Evaluating Keap for Medical AI Calling

What You Are Actually Testing When You Evaluate Medical AI Calling

Medical AI calling is not one thing. It covers inbound call handling (answering the phone, capturing the patient's reason for calling, logging it in the practice management system), appointment reminders (calling patients before they miss a visit), and outbound campaigns (calling patients to reschedule, follow up on results, or invite them for preventive care). Each has different compliance demands, different failure modes, and different ways of measuring whether it works. Start by writing down which of these three your practice actually needs. A busy GP surgery drowning in unanswered calls needs different technology from a dental practice that wants to cut no-shows, and that practice needs different technology again from a clinic running a recall campaign for cervical screening.

The core mechanism is always the same: the AI agent picks up the call, listens to what the patient says, understands the intent, and either solves the problem or transfers to a human with a summary already written in the patient record. That summary matters. It is not enough to know the agent handled the call; a practice manager needs to see what was said, when, by whom (human or AI), and be able to audit it on request. Patient confidentiality is non-negotiable, and so is the ability to back out of the AI system entirely if a patient asks for a human. The agent must recognize those requests instantly and hand over without reading the patient's full record to a recorded line. These are not edge cases; they are the core of what safe medical calling looks like.

Week One: What to Test in Your Trial

Start with a single, narrow use case. Do not try inbound, outbound and reminders at once. Pick the one that would save your practice the most time if it worked. For a surgery, that is usually inbound call handling. For a dental practice, it is often appointment reminders. Set up the trial to run parallel to your current process, not replacing it yet. Patients should not notice the trial is happening. The AI should handle incoming calls, log the interaction, and route the patient to a human if the call requires clinical judgment or if the patient requests one.

In week one, run this test: have the system answer all calls during a single quiet day, perhaps a Tuesday morning. Let it handle what it can. Write down every call that came in, what the AI did with it, whether the patient was satisfied, and whether the summary in the practice management system matched what actually happened. Count the calls yourself; do not rely on the vendor's dashboard. If 20 calls came in and the system handled 16 without human involvement, but 3 of those 16 had summaries so vague that your receptionist would have to call the patient back to book them, then you have not really reduced your work by 16 calls, but by 13. That is the number that matters.

How to Evaluate Keap for Medical AI Calling: The Metrics That Count

After a week, measure these five things. First, the completion rate: of the calls the AI handled, what percentage did not require a human callback or follow-up? A call is only truly handled if a patient gets what they called for without ringing back. Second, the handoff quality: when a call needed a human, how clear was the summary the AI left in the notes? Read five of these summaries and ask yourself: could a staff member pick up from this note and serve the patient on the first attempt? Third, the audit trail completeness: can you pull a report showing every call the AI handled, its duration, the date and time, the outcome, and the full transcript? If the practice is ever asked to show a regulator, health insurer, or lawyer a record of patient interactions, you need to produce this in minutes, not hours.

Fourth, the patient experience during handoff: did the system ask the patient's permission before transferring them to a human, or did it silently pass them over? Did it repeat the patient's issue to confirm it understood, or did it just route them cold? Listen to three of these handoffs yourself. Fifth, the escalation pattern: which types of calls did the AI refuse to handle, and which did it attempt and fail at? Note the categories. If the AI is refusing to handle all calls from patients with complex medication histories, but your practice has a lot of those patients, then the AI has not solved your problem, only changed its shape.

Create a simple spreadsheet: date, time, caller name, reason (appointment, prescription refill, results query, etc.), did AI handle it fully (yes/no), did it require a human callback (yes/no), was the summary usable (yes/no), handoff quality (clear/vague/missing). After a week, you will know whether this technology is worth extending to a second week, or whether to stop and ask the vendor why it did not work.

The Questions to Put in Writing Before You Commit

Before signing anything, send the vendor a written list of questions and ask for written answers. Do not accept verbal assurances or a promise to check with the product team. If they cannot answer in writing, that is a signal to pause. Ask: "Can the system recognise when a patient asks to speak to a human and hand over immediately without reading further information from the patient record?" This is a patient safety question and a consent question. The answer should be yes, with a description of how it works. "What patient data does the AI system access during a call, and how is access controlled?" If the AI reads the entire patient record to every caller, that is a problem. It should read only what is necessary (appointment dates, maybe medication list if relevant to the call) and nothing more.

Ask: "Can we retrieve a full audit log of every interaction, including rejected calls, transcripts, and the AI's reasoning for its decisions?" You need this for compliance and for understanding why the system failed on specific calls. "What happens to call recordings and transcripts after they are stored? How long are they kept, and can we delete them on request?" This relates to data retention and patient rights under GDPR and UK GDPR. Ask: "Does the system alert staff if a call contains certain keywords, such as references to self-harm, abuse, or acute symptoms?" A good AI system will flag these for immediate human review, not just log them. The answer that should end a sales process: "We do not have that functionality and it is not on the roadmap." If the AI system cannot flag a patient who mentions chest pain or suicidal ideation, it is not safe for medical use.

When This Technology Is the Wrong Choice

Medical AI calling is powerful when the call follows a predictable path and the outcome is administrative. It works well for confirming appointments, sending reminders, collecting basic information for a routine visit, and routing patients to the right department. It struggles when the call is complex, emotionally charged, or requires clinical judgment. A patient ringing because they are worried about a symptom needs a human. A patient who is confused, angry, or distressed needs a human. The AI will attempt to handle these calls, and will often fail, leaving the patient frustrated and your staff with a garbled note to sort out.

