AI-Powered Automation

How it works

1. 01AI integrates with your billing system and EMR to analyze historical claims data
2. 02System identifies patterns: CPT mismatches, chronic care gaps, upcoding risks, and denial trends
3. 03Flags missed billing opportunities you can address going forward
4. 04Monitors for patterns that could trigger fraud audits 3-5 years later
5. 05Provides actionable alerts with explanations so you can make informed decisions

Before and after

Primary care practice with 3 physicians seeing 60 patients/day, annual revenue $1.8M

Before

Practice billed correctly based on what they knew, but EMR didn't flag that 40% of diabetic patients qualified for chronic care management (CCM) codes worth $60-120/patient/month. Also had pattern of under-documenting E&M complexity, leaving money on table. No visibility into payer denial trends.

After

AI flagged CCM opportunity for 85 patients ($6,800/month potential). Identified under-documented visits worth additional $15K annually. Caught a Medicare upcoding pattern that could have triggered audit in 3 years, but it was corrected before it became an issue. Practice added $95K in compliant annual revenue and eliminated future audit risk.

Common questions

Does this work with our EMR system?

Yes, we integrate with most major EMRs including Epic, Cerner, Athenahealth, NextGen, eClinicalWorks, and DrChrono. If you use a less common system, we can likely work with claims exports.

Will this flag things we're doing wrong?

The goal is protection and optimization, not punishment. It flags potential issues so you can address them proactively, before auditors do. You stay in control of all decisions.

How much revenue lift can we expect?

It varies by practice, but most see 5-15% revenue improvement by capturing previously missed opportunities like CCM codes, appropriate E&M levels, and gap closure. The compliance protection is often even more valuable. Avoiding one clawback can save tens of thousands.

How it works

1. 01AI monitors incoming faxes, EMR messages, and external records
2. 02System automatically reads lab results, imaging reports, and specialist notes
3. 03Extracts key clinical data: diagnoses, test results, recommendations, follow-up needs
4. 04Generates plain-language summaries with suggested chart updates and action items
5. 05Sends summaries to provider for approval before updating EMR

Before and after

Family medicine practice receiving 30-50 external documents daily (labs, imaging, specialist notes)

Before

Medical assistant and nurses spent 2+ hours daily opening faxes, reading through dense reports, highlighting key findings, and manually entering summaries into charts. Critical follow-ups sometimes got lost in the noise. Specialist notes sat unreviewed for days.

After

AI processes all incoming documents and delivers clean summaries: "Patient MRI shows mild degenerative changes, no acute findings. Orthopedist recommends PT and follow-up in 6 weeks if no improvement." Provider reviews summary in 30 seconds, approves chart update with one click. Time saved: 8-10 hours per week. Zero missed follow-ups.

Common questions

What types of documents can it process?

Lab results, imaging reports (X-ray, MRI, CT), specialist consultation notes, hospital discharge summaries, pathology reports. Essentially any clinical document that arrives via fax, EMR message, or upload.

Does the provider still review everything?

Yes! AI does the first-pass reading and summarization. The provider reviews the summary and approves any chart updates. This maintains clinical oversight while eliminating the time-consuming extraction work.

How does it handle urgent findings?

AI flags critical results (e.g., "critical lab value," "urgent imaging finding") and escalates them for immediate provider attention, ensuring nothing urgent gets buried.

How it works

1. 01AI analyzes historical appointment data: who canceled, when, and under what circumstances
2. 02System identifies patterns: time of day, day of week, patient demographics, appointment type, booking lead time
3. 03Flags high-risk appointments 24-48 hours in advance
4. 04Triggers automated reminder calls/texts to high-risk patients
5. 05Suggests double-booking low-risk slots or filling from waitlist to maximize schedule density

Before and after

Pediatric practice with 80 appointments per day, averaging 12% no-show rate (9-10 lost slots daily)

Before

Practice sent standard reminders to everyone. No-shows were unpredictable. Some days had 15+ empty slots, other days were smooth. Lost revenue: ~$120-180 per empty slot = $1,400-1,800/day. Providers finished early some days, stayed late others. Inefficient and frustrating.

