Advances in computing technology promise to aid in achieving the goals of healthcare. We review how such changes can support each of the goals of healthcare as identified by the U.S. Institute of Medicine： safety, ef...Advances in computing technology promise to aid in achieving the goals of healthcare. We review how such changes can support each of the goals of healthcare as identified by the U.S. Institute of Medicine： safety, effectiveness, patient-centricity, timeliness, efficiency, and equitability. We also describe current foci of computing technology research aimed at realizing the ambitious goals for health information technology that have been set by the American Recovery and Reinvestment Act of 2009 and the Health Reform Act of 2010. Finally, we mention efforts to build health information technologies to support improved healthcare delivery in developing countries.展开更多
Conducting thematic analysis in qualitative research can be laborious and time-consuming.We propose and evaluate the feasibility of using Generative Pre-trained Transformer(GPT)models to assist public health researche...Conducting thematic analysis in qualitative research can be laborious and time-consuming.We propose and evaluate the feasibility of using Generative Pre-trained Transformer(GPT)models to assist public health researchers in extracting themes from interview transcripts.Carefully engineered prompts were used to sequentially extract and synthesize transcripts into a concise set of study-level themes relevant to the study’s goals.An evaluation using a 5-point Likert scale(0−4)assessed GPTgenerated themes across 11 published studies based on four criteria:succinctness,alignment with researcher-identified themes,quality of explanations,and relevance of quotes.Across all four criteria,the scores averaged 3.05(95%Confidence Interval(CI):[2.93,3.16]).Our findings indicate that at least half of the GPT-generated themes align with those in published studies,exhibiting succinctness with minimal repetition,substantial depth of explanations,and relevant quotations.Despite these promising results,practices such as complementing outputs with field-specific knowledge are recommended.展开更多
基金supported by grants U54 LM008748 and R01 LM009723 from the National Library of MedicineR01 EB001659 from the National Institute of Biomedical Imaging and BioengineeringONC #10510949 from the Office of the National Coordinator for Health Information Technology
文摘Advances in computing technology promise to aid in achieving the goals of healthcare. We review how such changes can support each of the goals of healthcare as identified by the U.S. Institute of Medicine： safety, effectiveness, patient-centricity, timeliness, efficiency, and equitability. We also describe current foci of computing technology research aimed at realizing the ambitious goals for health information technology that have been set by the American Recovery and Reinvestment Act of 2009 and the Health Reform Act of 2010. Finally, we mention efforts to build health information technologies to support improved healthcare delivery in developing countries.
基金supported in part by the Washington University Center for Diabetes Translation Research(WU-CDTR)under Grant Number P30DK092950 from the National Institute of Diabetes and Digestive and Kidney Diseases(NIDDK)。
文摘Conducting thematic analysis in qualitative research can be laborious and time-consuming.We propose and evaluate the feasibility of using Generative Pre-trained Transformer(GPT)models to assist public health researchers in extracting themes from interview transcripts.Carefully engineered prompts were used to sequentially extract and synthesize transcripts into a concise set of study-level themes relevant to the study’s goals.An evaluation using a 5-point Likert scale(0−4)assessed GPTgenerated themes across 11 published studies based on four criteria:succinctness,alignment with researcher-identified themes,quality of explanations,and relevance of quotes.Across all four criteria,the scores averaged 3.05(95%Confidence Interval(CI):[2.93,3.16]).Our findings indicate that at least half of the GPT-generated themes align with those in published studies,exhibiting succinctness with minimal repetition,substantial depth of explanations,and relevant quotations.Despite these promising results,practices such as complementing outputs with field-specific knowledge are recommended.
