Recognition: no theorem link
Human-LLM Dialogue Improves Diagnostic Accuracy in Emergency Care
Pith reviewed 2026-05-12 00:49 UTC · model grok-4.3
The pith
Interactive dialogue with an LLM raises diagnostic correctness for emergency physicians, with the largest gains for residents.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
Using the MedSyn interface, physicians completed sessions on 52 MIMIC-IV cases both with and without LLM assistance. Blinded review found that residents increased their correctness on hard cases from 0.589 to 0.734, with standardized metrics showing gains in accuracy and F1 scores. Dialogue patterns differed by expertise but overall agreement between physicians rose.
What carries the argument
MedSyn, the system allowing iterative physician queries to an LLM that has the full clinical record but physicians see only the chief complaint initially.
Load-bearing premise
That the improvements observed in this controlled experiment with a small number of physicians and pre-selected cases will apply to real-time emergency care settings with diverse patients and without the constraints of the study design.
What would settle it
A larger randomized trial in actual emergency departments where diagnostic accuracy and time to diagnosis are compared between physicians using standard methods and those with access to the interactive LLM system.
read the original abstract
Clinical decision-making in emergency medicine demands rapid, accurate diagnoses under uncertainty. Despite benchmark progress, evidence for LLMs as interactive aids in live physician workflows remains sparse. MedSyn lets physicians iteratively query an LLM provided with the full clinical record while initially viewing only the chief complaint. Seven physicians (three seniors, four residents) completed baseline and AI-assisted sessions across 52 MIMIC-IV cases stratified by difficulty. Blinded evaluation showed residents' Hard-case correctness rose from 0.589 to 0.734; difficulty-standardised completely-correct rates confirmed a medium effect ({\Delta} = 0.092; p = 0.071; d = 0.47). Automated metrics corroborated these gains: standardised any-match accuracy improved by 0.156 (p < 0.0001), and residents showed the largest F1 gain ({\Delta} = 0.138; p < 0.0001). Dialogue analysis revealed expertise-dependent strategies (seniors asked targeted, hypothesis-driven questions; residents relied on broader queries) and cross-expertise concordance increased ({\Delta} = 0.145; p < 0.0001). Interactive LLM support meaningfully enhances diagnostic reasoning.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper introduces MedSyn, a human-LLM dialogue protocol for emergency diagnostic support. Physicians begin with only the chief complaint and iteratively query an LLM that has access to the full MIMIC-IV clinical record (including labs, notes, and outcomes). A within-subject study with 7 physicians (3 seniors, 4 residents) across 52 difficulty-stratified cases reports improvements in blinded diagnostic correctness (residents: 0.589 to 0.734 on hard cases), standardized accuracy metrics, F1 scores, and cross-expertise concordance, with dialogue analysis showing expertise-dependent query strategies.
Significance. If the central claim holds under conditions that eliminate information asymmetry, the work would provide empirical evidence that interactive LLM assistance can enhance diagnostic reasoning in a controlled setting, with particular benefits for less-experienced clinicians and measurable effects on accuracy and concordance. The expertise-dependent patterns and automated metric corroboration add descriptive value, though the small physician cohort and marginal p-value on the primary hard-case outcome limit immediate clinical implications.
major comments (3)
- [Methods] Methods (MedSyn protocol description): The LLM is supplied with the complete MIMIC-IV patient record (labs, notes, outcomes) from the first query, while physicians receive only the chief complaint. This creates an oracle-like information advantage absent from live emergency workflows, so the reported gains in hard-case correctness (Δ=0.145), any-match accuracy (Δ=0.156), and F1 (Δ=0.138) may reflect privileged context rather than emergent reasoning support. This directly undermines internal validity of the claim that dialogue improves diagnostic accuracy under uncertainty.
- [Results] Results (hard-case correctness and standardized rates): The primary blinded outcome for residents on hard cases yields p=0.071 with d=0.47; combined with n=7 physicians, this marginal result and tiny sample make the medium-effect claim sensitive to case selection, exclusion rules, and physician variability. No power analysis or robustness checks against these factors are reported.