After

AI predicted high-risk appointments with 75% accuracy. Front desk called high-risk patients personally 48 hours out, reducing no-shows by 40% in that group. For predicted cancellations, they double-booked or filled from waitlist. Result: No-show rate dropped from 12% to 7%, adding 15-20 visits per week. Annual revenue impact: ~$180K.

Common questions

How accurate are the predictions?

Accuracy improves over time as the system learns your patient patterns. Most practices see 70-80% accuracy after 30 days. Even 70% accuracy makes a significant impact, and you're acting on the right appointments most of the time.

What if we accidentally double-book and both show up?

The system flags low-risk double-bookings only. If both show, you have a busy day, but that's better than empty slots. Most practices see this happen less than 5% of the time, and the revenue gain far outweighs occasional crowding.

Does it work for specialty or procedure appointments?

Yes! The AI learns patterns for different appointment types. Procedure appointments often have different no-show patterns than routine follow-ups, and the system accounts for this.

How it works

1. 01Patient refill request arrives via phone, portal, or pharmacy fax
2. 02AI pulls patient chart and gathers context: last visit date, current medication list, refill history, controlled substance status
3. 03System checks refill eligibility: Is it too early? Is an appointment required? Any drug interactions or safety flags?
4. 04Drafts recommended disposition: "Approve 90-day supply" or "Require office visit, last seen 18 months ago"
5. 05Provider reviews summary and approves/modifies in seconds

Before and after

Internal medicine practice with 3 providers receiving 40-60 refill requests daily

Before

Nurse or MA fielded each refill request, pulled chart, checked last visit, looked up med history, flagged controlled substances, and left message for provider. Provider reviewed and made decision. Each request took 5-7 minutes of combined staff/provider time. Patients waited hours or days for responses. Some requests fell through cracks.

After

AI pre-screens all refills. Nurse receives summary: "Patient John Doe requesting lisinopril refill. Last visit 4 months ago, no appointment needed per protocol. No red flags. Recommend approve 90-day supply." Nurse reviews in 30 seconds, provider approves in 10 seconds. Time per refill: under 1 minute. Patients get faster responses. Practice saves 3-4 hours daily.

Common questions

How does it handle controlled substances?

The system flags all controlled substances and applies stricter protocols: checking refill frequency, last visit requirements, and state-specific regulations. These always require provider review. AI just does the prep work.

What if a patient needs an appointment for the refill?

AI flags patients who haven't been seen within your practice's protocol timeframe (e.g., 12 months for chronic meds). It drafts a message explaining they need an appointment and can even offer scheduling options.

Does it check for drug interactions?

Yes, it cross-references the refill request against the patient's current medication list and flags potential interactions or safety concerns for provider review.

How it works

1. 01Lab results arrive in EMR or via interface
2. 02AI reads results and translates medical terminology into patient-friendly language
3. 03Generates personalized message in your tone: "Your HbA1c is 7.2%, which means your average blood sugar has been slightly elevated over the past 3 months. We'd like to adjust your medication and schedule a follow-up in 8 weeks."
4. 04Provider reviews message, makes edits if needed, and approves
5. 05Message sent via patient portal or secure text, with no manual typing

Before and after

Family medicine practice with 2 providers seeing 50 patients/day, sending 30-40 lab result messages daily

Before

Providers spent 15-20 minutes at end of each day reviewing lab results and manually typing patient messages. Messages were often rushed and brief, leading to confused patients calling the next day for clarification. Some results sat in the queue for 48+ hours. Patients felt anxious waiting.

After

AI drafts messages for every result. Provider reviews queue in 5-10 minutes, tweaking a few and approving the rest. Messages go out same-day with clear, empathetic explanations. Patient callback volume dropped 40% because messages answered common questions upfront. Provider stress reduced, patient satisfaction increased.

Common questions

How personalized are the messages?

Very. The AI considers the patient's specific results, history, current treatment plan, and your communication style. Messages don't feel robotic. They sound like you wrote them, because the AI learns your tone during setup.

Can it handle abnormal results that need urgent follow-up?

Yes! AI flags abnormal or critical results and drafts messages with appropriate urgency: "Your lab shows [issue]. Please call our office today to schedule an appointment." It escalates critical results to provider immediately.

What if the provider wants to add something specific?

Providers can edit any message before sending. Most make minor tweaks or approve as-is. The system learns from edits over time, getting better at matching your preferences.