Pub Date : 2024-11-18DOI: 10.1038/s41746-024-01341-z
Elizabeth J Enichen, Kimia Heydari, Serena Wang, Grace C Nickel, Joseph C Kvedar
{"title":"The utility of personal wearable data in long COVID and personalized patient care.","authors":"Elizabeth J Enichen, Kimia Heydari, Serena Wang, Grace C Nickel, Joseph C Kvedar","doi":"10.1038/s41746-024-01341-z","DOIUrl":"https://doi.org/10.1038/s41746-024-01341-z","url":null,"abstract":"","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"7 1","pages":"326"},"PeriodicalIF":12.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142667840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-18DOI: 10.1038/s41746-024-01307-1
Rohit Singla, Nikola Pupic, Seyed-Aryan Ghaffarizadeh, Caroline Kim, Ricky Hu, Bruce B. Forster, Ilker Hacihaliloglu
The integration of artificial intelligence (AI) education into medical curricula is critical for preparing future healthcare professionals. This research employed the Delphi method to establish an expert-based AI curriculum for Canadian undergraduate medical students. A panel of 18 experts in health and AI across Canada participated in three rounds of surveys to determine essential AI learning competencies. The study identified key curricular components across ethics, law, theory, application, communication, collaboration, and quality improvement. The findings demonstrate substantial support among medical educators and professionals for the inclusion of comprehensive AI education, with 82 out of 107 curricular competencies being deemed essential to address both clinical and educational priorities. It additionally provides suggestions on methods to integrate these competencies within existing dense medical curricula. The endorsed set of objectives aims to enhance AI literacy and application skills among medical students, equipping them to effectively utilize AI technologies in future healthcare settings.
{"title":"Developing a Canadian artificial intelligence medical curriculum using a Delphi study","authors":"Rohit Singla, Nikola Pupic, Seyed-Aryan Ghaffarizadeh, Caroline Kim, Ricky Hu, Bruce B. Forster, Ilker Hacihaliloglu","doi":"10.1038/s41746-024-01307-1","DOIUrl":"10.1038/s41746-024-01307-1","url":null,"abstract":"The integration of artificial intelligence (AI) education into medical curricula is critical for preparing future healthcare professionals. This research employed the Delphi method to establish an expert-based AI curriculum for Canadian undergraduate medical students. A panel of 18 experts in health and AI across Canada participated in three rounds of surveys to determine essential AI learning competencies. The study identified key curricular components across ethics, law, theory, application, communication, collaboration, and quality improvement. The findings demonstrate substantial support among medical educators and professionals for the inclusion of comprehensive AI education, with 82 out of 107 curricular competencies being deemed essential to address both clinical and educational priorities. It additionally provides suggestions on methods to integrate these competencies within existing dense medical curricula. The endorsed set of objectives aims to enhance AI literacy and application skills among medical students, equipping them to effectively utilize AI technologies in future healthcare settings.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-10"},"PeriodicalIF":12.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01307-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-18DOI: 10.1038/s41746-024-01335-x
Hong Yeul Lee, Soomin Chung, Dongwoo Hyeon, Hyun-Lim Yang, Hyung-Chul Lee, Ho Geol Ryu, Hyeonhoon Lee
Delirium can result in undesirable outcomes including increased length of stays and mortality in patients admitted to the intensive care unit (ICU). Dexmedetomidine has emerged for delirium prevention in these patients; however, optimal dosing is challenging. A reinforcement learning-based Artificial Intelligence model for Delirium prevention (AID) is proposed to optimize dexmedetomidine dosing. The model was developed and internally validated using 2416 patients (2531 ICU admissions) and externally validated on 270 patients (274 ICU admissions). The estimated performance return of the AID policy was higher than that of the clinicians’ policy in both derivation (0.390 95% confidence interval [CI] 0.361 to 0.420 vs. −0.051 95% CI −0.077 to −0.025) and external validation (0.186 95% CI 0.139 to 0.236 vs. −0.436 95% CI −0.474 to −0.402) cohorts. Our finding indicates that AID might support clinicians’ decision-making regarding dexmedetomidine dosing to prevent delirium in ICU patients, but further off-policy evaluation is required.