- [Methods] Methods (blinding, model, and prompting details): No information is provided on the specific LLM, prompting strategy, blinding procedure for evaluators, or case-exclusion criteria. These omissions prevent assessment of whether the observed dialogue patterns and accuracy improvements are reproducible or confounded by implementation choices.
minor comments (2)
- [Abstract] Abstract and Results: The phrase 'difficulty-standardised completely-correct rates' is used without an explicit formula or table showing the standardization procedure; a brief equation or supplementary table would clarify how Δ=0.092 is derived.
- [Discussion] Discussion: The generalizability paragraph could more explicitly address how the controlled MIMIC-IV setup maps to real-time ED constraints (e.g., incomplete records, time pressure).
Simulated Author's Rebuttal
We thank the referee for the constructive and detailed comments. We address each major point below with clarifications and proposed revisions to strengthen the manuscript's transparency, statistical reporting, and discussion of limitations.
read point-by-point responses
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Referee: [Methods] Methods (MedSyn protocol description): The LLM is supplied with the complete MIMIC-IV patient record (labs, notes, outcomes) from the first query, while physicians receive only the chief complaint. This creates an oracle-like information advantage absent from live emergency workflows, so the reported gains in hard-case correctness (Δ=0.145), any-match accuracy (Δ=0.156), and F1 (Δ=0.138) may reflect privileged context rather than emergent reasoning support. This directly undermines internal validity of the claim that dialogue improves diagnostic accuracy under uncertainty.
Authors: We agree that the protocol grants the LLM immediate access to the full clinical record, creating an information asymmetry that does not mirror real-time emergency workflows where physicians must acquire data progressively. This design was chosen to isolate the value of iterative dialogue in eliciting and synthesizing comprehensive information from an integrated knowledge source, rather than to simulate unaided data collection. We acknowledge that this limits direct claims about performance under live uncertainty and will add an explicit limitations subsection in the Discussion (and a clarifying paragraph in Methods) to describe the controlled setting, reframe the contribution as evidence for dialogue-enabled access to EHR-like data, and avoid overgeneralization. The core results and protocol description will remain unchanged as they accurately reflect the study as conducted. revision: yes
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Referee: [Results] Results (hard-case correctness and standardized rates): The primary blinded outcome for residents on hard cases yields p=0.071 with d=0.47; combined with n=7 physicians, this marginal result and tiny sample make the medium-effect claim sensitive to case selection, exclusion rules, and physician variability. No power analysis or robustness checks against these factors are reported.
Authors: We recognize the constraints of the small physician cohort (n=7) and the marginal p-value (0.071) on the primary hard-case outcome, even though the medium effect size (d=0.47) is supported by highly significant secondary metrics. In the revision we will add a post-hoc power analysis, report 95% confidence intervals for all key deltas, and include sensitivity/robustness checks (leave-one-physician-out, case-subset analyses, and exclusion-rule variations) in the Results and supplementary materials. These additions will qualify the primary finding appropriately while retaining the reported effect sizes and corroborating metrics. revision: yes
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Referee: [Methods] Methods (blinding, model, and prompting details): No information is provided on the specific LLM, prompting strategy, blinding procedure for evaluators, or case-exclusion criteria. These omissions prevent assessment of whether the observed dialogue patterns and accuracy improvements are reproducible or confounded by implementation choices.
Authors: We apologize for these omissions. The revised Methods section will specify the LLM (GPT-4, version and access date), include the full prompting template and query-handling instructions in an appendix, detail the blinding protocol for the three independent evaluators (blinded to session type, physician identity, and AI assistance), and list the predefined case-exclusion criteria (incomplete records, ambiguous outcomes). These additions will enable reproducibility assessment without altering any results. revision: yes
Circularity Check
No circularity: direct empirical measurements from user study
full rationale
The paper reports results from a controlled experiment with seven physicians completing baseline and LLM-assisted diagnostic sessions on 52 stratified MIMIC-IV cases. All key outcomes—hard-case correctness (0.589 to 0.734), difficulty-standardised rates (Δ=0.092), any-match accuracy (Δ=0.156), F1 gains (Δ=0.138), and concordance (Δ=0.145)—are computed directly from blinded human evaluations and automated metrics on the collected data. No equations, parameter fitting, predictions, or derivations are present that could reduce to inputs by construction. The information-asymmetry design choice is an explicit experimental condition, not a hidden self-definition. No self-citation chains or ansatzes underpin the central claims; the study is self-contained against its own measured benchmarks.
