Referral Workflow
Interventional pain referrals come by phone and require accurate capture of referring provider, diagnosis, and procedure urgency. This page explains how AI receptionists gather referral details and return them as structured summaries for your team to review and file.
Referring providers call to send a patient for interventional pain management. The AI receptionist captures the provider name, facility, diagnosis, procedure code, and clinical rationale. This information is returned as a structured referral summary that your clinical team reviews and files. No more 'the doctor called but we didn't get the details.'
During the intake call, the AI gathers patient name, date of birth, phone, email, insurance information, and appointment preference. The new patient record with demographics and insurance is written to your EMR and the appointment is booked in real time. Your clinical staff review the referral notes and structured summary; they don't create chart entries from scratch.
The AI asks about symptom urgency and procedure timeline ('Is this urgent?' 'When does the patient want to be seen?'). High-priority referrals are flagged in your system so your clinical team triages scheduling accordingly. Routine referrals are batched and scheduled during normal availability.
Insurance information is collected during the referral call. Your staff can immediately flag whether prior authorization will be needed based on procedure code and insurance type. Prior auth can be initiated right away rather than discovered after scheduling.
Every referral call answered
No voicemail for incoming referrals—day or night, referring providers reach intake immediately
Referral data structured and ready to file
Provider, diagnosis, procedure code, and clinical urgency captured and formatted for your team to review
New patient record created on intake
Demographics and insurance synced to EMR during the same call; appointment booked automatically
Priority referrals flagged for clinical review
Urgent cases, out-of-network referrals, and prior auth requirements surfaced to your clinical team immediately
Referral sources often identify themselves immediately: 'I'm calling from Dr. Jones' office with a referral' or 'I'm a primary care provider sending you a patient.' The AI listens for these cues. You can also configure the system to ask upfront: 'Are you a patient, referring provider, or insurance company?' This routes calls appropriately.
The AI gathers: referring provider name and facility, patient name and date of birth, diagnosis, procedure code (CPT if known), procedure urgency, relevant medical history, and patient contact information. This is returned as a structured referral summary that your clinical team can review and file into the patient's chart.
Yes. During the intake call, the AI collects patient name, date of birth, phone, email, insurance information, and appointment time. Patient demographics and insurance are written to your EMR as a new patient record and an appointment is booked. Your staff see a populated chart with the referral summary attached, not a blank intake form.
The AI asks about the referring provider and facility affiliation. If the referral is flagged as out-of-network or if the procedure typically requires prior authorization, the system alerts your clinical team so they can prioritize authorization before scheduling the patient.
Yes. Many practices prefer the AI to book a time during the referral call if the patient is on the phone, or the AI can schedule based on the referring provider's request and have the patient confirm by callback. Appointment confirmation and reminder details are sent to the patient via text and email.
The AI syncs the new patient record (demographics and insurance) and appointment booking directly to your EMR. Referral details and clinical notes are returned as a structured summary for your staff to review and paste into the patient's chart. No duplicate data entry required.
See how MedReception AI handles after-hours calls, scheduling, intake, and patient communication for medical practices like yours.
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