谵妄会导致不良后果，包括延长重症监护室（ICU）患者的住院时间和死亡率。右美托咪定可用于预防这些患者的谵妄，但最佳剂量的确定却很困难。我们提出了一种基于强化学习的人工智能谵妄预防模型（AID）来优化右美托咪定的剂量。该模型由 2416 名患者（2531 名入住 ICU 的患者）开发并进行了内部验证，由 270 名患者（274 名入住 ICU 的患者）进行了外部验证。在衍生（0.390 95% 置信区间 [CI] 0.361 至 0.420 vs. -0.051 95% CI -0.077 至 -0.025）和外部验证（0.186 95% CI 0.139 至 0.236 vs. -0.436 95% CI -0.474 至 -0.402）队列中，AID 政策的估计绩效回报率均高于临床医生政策。我们的研究结果表明，AID可帮助临床医生做出右美托咪定剂量的决策，以预防ICU患者出现谵妄，但还需要进一步的政策外评估。
{"title":"Reinforcement learning model for optimizing dexmedetomidine dosing to prevent delirium in critically ill patients","authors":"Hong Yeul Lee, Soomin Chung, Dongwoo Hyeon, Hyun-Lim Yang, Hyung-Chul Lee, Ho Geol Ryu, Hyeonhoon Lee","doi":"10.1038/s41746-024-01335-x","DOIUrl":"10.1038/s41746-024-01335-x","url":null,"abstract":"Delirium can result in undesirable outcomes including increased length of stays and mortality in patients admitted to the intensive care unit (ICU). Dexmedetomidine has emerged for delirium prevention in these patients; however, optimal dosing is challenging. A reinforcement learning-based Artificial Intelligence model for Delirium prevention (AID) is proposed to optimize dexmedetomidine dosing. The model was developed and internally validated using 2416 patients (2531 ICU admissions) and externally validated on 270 patients (274 ICU admissions). The estimated performance return of the AID policy was higher than that of the clinicians’ policy in both derivation (0.390 95% confidence interval [CI] 0.361 to 0.420 vs. −0.051 95% CI −0.077 to −0.025) and external validation (0.186 95% CI 0.139 to 0.236 vs. −0.436 95% CI −0.474 to −0.402) cohorts. Our finding indicates that AID might support clinicians’ decision-making regarding dexmedetomidine dosing to prevent delirium in ICU patients, but further off-policy evaluation is required.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-13"},"PeriodicalIF":12.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01335-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-18DOI: 10.1038/s41746-024-01330-2
Sanjay Basu, Dean Schillinger, Sadiq Y. Patel, Joseph Rigdon
Population health initiatives often rely on cold outreach to close gaps in preventive care, such as overdue screenings or immunizations. Tailoring messages to diverse patient populations remains challenging, as traditional A/B testing requires large sample sizes to test only two alternative messages. With increasing availability of large language models (LLMs), programs can utilize tiered testing among both LLM and manual human agents, presenting the dilemma of identifying which patients need different levels of human support to cost-effectively engage large populations. Using microsimulations, we compared both the statistical power and false positive rates of A/B testing and Sequential Multiple Assignment Randomized Trials (SMART) for developing personalized communications across multiple effect sizes and sample sizes. SMART showed better cost-effectiveness and net benefit across all scenarios, but superior power for detecting heterogeneous treatment effects (HTEs) only in later randomization stages, when populations were more homogeneous and subtle differences drove engagement differences.
{"title":"Simulating A/B testing versus SMART designs for LLM-driven patient engagement to close preventive care gaps","authors":"Sanjay Basu, Dean Schillinger, Sadiq Y. Patel, Joseph Rigdon","doi":"10.1038/s41746-024-01330-2","DOIUrl":"10.1038/s41746-024-01330-2","url":null,"abstract":"Population health initiatives often rely on cold outreach to close gaps in preventive care, such as overdue screenings or immunizations. Tailoring messages to diverse patient populations remains challenging, as traditional A/B testing requires large sample sizes to test only two alternative messages. With increasing availability of large language models (LLMs), programs can utilize tiered testing among both LLM and manual human agents, presenting the dilemma of identifying which patients need different levels of human support to cost-effectively engage large populations. Using microsimulations, we compared both the statistical power and false positive rates of A/B testing and Sequential Multiple Assignment Randomized Trials (SMART) for developing personalized communications across multiple effect sizes and sample sizes. SMART showed better cost-effectiveness and net benefit across all scenarios, but superior power for detecting heterogeneous treatment effects (HTEs) only in later randomization stages, when populations were more homogeneous and subtle differences drove engagement differences.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-8"},"PeriodicalIF":12.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01330-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-18DOI: 10.1038/s41746-024-01333-z
Dongju Lim, Jaegwon Jeong, Yun Min Song, Chul-Hyun Cho, Ji Won Yeom, Taek Lee, Jung-Been Lee, Heon-Jeong Lee, Jae Kyoung Kim
Wearable devices enable passive collection of sleep, heart rate, and step-count data, offering potential for mood episode prediction in mood disorder patients. However, current models often require various data types, limiting real-world application. Here, we develop models that predict future episodes using only sleep-wake data, easily gathered through smartphones and wearables when trained on an individual’s sleep-wake history and past mood episodes. Using mathematical modeling to longitudinal data from 168 patients (587 days average clinical follow-up, 267 days wearable data), we derived 36 sleep and circadian rhythm features. These features enabled accurate next-day predictions for depressive, manic, and hypomanic episodes (AUCs: 0.80, 0.98, 0.95). Notably, daily circadian phase shifts were the most significant predictors: delays linked to depressive episodes, advances to manic episodes. This prospective observational cohort study (ClinicalTrials.gov: NCT03088657, 2017-3-23) shows sleep-wake data, combined with prior mood episode history, can effectively predict mood episodes, enhancing mood disorder management.
