Evidence-backed symptom and health data analysis workspace

For

Clinics, care teams and health service operators supporting patients who need to understand symptoms and health data

Solves

Patients describe symptoms and hold scattered lab results, logs and documents, while clinicians lack a single reviewed workspace to turn that material into evidence-backed guidance.

Delivers

Clinician-reviewed, evidence-linked health reports

Built in

about 6 weeks of creation time, MVP in 7 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce repeated intake and document chasing while keeping clinical decisions with licensed professionals.

1. Capture symptom descriptions in plain language.
2. Run structured symptom questionnaires.
3. Interpret uploaded lab results with biomarker explanations.
4. Extract key data points from PDFs and Word documents.
5. Search across a patient's document set with context.
6. Log daily symptoms, sleep, mood and medications.
7. Detect patterns and correlations across logged data.
8. Tailor tracking to specific chronic conditions.
9. Generate personalized health reports with possible causes and care guidance.
10. Link every answer to peer-reviewed literature and clinical guidelines.
11. Cover a broad range of medical specialties.
12. Route cases to licensed doctors for review or second opinion.
13. Support live video consultations.
14. Record prescriptions and lab test orders after consultation.
15. Produce condensed PDF summaries for appointments.
16. Compare the reviewed result with the recorded baseline and value assumptions.
17. Capture corrections and named-clinician approval before consequential use.
18. Export a versioned clinician-reviewed, evidence-linked health report with source references and unresolved questions.

Everything these tools do, in one app

• Symptom Assessment Helps users identify possible causes of their symptoms through questionnaires or AI analysis.Found in Ubie, Symptom Checker AI, Yesil Health and 1 more
• Personalized Health Reports Generates tailored reports with potential causes, treatment options, and guidance on when to seek care.Found in Ubie, Docus
• Lab Test Interpretation Interprets lab results and provides detailed biomarker explanations and health reports.Found in Docus
• Expert Medical Review Provides access to licensed doctors or medical experts for second opinions or video consultations.Found in Ubie, Docus, Doctronic AI + Human Doctors
• Health Data Logging Allows users to record daily health metrics such as symptoms, sleep, mood, and medications.Found in Juno
• Pattern Recognition Analyzes logged data to identify correlations and trends over time.Found in Juno
• PDF Report Generation Creates condensed PDF summaries of health data for medical appointments.Found in Juno
• Condition-Specific Support Tailors tracking and insights for specific chronic conditions like fibromyalgia, long COVID, and others.Found in Juno
• Natural Language Input Allows users to describe symptoms or health queries in plain language.Found in Symptom Checker AI, Yesil Health
• Evidence-Based Responses Generates answers based on peer-reviewed literature and clinical guidelines.Found in Yesil Health
• Broad Medical Coverage Addresses a wide range of health topics across many medical specialties.Found in Yesil Health
• 24/7 Availability Provides health assistance at any time, often with quick response times.Found in Yesil Health, Doctronic AI + Human Doctors
• Video Consultations Enables live video visits with licensed doctors for medical advice.Found in Doctronic AI + Human Doctors
• Prescriptions and Test Orders Allows doctors to prescribe medications or order lab tests after consultation.Found in Doctronic AI + Human Doctors
• Data Security Compliance Adheres to health data privacy standards like HIPAA and GDPR.Found in Docus
• Document Search Enables context-aware search across large volumes of documents.Found in Docus.ai
• Data Extraction Automatically extracts key data points and summaries from documents.Found in Docus.ai
• Multi-Format Support Supports various document formats including PDFs and Word files.Found in Docus.ai
• Collaboration Tools Allows teams to review and annotate documents together.Found in Docus.ai
• Content Creation Generates written content for articles, blogs, and social media.Found in Keepo
• Editing and Rewriting Improves clarity and tone of existing text.Found in Keepo
• Customizable Templates Provides templates for different content formats.Found in Keepo
• Multi-Language Support Supports content generation in multiple languages.Found in Keepo
• API Integration Offers a REST API for developers to integrate health consultation capabilities.Found in Yesil Health

What goes in, what comes out

The workflow

1. In
   Start with

   Consented symptom descriptions, lab results, daily health logs and medical documents

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect consented symptom descriptions

4. 3

   Lab results

5. 4

   Daily logs and medical documents

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Clinician-reviewed, evidence-linked health reports

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One approved document format set and one chronic-condition template; final diagnosis, prescribing and care decisions remain with licensed clinicians. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Patient intake and consent, Editable clinical review workspace, Patient report and follow-up. Use a case list for patients, a large central review canvas, and a right-hand panel for evidence references, flags and comments. Let reviewers compare draft and approved versions side by side. Display draft, changes requested and clinician-approved states. Provide a patient-facing report link with plain-language explanations. Make the task-specific outcome clinician-reviewed, evidence-linked health reports visible beside its evidence, review state and value baseline.