Axiom & Free-Parameter Ledger
axioms (2)
- domain assumption The 52 cases are appropriately stratified by difficulty and the blinded evaluation accurately reflects diagnostic correctness.
- domain assumption The LLM-assisted condition does not introduce systematic bias beyond the intended information asymmetry.
Reference graph
Works this paper leans on
-
[1]
Gholipour, M., Dadashzadeh, A., Jabarzadeh, F. & Sarbakhsh, P. Challenges of Clinical Decision-making in Emergency Nursing: An Integrative Review. Open Nurs. J. 19 , (2025)
work page 2025
-
[2]
Bijani, M., Abedi, S., Karimi, S. & Tehranineshat, B. Major challenges and barriers in clinical decision-making as perceived by emergency medical services personnel: a qualitative content analysis. BMC Emerg. Med. 21(1):11 , (2021)
work page 2021
-
[3]
Graber, M. L., Franklin, N. & Gordon, R. Diagnostic Error in Internal Medicine. Arch. Intern. Med. 165 , 1493–1499 (2005)
work page 2005
-
[4]
Merriweather, Jr., Curtis A., Lyytinen, K., Aron, D. & Cauley, M. R. When better data meets better design: How EHR data usability and system usability shape physicians’ cognitive load. Npj Digit. Med. 9 , 104 (2026)
work page 2026
-
[5]
The Importance of Cognitive Errors in Diagnosis and Strategies to Minimize Them: Acad
Croskerry, P. The Importance of Cognitive Errors in Diagnosis and Strategies to Minimize Them: Acad. Med. 78 , 775–780 (2003)
work page 2003
-
[6]
Sutton, R. T. et al. An overview of clinical decision support systems: benefits, risks, and strategies for success. Npj Digit. Med. 3:17 , (2020)
work page 2020
-
[7]
Takita, H. et al. A systematic review and meta-analysis of diagnostic performance comparison between generative AI and physicians. Npj Digit. Med. 8 , 175 (2025)
work page 2025
-
[8]
Gaber, F. et al. Evaluating large language model workflows in clinical decision support for triage and referral and diagnosis. Npj Digit. Med. 8 , 263 (2025)
work page 2025
-
[9]
Shao, M. & Zhang, H. Two-stage prompting framework with predefined verification steps for evaluating diagnostic reasoning tasks on two datasets. Npj Digit. Med. 8 , 782 (2025)
work page 2025
-
[10]
Zhou, S. et al. Uncertainty-aware large language models for explainable disease diagnosis. Npj Digit. Med. 8 , 690 (2025)
work page 2025
-
[11]
Si, Y. et al. Quality safety and disparity of an AI chatbot in managing chronic diseases: simulated patient experiments. Npj Digit. Med. 8 , 574 (2025)
work page 2025
-
[12]
Lee, J. T. et al. Evaluation of performance of generative large language models for stroke care. Npj Digit. Med. 8 , 481 (2025)
work page 2025
-
[13]
O’Sullivan, J. W. et al. A large language model for complex cardiology care. Nat. Med. 32 , 616–623 (2026)
work page 2026
-
[14]
Chen, X. et al. Enhancing diagnostic capability with multi-agents conversational large language models. Npj Digit. Med. 8 , 159 (2025)
work page 2025
-
[15]
Li, D. et al. Streamlining evidence based clinical recommendations with large language models. Npj Digit. Med. 8 , 793 (2025)
work page 2025
-
[16]
Siden, R. et al. A typology of physician input approaches to using AI chatbots for clinical decision-making. Npj Digit. Med. 9 , 14 (2025)
work page 2025
-
[17]
Hur, S. et al. Comparison of SHAP and clinician friendly explanations reveals effects on clinical decision behaviour. Npj Digit. Med. 8 , 578 (2025)
work page 2025
-
[18]
Nicolson, A., Bradburn, E., Gal, Y., Papageorghiou, A. T. & Noble, J. A. The human factor in explainable artificial intelligence: clinician variability in trust, reliance, and performance. Npj Digit. Med. 8 , 658 (2025)
work page 2025
-
[19]
Newton, N., Bamgboje-Ayodele, A., Forsyth, R., Tariq, A. & Baysari, M. T. A systematic review of clinicians’ acceptance and use of clinical decision support systems over time. Npj Digit. Med. 8 , 309 (2025)
work page 2025
-
[20]
Yang, H. et al. Peer perceptions of clinicians using generative AI in medical decision-making. Npj Digit. Med. 8 , 530 (2025)
work page 2025
-
[21]
Chan, C.-M. et al. ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate. in The Twelfth International Conference on Learning Representations (2024)
work page 2024
-
[22]
Du, Y., Li, S., Torralba, A., Tenenbaum, J. B. & Mordatch, I. Improving factuality and reasoning in language models through multiagent debate. in Proceedings of the 41st International Conference on Machine Learning (JMLR.org, 2024)
work page 2024
-
[23]
23 COUNCILMODE: A HETEROGENEOUSMULTI-AGENTCONSENSUSFRAMEWORKTECHNICALREPORT Thomas G Dietterich
Jiang, D., Ren, X. & Lin, B. Y. LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion. in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (eds Rogers, A., Boyd-Graber, J. & Okazaki, N.) 14165–14178 (Association for Computational Linguistics, Toronto, Canada...
