AI Pre-Visit Chart Summaries: What They Read, What They Miss, and a Pre-Charting Workflow

Most of the AI-scribe conversation is about what happens during and after a visit: the recording, the draft note, the edit-and-sign. But a growing share of the 2026 product releases is aimed at the five minutes before a visit, when a clinician opens the chart and tries to remember who this patient is and what happened last time.

Epic now batch-generates note summaries for every patient on a clinician's schedule. Abridge's August 2026 release centered on pre-visit summaries. Suki, Freed, Heidi and Commure all ship some version of "here's what you need to know before you walk in."

This guide covers what these tools actually summarize (often less than you'd assume), what the research says about time savings and errors, how pre-charting interacts with time-based E/M billing, and a workflow for using a summary without letting it replace the chart.

What "AI pre-charting" means in 2026

Vendors use "pre-charting" for two different things, and it helps to keep them apart:

• Pre-visit chart summaries. The AI reads prior documentation and produces a short overview: active problems, what changed since the last visit, the last plan, open follow-ups. This is the newer category and the focus of this guide.
• Pre-populating the note. The clinician dictates or types context before the visit ("follow-up for A1c, started metformin in June") so the ambient scribe has it when it drafts the note. This is older and simpler, closer to a sticky note than to a summary.

Several products do both, and some ask-the-chart chat assistants ("what was her last LDL?") blur the line further.

What each tool actually summarizes

The most important question about any pre-visit summary is what it can see. A summary built only from your scribe's own past notes is a very different tool from one that reads the whole EHR. From each vendor's own documentation:

• Epic's outpatient chart summarization summarizes clinical notes only: progress notes, consults, procedure notes, H&Ps, discharge summaries and ED provider notes. According to the UCLA trial protocol describing the tool, labs and imaging are not included and still need manual review. It handles up to 30 notes or 24,000 characters. Summaries for scheduled patients are batch-generated about 36 hours before the appointment, and a "focus on" prompt can center a summary on one topic. The same protocol says Epic generates more than 16 million summaries a month across its summarization tools.
• Abridge pre-visit summaries (August 28, 2026 release) open with an overview of the patient's history, active problems and what changed since the last visit, followed by a chronological list of prior encounters. Abridge says every detail links back to its source in the chart. The announcement doesn't say which data types are included.
• Suki launched Patient Summaries in December 2024. Suki says they are "generated from previous patient documentation and chart information within the EHR," and its later product post lists prior visits, medications, labs and problems.
• Freed generates a patient context summary on Premier and Groups accounts from earlier notes in your Freed account, so it needs at least one prior Freed visit for that patient. Freed's help center is explicit that it "does not automatically import details from prior visits." Its pre-charting is mainly a context box you fill by typing or dictating. The help center doesn't describe pulling data from your EHR.
• Heidi Health added an "Automatically include past sessions" setting (Settings → Data management, web only) in its August 5, 2026 release notes. With a patient linked, their most recent Heidi sessions come along as context for the new note. A Patients sidebar shows history, medications, allergies, files and past sessions before the visit starts.
• Augmedix, now part of Commure, offers pre-visit summaries through Commure's Patient360 platform. Commure says it unifies "a patient's full EHR history," with expandable citations back to the source note, lab or imaging report. The June 29, 2026 description doesn't state availability or pricing.

The practical split: scribe-history summaries (Freed, Heidi) only know what was said in visits recorded with that tool. EHR-connected summaries (Epic, Abridge, Suki, Commure) can see more, but even Epic's version stops at notes.

Neither type reliably covers outside records, such as the referral packet that arrived by fax, the hospital discharge from another system or a specialist's letter scanned as a PDF. Those are often what matter most at a new-patient or post-hospital visit.

What the evidence says about time savings

The evidence is thin and early.

• NYU Langone (JAMIA Open, 2025). Silberlust et al. deployed AI-generated encounter summaries to 22 ambulatory providers and compared six months of Epic Signal data before and after. Among the 17 providers with complete data, average chart-review time per visit fell from 3:22 to 3:04, or about 18 seconds per visit (8,345 visits before, 7,924 after). Providers who spent the most time reviewing at baseline saw the biggest drop: each extra minute of baseline review was associated with an 8.6-second greater reduction (P = .046). Most providers rated the summaries accurate and time-saving. (JAMIA Open 2025;8(5):ooaf096, doi:10.1093/jamiaopen/ooaf096. The paper doesn't name the vendor.)
• UCLA randomized trial (in progress). UCLA is running a randomized trial of Epic's chart summarization tool across roughly 284 ambulatory physicians and advanced practice providers (NCT07438743). The primary outcome is pre-charting task load at 90 days. Secondary outcomes include chart time, burnout, safety events and clinician-reported summary errors. Enrollment started February 23, 2026, and randomization is stratified by whether the clinician already has an AI scribe license. No results had been posted as of late October 2026. It will be the first randomized evidence on this category.

An 18-second average is modest, but the NYU finding about heavy chart reviewers matters more. Pre-charting summaries probably help most for complex, infrequently seen patients (a pediatric specialist seeing a child every 6 to 12 months, a geriatric patient with a thick chart). They probably help least for a quick follow-up you saw three weeks ago.

What the evidence says about errors

There's no large published error study of a commercial pre-charting product yet, but research on LLM clinical summarization points to two consistent patterns.

