Patient Call Resolution Drives Satisfaction

Practices assume some calls are just harder than others — a prior-authorization request feels more likely to end in frustration than a routine appointment confirmation. We wanted to test that assumption against real data, using the same call-transcript analysis behind our earlier look at nearly half a million patient calls. CallMyDoc has now handled more than 29 million patient calls over 11+ years across 44 states plus DC and the US Virgin Islands, with zero breaches and zero lost calls — so we pulled a fresh, current sample to check whether the "harder topics cause more frustration" assumption actually holds.

It doesn't. What predicts frustration isn't the reason for the call. It's whether the call gets closed out.

1. The resolution gap is the whole story

We classified 1,982 recent patient calls from the last six months — a fresh, randomly sampled window, not a cherry-picked one — for two things: does the transcript show any sign of frustration (long waits, "still waiting," similar language), and does the call sound resolved, headed to a callback, or unresolved. The gap by resolution status is stark:

• Resolved on first contact: ~1% show frustration signals (1 of 75 sampled calls).
• Needs a callback: ~14% show frustration signals (144 of 1,015 sampled calls).
• Unresolved: ~20% show frustration signals (107 of 523 sampled calls).

A call that gets closed on the spot is dramatically less likely to leave the patient frustrated than one that doesn't — by roughly an order of magnitude. That gap is bigger than almost anything else we tested, including the topic of the call itself.

Why "unresolved" happens — it's rarely about the medicine

None of the calls behind that 20% figure are unresolved because the clinical question was too hard. Almost every one follows the same mechanical pattern: the patient calls, reaches a voicemail or an overflow queue, leaves a message, and waits. Somebody eventually listens to the message, writes down a callback number and a rough summary, and adds it to a stack. Hours or a day later, someone calls back — sometimes reaching the patient, sometimes not, sometimes with only the handwritten note to go on because the person calling back wasn't the one who took the original message. Every one of those handoffs is a place the loop can fail to close, and none of them have anything to do with whether the underlying medical or administrative issue was actually complicated.

That's consistent with what the topic breakdown below shows: even the "hardest" categories — prior authorizations, referrals — aren't dramatically more frustrating than routine ones. What's dramatically more frustrating is the version of any of those calls that goes through two or three handoffs before it's actually closed.

2. It's (mostly) not about the topic

If frustration tracked clinical or administrative difficulty, you'd expect "harder" categories — prior authorizations, referrals, billing disputes — to run well above routine ones like appointment confirmations. They run a little higher, but nowhere near the gap resolution status produces:

• Billing & insurance questions: ~25% frustration
• Prescription issues: ~24% frustration
• Referrals and prior authorizations: ~21% frustration
• Medication questions: ~19% frustration
• Order requests (labs, imaging): ~16% frustration
• Appointments, refills, callbacks, clinical questions: all cluster around 10–12% frustration
• Test-result inquiries — the calmest category we measured: ~6% frustration

The spread across every topic we measured — from the calmest (test results) to the most contentious (billing) — is about 19 percentage points. The spread between a resolved call and an unresolved one, regardless of topic, is nearly the same size on its own. Topic matters at the margins. Resolution is the main driver.

3. Billing calls are exactly what they say they are

As a check on how well the underlying classifier holds up, we looked at calls the model tagged as being about billing or insurance, and asked how often those same calls also triggered a separate cost/billing-language flag. The agreement was 100% (69 of 69 sampled calls) — nearly identical to the near-total agreement rate (99.65%) we found on this exact check the last time we ran it on a much larger dataset. When a patient calls about a bill, the data says so consistently, which is part of why we trust the resolution finding above and not just the topic breakdown.

What this means for a practice — and what actually fixes it

The honest version: CallMyDoc doesn't make a prior authorization approve faster or a lab result come back sooner. It doesn't diagnose anything, and it never assesses or scores clinical urgency — a human makes every medical call, every time. What it targets is the operational failure sitting underneath most of that "unresolved" 20%: a call that goes to voicemail, gets logged as a vague note, or depends on someone remembering to call back before the patient calls back again, angrier. It's clinical communication infrastructure, not a front-desk replacement — the goal is that the loop closes mechanically, not that it depends on any one person remembering to close it.

That's a mechanical problem, and it has a mechanical fix, in two parts.

The automatable slice never enters the callback queue at all. Refill requests, appointment confirmations and reschedules, and similar routine, structured requests — an average of 47% of call volume across practices — get closed out immediately without a callback. (Full patient self-scheduling by phone is available today on athenahealth; the other two EMRs get the same call automation and documentation, without the self-schedule step.) A call that never becomes a callback can't become an unresolved one either.

Everything else still gets handled by a person — but with the handoff removed. When a call needs staff or a provider, CallMyDoc documents it directly into the patient's chart in the practice's EMR — athenahealth, Veradigm, or Altera TouchWorks — with what the patient said, not a paraphrase on a sticky note. Whoever follows up sees the actual request, not a secondhand summary, which is what determines whether that callback closes in one round or turns into the kind of repeat, unresolved contact this data shows patients notice and react to. The same documentation also gives a practice, for the first time, a way to actually measure its own resolution and callback rates instead of guessing — most practices today have no record of how many calls come back unresolved, because a paper message slip doesn't produce a report.

A quick self-check for practice managers

If you want a rough read on whether your own phones have this problem before you look at any vendor's numbers, three questions tend to surface it fast:

• Can you pull a report right now showing what percentage of yesterday's calls required more than one contact to close? Most practices can't — the answer lives in scattered voicemail boxes and paper message slips, not a system.
• When a callback happens, does the person making it see what the patient originally said, or just a name and a callback reason written by someone else?
• Do refill and scheduling requests — the calls that shouldn't need a human at all — still take a full callback cycle at your practice?

If the honest answer to any of those is "we don't know" or "no," that's the same gap this data points to: not a clinical difficulty problem, an operational one.

Methodology, and where this sample is thin

This finding comes from a fresh, randomly sampled batch of 1,982 recent patient call transcripts (trailing six months), aggregated and fully de-identified — no patient names, phone numbers, or other identifiers were used, stored, or shown at any point. Classification reuses the same taxonomy and disambiguation rules validated on our earlier 494,000-call census, run through Claude via Amazon Bedrock under our existing BAA. One caveat worth stating plainly: the "resolved" bucket in this sample is the smallest (75 calls), so while the direction of the finding is unambiguous, the exact 1% figure for that group carries a wider margin than the larger callback and unresolved buckets. We'll continue to expand this sample and will update the numbers if a larger pull moves them.

Go deeper: more from the call data

This post is a companion to our larger look at patient call patterns. See also:

• What 494,000+ patient phone calls reveal — the full picture: urgency, callers, timing, and specialty patterns.
• How often patients have to call back — the phone-tag problem behind the "callback" bucket above.
• The full breakdown of why patients call.

Book a demo → and see how your practice's resolution rate compares.

Methodology: Findings are aggregated and de-identified, drawn from a fresh, randomly sampled batch of 1,982 recent patient call transcripts (trailing six months) handled by CallMyDoc, part of a corpus of 29M+ calls across 44 states plus DC and the US Virgin Islands. Resolution and frustration classifications were produced by an automated classifier; the "resolved" subgroup (n=75) is small enough that its exact percentage should be read directionally. No patient-identifying information was used or included. Percentages are rounded.

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