Explore results →SCRIBE RESULTS / 22 SEPTEMBER 2026
Averages across 5 graded runs of 12 consultations, the same audio and instruction for every scribe. Notes graded on default template.
Freed averaged 8.33 errors per note under the Weiner et al. standard, ranked 2 of 6, against the 5.0 Weiner et al. (2020) found in doctor-written notes, on a stricter test than theirs. Clinical coverage was 87%, ranked 2 of 6.
It ranked 5 of 6 for time to a finalised note at 4m 40s and 6 of 6 for word error rate at 23.1%. It averaged 2.47 hallucination flags per note, and none of its notes were ready to finalise as written.
| Measure | Average | Run range | Rank |
|---|---|---|---|
| Time to a finalised noteWait from pressing stop to the graded note, plus review time: how long until the note can be finalised if it is reviewed straight away. | 4m 40s | 4m 22s–5m 12s | 5 of 6 |
| Notes ready to finalise as writtenShare of notes that captured every fact in the consultation's inventory in full with no hallucination flags, so nothing needed editing. | 0% | 0%–0% | 4 of 6 |
| Editing effort0% means no editing required; 100% means writing the full note by hand. Fixes are weighted by the work they take: partial fact 1, missing fact 2, hallucination flag 3, and +2 when a fact is captured wrongly. | 25% | 20%–39% | 4 of 6 |
| Review time per noteEstimated clinician time to proofread and fix one note (190 wpm reading, 5 s to recall and 40 wpm to type each missing or partial fact, 12 s to verify and delete each hallucination flag, including a wrong fact before it is retyped). | 4m 5s | 3m 49s–4m 36s | 5 of 6 |
| Worst note, any runThe single slowest note to review and fix across every case and run. | 8m | — | 4 of 6 |
| Time saved per noteTime to write the note yourself (8.1 min, the median in Apathy et al. 2023) minus review time. | 4m 1s | 3m 30s–4m 17s | 5 of 6 |
| Faster than writing it yourselfHow much quicker reviewing and fixing a note is than writing it yourself (8.1 min, the median in Apathy et al. 2023). Negative means slower. | 50% | 43%–53% | 5 of 6 |
| Errors per note (Weiner et al. standard)Relevant facts the note missed or captured only partly, plus every hallucination flag (a wrong detail counts twice, as in Weiner et al.), against an inventory of every fact said that relates to the patient's conditions, symptoms or signs (Weiner et al. 2020). Average per note, across 12 cases. | 8.33 | 7.33–10.25 | 2 of 6 |
| Note errors (Weiner et al. standard)Relevant facts the note missed or captured only partly, plus every hallucination flag (a wrong detail counts twice, as in Weiner et al.), against an inventory of every fact said that relates to the patient's conditions, symptoms or signs (Weiner et al. 2020). Total per run, across 12 cases. The flat count is in Hallucination flags. | 100.0 | 88.0–123.0 | 2 of 6 |
| Errors reaching the record, per 100 notes (estimate)Note errors (Weiner et al. standard) per 100 notes, times 70%: in Biro et al. (2025), GPs reviewing AI drafts left each planted error uncorrected 65–75% of the time. An estimate of what a typical review would let through, across 12 cases. | 583 | 513–718 | 2 of 6 |
| Fewer errors than doctor-written notesCompared with the 5.0 errors per note Weiner et al. (2020) found in physicians' notes checked against recordings of the visit. Our test counts more strictly than theirs, so this favours the doctors. 100% means no errors; below 0% means more errors than the doctors. Across 11 cases. | -67% | -105%–-47% | 2 of 6 |
| Hallucination flags per note (Weiner et al. standard)Every clinical element the note documents that the consultation does not support, including wrong values, sides, doses and timings: Weiner et al.’s (2020) errors of commission. Paraphrase and clinical terms for what was said do not count. Average per note, across 12 cases. | 2.47 | 1.75–4.17 | 4 of 6 |
| Edit actions per noteMissing items, partial items and hallucination flags a clinician would need to fix, per note. | 8.33 | 7.33–10.25 | 2 of 6 |
| Clinical coverageShare of each consultation's fact inventory captured by the note (partial counts as half). | 87% | 86%–88% | 2 of 6 |
| Significant transcript errors per runMis-heard words that change clinical meaning, rated blind, across 12 cases. | 22.8 | 19.0–25.0 | 4 of 6 |
| Word error ratePooled word error rate per run against the human reference. Every word counts equally, including fillers. | 23.1% | 21.9%–25.2% | 6 of 6 |
| Stop to prompted noteMedian time from pressing stop to the note written to our instruction, per run. | 49s | 45s–51s | 6 of 6 |
| Data per consultAverage network data per consultation, per run. | 17.1 MB | 16.7 MB–17.4 MB | 2 of 6 |
| Avg RAM (stress test)Physical memory footprint (macOS `footprint`, matching Activity Monitor's Memory column) sampled every minute during one continuous 50-minute stress-test consultation — separate from the 12-case suite above. Every scribe recorded this visit at the same time on one machine, so absolute values reflect shared contention between them, not isolated real-world usage. Compare scribes to each other here, not to a device's spec sheet. | 1300 MB | 1273 MB–1324 MB | 4 of 6 |
| Avg CPU (stress test)CPU used per minute during the same continuous 50-minute stress-test consultation, measured as cumulative CPU-seconds consumed divided by wall-clock time (can exceed 100% when a scribe uses more than one core). Every scribe ran concurrently on one machine. | 42.4% | 38.4%–45.3% | 3 of 6 |
Definitions and weights are in the methodology. Compare Freed head to head ↗
3 August 2026
After 6 to 12 months of use, this reviewer says Freed lets them focus on patients and finish work on time, and that it suits someone who isn't technical. They would prefer an automatic EHR link over copying notes or using the browser version.
Summarised from a review on Capterra. Not verified by ScribeStandard. Read the original
16 May 2026
With over a year of use, this doctor closes notes straight away and no longer charts after hours. They praise the price, visit summaries and customer service, but lost three visits when their computer was muted and Freed gave no obvious alert.
Summarised from a review on Capterra. Not verified by ScribeStandard. Read the original
8 December 2024
A primary care nurse practitioner reports saving 10 to 15 minutes per patient after about a year of use, and no longer charting at home. They found it easy to use and were impressed by how it filters out conversation and spells medical terms correctly.
Summarised from a review on Capterra. Not verified by ScribeStandard. Read the original