Can Electronic care planning using AI Summarization Yield equal Documentation Quality? (EASY eDocQ)

ImportanceData, information and knowledge in health care has expanded exponentially over the last 50 years, leading to significant challenges with information overload and complex, fragmented care plans. Generative AI has the potential to facilitate summarization and integration of knowledge and wisdom to through rapid integration of data and information to enable efficient care planning. ObjectiveOur objective was to understand the value of AI generated summarization through short synopses at the care transition from hospital to first outpatient visit. DesignUsing a de-identified data set of recently hospitalized patients with multiple chronic illnesses, we used the data-information-knowledge-wisdom framework to train clinicians and an open-source generative AI Large Language Model system to produce summarized patient assessments after hospitalizations. Both sets of synopses were judged blinded in random order by clinician judges. ParticipantsDe-identified patients had multiple chronic conditions and a recent hospitalization. Raters were physicians at various levels of training. Main outcomeAccuracy, succinctness, synthesis and usefulness of synopses using a standardized scale with scores > 80% indicating success. ResultsAI and clinicians summarized 80 patients with 10% overlap. In blinded trials, AI synopses were rated as useful 75% of the time versus 76% for human generated ones. AI had lower succinctness ratings for the Data synopsis task (55-67%) versus human (84-86%). For accuracy and synthesis, AI had near equal or better scores in other domains (AI: 72%-79%, humans: 68%-84%), with best scores from AI in Wisdom. Interrater agreement was moderate, indicating different preferences for synopsis content, and did not vary between AI and human-created synopses. DiscussionAI-created synopses that were nearly equivalent to human-created ones; they were slightly longer and did not always synthesize individual data elements compared to humans. Given their rapid introduction into clinical care, our framework and protocol for evaluation of these tools provides strong benchmarking capabilities for developers and implementers. Key PointsO_ST_ABSQuestionC_ST_ABSCan a Generative AI Large Language Model be trained to generate accurate and useful patient synopses through chart summarization for use in outpatient settings after hospital discharge? FindingsUsing a Data-Information-Knowledge-Wisdom framework, clinicians and an open-source AI system were trained to summarize charts; these synopses were rated blindly using a standardized index. Synopses from the AI were rated as useful 75% of the time versus 76% for human generated ones, AI synopses scored highest in Wisdom for accuracy and synthesis. Interrater agreement was moderate but did not vary between AI and human. MeaningThis study provides a concrete, replicable protocol for benchmarking LLM summarization outputs and demonstrates general equivalence to human-created synopses for outpatient use after care transitions.