{"id":"b792d58a-6b4f-41c6-bc88-10293f14c8c1","arxiv_id":"2606.08442","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"Mixed-methods research finds that AI tools in medicine focus on single-encounter documentation while missing the temporal and interpretive structures central to physicians' longitudinal clinical reasoning.","lead":"This study uses interviews and surveys to map how physicians reason through clinical problems over time and how current AI tools align with that process. The results identify specific gaps in AI design that could guide development of more useful clinical support systems.","discovery_kind":"new_application","skeptic_critique":{"model":"grok-4.3","headline":"Mixed-methods design's ability to empirically ground temporal and multi-encounter reasoning structures remains the weakest link","rationale":"The reader's weakest assumption directly identifies the methodological gap that must hold for the strongest claim to be supported. Full text access does not remove the need to inspect the concrete data-collection and analysis steps; the concern is internal to the design rather than external consensus.","tokens_in":1770,"tokens_out":308,"duration_ms":12947,"concrete_test":"In the methods and results sections, locate the interview guide, survey items, and qualitative coding framework; verify whether any prompts or codes explicitly target reasoning across encounters (e.g., 'describe how you track changes over visits') and report inter-rater reliability or example excerpts. If no such items exist or if analysis remains at single-encounter granularity, re-analyze the raw response excerpts for temporal references and test whether the 'largely implicit and physician-driven' conclusion still holds.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim requires that interview and survey data can reliably surface and distinguish 'temporal or interpretive structures' spanning multiple encounters from encounter-level tasks. This hinges on the assumption that self-reported accounts from physicians will expose implicit, context-sensitive processes that AI omits. Without explicit protocols for eliciting multi-encounter cases, coding schemes that tag temporal elements, or validation against observed decisions, the distinction between 'often omit' and physician-driven aspects rests on untested interpretive steps rather than direct evidence.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper presents findings from a mixed-methods study combining qualitative interviews with structured survey data to characterize physicians' clinical reasoning as a temporally extended, context-sensitive process and to assess its alignment with current AI systems. The central claims are that AI tools are deployed mainly for encounter-level tasks such as documentation and summarization, that AI-generated outputs frequently omit temporal and interpretive structures essential to multi-encounter decision-making, and that core longitudinal reasoning remains implicit and physician-driven. The work offers a unified framework for these processes and identifies design directions for better human-AI alignment in clinical workflows.","tokens_in":1865,"tokens_out":473,"duration_ms":15965,"significance":"If the empirical distinctions between encounter-level and longitudinal reasoning hold under scrutiny, the results would usefully direct AI development away from isolated-task automation toward systems that better support multi-encounter cognition and uncertainty management. The topic addresses a recognized gap between benchmark-driven medical AI and real-world clinical practice; a well-supported account could inform both system design and policy on AI integration.","major_comments":[{"comment":"Methods section: the mixed-methods protocol is described at a high level but supplies no information on sample size, recruitment, exclusion criteria, interview guides for surfacing multi-encounter cases, coding schemes that tag temporal or interpretive elements, inter-rater reliability, or any triangulation against observed decisions or EHR data. These omissions are load-bearing for the claim that the data reliably distinguish AI-supported encounter tasks from physician-driven longitudinal reasoning.","section":"Methods"},{"comment":"Results/Findings: the assertions that AI representations 'often omit temporal or interpretive structures' and that 'core aspects of reasoning... remain largely implicit and physician-driven' are presented as direct outcomes of the interviews and surveys, yet no quantitative frequencies, example coded excerpts, or validation steps are referenced to ground the distinction between 'often' and 'largely.'","section":"Results"}],"minor_comments":[{"comment":"The abstract states headline findings without any methodological parameters (N, response rate, analysis approach), which weakens the reader's ability to assess the scope of the empirical claims even before reaching the full Methods section.","section":"Abstract"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We appreciate the referee's constructive feedback, which highlights important areas for improving methodological transparency and empirical grounding. We address each major comment below and will make substantial revisions to the manuscript.","responses":[{"response":"We agree that the current Methods section provides only a high-level description and lacks these critical details. In the revised manuscript, we will expand the section to report the sample sizes for interviews and surveys, recruitment strategies and exclusion criteria, the interview guide with specific prompts for multi-encounter cases, the coding scheme including tags for temporal and interpretive elements, inter-rater reliability metrics, and any triangulation with EHR data or observed decisions. These additions will directly address the load-bearing concerns for our claims.","revision_made":"yes","referee_comment":"[Methods] Methods section: the mixed-methods protocol is described at a high level but supplies no information on sample size, recruitment, exclusion criteria, interview guides for surfacing multi-encounter cases, coding schemes that tag temporal or interpretive elements, inter-rater reliability, or any triangulation against observed decisions or EHR data. These omissions are load-bearing for the claim that the data reliably distinguish AI-supported encounter tasks from physician-driven longitudinal reasoning."},{"response":"We acknowledge that the Results section would be strengthened by more explicit quantitative and qualitative grounding. We will revise to include survey-based frequencies (e.g., proportions indicating omission of temporal structures), representative coded interview excerpts illustrating the themes, and details on validation or triangulation steps. This will better substantiate the characterizations and the distinction between encounter-level and longitudinal reasoning.","revision_made":"yes","referee_comment":"[Results] Results/Findings: the assertions that AI representations 'often omit temporal or interpretive structures' and that 'core aspects of reasoning... remain largely implicit and physician-driven' are presented as direct outcomes of the interviews and surveys, yet no quantitative frequencies, example coded excerpts, or validation steps are referenced to ground the distinction between 'often' and 'largely.'"