{"title":"Accurately predicting mood episodes in mood disorder patients using wearable sleep and circadian rhythm features","authors":"Dongju Lim, Jaegwon Jeong, Yun Min Song, Chul-Hyun Cho, Ji Won Yeom, Taek Lee, Jung-Been Lee, Heon-Jeong Lee, Jae Kyoung Kim","doi":"10.1038/s41746-024-01333-z","DOIUrl":"10.1038/s41746-024-01333-z","url":null,"abstract":"Wearable devices enable passive collection of sleep, heart rate, and step-count data, offering potential for mood episode prediction in mood disorder patients. However, current models often require various data types, limiting real-world application. Here, we develop models that predict future episodes using only sleep-wake data, easily gathered through smartphones and wearables when trained on an individual’s sleep-wake history and past mood episodes. Using mathematical modeling to longitudinal data from 168 patients (587 days average clinical follow-up, 267 days wearable data), we derived 36 sleep and circadian rhythm features. These features enabled accurate next-day predictions for depressive, manic, and hypomanic episodes (AUCs: 0.80, 0.98, 0.95). Notably, daily circadian phase shifts were the most significant predictors: delays linked to depressive episodes, advances to manic episodes. This prospective observational cohort study (ClinicalTrials.gov: NCT03088657, 2017-3-23) shows sleep-wake data, combined with prior mood episode history, can effectively predict mood episodes, enhancing mood disorder management.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-13"},"PeriodicalIF":12.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01333-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-18DOI: 10.1038/s41746-024-01315-1
Eyal Klang, Donald Apakama, Ethan E. Abbott, Akhil Vaid, Joshua Lampert, Ankit Sakhuja, Robert Freeman, Alexander W. Charney, David Reich, Monica Kraft, Girish N. Nadkarni, Benjamin S. Glicksberg
Large language models (LLMs) can optimize clinical workflows; however, the economic and computational challenges of their utilization at the health system scale are underexplored. We evaluated how concatenating queries with multiple clinical notes and tasks simultaneously affects model performance under increasing computational loads. We assessed ten LLMs of different capacities and sizes utilizing real-world patient data. We conducted >300,000 experiments of various task sizes and configurations, measuring accuracy in question-answering and the ability to properly format outputs. Performance deteriorated as the number of questions and notes increased. High-capacity models, like Llama-3–70b, had low failure rates and high accuracies. GPT-4-turbo-128k was similarly resilient across task burdens, but performance deteriorated after 50 tasks at large prompt sizes. After addressing mitigable failures, these two models can concatenate up to 50 simultaneous tasks effectively, with validation on a public medical question-answering dataset. An economic analysis demonstrated up to a 17-fold cost reduction at 50 tasks using concatenation. These results identify the limits of LLMs for effective utilization and highlight avenues for cost-efficiency at the enterprise scale.
