K253281 · Updoc, Inc. · NDC · Dec 23, 2025 · Anesthesiology
Device Facts
Record ID
K253281
Device Name
UpDoc
Applicant
Updoc, Inc.
Product Code
NDC · Anesthesiology
Decision Date
Dec 23, 2025
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 868.1890
Device Class
Class 2
Attributes
Software as a Medical Device, Therapeutic, PCCP
Indications for Use
UpDoc is a software as a medical device (SaMD) intended to provide medication management for patients aged 18 years and older who have been diagnosed with type 2 diabetes. UpDoc provides patients with insulin treatment plan instructions based on a healthcare provider-specified treatment plan. UpDoc contains two user-interactive software components: Patient User Interface (UpDoc mobile application): Intended for use by patients with type 2 diabetes as an aid in optimizing insulin management. Patients use the mobile application to log blood glucose, meal, symptom, and medication adherence data, and receive treatment plan instructions. Data may be entered manually or reported via voice or text-based interactions. The application may also receive blood glucose data via a Bluetooth-enabled glucometer or continuous glucose monitor. Healthcare Provider User Interface (UpDoc web portal): Intended for use by trained healthcare providers to configure and manage the patient-specific insulin treatment plan. This includes insulin dosing instructions (type, starting and maximum doses, adjustment algorithm, and blood glucose targets) and safety protocols to address non-emergency hypoglycemia, hyperglycemia, and related symptoms. Insulin instructions are computed in UpDoc’s cloud-based application based on the healthcare provider-defined treatment parameters.
Device Story
UpDoc is a cloud-based SaMD for type 2 diabetes insulin management. Inputs include patient-reported blood glucose (manual, voice, text, or Bluetooth-connected CGM/glucometer), meal data, medication adherence, and symptom logs. HCPs use a web portal to configure individualized treatment plans, including insulin types, dosing targets, and safety protocols. The cloud-based Clinical Service computes insulin instructions based on these HCP-defined parameters. The patient mobile app delivers these instructions and provides safety alerts (e.g., eat something sweet, contact provider). The system includes safety locks that trigger if newly reported symptoms or non-protocol-compliant data are detected, preventing inappropriate dose increases. The device supports HCPs by providing longitudinal data logs and treatment history. It benefits patients by facilitating adherence to provider-directed insulin titration and improving safety through automated symptom-based system locks.
Clinical Evidence
No clinical testing was performed. Substantial equivalence is supported by non-clinical testing, including software verification/validation per IEC 62304, cybersecurity assessment, and human factors/usability engineering.
Technological Characteristics
SaMD architecture comprising a patient mobile app, provider web portal, and cloud-based Clinical Service. Operates via deterministic insulin titration algorithms based on HCP-defined parameters. Connectivity includes Bluetooth for glucose monitor integration. Software classified as major level of concern. Complies with IEC 62304, cybersecurity guidance, and human factors standards.
Indications for Use
Indicated for patients aged 18+ with type 2 diabetes to provide insulin treatment plan instructions. Contraindicated for patients with type 1 diabetes.
Regulatory Classification
Identification
A predictive pulmonary-function value calculator is a device used to calculate normal pulmonary-function values based on empirical equations.
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FDA
U.S. FOOD & DRUG
ADMINISTRATION
# 510(k) SUBSTANTIAL EQUIVALENCE DETERMINATION DECISION SUMMARY
## I Background Information:
A 510(k) Number
K253281
B Applicant
UpDoc, Inc.
C Proprietary and Established Names
UpDoc (01-00-US)
D Regulatory Information
| Product Code(s) | Classification | Regulation Section | Panel |
| --- | --- | --- | --- |
| NDC | Class II | 21 CFR 868.1890 - Predictive pulmonary-function value calculator | Clinical Chemistry |
## E Purpose for Submission:
- New device
- Establish a Predetermined Change Control Plan (PCCP)
## II Intended Use/Indications for Use:
A Intended Use(s):
See Indications for Use below.
B Indication(s) for Use:
UpDoc is a software as a medical device (SaMD) intended to provide medication management for patients aged 18 years and older who have been diagnosed with type 2 diabetes.
