[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-458187-105":59,"doc-detail-458187-en":135},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":128,"head_meta":130,"extra_data":132,"updated_unix":134},105,"en","a-typology-of-physician-input-approaches-to-using-ai-chatbots-for-clinical-decision-making","A typology of physician input approaches to using AI chatbots for clinical decision-making","","Recent work suggests that when physicians have access to a large language model (LLM) chatbot during clinical reasoning tests, performance may not improve relative to using the same chatbot alone. This study examines how physicians use LLM chatbots in reasoning tasks and whether the amount of clinical case content entered in the prompt changes outcomes. Semi-structured interviews produced an input typology, which was validated using coded chat logs from two randomized controlled trials and compared with linear mixed-effects models. Four input types emerged: copy-paster, selective copy-paster, summarizer, and searcher. No single type yielded higher case scores.",{"@graph":69,"@context":127},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/a-typology-of-physician-input-approaches-to-using-ai-chatbots-for-clinical-decision-making/458187/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/a-typology-of-physician-input-approaches-to-using-ai-chatbots-for-clinical-decision-making/458187.png","ImageObject",300,407,{"name":92,"@type":93},"Aurelia","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119,123],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the study address about AI chatbots in clinical reasoning?","Question",{"text":113,"@type":114},"The study investigates how physicians approach using LLM chatbots during clinical reasoning tasks and whether entering more or less case content affects performance.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How was the typology of physician input approaches developed?",{"text":118,"@type":114},"It was built from semi-structured interviews with U.S. physicians using an LLM chatbot, then refined by coding physician chat logs from two randomized controlled trials.",{"name":120,"@type":111,"acceptedAnswer":121},"What input approach types were identified in the study?",{"text":122,"@type":114},"Four types were found: copy-paster (entire case), selective copy-paster (pieces of a case), summarizer (user-generated case summary), and searcher (short queries).",{"name":124,"@type":111,"acceptedAnswer":125},"Did any input type lead to higher clinical reasoning scores?",{"text":126,"@type":114},"No single input type was associated with higher scoring on clinical cases; the study notes other factors like prompting strategies, cognitive engagement, and interpreting outputs may matter more.","https://schema.org",{"og:url":83,"og:type":129,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":131,"canonical":83},"index,follow",{"doc_id":133,"site_id":62},458187,1790817638,{"code":4,"msg":5,"data":136},{"doc_id":133,"user_id":137,"nickname":92,"user_avatar":138,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":139,"file_id":140,"file_url":141,"file_type":142,"file_size":143,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":34,"language":144,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":145,"faqs":146,"seo_title":147,"seo_description":67,"update_tm":148,"read_time":149,"preview_page_count":4},1099514068365,"https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1794309523707217728","npj | digital medicine Article  \n\n| Published in partnership with Seoul National University Bundang Hospital | |\n| --- | --- |\n| [https://doi.org/10.1038/s41746-025-02184-y](https://doi.org/10.1038/s41746-025-02184-y) |  |\n| A typology of physician input approaches to using AI chatbots for clinical decisionmaking\u003Cbr> Check for updates |  |\n\nRachel Siden1 , Hannah Kerman2,3, Robert J. Gallo4,5, Joséphine A. Cool2,3, Jason Hom1,6, Ethan Goh1,7,8, Neera Ahuja1,6, Paul Heidenreich1, Lisa Shieh1,6, DanielYang9, Jonathan H. Chen1,7,8,10, Adam Rodman2,3,10 & Laura M. Holdsworth1  \nRecent studies have found that physicians with access toa large language model(LLM)chatbot during clinical reasoning tests may score no better to worse compared to the same chatbot performing alone with an input that included the entire clinical case. This study explores how physicians approach using LLM chatbots during clinical reasoning tasks and whether the amount of clinical case content included in the input affects performance. We conducted semi-structured interviews with U.S. physicians on experiences using an LLM chatbot and developed a typology based on input patterns. We then analyzed physician chat logs from two randomized controlled trials, coding each clinical case to an input approach type. Lastly, we used a linear mixed-effects model to compare the case scores of different input approach types. We identified four input approach types based on patterns of content amount: copy-paster (entire case), selective copy-paster (pieces of a case), summarizer (usergenerated case summary), and searcher (short queries) . Copy-pasting and searching were utilized most. No single type was associated with scoring higher on clinical cases. Other factors such as different prompting strategies, cognitive engagement, and interpretation of the outputs may have more impact and should be explored in future studies.  \nLarge language model (LLM) chatbots (such as ChatGPT, Perplexity, or Claude) that users can interact with conversationally have proliferated in recent years1, including in clinical contexts, and are increasingly being used for generating differential diagnoses2,3. The most recent generation of LLM chatbots have demonstrated even greater abilities in addressing complex clinical cases with a high degree of accuracy in controlled settings with researcher-created prompts4,5.  \nThe inputs toLLM chatbots can significantlyinfluence the accuracy of outputs produced6,7. Prompt engineering, the practice of optimizing LLM chatbot outputs with structured inputs, has emerged as an important field of study8. Such inputs can include assigning a role, defining timelines, describing the setting, and providing context9, which are additional details that are not typically used with traditional search engines. Prompt engineeringbest practices are not always intuitive to lay  \nor untrained users, who may employ a variety of different input styles when interacting with LLM chatbots 10, 11. When evaluating LLM chatbot outputs for accuracy and applicability in clinical settings, it will be important to consider not just how LLM chatbots perform under ideal conditions, but also how well they perform with everyday human users. In previous work evaluating the accuracy ofchatbot responses to clinical cases, researchers found that a LLM chatbot alone (using structured prompts which contained the entire clinical vignette) scored higher on clinical reasoning cases than physicians working through the same cases with access to the same LLM chatbot12, 13. It is unknown whether the difference in scoring could be attributed to different styles of inputting content into the LLM chatbot. Specifically, ifa key difference was due to how the chatbot was given the entire case vignette, whereas physicians chose for themselves how much content to input.  \n1Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA. 2Division of Hospital Medicine, Beth Israel Deaconess Medical Ce","cbCaigkkhAnb9XqJ","https://ap.wps.com/l/cbCaigkkhAnb9XqJ","pdf",748984,"English","# Background\n## LLM chatbots in clinical contexts\n## Input content and prompt engineering\n# Study Design\n## Sequential mixed methods\n## Interviews and typology development\n## Chat-log coding and statistical comparison\n# Input Approach Typology\n## Four input types by content amount\n## Usage patterns and performance findings\n# Implications and Future Work\n## Factors beyond input type","[{\"question\":\"What problem does the study address about AI chatbots in clinical reasoning?\",\"answer\":\"The study investigates how physicians approach using LLM chatbots during clinical reasoning tasks and whether entering more or less case content affects performance.\"},{\"question\":\"How was the typology of physician input approaches developed?\",\"answer\":\"It was built from semi-structured interviews with U.S. physicians using an LLM chatbot, then refined by coding physician chat logs from two randomized controlled trials.\"},{\"question\":\"What input approach types were identified in the study?\",\"answer\":\"Four types were found: copy-paster (entire case), selective copy-paster (pieces of a case), summarizer (user-generated case summary), and searcher (short queries).\"},{\"question\":\"Did any input type lead to higher clinical reasoning scores?\",\"answer\":\"No single input type was associated with higher scoring on clinical cases; the study notes other factors like prompting strategies, cognitive engagement, and interpreting outputs may matter more.\"}]","A typology of physician input approaches to using AI chatbots for clinical decision-making | PDF",1790751990,18]