Accounts and administration

Patient consent records, document versions, clinician comments, approval states, usage allowances, review limits, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Patient-owned records, authorized lab exports and permitted clinical sources. Cloud document storage, EHR import/export and care destinations. Start with file exchange and validate destination specifications before promising direct EHR write-back. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   7 days

   One buyer segment, one recurring use case; first modules: capture symptom descriptions in plain language; run structured symptom questionnaires; interpret uploaded lab results with biomarker explanations; extract key data points from PDFs and Word documents. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   8 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   3 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will clinics, care teams and health service operators supporting patients who need to understand symptoms and health data use it to solve "patients describe symptoms and hold scattered lab results, logs and documents, while clinicians lack a single reviewed workspace to turn that material into evidence-backed guidance"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Clinician-accepted reports per review hour and corrections after clinical sign-off.
4. Measure, then decide. Track clinician-accepted reports per review hour and corrections after clinical sign-off; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One approved document format set and one chronic-condition template; final diagnosis, prescribing and care decisions remain with licensed clinicians. Implement one approved input format, a bounded representative case set and the first four task modules: capture symptom descriptions in plain language; run structured symptom questionnaires; interpret uploaded lab results with biomarker explanations; extract key data points from PDFs and Word documents. Support the remaining modules with operator review: search across a patient's document set with context; log daily symptoms, sleep, mood and medications; detect patterns and correlations across logged data; tailor tracking to specific chronic conditions; generate personalized health reports with possible causes and care guidance; link every answer to peer-reviewed literature and clinical guidelines; cover a broad range of medical specialties; route cases to licensed doctors for review or second opinion; support live video consultations; record prescriptions and lab test orders after consultation; produce condensed PDF summaries for appointments. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around clinician-reviewed, evidence-linked health reports. Retain the explicit scope boundary: One approved document format set and one chronic-condition template; final diagnosis, prescribing and care decisions remain with licensed clinicians.

What the build depends on. Document upload and preview, asynchronous processing jobs, editable version history, clinician access and tested export formats. High-fidelity clinical use requires specialist clinical QA. Obtain representative authorized cases, baseline measurements, licensed clinician reviewers and a buyer-side decision owner. Specific limitation: One approved document format set and one chronic-condition template; final diagnosis, prescribing and care decisions remain with licensed clinicians.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined patient case package. Offer a monthly production allowance after repeat demand. Quote complex integrations or specialist clinical review separately. These are test prices, not market benchmarks. Package the initial sale as one bounded clinician-reviewed, evidence-linked health report. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce repeated intake and document chasing while keeping clinical decisions with licensed professionals. Demonstrate a concrete clinician-reviewed, evidence-linked health report using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Clinics, care teams and health service operators professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample clinician-reviewed, evidence-linked health report from a small authorized input set, with a transparent calculation of clinician-accepted reports per review hour and corrections after clinical sign-off and no promised savings.

The first 30 days

1. Week 1: interview five clinics, care teams and health service operators supporting patients who need to understand symptoms and health data and inspect a recent example of patients describe symptoms and hold scattered lab results, logs and documents, while clinicians lack a single reviewed workspace to turn that material into evidence-backed guidance.
2. Week 2: prepare a consented or synthetic demonstration of the task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure clinician-accepted reports per review hour and corrections after clinical sign-off, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Clinician-accepted reports per review hour and corrections after clinical sign-off. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Clinician-accepted reports per review hour and corrections after clinical sign-off; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs clinician-reviewed, evidence-linked health reports. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved clinical templates, evidence links and review examples, together with reliable delivery for a narrow care niche. Build a permissioned library of representative task cases, clinician corrections and verified operating constraints for clinics, care teams and health service operators supporting patients who need to understand symptoms and health data. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Docus.ai, Ubie, Docus, Symptom Checker AI, Juno, Keepo, Yesil Health and Doctronic AI + Human Doctors. Compare this product with the buyer's present method on clinician-accepted reports per review hour and corrections after clinical sign-off. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, document processing, storage, clinician reviewer hours, patient revision rounds and licensed source material. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of clinician-reviewed, evidence-linked health reports. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve patient consent, source attribution, evidence accuracy and usage permissions. Licensed clinicians approve substantive changes and care scope. One approved document format set and one chronic-condition template; final diagnosis, prescribing and care decisions remain with licensed clinicians. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.