{"title":"A strategy for cost-effective large language model use at health system-scale","authors":"Eyal Klang, Donald Apakama, Ethan E. Abbott, Akhil Vaid, Joshua Lampert, Ankit Sakhuja, Robert Freeman, Alexander W. Charney, David Reich, Monica Kraft, Girish N. Nadkarni, Benjamin S. Glicksberg","doi":"10.1038/s41746-024-01315-1","DOIUrl":"10.1038/s41746-024-01315-1","url":null,"abstract":"Large language models (LLMs) can optimize clinical workflows; however, the economic and computational challenges of their utilization at the health system scale are underexplored. We evaluated how concatenating queries with multiple clinical notes and tasks simultaneously affects model performance under increasing computational loads. We assessed ten LLMs of different capacities and sizes utilizing real-world patient data. We conducted >300,000 experiments of various task sizes and configurations, measuring accuracy in question-answering and the ability to properly format outputs. Performance deteriorated as the number of questions and notes increased. High-capacity models, like Llama-3–70b, had low failure rates and high accuracies. GPT-4-turbo-128k was similarly resilient across task burdens, but performance deteriorated after 50 tasks at large prompt sizes. After addressing mitigable failures, these two models can concatenate up to 50 simultaneous tasks effectively, with validation on a public medical question-answering dataset. An economic analysis demonstrated up to a 17-fold cost reduction at 50 tasks using concatenation. These results identify the limits of LLMs for effective utilization and highlight avenues for cost-efficiency at the enterprise scale.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-12"},"PeriodicalIF":12.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01315-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-17DOI: 10.1038/s41746-024-01324-0
Bettina Freitag, Marie Uncovska, Sven Meister, Christian Prinz, Leonard Fehring
Regulated mobile health applications are called digital health applications (“DiGA”) in Germany. To qualify for reimbursement by statutory health insurance companies, DiGA have to prove positive care effects in scientific studies. Since the empirical exploration of DiGA cost-effectiveness remains largely uncharted, this study pioneers the methodology of cohort-based state-transition Markov models to evaluate DiGA for depression. As health states, we define mild, moderate, severe depression, remission and death. Comparing a future scenario where 50% of patients receive supplementary DiGA access with the current standard of care reveals a gain of 0.02 quality-adjusted life years (QALYs) per patient, which comes at additional direct costs of ~1536 EUR per patient over a five-year timeframe. Influencing factors determining DiGA cost-effectiveness are the DiGA cost structure and individual DiGA effectiveness. Under Germany’s existing cost structure, DiGA for depression are yet to demonstrate the ability to generate overall savings in healthcare expenditures.
{"title":"Cost-effectiveness analysis of mHealth applications for depression in Germany using a Markov cohort simulation","authors":"Bettina Freitag, Marie Uncovska, Sven Meister, Christian Prinz, Leonard Fehring","doi":"10.1038/s41746-024-01324-0","DOIUrl":"10.1038/s41746-024-01324-0","url":null,"abstract":"Regulated mobile health applications are called digital health applications (“DiGA”) in Germany. To qualify for reimbursement by statutory health insurance companies, DiGA have to prove positive care effects in scientific studies. Since the empirical exploration of DiGA cost-effectiveness remains largely uncharted, this study pioneers the methodology of cohort-based state-transition Markov models to evaluate DiGA for depression. As health states, we define mild, moderate, severe depression, remission and death. Comparing a future scenario where 50% of patients receive supplementary DiGA access with the current standard of care reveals a gain of 0.02 quality-adjusted life years (QALYs) per patient, which comes at additional direct costs of ~1536 EUR per patient over a five-year timeframe. Influencing factors determining DiGA cost-effectiveness are the DiGA cost structure and individual DiGA effectiveness. Under Germany’s existing cost structure, DiGA for depression are yet to demonstrate the ability to generate overall savings in healthcare expenditures.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-13"},"PeriodicalIF":12.4,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01324-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-13DOI: 10.1038/s41746-024-01320-4
Mengyan Li, Xiaoou Li, Kevin Pan, Alon Geva, Doris Yang, Sara Morini Sweet, Clara-Lea Bonzel, Vidul Ayakulangara Panickan, Xin Xiong, Kenneth Mandl, Tianxi Cai
Electronic Health Record (EHR) systems are particularly valuable in pediatrics due to high barriers in clinical studies, but pediatric EHR data often suffer from low content density. Existing EHR code embeddings tailored for the general patient population fail to address the unique needs of pediatric patients. To bridge this gap, we introduce a transfer learning approach, MUltisource Graph Synthesis (MUGS), aimed at accurate knowledge extraction and relation detection in pediatric contexts. MUGS integrates graphical data from both pediatric and general EHR systems, along with hierarchical medical ontologies, to create embeddings that adaptively capture both the homogeneity and heterogeneity between hospital systems. These embeddings enable refined EHR feature engineering and nuanced patient profiling, proving particularly effective in identifying pediatric patients similar to specific profiles, with a focus on pulmonary hypertension (PH). MUGS embeddings, resistant to negative transfer, outperform other benchmark methods in multiple applications, advancing evidence-based pediatric research.
