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ABSTRACT:
SUBMITTER: Graham EM
PROVIDER: S-EPMC4938149 | biostudies-literature | 2016 May
REPOSITORIES: biostudies-literature
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Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies 20160501 5
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Project description:ImportanceThe emergence and promise of generative artificial intelligence (AI) represent a turning point for health care. Rigorous evaluation of generative AI deployment in clinical practice is needed to inform strategic decision-making.ObjectiveTo evaluate the implementation of a large language model used to draft responses to patient messages in the electronic inbox.Design, setting, and participantsA 5-week, prospective, single-group quality improvement study was conducted from July 10 through August 13, 2023, at a single academic medical center (Stanford Health Care). All attending physicians, advanced practice practitioners, clinic nurses, and clinical pharmacists from the Divisions of Primary Care and Gastroenterology and Hepatology were enrolled in the pilot.InterventionDraft replies to patient portal messages generated by a Health Insurance Portability and Accountability Act-compliant electronic health record-integrated large language model.Main outcomes and measuresThe primary outcome was AI-generated draft reply utilization as a percentage of total patient message replies. Secondary outcomes included changes in time measures and clinician experience as assessed by survey.ResultsA total of 197 clinicians were enrolled in the pilot; 35 clinicians who were prepilot beta users, out of office, or not tied to a specific ambulatory clinic were excluded, leaving 162 clinicians included in the analysis. The survey analysis cohort consisted of 73 participants (45.1%) who completed both the presurvey and postsurvey. In gastroenterology and hepatology, there were 58 physicians and APPs and 10 nurses. In primary care, there were 83 physicians and APPs, 4 nurses, and 8 clinical pharmacists. The mean AI-generated draft response utilization rate across clinicians was 20%. There was no change in reply action time, write time, or read time between the prepilot and pilot periods. There were statistically significant reductions in the 4-item physician task load score derivative (mean [SD], 61.31 [17.23] presurvey vs 47.26 [17.11] postsurvey; paired difference, -13.87; 95% CI, -17.38 to -9.50; P < .001) and work exhaustion scores (mean [SD], 1.95 [0.79] presurvey vs 1.62 [0.68] postsurvey; paired difference, -0.33; 95% CI, -0.50 to -0.17; P < .001).Conclusions and relevanceIn this quality improvement study of an early implementation of generative AI, there was notable adoption, usability, and improvement in assessments of burden and burnout. There was no improvement in time. Further code-to-bedside testing is needed to guide future development and organizational strategy.
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Project description: Not available
| S-EPMC11581472 | biostudies-literature
Project description:This paper proposes a method to predict fluctuations in the prices of cryptocurrencies, which are increasingly used for online transactions worldwide. Little research has been conducted on predicting fluctuations in the price and number of transactions of a variety of cryptocurrencies. Moreover, the few methods proposed to predict fluctuation in currency prices are inefficient because they fail to take into account the differences in attributes between real currencies and cryptocurrencies. This paper analyzes user comments in online cryptocurrency communities to predict fluctuations in the prices of cryptocurrencies and the number of transactions. By focusing on three cryptocurrencies, each with a large market size and user base, this paper attempts to predict such fluctuations by using a simple and efficient method.
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Project description:Recent evidence shows that US politicians' conception of honesty has undergone a bifurcation, with authentic but evidence-free "belief-speaking" becoming more prominent and differentiated from evidence-based "fact-speaking". Here we examine the downstream consequences of those two ways of conceiving honesty by investigating user engagement with fact-speaking and belief-speaking texts by members of the US Congress on Twitter (now X). We measure the conceptions of honesty of a sample of tweets and replies using computational text processing, and check whether the conceptions of honesty in the tweets align with those in their replies. We find that the conceptions of honesty used in replies align with those of the tweets, suggesting a "contagion". Notably, this contagion replicates under controlled experimental conditions. Our study highlights the crucial role of political leaders in setting the tone of the conversation on social media.
| S-EPMC11802837 | biostudies-literature
Project description:ImportanceTimely tests are warranted to assess the association between generative artificial intelligence (GenAI) use and physicians' work efforts.ObjectiveTo investigate the association between GenAI-drafted replies for patient messages and physician time spent on answering messages and the length of replies.Design, setting, and participantsRandomized waiting list quality improvement (QI) study from June to August 2023 in an academic health system. Primary care physicians were randomized to an immediate activation group and a delayed activation group. Data were analyzed from August to November 2023.ExposureAccess to GenAI-drafted replies for patient messages.Main outcomes and measuresTime spent (1) reading messages, (2) replying to messages, (3) length of replies, and (4) physician likelihood to recommend GenAI drafts. The a priori hypothesis was that GenAI drafts would be associated with less physician time spent reading and replying to messages. A mixed-effects model was used.ResultsFifty-two physicians participated in this QI study, with 25 randomized to the immediate activation group and 27 randomized to the delayed activation group. A contemporary control group included 70 physicians. There were 18 female participants (72.0%) in the immediate group and 17 female participants (63.0%) in the delayed group; the median age range was 35-44 years in the immediate group and 45-54 years in the delayed group. The median (IQR) time spent reading messages in the immediate group was 26 (11-69) seconds at baseline, 31 (15-70) seconds 3 weeks after entry to the intervention, and 31 (14-70) seconds 6 weeks after entry. The delayed group's median (IQR) read time was 25 (10-67) seconds at baseline, 29 (11-77) seconds during the 3-week waiting period, and 32 (15-72) seconds 3 weeks after entry to the intervention. The contemporary control group's median (IQR) read times were 21 (9-54), 22 (9-63), and 23 (9-60) seconds in corresponding periods. The estimated association of GenAI was a 21.8% increase in read time (95% CI, 5.2% to 41.0%; P = .008), a -5.9% change in reply time (95% CI, -16.6% to 6.2%; P = .33), and a 17.9% increase in reply length (95% CI, 10.1% to 26.2%; P < .001). Participants recognized GenAI's value and suggested areas for improvement.Conclusions and relevanceIn this QI study, GenAI-drafted replies were associated with significantly increased read time, no change in reply time, significantly increased reply length, and some perceived benefits. Rigorous empirical tests are necessary to further examine GenAI's performance. Future studies should examine patient experience and compare multiple GenAIs, including those with medical training.
| S-EPMC11019394 | biostudies-literature
Project description:The anti-vaccine movement has gained traction in many countries since the COVID-19 pandemic began. However, their aggressive behaviour through replies on Twitter-a form of directed messaging that can be sent beyond follow-follower relationships-is less understood, and even less is known about the language use differences of this behaviour. We conducted a comparative study of anti-vaxxers' aggressive behaviours by analysing a longitudinal dataset of COVID-19 tweets in English and Japanese. We found two common features across these languages. First, anti-vaxxers most actively transmit targeted messages or replies to users with different beliefs, especially to neutral accounts, with significantly toxic and negative language, and these replies are often directed to posts about vaccine operations. Second, influential users with many followers and verified accounts are more likely to receive the most toxic replies from the anti-vaxxers. However, pro-vaccine accounts with a few followers receive highly toxic replies in English, which is different from the Japanese case. These results provide insights into both language-dependent and independent countermeasures against anti-vaxxers' aggressive behaviour.
| S-EPMC9255452 | biostudies-literature