}],"tokens_in":1432,"tokens_out":429,"duration_ms":20356,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main takeaway is that current AI gets used mostly for single-encounter jobs like documentation, while doctors' reasoning often tracks patterns and interpretations over multiple visits in ways the systems do not yet capture. That longitudinal angle is the piece the authors treat as their empirical contribution.\n\nThe work does a straightforward job of laying out the contrast between encounter-level AI tasks and the more extended, context-sensitive parts of clinical cognition. Mixing interviews for depth with survey data for patterns is a common approach in this area, and it can surface practical mismatches if the coding and sampling are handled carefully.\n\nThe soft spot is the missing methodological detail. The abstract gives no sample size, no description of how multi-encounter cases were elicited, no coding scheme for temporal or interpretive elements, and no validation steps. Without those, the claims that AI representations \"often omit\" key structures or that core reasoning stays \"physician-driven\" rest on interpretive steps that are not shown. The stress-test note correctly identifies this as the load-bearing assumption.\n\nThis is aimed at researchers working on medical AI, clinical decision support, or human-AI collaboration in healthcare. A reader in that niche might pick up some concrete directions for workflow alignment, but only once the data collection and analysis procedures are visible and defensible.\n\nI would bring it to a reading group to talk through the longitudinal framing, though with the caveat that the evidence needs checking. I would not cite it yet. It is worth sending for peer review so referees can assess whether the mixed-methods design actually grounds the reported mismatches.","headline":"The paper flags temporal mismatches between AI tools and how physicians reason across visits, but the abstract leaves the mixed-methods evidence too thin to judge the strength of those claims.","tokens_in":2370,"tokens_out":393,"would_cite":false,"duration_ms":18102,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Physicians reason across multiple encounters using temporal structures that current AI largely omits.","keywords":["clinical reasoning","human-AI collaboration","longitudinal cognition","AI in medicine","mixed-methods study","clinical decision-making","electronic health records"],"falsifier":"A direct comparison showing that AI summaries already preserve the same temporal and interpretive structures physicians use across encounters would falsify the claim of partial alignment.","tokens_in":2684,"feed_emoji":"🩺","tokens_out":647,"duration_ms":22076,"temperature":0.7,"pith_summary":"The paper establishes that clinical reasoning is a context-sensitive process that unfolds over time and across patient encounters, relying on implicit temporal and interpretive links. Current AI tools, by contrast, are used mainly for single-encounter tasks such as documentation and summarization and therefore capture only part of how physicians actually decide. A sympathetic reader would care because this mismatch explains persistent problems like hallucinations and shows why AI has not yet become a reliable partner in complex care. The mixed-methods study of interviews and surveys supplies the evidence for both the structure of reasoning and the specific gaps in existing systems.","feed_headline":"AI stays at single visits while doctors track patients across time","feed_subtitle":"Mixed-methods study finds current tools omit the temporal structures central to real clinical reasoning.","key_machinery":"The mixed-methods account of clinical reasoning as a context-sensitive, temporally extended process that reveals mismatches with encounter-level AI applications.","core_discovery":"Findings indicate that current AI systems are primarily deployed for encounter-level tasks such as documentation and summarization, and only partially align with physicians' underlying reasoning processes. In particular, AI-generated representations often omit temporal or interpretive structures central to clinical decision-making, while core aspects of reasoning, especially those spanning multiple encounters, remain largely implicit and physician-driven. By integrating fine-grained qualitative insights with broader quantitative patterns, this study offers a unified framework for understanding clinical reasoning as a context-sensitive, temporally extended process and identifies key mismatche","pith_inferences":["Training datasets for medical AI would need to shift from isolated encounters to full patient timelines to close the observed gap.","EHR interfaces could be restructured to surface the longitudinal links that physicians currently maintain mentally.","Regulatory or design standards for clinical AI might eventually require evidence of support for multi-encounter reasoning."],"forward_implications":["AI systems should be redesigned to incorporate temporal structures that span multiple encounters rather than remaining limited to single-visit documentation.","Development efforts must address the implicit, physician-driven aspects of reasoning that occur under conditions of uncertainty and constraint.","A unified framework for context-sensitive reasoning can supply concrete directions for building AI that augments rather than replaces clinical workflows.","Better alignment would help meet the dual demands of speed and care quality while reducing hallucinations and sycophancy."],"fun_headline_variants":["AI tools stick to single visits, ignoring doctors' longitudinal reasoning","Doctors track patient histories across time, AI limited to encounter tasks","AI omits temporal structures key to real clinical reasoning","Current AI design misses multi-encounter cognition in medicine","Physicians drive longitudinal reasoning that AI workflows overlook"],"cache_read_input_tokens":64,"weakest_assumption_plain":"That interviews combined with structured survey data can produce a comprehensive picture of how clinical reasoning unfolds over multiple encounters.","fun_headline_variants_meta":{"raw":{"variants":["AI tools stick to single visits, ignoring doctors' longitudinal reasoning","Doctors track patient histories across time, AI limited to encounter tasks","AI omits temporal structures key to real clinical reasoning","Current AI design misses multi-encounter cognition in medicine","Physicians drive longitudinal reasoning that AI workflows overlook"]},"model":"grok-4.3","cost_usd":0.002505,"raw_usage":{"total_tokens":1469,"prompt_tokens":719,"num_sources_used":0,"completion_tokens":77,"cost_in_usd_ticks":25049500,"prompt_tokens_details":{"text_tokens":719,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":673,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":719,"tokens_out":77,"duration_ms":4881,"temperature":1.0,"reasoning_tokens":673,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-27T18:06:53.088495+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A direct comparison showing that AI summaries already preserve the same temporal and interpretive structures physicians use across encounters would falsify the claim of partial alignment.","supporting_citations":[],"review_version":1}