UpDoc provides patients with insulin treatment plan instructions based on a healthcare provider-specified treatment plan.
UpDoc contains two user-interactive software components:
Food and Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993-0002
www.fda.gov
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- Patient User Interface (UpDoc mobile application): Intended for use by patients with type 2 diabetes as an aid in optimizing insulin management. Patients use the mobile application to log blood glucose, meal, symptom, and medication adherence data, and receive treatment plan instructions. Data may be entered manually or reported via voice or text-based interactions. The application may also receive blood glucose data via a Bluetooth-enabled glucometer or continuous glucose monitor.
- Healthcare Provider User Interface (UpDoc web portal): Intended for use by trained healthcare providers to configure and manage the patient-specific insulin treatment plan. This includes insulin dosing instructions (type, starting and maximum doses, adjustment algorithm, and blood glucose targets) and safety protocols to address non-emergency hypoglycemia, hyperglycemia, and related symptoms.
Insulin instructions are computed in UpDoc’s cloud-based application based on the healthcare provider-defined treatment parameters.
## C Special Conditions for Use Statement(s):
Rx - For Prescription Use Only.
UpDoc should not be used in the following situations:
- Patients with type 1 diabetes
## III Device Description
UpDoc is a software as a medical device (SaMD) designed to assist patients aged 18 years and older with insulin management for type 2 diabetes. Healthcare providers (HCPs) set an individualized treatment plan for their patients that includes monitoring and insulin titration instructions. UpDoc engages with patients to help them follow their designated treatment plan and supports HCPs in monitoring reported health data, medication adherence, and treatment progress.
UpDoc is composed of three modular software components: a provider-facing web portal (UpDoc Provider Portal), a patient mobile application (UpDoc Patient App), and a cloud-based application consisting of a Conversation Service (UpDoc Agent) and a Clinical Service. These components work together to support safe and effective provider-directed insulin therapy.
## IV Substantial Equivalence Information:
### A Predicate Device Name(s):
d-Nav System
### B Predicate 510(k) Number(s):
K181916
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# C Comparison with Predicate(s):
| Device & Predicate Device(s): | K253281 | K181916 |
| --- | --- | --- |
| Device Trade Name | UpDoc | d-Nav System |
| General Device Characteristic Similarities | | |
| Intended Use/Indications For Use | UpDoc is a software as a medical device (SaMD) intended to provide medication management for patients aged 18 years and older who have been diagnosed with type 2 diabetes. UpDoc provides patients with insulin treatment plan instructions based on a healthcare provider (HCP)-specified treatment plan. | Same |
| Intended Users | • Health Care Provider (HCP) • Patients with Type 2 diabetes | Same |
| Use Environments | Healthcare settings and home environments | Same |
| Rx/OTC | Rx | Same |
| General Device Characteristic Differences | | |
| Supported Insulin | • Glargine U-100 • Glargine U-300 • Degludec U-100 • Degludec U-200 • NPH • Regular Insulin U-100 • Insulin Aspart • Insulin Glulisine • Insulin Lispro • Humulin 70/30 • Novolin 70/30 | • Lantus • Basaglar • Tresibo • Toujeo • Humalog • NovoLog • Apidra • Humalog Mix 75/25 • Novolog Mix 70/30 • Humulin 70/30 |
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| | • Humalog Mix 75/25
• Humalog Mix 50/50
• Novolog Mix 70/30 | • Novolin 70/30 |
| --- | --- | --- |
In addition to the similarities and differences between the candidate and predicate devices listed in the table above, the candidate device has an authorized PCCP to make specific adjustments to default values or clinical definitions, update insulin product references, add insulin dosing features, make user interface/user experience enhancements, and implement alternative data input methods. See Section VI.C for more information.
## V Standards/Guidance Documents Referenced:
IEC 62304 and FDA's guidance Content of Premarket Submissions for Device Software Functions
FDA's guidance Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions
FDA's guidance Applying Human Factors and Usability Engineering to Medical Devices
## VI Performance Characteristics:
### A. Non-Clinical Performance
Not applicable.
### B. Clinical Studies:
Not applicable.
