OneTouch Ultra2, OneTouch UltraMini, Aviva, Compact Plus glucose meters) and a server.
The Health-e-Connect Programmer is a PC based application software used to register a patient's glucose meter with the Health-e-Connect System and upload historical patient blood glucose level data to Health-e-Connect system's web-based servers. The home based application software collects data from blood glucose meters and transmits the data over the home's existing internet connection where it is uploaded to the Health-e-Connect System's web-based servers.
The server is a web-based application that collects, stores and displays historical patient blood sugar levels. It also allows patients, healthcare providers, patient relatives and other healthcare providers involved in the case to send messages to each other and share patient information. This communication is retrospective and not a real-time alert or alarm. The Health-e-Connect System is a tool to monitor patients remotely and motivate them through notifications.
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2. Principles of Operation:
The operating system requirements for the Health-e-Connect System are: Windows XP, Windows Vista 32 bit and 64 bit, or Windows 7.
3. Modes of Operation:
Does the applicant’s device contain the ability to transmit data to a computer, webserver, or mobile device?
Yes ☐ X or No ☐
Does the applicant’s device transmit data to a computer, webserver, or mobile device using wireless transmission:
Yes ☐ or No ☐ X
4. Specimen Identification:
Specimen identification is based on time and date of testing
5. Specimen Sampling and Handling:
Data transmission from glucose meters using capillary whole blood samples
6. Calibration:
Glucose meter specific. See statement below under section J.
7. Quality Control:
Glucose meter specific. See statement below under section J.
8. Software:
FDA has reviewed applicant’s Hazard Analysis and Software Development processes for this line of product types:
Yes ☐ X or No ☐
F. Regulatory Information:
1. Regulation section:
21 CFR § 862.1345 Glucose Test System
21 CFR § 862.2100 Calculator/Data Processing Module for Clinical Use
2. Classification:
Class II, I (respectively)
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3. Product code:
NBW, Blood Glucose Test System, Over-the-Counter
JQP, Calculator/Data Processing Module for Clinical Use
4. Panel:
75 (Clinical Chemistry)
G. Intended Use:
1. Indication(s) for Use:
The Health-e-Connect System is intended for use in the home and clinical settings by people with diabetes and healthcare providers as an aid in the review, analysis and evaluation of historical glucose test results and associated usage data in support of an effective diabetes management program.
2. Special Conditions for Use Statement(s):
None
H. Substantial Equivalence Information:
1. Predicate Device Name(s) and 510(k) numbers:
Express MD Solutions, LLC; Electronic House Call System; k090801
2. Comparison with Predicate Device:
| Item | Candidate
Health-e-connect System | Predicate
Electronic House Call System (k090801) |
| --- | --- | --- |
| Intended Use | The Health-e-Connect System is intended for use in the home and clinical settings by people with diabetes and healthcare providers as an aid in the review, analysis and evaluation of historical glucose test results and associated usage data in support of an effective diabetes management program. | Same |
| Data Sources | Electronic upload of data from supported blood glucose meters and manual entry of data | Same |
| Compatibility | For use with the supported glucose meters | Same |
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I. Special Control/Guidance Document Referenced (if applicable):
No standards cited
J. Performance Characteristics:
1. Analytical Performance:
The performance characteristics listed below as applicable, were presented in the specific glucose meter clearances under (k051839, k070850, k092602, k010553, k053529, k061118, k060470, k062347, k081389, k043474).
a. Accuracy:
See above statement under section J.
b. Precision/Reproducibility:
See above statement under section J.
c. Linearity:
See above statement under section J.
d. Carryover:
See above statement under section J.
e. Interfering Substances:
See above statement under section J.
2. Other Supportive Instrument Performance Data Not Covered Above:
a) The sponsor conducted bench testing using all 10 claimed glucose meters: FreeStyle Freedom, FreeStyle Feedom Lite, FreeStyle Lite, Precision Xtra, Breeze2, Contour, OneTouch Ultra2, OneTouch UltraMini, Aviva, Compact Plus. The testing verified completeness and accuracy of blood glucose value transfer, data rollover, correct time stamps, serial number and model, and HeC identified flags such as control solutions used. The study confirmed the ability of the HeC System to capture and record glucose data accurately when transferred from each of the claimed glucose meters.
b) The usability study consisted of three components: 1) 22 lay-users with varying demographics (age, sex, and education level) were included in a usability study for electronically uploading data from glucose meters. Following the study the study participants also completed a questionnaire in
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response to whether the data transmission feature is easy to use. 2) 2 health care professionals completed surveys following the study. 3) 28 study participants with varying demographics (age, sex, and education level) performed manual data entry for at least 2 days worth of data. These participants completed a survey following the study. The sponsor concluded that the user's responses indicated that data transmission function was easy to operate by following the instructions provided with the system.
c) The following documentation related to the software was reviewed and found to be acceptable: level of concern, software description, device hazard analysis, software requirements specifications, software design specification, software development environment description, and verification and validation testing.
d) The sponsor performed a readability study and obtained a Flesch-Kincaid Grade Level Score of 7.5 for the User Manual. The readability of the labeling was also assessed as part of the usability studies and surveys.
## K. Proposed Labeling:
The labeling is sufficient and it satisfies the requirements of 21 CFR Part 809.10.
## L. Conclusion:
The submitted information in this premarket notification is complete and supports a substantial equivalence decision.
Two short videos show you everything — or skip straight to the written tutorial if you'd rather read. You can reopen this any time from the Tutorial button in the top bar.
Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.
