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Pulse-MD Urgent Care cut missed calls from 26% to zero in 30 days
Pulse-MD was missing 1 in 4 calls until Hello Patient's AI agent started answering them all. In month one, the agent handled 4,886 patient interactions and returned 5+ weeks of staff time.
Pulse-MD Urgent Care cut missed calls from 26% to zero in 30 days

Highlights
- 4,886 patient interactions handled in month one
- 0 dropped, abandoned, or missed calls. Down from 26% missed.
- Full EHR-integrated scheduling
- 30% containment rate. Hit target in the first month.
- 208 staff hours saved in a month. Over 5 weeks of an FTE.
About Pulse-MD
Pulse-MD is a multi-location urgent care provider serving patients across New York. In urgent care, every inbound call carries real clinical weight. Patients are calling about same-day symptoms, injuries, and time-sensitive concerns where delay isn't an option.
Executive Summary
In early 2026, Pulse-MD's team looked at their inbound call data and didn't like what they saw. Across 69,379 calls network-wide, 23% of patients hung up before being answered, over 16,000 abandoned calls. At their worst-performing line, only 7% of calls were getting picked up.
After partnering with Hello Patient and rolling out an AI agent built for medical practices, Pulse-MD eliminated missed calls entirely in the first 30 days. The agent handled 4,886 patient interactions, contained 30% of calls end-to-end without staff involvement, and returned 208 hours of capacity to the front-desk team.
The Challenge
Faisal Ashraf, Chief Operations Officer at Pulse-MD, knew the front-desk team was working hard and still falling behind. Urgent care call volume comes in spikes, and even well-staffed lines couldn't catch every call when volume peaked.
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The data made the gap impossible to ignore:
- 69,379 inbound calls across the network
- 23% abandonment rate, 16,028 patients hung up before reaching anyone
- 26% missed call rate, 18,086 calls never answered
- Worst-performing line: 7% answer rate (93% of patients calling that line never reached the practice)
- Network-wide answer rate of just 73%
For an urgent care group, this is a clinical and revenue problem as much as it was an operations problem. Pulse-MD needed a solution that could answer every inbound call instantly regardless of volume or time of day, handle the most common urgent care inquiries (locations, wait times, services, scheduling), resolve routine questions end-to-end without needing a human, roll out quickly across all locations, and maintain the warm, clinical-grade patient experience Pulse-MD is known for.
Why Hello Patient
Pulse MD evaluated several voice AI solutions. Three things made Hello Patient the clear choice.
1. Clinical-grade voice quality
The product test made the decision in minutes. Low latency, natural intonation, no awkward pauses.
2. Speed of deployment
Hello Patient delivered a functional prototype in weeks, not months. Full deployment followed shortly after, at a fraction of the implementation cost and timeline of competing solutions.
3. Genuine partnership
Beyond the product, Pulse-MD pointed to how closely the Hello Patient team worked with them through launch and the weeks that followed.
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The Impact
Results in month one exceeded what Pulse-MD expected from any voice automation solution. Where 1 in 4 patients used to hang up before reaching the practice, every patient now reaches the agent instantly. The network's answer rate went from 73% before go-live to 100% in the first 30 days, the worst-performing line went from a 7% answer rate to 100%, and the 16,028 callers a month who used to hang up before reaching anyone dropped to zero. No hold times and no rollovers.
In late June, Pulse-MD turned on Hello Patient web chat on their website, so patients now get the same fast answer whether they call or type. The chat answers patient questions about services, hours, locations, and cost, and routes anything it shouldn't handle to the right place.
Three operational shifts Pulse MD observed in 30 days:
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Beyond the numbers, Pulse-MD saw the change in two places, the patient experience and how the front-desk team spent its day.
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What's Next?
Pulse-MD continues to see containment climb week over week as the agent learns more workflows and edge cases. Web chat on their website, powered by Hello Patient, went live in June 2026, giving patients the same fast and helpful answers they need. The team is now exploring more use cases, including proactive outbound outreach and waitlist management.
For urgent care and same-day care providers looking to eliminate missed calls and recover staff capacity, Pulse-MD's first 30 days with Hello Patient offer a clear blueprint. In urgent care, response time defines patient care quality, and the agent makes it instant.
Methodology: Results measured across Pulse MD's first 30 days of Mia deployment, April 1 – April 30, 2026, across 4 urgent care locations. All call-volume baselines are drawn from Pulse MD's pre-deployment call data.
In urgent care, a missed call isn't just a missed appointment. It's a patient who ends up at the ER, walks into a competitor's clinic, or gives up on seeking care altogether.
Arman Choudhury
Informatics Specialist, Pulse MD

The Hello Patient team treated our deployment like their own product launch. Weekly call audits, prompt refinement, workflow tuning — they were in the weeds with us from day one.
Arman Choudhury
Informatics Specialist, Pulse MD


Answer Rates
100%

Containment
30%
of calls resolved without a human

Time Savings
5+ weeks
New patient-experience baseline

Every caller reaches the practice on the first try now, around the clock, and gets the same warm answer whether the lobby is quiet or packed.
Front-desk team refocused