### C. Other Supportive Instrument Performance Characteristics Data
#### Usability:
The sponsor provided comprehensive human factors engineering protocols and study results demonstrating that intended users can safely and effectively perform all critical tasks associated with the modified device features. The study participants were representative of the device's intended user population, including:
- Patients - adults over 18 years of age with type 2 diabetes across varying levels of technology experience and diabetes management proficiency
- Healthcare providers - qualified clinicians responsible for diabetes care management and treatment plan configuration
The human factors validation studies adequately demonstrated that users can operate the device safely without use-related errors that could compromise patient safety or treatment effectiveness. The results support a determination of substantial equivalence to the predicate device regarding usability and user interface design.
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Software:
The sponsor provided comprehensive software documentation in accordance with FDA's "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" (May 11, 2005). The documentation was consistent with the requirements for software classified as having a major level of concern, reflecting the device's potential impact on patient safety through insulin dosing recommendations.
The software documentation package included appropriate:
- Software requirements and design specifications demonstrating traceability between intended use and software functionality
- Verification and validation protocols with evidence of comprehensive testing across anticipated use scenarios
- Risk analysis and mitigation strategies addressing potential software-related hazards and their clinical implications
- Cybersecurity considerations appropriate for the device's connectivity and data handling capabilities
The submitted software documentation was determined to be acceptable and sufficient to support the safety and effectiveness determination for this 510(k) submission.
Predetermined Change Control Plan (PCCP):
The sponsor proposes the following six categories of modifications in their PCCP plan:
1. Specific adjustments to default values or clinical definitions
a. Adjusting the default goal fasting blood glucose range for titration
b. Modifying the number of blood glucose readings used to calculate an average
c. Updating the maximum configurable insulin dose
d. Modifying system-recognized symptom definitions (e.g., "hypoglycemia symptoms")
2. Updates to insulin product references
a. Adding references to newly approved FDA insulin formulations or biosimilars
b. Updating insulin-specific dosing ranges in alignment with clinical practice guidelines
3. Addition of insulin-dosing features
a. Supporting meal-related delivery logic (e.g., carb counting, pre-meal BG corrections)
b. Supporting insulin sliding scales
4. User Interface (UI)/User eXperience (UX) enhancements (e.g., multilingual support, iconography, or layout adjustments).
a. Addition of language support (e.g., Spanish, Mandarin) for patient instructions and app UI
b. Modification of provider portal layout or visualizations to improve workflow efficiency (e.g., summary dashboards, filter tools)
c. Changes to patient app appearance (e.g., colors, icons, glucose graph display) that improve usability without affecting functionality
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5. Alternative data input methods
a. Obtaining an existing input (e.g., blood glucose readings, insulin adherence data) via a new method or channel (e.g., Apple HealthKit, new CGM interface, external log)
b. Supporting alternate patient-reported adherence input methods (e.g., external logs instead of voice input)
c. Enabling additional input modes (e.g., voice commands, tap-to-log, device syncing)
As part of this PCCP, UpDoc will verify the updated software following a testing framework that includes automated unit testing, integration testing, and regression testing with 100% pass rate requirements across all modification categories. Validation activities encompass clinical governance review by subject matter experts, technical review for compatibility assessment, and risk-based human factors evaluations. Performance requirements mandate that all modifications maintain deterministic insulin dosing logic without altering core clinical decision-making, preserve system accuracy and response times, and ensure data imported from alternative sources matches manually entered values exactly with zero tolerance for deviation and incorrect unit conversion rates of zero.
Users will be notified of modifications through multiple channels including change summaries made available in both the UpDoc Provider Portal and Patient App, updated labeling and Instructions for Use documentation, brief in-app guided tours for significant layout changes, and comprehensive training materials including FAQs and training modules for healthcare providers when new features are introduced. The system ensures transparency through formal documentation within the Quality Management System, traceability matrices linking modifications to their rationale and validation activities, and maintenance of detailed change control records that provide full audit trails for regulatory compliance and user awareness.
VII Proposed Labeling:
The labeling supports the finding of substantial equivalence for this device.
VIII Conclusion:
The submitted information in this premarket notification is complete and supports a substantial equivalence decision.
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Learn the FDA Browser
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.