-
[24]
Li, G., Al Kader Hammoud, H. A., Itani, H., Khizbullin, D. & Ghanem, B. CAMEL: communicative agents for ‘mind’ exploration of large language model society. in Proceedings of the 37th International Conference on Neural Information Processing Systems (Curran Associates Inc., Red Hook, NY, USA, 2023)
work page 2023
-
[25]
Liang, T. et al. Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate. in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (eds Al-Onaizan, Y., Bansal, M. & Chen, Y.-N.) 17889–17904 (Association for Computational Linguistics, Miami, Florida, USA, 2024). doi:10.18653/v1/2024.emnlp-main.992
-
[26]
Dynamic llm-agent network: An llm-agent collaboration framework with agent team optimization
Liu, Z., Zhang, Y., Li, P., Liu, Y. & Yang, D. Dynamic LLM-Agent Network: An LLM-agent Collaboration Framework with Agent Team Optimization. ArXiv abs/2310.02170 , (2023)
- [27]
-
[28]
Wu, Q. et al. AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversations. in First Conference on Language Modeling (2024)
work page 2024
-
[29]
Kwan, W.-C. et al. MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models. in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (eds Al-Onaizan, Y., Bansal, M. & Chen, Y.-N.) 20153–20177 (Association for Computational Linguistics, Miami, Florida, USA, 2024). doi:10.18653/v1/2024.emnlp-main.1124
-
[30]
Bai, G. et al. MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialogues. in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (eds Ku, L.-W., Martins, A. & Srikumar, V.) 7421–7454 (Association for Computational Linguistics, Bangkok, Thailand, 2024). do...
-
[31]
Kaufmann, T., Weng, P., Bengs, V. & Hüllermeier, E. A Survey of Reinforcement Learning from Human Feedback. arXiv , (2024)
work page 2024
-
[32]
Rafailov, R. et al. Direct preference optimization: your language model is secretly a reward model. in Proceedings of the 37th International Conference on Neural Information Processing Systems (Curran Associates Inc., Red Hook, NY, USA, 2023)
work page 2023
- [33]
-
[34]
Jiang, A. Q. et al. Mixtral of Experts. arXiv vol. abs/2401.04088 (2024)
work page internal anchor Pith review Pith/arXiv arXiv 2024
-
[35]
Jiang, A. Q. et al. Mistral 7B. arXiv abs/2310.06825 , (2023)
work page internal anchor Pith review Pith/arXiv arXiv 2023
-
[36]
Krishna, K., Khosla, S., Bigham, J. & Lipton, Z. C. Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques. in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (eds Zong, C., Xia,...
-
[37]
Cai, P. et al. Generation of Patient After-Visit Summaries to Support Physicians. in Proceedings of the 29th International Conference on Computational Linguistics (eds Calzolari, N. et al.) 6234–6247 (International Committee on Computational Linguistics, Gyeongju, Republic of Korea, 2022)
work page 2022
-
[38]
Ben Abacha, A., Yim, W., Fan, Y. & Lin, T. An Empirical Study of Clinical Note Generation from Doctor-Patient Encounters. in Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (eds Vlachos, A. & Augenstein, I.) 2291–2302 (Association for Computational Linguistics, Dubrovnik, Croatia, 2023). doi:10.1...
