# PriMock57: A Dataset Of Primary Care Mock Consultations

arXiv: https://arxiv.org/abs/2204.00333 · HTML: https://mlsanity.com/paper/2204.00333/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 47 references that are in the map.
- First posted on arXiv 1 Apr 2022.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports](https://mlsanity.com/paper/1911.02541/) (2019)
- [QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions](https://mlsanity.com/paper/1910.10261/) (2019)
- [Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization](https://mlsanity.com/paper/2110.07356/) (2021)
- [Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques](https://mlsanity.com/paper/2005.01795/) (2020)
- [Leveraging BERT for Extractive Text Summarization on Lectures](https://mlsanity.com/paper/1906.04165/) (2019)
- [Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures](https://mlsanity.com/paper/2009.08666/) (2020)
- [Leveraging Pretrained Models for Automatic Summarization of Doctor-Patient Conversations](https://mlsanity.com/paper/2109.12174/) (2021)
- [Topic-aware Pointer-Generator Networks for Summarizing Spoken Conversations](https://mlsanity.com/paper/1910.01335/) (2019)
- [Ontology-Aware Clinical Abstractive Summarization](https://mlsanity.com/paper/1905.05818/) (2019)
- [Speech recognition for medical conversations](https://mlsanity.com/paper/1711.07274/) (2017)
- [Understanding Medical Conversations: Rich Transcription, Confidence Scores & Information Extraction](https://mlsanity.com/paper/2104.02219/) (2021)
- [A preliminary study on evaluating Consultation Notes with Post-Editing](https://mlsanity.com/paper/2104.04402/) (2021)
- [Medication Regimen Extraction From Medical Conversations](https://mlsanity.com/paper/1912.04961/) (2019)

## Built on by (most cited first, 30 of 31)

- [ACI-BENCH: a Novel Ambient Clinical Intelligence Dataset for Benchmarking Automatic Visit Note Generation](https://mlsanity.com/paper/2306.02022/) (2023)
- [The Sound of Healthcare: Improving Medical Transcription ASR Accuracy with Large Language Models](https://mlsanity.com/paper/2402.07658/) (2024)
- [A Meta-Evaluation of Faithfulness Metrics for Long-Form Hospital-Course Summarization](https://mlsanity.com/paper/2303.03948/) (2023)
- [UMASS_BioNLP at MEDIQA-Chat 2023: Can LLMs generate high-quality synthetic note-oriented doctor-patient conversations?](https://mlsanity.com/paper/2306.16931/) (2023)
- [User-Driven Research of Medical Note Generation Software](https://mlsanity.com/paper/2205.02549/) (2022)
- [We Should Evaluate Real-World Impact](https://mlsanity.com/paper/2507.05973/) (2025)
- [The timing bottleneck: Why timing and overlap are mission-critical for conversational user interfaces, speech recognition and dialogue systems](https://mlsanity.com/paper/2307.15493/) (2023)
- [Consultation Checklists: Standardising the Human Evaluation of Medical Note Generation](https://mlsanity.com/paper/2211.09455/) (2022)
- [LLM-based speaker diarization correction: A generalizable approach](https://mlsanity.com/paper/2406.04927/) (2024)
- [WangLab at MEDIQA-Chat 2023: Clinical Note Generation from Doctor-Patient Conversations using Large Language Models](https://mlsanity.com/paper/2305.02220/) (2023)
- [Improving Clinical Note Generation from Complex Doctor-Patient Conversation](https://mlsanity.com/paper/2408.14568/) (2024)
- [Generating Synthetic Doctor-Patient Conversations for Long-form Audio Summarization](https://mlsanity.com/paper/2604.06138/) (2026)
- [WER is Unaware: Assessing How ASR Errors Distort Clinical Understanding in Patient Facing Dialogue](https://mlsanity.com/paper/2511.16544/) (2025)
- [GDPR Compliant Collection of Therapist-Patient-Dialogues](https://mlsanity.com/paper/2211.12360/) (2022)
- [FactsR: A Safer Method for Producing High Quality Healthcare Documentation](https://mlsanity.com/paper/2505.10360/) (2025)
- [MedSynth: Realistic, Synthetic Medical Dialogue-Note Pairs](https://mlsanity.com/paper/2508.01401/) (2025)
- [TriageSim: A Conversational Emergency Triage Simulation Framework from Structured Electronic Health Records](https://mlsanity.com/paper/2603.10035/) (2026)
- [MedMosaic: A Challenging Large Scale Benchmark of Diverse Medical Audio](https://mlsanity.com/paper/2605.00969/) (2026)
- [A Typology of Synthetic Datasets for Dialogue Processing in Clinical Contexts](https://mlsanity.com/paper/2505.03025/) (2025)
- [A Text-To-Text Alignment Algorithm for Better Evaluation of Modern Speech Recognition Systems](https://mlsanity.com/paper/2509.24478/) (2025)
- [Agentic-DuplexGen: Decoupling Content, Timing, and Acoustics for Synthetic Dialogue Speech](https://mlsanity.com/paper/2608.16053/) (2026)
- [One note in three: a verified census of three deployed AI scribes, and the instrument that counted it](https://mlsanity.com/paper/2608.31017/) (2026)
- [Understanding Zero-shot Rare Word Recognition Improvements Through LLM Integration](https://mlsanity.com/paper/2502.16142/) (2025)
- ["Where does it hurt?" -- Dataset and Study on Physician Intent Trajectories in Doctor Patient Dialogues](https://mlsanity.com/paper/2508.19077/) (2025)
- [Multimodal Hidden Markov Models for Persistent Emotional State Tracking](https://mlsanity.com/paper/2605.12838/) (2026)
- [A Dataset of Robot-Patient and Doctor-Patient Medical Dialogues for Spoken Language Processing Tasks](https://mlsanity.com/paper/2605.26747/) (2026)
- [When Multiple Scripts Matter: Evaluating ASR in Clinical Settings](https://mlsanity.com/paper/2606.17826/) (2026)
- [Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies](https://mlsanity.com/paper/2608.05993/) (2026)
- [Speech-to-SOAP: End-to-End Summarization of Medical Dialogues: KIT@BeTraC 2026](https://mlsanity.com/paper/2608.24327/) (2026)
- [MedWER: A Reproducible, Model-Free Evaluation Protocol for Medical Speech Recognition](https://mlsanity.com/paper/2609.05728/) (2026)

## Abstract (arXiv)

Recent advances in Automatic Speech Recognition (ASR) have made it possible to reliably produce automatic transcripts of clinician-patient conversations. However, access to clinical datasets is heavily restricted due to patient privacy, thus slowing down normal research practices. We detail the development of a public access, high quality dataset comprising of57 mocked primary care consultations, including audio recordings, their manual utterance-level transcriptions, and the associated consultation notes. Our work illustrates how the dataset can be used as a benchmark for conversational medical ASR as well as consultation note generation from transcripts.