The agent resolved 30% of calls on its own in month one, letting the front desk focus on patient support and clinical handoffs.
but what can we do for you?
Book a free 1:1 call and let's find out.
Book a CallHow does an AI receptionist work for multi-location urgent care?
An AI receptionist like Mia answers every inbound patient call instantly across all of the group's locations, regardless of call volume, hour, or day. Mia handles all calls with a 100% answer rate and answers questions about wait times, hours, services, insurance, and scheduling for available appointment slots. She works within the rules and protocols your team sets, escalates anything clinical or out of scope to a real person, and logs every interaction for review.
For multi-location urgent care groups, the practical effect is that one central AI agent covers every clinic's phones simultaneously. Patients calling different locations of your clinic at different times of the day all get the same instant, warm response. Our case study with Pulse MD showed a multi-location urgent care network go from a 26% missed-call rate to zero missed calls in the first 30 days.
How does an AI receptionist handle clinical triage in an urgent care setting?
Mia, our AI receptionist, follows the triage rules your team sets. When a call requires clinical judgment, Mia escalates to a real person on your team or directs the patient to the appropriate emergency resource per your protocols. These calls include symptom-severity questions, requests for an X-ray after a possible broken bone or injury, "Should I come in or go to the ER?", medication questions, and anything outside Mia's approved scope.
Hello Patient is HIPAA-compliant by design and signs a BAA with every practice. Mia never gives medical advice on her own and never improvises around your clinical guidelines. In practice, Mia contains the routine, non-clinical calls end-to-end, while every clinical or out-of-scope call is routed to staff exactly as the practice's protocols require.
How does an AI receptionist integrate with our urgent care EHR?
Hello Patient's AI receptionist integrates with the systems urgent care groups already run on, including ModMed, AdvancedMD, Athena, eClinicalWorks, NextGen, and Veradigm, plus the major scheduling and practice-management platforms. Depending on what your group uses and which workflows you want Mia to handle, Mia can look up patient information, read the clinic's schedule, find a patient's existing appointments, book new appointments, reschedule or cancel them, create new patient accounts, and leave notes on an appointment so the conversation context carries over to your team.
Beyond call handling, Mia captures structured data on every interaction. Hello Patient's analytics surface what patients are actually calling about, what's getting transferred to staff and why, and how call mix varies across locations. For multi-location urgent care groups, this means you can benchmark clinic-by-clinic, seeing which sites field the most X-ray and injury inquiries, where insurance questions are spiking, or whether one location transfers at a higher rate than the others, and why. The data feeds back into operational decisions about staffing, training, and workflow.
Mia works alongside your existing phone system, scheduling tools, or EHR. For groups still consolidating systems across newly acquired locations, Mia can present a unified patient-facing experience across mismatched back-end stacks while the consolidation runs in parallel.
How long does it take to set up an AI receptionist at an urgent care practice?
Most groups go live within four to eight weeks, depending on the number of locations, the complexity of their scheduling rules, and how quickly clinical and operational leaders can review and approve the workflows the AI receptionist should cover. A functional prototype is typically ready in a few weeks, with full deployment shortly after.
The first phase is workflow mapping with your operations and clinical teams: location list, hours, services, triage rules, escalation paths, and scheduling logic. The second phase is integration with your scheduling system, EHR, and existing phone infrastructure. The third phase is pilot calls with real patients at a subset of locations, followed by full rollout. There's no new software for your front-desk team to learn. Mia, our AI receptionist, operates inside your existing workflow.
How do you measure the ROI of an AI receptionist in multi-location urgent care?
ROI from an AI receptionist like Mia shows up in three operational metrics a multi-location operator can pull from their own call data before and after deployment.
Missed-call rate: the share of inbound calls that never reached a person. Every missed call in urgent care is a same-day patient who went elsewhere or didn't get care.
Containment rate: the share of calls handled end-to-end without staff involvement, covering hours, locations, scheduling, and intake.
Front-desk capacity returned: the staff-hours pulled off routine inbound and redirected to in-clinic work.
Our case study with Pulse MD showed all three move in the first 30 days. A 26% missed-call rate dropped to 0%, Mia reached 30% containment, and the group recovered 208 hours (about five weeks of an FTE) across four locations.
What happens when a patient needs to speak to a real person?
We support live escalation to a real person whenever the call falls outside Mia's approved scope. An AI receptionist like Mia can handle clinical questions, complex insurance situations, complaints, or anything the patient or your protocols require a human for. Escalation paths are configurable per group. During open hours, Mia can transfer the call live or take a callback request with full context for staff to pick up. For after-hours calls, Mia can leave a voicemail for your team, create a ticket in Slack or your existing workflow tools, or route to your answering service for live triage. Your team starts the next morning with a complete picture of what came in overnight.
The patient never has to repeat themselves. The full conversation context (what they called about, what Mia already answered, and why they need a person) is passed to the staff member or the callback queue, so the human pickup starts informed instead of cold.
Is Hello Patient's AI receptionist HIPAA-compliant?
Yes. Hello Patient is HIPAA-compliant by design and signs a BAA with every practice. Mia uses your approved protocols, your knowledge base, and your escalation rules, and logs every interaction for review. PHI is handled per your group's data-handling policies. When a question requires clinical judgment or falls outside Mia's approved scope, Mia escalates to the right person on your team.
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