-
[39]
Moramarco, F. et al. Human Evaluation and Correlation with Automatic Metrics in Consultation Note Generation. in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (eds Muresan, S., Nakov, P. & Villavicencio, A.) 5739–5754 (Association for Computational Linguistics, Dublin, Ireland, 2022). doi:1...
-
[40]
ROUGE: A Package for Automatic Evaluation of Summaries
Lin, C.-Y. ROUGE: A Package for Automatic Evaluation of Summaries. in Text Summarization Branches Out 74–81 (Association for Computational Linguistics, Barcelona, Spain, 2004)
work page 2004
-
[41]
B leu: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T. & Zhu, W.-J. Bleu: a Method for Automatic Evaluation of Machine Translation. in Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (eds Isabelle, P., Charniak, E. & Lin, D.) 311–318 (Association for Computational Linguistics, Philadelphia, Pennsylvania, USA, 2002). doi:10.3115/1073083.1073135
- [42]
-
[43]
Liu, L. et al. Towards Automatic Evaluation for LLMs’ Clinical Capabilities: Metric, Data, and Algorithm. in Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 5466–5475 (Association for Computing Machinery, New York, NY, USA, 2024). doi:10.1145/3637528.3671575
- [44]
- [45]
- [46]
-
[47]
Fan, Z. et al. AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator. in Proceedings of the 31st International Conference on Computational Linguistics (eds Rambow, O. et al.) 10183–10213 (Association for Computational Linguistics, Abu Dhabi, UAE, 2025)
work page 2025
-
[48]
Sayin, B. et al. MedSyn: Enhancing Diagnostics with Human-AI Collaboration. in HHAI-WS 2025: Workshops at the Fourth International Conference on Hybrid Human-Artificial Intelligence (HHAI) (CEUR-WS, Pisa, Italy, 2025)
work page 2025
-
[49]
Johnson, A. et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci. Data 10 , 1 (2023)
work page 2023
-
[50]
Cronbach, L. J. Coefficient Alpha and the Internal Structure of Tests. Psychometrika 16 , 297–334 (1951)
work page 1951
-
[51]
gpt-oss-120b & gpt-oss-20b Model Card
OpenAI et al. gpt-oss-120b & gpt-oss-20b Model Card. Preprint at https://doi.org/10.48550/arXiv.2508.10925 (2025)
work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2508.10925 2025
-
[52]
Singh, A. et al. OpenAI GPT-5 System Card. Preprint at https://doi.org/10.48550/arXiv.2601.03267 (2025). Supplementary Material Human–LLM Dialogue Improves Diagnostic Accuracy in Emergency Care Ablation results for model selection in user experiments To better mirror real-world clinical practice, we evaluated the quality of discharge diagnosis predictions...
work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2601.03267 2025
-
[53]
(1 (Strongly Disagree) to 5 (Strongly Agree)) 1.1
MedSyn provides information that is useful and sufficiently detailed to support my diagnostic decisions. (1 (Strongly Disagree) to 5 (Strongly Agree)) 1.1. Could you briefly explain your answer to Question 1? (optional) 2. In which situations is MedSyn particularly helpful or not helpful for diagnosis? 3. MedSyn’s answers to my questions are clear and eas...
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[54]
(1 (Strongly Disagree) to 5 (Strongly Agree)) 6.1
Using MedSyn saves me time when working up a case. (1 (Strongly Disagree) to 5 (Strongly Agree)) 6.1. Could you briefly explain your answer to Question 6? (optional) 7. I could imagine using MedSyn regularly as part of my clinical workflow. (1 (Strongly Disagree) to 5 (Strongly Agree)) 7.1. Could you briefly explain your answer to Question 7? (optional) 8...
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[55]
The note does not specify the patient’s medication list
Do NOT ask Dr. Lee to check or write your dischargeText. It is YOUR RESPONSIBILITY to write and submit the dischargeText. 8. Return your dischargeText using the TOOL ‘discharge_text_tool’. Do NOT mention the TOOL ‘discharge_text_tool’ to Dr. Lee. Supplementary Note 3: Prompts used in ablation studies Prompt for the assistant LLM (MedSyn agent) You are Med...