• Omissions are the dominant failure. In a UCSF study of GPT-4 summaries of 100 emergency department encounters, only 33% were error-free. 47% omitted clinically relevant information, 42% contained a hallucination and 10% had an inaccuracy. Most errors were rated low-harm (mean 0.57 on a 0–7 scale), but one hallucination scored 6. Omissions clustered in the physical exam and history-of-present-illness sections. (Williams et al., PLOS Digital Health 2025;4(6):e0000899.)
• Good summaries are achievable. In a Stanford study across four summarization tasks, 10 physicians rated the best-adapted LLM summaries equivalent (45%) or superior (36%) to summaries written by medical experts. (Van Veen et al., Nature Medicine 2024;30(4):1134–1142.)

These are research models on research data, not the products above. But the takeaway is consistent with our AI scribe accuracy research roundup: a summary is more likely to leave something out than to make something up, and omissions are the harder error to catch. A false statement can be checked against the chart. A missing fact never prompts you to look.

How pre-charting interacts with E/M billing

This part of the topic gets little attention, and it matters for practices that code office visits by time.

Since the 2021 office E/M changes, you can choose a level based on medical decision making (MDM) or total time on the date of the encounter. As the AAFP's coding guidance puts it, total time "includes prepping for the visit (e.g. chart review)" but "does not include staff time or time spent by the physician or QHP outside the date of the visit." For established patients, the thresholds are 10–19 minutes for 99212, 20–29 for 99213, 30–39 for 99214 and 40–54 for 99215.

Three consequences:

1. If AI makes pre-charting faster, time-based totals drop. A clinician who used to spend six minutes on chart review and now spends two has four fewer billable minutes. On a visit near a threshold, that can mean a lower code. That's not a reason to avoid the tool, but you should know about it before you start.
2. Reviewing the summary is still your time; generating it isn't. Time you personally spend reading the summary and checking it against the chart on the date of service counts. The AI's batch generation, done 36 hours early in Epic's case, doesn't, and neither does staff time.
3. Don't let a summary inflate MDM. The MDM data category includes "review of prior external note(s) from each unique source." Credit that only for records you actually reviewed, not for sources that appeared inside an AI summary you skimmed. Ask your coder how your practice will document reviews prompted by an AI summary. For the broader audit picture, see our guide to AI scribes and coding intensity.

A pre-charting workflow that keeps the chart in charge

1. Know what your summary can see. Write down, for your specific tool, whether it reads notes only, notes plus labs and medications, or only its own past sessions. Put it on a tip sheet. Most pre-charting errors in practice will come from assuming coverage the tool doesn't have.

2. Use the summary to orient, then go to the sources for anything you'll act on. Read the summary first for the overview. Before you change a medication, interpret a trend or follow up on an abnormal result, open the source. With tools that cite sources (Abridge, Commure), that's one click. With tools that don't, it's a chart search.

3. Always check what notes-only summaries miss. For an Epic-style notes summary, labs and imaging need a separate look. For any tool, check outside records, recent results that arrived after the last note, and medication changes made by other prescribers.

4. Watch for staleness. A summary generated 36 hours ago doesn't know about yesterday's ED visit or this morning's lab. For same-day add-ons, regenerate or check the chart directly.

5. Be careful carrying summary text into the note. If your scribe uses past sessions as context (Heidi's auto-include, or text you paste into Freed's context box), old facts can resurface in today's note as if they were reconfirmed. Treat anything in the draft that wasn't said in today's visit as needing verification. Our rollout guide covers note-review habits in more detail.

6. Report misses. The UCLA trial collects clinician-reported summary errors as a safety outcome. Your practice should too, even if only in a shared document. A pattern of the same omission, such as allergies from outside records or a specialist's plan, tells you what to check manually.

7. Decide your coding approach up front. If you code by time, track how pre-charting time changes after rollout. If you code by MDM, write down how AI-assisted record review will be documented.

Privacy and BAA basics

EHR-embedded summaries (Epic) run inside a system you already have agreements for. Standalone tools that read your chart or store prior sessions need a signed business associate agreement before PHI goes in. Heidi says it executes one on every plan, and Freed, Suki and Abridge offer them; see our HIPAA guide for AI tools for what a BAA does and doesn't cover. If a tool stores past sessions to build summaries, ask how long they're kept and whether deleting a note also removes it from future summaries. Freed's help center notes that deleted notes no longer feed its pre-charting.

Who benefits most

• Specialists and pediatric subspecialists with long gaps between visits. Abridge's release quoted an Akron Children's physician noting that "it may be 6 to 12 months between patient visits in pediatric care." See AI tools for pediatricians.
• Practices with complex, multimorbid panels, such as primary care and family medicine, where the NYU data suggest heavy chart reviewers gain the most.
• Less benefit: quick, frequent follow-ups where the clinician already knows the patient, and new patients whose records live outside the system.

If you're comparing scribes with pre-visit features, start with the AI medical scribe directory or the family medicine scribe comparison. Ask each vendor three questions: what data the summary reads, whether it cites sources, and how fresh it is at visit time.

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This article is for informational purposes only and is not legal, billing or medical advice. Product features change often, so verify capabilities, data sources and BAA terms directly with each vendor, and confirm coding practices with your billing team or payer. Features and study details were checked as of October 26, 2026.