Do not buy AI calling if your practice does not have the staff bandwidth to review the AI's work. If your practice is understaffed and you are hoping the AI will let you cut further, you will be disappointed. The AI will filter the simplest 30 to 40 percent of calls and hand everything else to your team, with notes you will need to check. That is the honest trade-off. It is still valuable if you have staff to review the work, but if you are trying to run the surgery with fewer bodies, this is a cost centre you cannot afford yet.

Do not buy it if you cannot commit to training staff to use the audit trail and review AI decisions. If the system is running in the background and nobody checks whether it is actually working, or whether it is making mistakes, you will have compliance and safety problems within weeks. Assign someone the job of reviewing AI calls and summaries every week. This is not a hands-off tool.

Worked Example: A Five-Chair Dental Practice

Imagine a five-chair dental practice with 35 patient calls per day on average. The practice manager estimates that 40 percent of those calls are appointment requests or reschedules, 30 percent are inquiry calls (prices, how long a procedure takes, insurance), and 30 percent are clinical follow-ups or emergency calls. The practice currently employs one full-time receptionist and answers about 75 percent of calls; the rest go to voicemail and get returned at the end of the day. The receptionist estimates she spends about 12 hours a week on call handling and scheduling. If AI calling handled the appointment and inquiry calls, that would be roughly 24 calls a day (40 percent plus 30 percent of 35). If the AI completed 80 percent of those without a human callback (a realistic figure), that would be 19 calls the receptionist does not have to handle. At roughly 5 minutes per call, that is 95 minutes a week recovered, or about 2 hours. That is real time, but it is not transformative. The receptionist still works 40 hours a week; she just spends 38 on other tasks instead of 40 on calls.

If, however, the practice currently misses 9 calls a day (25 percent of 35), and one in three of those would have booked an appointment if they had reached a human, then the missed revenue is 3 appointments a day that do not happen. At, say, £180 per routine appointment, that is £540 a day, or £2,700 a week in lost revenue (assuming a five-day week). An AI system that answers the missed calls and books even 60 percent of them would recover roughly £1,620 a week in appointments. That is the true value proposition for this practice: not time saved, but revenue recovered and patient satisfaction improved. Before you buy, calculate this figure for your own practice. If it is small, the business case is weak. If it is large, the business case is strong.

How to Compare Vendors and Make a Decision

You will be comparing AI calling systems across a few key dimensions. First, integration: does the system write to your practice management software automatically, or do you have to export calls and import them manually? If it is the latter, the receptionist is still doing work. Second, customization: can you set the AI's behavior for your practice, or is it generic? Can you tell it which calls to accept, which to escalate, and which to reject? Third, support: when the AI makes a mistake and a patient is unhappy, what happens? Do you get a person on the phone within hours, or do you submit a ticket and wait for an email? Medical practices cannot afford slow support.

Fourth, compliance: we have already covered the audit trail and data handling questions. A vendor who cannot answer those questions clearly is not ready for healthcare. Fifth, cost: AI calling is usually sold on a per-minute basis or a monthly fee plus minutes. Calculate the cost based on your actual call volume. If your practice takes 35 calls a day and the AI handles 60 percent of them, that is 21 calls a day, or about 100 minutes a day if each call is 5 minutes long (some will be shorter, some longer). That is 2,000 minutes a month. At typical pricing, that could range from £240 to £400 a month depending on the vendor and the plan. Is that worth the revenue recovered or time saved? For the dental practice in the example above, yes. For a smaller practice, maybe not.

Healthcare-specific AI calling solutions need to be evaluated carefully because the stakes are higher than in other industries. A wrong call handled badly does not just waste time; it can affect patient safety and trust. Take the time to run a proper trial, measure real outcomes, and ask hard questions. Compare Sysevo and Keap side by side using this checklist, and you will have a clear picture of which system fits your practice. When you are ready to explore options in detail, book a call with our team to discuss your specific situation.

Frequently Asked Questions

Will an AI calling system work if our practice receives very few calls each day?

AI calling makes the strongest business case when you are missing significant numbers of calls or spending many hours on administrative calls. If your practice receives fewer than 20 calls a day, the time saved may be minimal. Calculate the revenue impact first. If you are not currently losing money due to missed calls, the value is lower.

What happens if the AI makes a clinical error and gives a patient wrong information?

This is why the audit trail is critical. A well-designed medical AI system should refuse to answer clinical questions entirely and route them to staff. Your trial testing should expose whether it does this reliably. If it attempts clinical calls and fails, that vendor is not safe for healthcare.

Can patients opt out of AI calling entirely?

Yes, and any vendor should support this. Ask in writing whether the system can flag individual patient records to always route to a human, and whether patients can request this during a call. Both should be immediate and automatic.

How long does it take to see a return on investment?

It depends entirely on your current call volume and missed call rate. If you recover five appointments a week at £150 each and the system costs £250 a month, payback happens within two months. If you save three hours a week of receptionist time at £18 an hour, payback takes much longer. Model your own numbers before you buy.

Do we need special permission from patients to use AI calling?

Check with your practice's legal advisor and your data protection officer. Under GDPR and UK GDPR, you should be transparent about the use of AI in patient communications. Most practices disclose this in patient information leaflets or on the phone system message itself. A vendor should be able to provide template language you can adapt.

What should I do if the trial reveals the AI is not working well?

Ask the vendor for specific support. Maybe the system needs tuning for your practice's specific workflows or patient population. But if after two weeks of tuning and adjustments the system is still failing on more than 40 percent of calls, or if it is making errors that matter clinically, stop and try a different vendor or tool. Do not go live with a system you do not trust.