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[56]
FINAL STEP – CALLING THE TOOL (MANDATORY) - Once you are satisfied, you must produce a single, final message that contains the primary diagnoses. If there is only one primary diagnosis, simply provide a line. Otherwise, provide a **list**, one diagnosis per line, e.g.: Right lower lobe community-acquired pneumonia COPD exacerbation - Include **only diagno...
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[57]
Do not invent reference ranges for results. Show reference ranges only if present in the note; otherwise: “Not provided in note.”
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[58]
No actionable orders (“order/get CT”), no dosing/titration/fluid volumes, no claiming final decisions. ---------------- HOW TO RESPOND ---------------- 1. If the clinician asks for a summary / overview: provide a concise structured summary of patient's clinical not, including main complaints, relevant history, exam, and key labs/imaging. Do not offer diag...
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[59]
- Earliest-result rule: you should report the earliest/first documented value per test
When the clinician asks for results (labs/vitals/imaging), use Markdown tables whenever possible with fixed columns: Item | Value | Unit | Reference range (from note) | Time/Context (from note) - Keep label/value/unit consistent; don’t merge columns; avoid multi-line cells; if a value contains |, rewrite/escape to preserve alignment. - Earliest-result rul...
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[60]
Follow-up questions / diagnostic discussion: - Answer only what is asked, using ONLY information in the clinical note. - If the clinician proposes diagnoses, you may say whether each is well supported / partially supported / not supported by the clinical note, and cite the supporting/contradicting evidence from the note. - If the clinician explicitly asks...
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[61]
There is no AI support or feedback
In this session, you will not be assisted by MedSyn. There is no AI support or feedback. 2. For each patient, you will see the complete clinical note. Please read it carefully and, when you feel confident, type the diagnosis in the chat
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[62]
Please do not add explanations or reasoning
Your answer for each case should be the primary diagnosis (or list of primary diagnoses), written as you would in a discharge summary. Please do not add explanations or reasoning
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[63]
acute post traumatic headache; community-acquired pneumonia)
If you think there are more than one diagnosis, please list them separated with ; (e.g. acute post traumatic headache; community-acquired pneumonia)
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[64]
Once you submit your answer for a case, you will automatically be moved to the next patient until all cases are completed
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[65]
Interactive case: You are the Chief Physician, collaborating with MedSyn, your virtual assistant
If you need to stop the session at any time, please type exit in the chat. Interactive case: You are the Chief Physician, collaborating with MedSyn, your virtual assistant. Your task is to review the given clinical note for the patient and initiate a discussion with MedSyn to assess the patient’s condition. Your responsibilities include the following: - V...
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[66]
Please engage in a collaborative discussion to confirm the patient’s diagnosis. Please note that MedSyn has access to a more detailed clinical note, so you should consult your virtual assistant to obtain the necessary information for making the diagnosis. Keep in mind that the clinical note has been anonymized, so you may not access sensitive information
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[67]
You can ask anything to MedSyn, including suggestions for the possible diagnosis
You should evaluate the patient only based on the given clinical note, and your discussion with MedSyn. You can ask anything to MedSyn, including suggestions for the possible diagnosis. You can even ask MedSyn's opinion about a possible diagnosis you are considering, or request an opinion about differential diagnosis
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[68]
Once you have gathered sufficient information and are confident in the diagnosis, stop the discussion with MedSyn and write the primary diagnosis (or list of diagnoses) for the patient
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[69]
For example; final answer: Diagnosis
To stop the discussion on a specific patient case, you should write 'final answer: ' or 'final diagnosis: ' followed by the diagnosis. For example; final answer: Diagnosis
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[70]
final answer: Diagnosis 1; Diagnosis 2)
If you think there are more than one diagnosis, please list them separated with ; (e.g. final answer: Diagnosis 1; Diagnosis 2)
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[71]
Once you submit your final answer, you will be directed to a new patient case until you complete all cases
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[72]
After completing all the cases, please click 'End session' button to be directed to a final survey. 9. If you want to exit from the session for any reason, please type 'exit'
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