Lotus Flow

Lotus Flow transforms patient intake from a slow, manual process into an intelligent, automated workflow. The solution captures information from digital forms, scanned documents, referrals, emails, and uploaded insurance cards, then uses AI to extract and organize patient demographics, insurance details, medical history, consent information, and appointment requests.

Before the information enters operational systems, Lotus Flow validates required fields, detects missing or inconsistent data, checks for duplicate patient records, and flags issues that need staff review. It can also connect with eligibility-verification services to confirm insurance coverage and identify potential authorization or documentation requirements before the patient arrives.

Once the patient’s information has been validated, Lotus Flow routes the case to the correct provider, department, location, or scheduling team based on specialty, urgency, insurance network, language preference, geography, and appointment availability. Straightforward cases can move through automatically, while exceptions are placed in a clearly prioritized review queue.

The solution can integrate with existing EHR, CRM, scheduling, billing, patient portal, and document-management platforms through APIs or secure middleware. Staff receive a centralized view of each intake case, including its current status, missing information, assigned owner, and next required action.

By reducing repetitive data entry and preventing incomplete intake packages from moving downstream, Lotus Flow helps healthcare organizations shorten registration time, reduce administrative errors, improve the patient experience, and allow staff to focus on higher-value patient support.

Typical implementation scope — $35,000

The implementation includes:

• Intake workflow and requirements discovery

• Configuration of supported forms and document types

• AI-based document classification and data extraction

• Demographic and insurance-data validation rules

• Duplicate-record and missing-information detection

• Patient-routing and exception-management workflows

• Integration with up to two existing healthcare systems

• Operations dashboard and intake-status reporting

• Security, access-control, and audit-log configuration

• User acceptance testing, staff training, and launch support

Expected business outcomes

A typical organization can use Lotus Flow to:

• Reduce manual intake and data-entry work

• Decrease incomplete or inaccurate patient records

• Identify insurance problems earlier

• Shorten the time from referral to scheduled appointment

• Reduce back-and-forth communication with patients

• Improve visibility into intake bottlenecks

• Create a faster and more consistent onboarding experience

Example patient journey

A patient uploads a referral, insurance card, identification, and completed registration form. Lotus Flow identifies each document, extracts the relevant information, compares the patient’s name and date of birth across the files, and checks that all required fields are present. It then validates the insurance information, determines the appropriate specialty and location, creates or updates the patient record, and routes the intake package to the correct scheduling team. Staff become involved only when the system detects an exception, such as missing information, an insurance mismatch, or a referral requiring review.

Investment: $35,000 for implementation, with optional ongoing support, additional integrations, and managed AI operations available as separate monthly services.

AI extraction and classification is where Lotus Flow turns raw uploaded documents into structured patient data.

What it processes:

• Digital intake forms, scanned documents, referrals, emails, and uploaded insurance cards — any format a patient or referring provider might send in

How it works:

1. Document classification — identifies what each uploaded file actually is (referral letter, insurance card, ID, registration form) so the system knows which extraction rules to apply
2. Field-level extraction — pulls structured data out of unstructured or semi-structured documents:
   • Demographics — name, date of birth, address, contact info
   • Insurance details — payer, member ID, group number, plan type (often read directly off a photographed insurance card)
   • Medical history — relevant conditions, referring diagnosis, prior treatment context
   • Consent information — signed consent forms and their scope
   • Appointment requests — requested specialty, provider, location, or timeframe
3. Cross-document consistency checks — compares extracted values across multiple documents from the same intake package (e.g., does the name and date of birth on the insurance card match the registration form and the ID?) to catch mismatches early, before they become downstream problems

Why this stage matters: It's the difference between a patient uploading a stack of PDFs and staff manually re-typing everything into the EHR versus the system doing that work automatically. It's also the foundation for everything after it — validation, eligibility checking, and routing all depend on the extracted fields being accurate and complete.

Output: A structured patient intake record (demographics, insurance, history, consent, appointment request) that gets handed to the validation and duplicate-detection stage next.

Cross-document consistency matching is the check that catches problems before they ever reach a human.

How it works:

When a patient uploads multiple documents in one intake package (say, a referral, an insurance card, an ID, and a completed registration form), each document goes through extraction independently, and the system ends up with the same fields — name, date of birth, address — pulled from four different sources.

The matching logic then:

1. Normalizes values before comparing — "Robert Smith" on an ID vs. "Bob Smith" on a form vs. "SMITH, ROBERT" on a referral all need to resolve to a comparable format before a mismatch can be flagged accurately. Same for dates (MM/DD/YYYY vs. DD-MM-YYYY vs. spelled-out month), addresses (abbreviations, unit numbers), and insurance member IDs (dashes, spacing).
2. Applies fuzzy matching with confidence scoring — not every difference is an error. "Robert" vs. "Bob" is a common nickname variant and might get a lower-severity flag than "Robert" vs. "Richard," which is a likely typo or a genuinely different person.
3. Weighs which fields matter most — a mismatched middle initial is lower priority than a mismatched date of birth or a mismatched insurance member ID, since the latter two are far more likely to indicate a real problem (wrong patient, expired card, data entry error at the referring office).
4. Produces a structured discrepancy report rather than a blanket pass/fail — so when a case does need staff review, the reviewer sees exactly which field disagreed across which two documents, not just "review needed."

Why it's structured this way: The goal is to only interrupt staff when something is genuinely uncertain. A minor formatting difference shouldn't generate a manual review task, but a real identity or eligibility mismatch should never silently pass through — that's the kind of error that causes downstream billing rejections or, worse, the wrong patient's information getting merged into a record.

Where it hands off: Cases that pass consistency checks move on to validation (missing required fields) and then eligibility verification. Cases with unresolved discrepancies get routed straight to the exception queue with the discrepancy report attached, so the reviewing staff member has full context immediately rather than having to re-derive it.