由于临床研究的高门槛，电子健康记录（EHR）系统对儿科尤为重要，但儿科 EHR 数据的内容密度往往较低。现有的电子病历代码嵌入是为普通患者量身定制的，无法满足儿科患者的独特需求。为了弥补这一缺陷，我们引入了一种迁移学习方法--MUltisource Graph Synthesis（MUGS），旨在儿科环境中进行准确的知识提取和关系检测。MUGS 整合了来自儿科和普通电子病历系统的图形数据以及分层医疗本体，创建了能自适应捕捉医院系统间同质性和异质性的嵌入。这些嵌入技术可实现完善的电子病历特征工程和细致入微的病人特征描述，在识别与特定特征相似的儿科病人（重点是肺动脉高压（PH））方面尤其有效。MUGS 嵌入抗负转移，在多种应用中优于其他基准方法，推动了循证儿科研究的发展。
{"title":"Multisource representation learning for pediatric knowledge extraction from electronic health records","authors":"Mengyan Li, Xiaoou Li, Kevin Pan, Alon Geva, Doris Yang, Sara Morini Sweet, Clara-Lea Bonzel, Vidul Ayakulangara Panickan, Xin Xiong, Kenneth Mandl, Tianxi Cai","doi":"10.1038/s41746-024-01320-4","DOIUrl":"10.1038/s41746-024-01320-4","url":null,"abstract":"Electronic Health Record (EHR) systems are particularly valuable in pediatrics due to high barriers in clinical studies, but pediatric EHR data often suffer from low content density. Existing EHR code embeddings tailored for the general patient population fail to address the unique needs of pediatric patients. To bridge this gap, we introduce a transfer learning approach, MUltisource Graph Synthesis (MUGS), aimed at accurate knowledge extraction and relation detection in pediatric contexts. MUGS integrates graphical data from both pediatric and general EHR systems, along with hierarchical medical ontologies, to create embeddings that adaptively capture both the homogeneity and heterogeneity between hospital systems. These embeddings enable refined EHR feature engineering and nuanced patient profiling, proving particularly effective in identifying pediatric patients similar to specific profiles, with a focus on pulmonary hypertension (PH). MUGS embeddings, resistant to negative transfer, outperform other benchmark methods in multiple applications, advancing evidence-based pediatric research.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-15"},"PeriodicalIF":12.4,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01320-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-11DOI: 10.1038/s41746-024-01306-2
James T. Anibal, Hannah B. Huth, Jasmine Gunkel, Susan K. Gregurick, Bradford J. Wood
In the future, large language models (LLMs) may enhance the delivery of healthcare, but there are risks of misuse. These methods may be trained to allocate resources via unjust criteria involving multimodal data - financial transactions, internet activity, social behaviors, and healthcare information. This study shows that LLMs may be biased in favor of collective/systemic benefit over the protection of individual rights and could facilitate AI-driven social credit systems.
{"title":"Simulated misuse of large language models and clinical credit systems","authors":"James T. Anibal, Hannah B. Huth, Jasmine Gunkel, Susan K. Gregurick, Bradford J. Wood","doi":"10.1038/s41746-024-01306-2","DOIUrl":"10.1038/s41746-024-01306-2","url":null,"abstract":"In the future, large language models (LLMs) may enhance the delivery of healthcare, but there are risks of misuse. These methods may be trained to allocate resources via unjust criteria involving multimodal data - financial transactions, internet activity, social behaviors, and healthcare information. This study shows that LLMs may be biased in favor of collective/systemic benefit over the protection of individual rights and could facilitate AI-driven social credit systems.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-10"},"PeriodicalIF":12.4,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01306-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-09DOI: 10.1038/s41746-024-01313-3
Edmond Pui Hang Choi, Chanchan Wu, Kitty Wai Ying Choi, Pui Hing Chau, Eric Yuk Fai Wan, William Chi Wai Wong, Janet Yuen Ha Wong, Daniel Yee Tak Fong, Eric Pui Fung Chow
Men who have sex with men (MSM) who use dating applications (apps) have higher rates of engaging in condomless anal sex than those who do not. Therefore, we conducted a two-arm randomized controlled trial to evaluate the effectiveness of an interactive web-based intervention in promoting safer sex among this population. The intervention was guided by the Theory of Planned Behavior and co-designed by researchers, healthcare providers, and MSM participants. The primary outcome was the frequency of condomless anal sex in past three months. Secondary outcomes included five other behavioral outcomes and two psychological outcomes. This trial was registered on ISRCTN (ISRCTN16681863) on 2020/04/28. A total of 480 MSM were enrolled and randomly assigned to the intervention or control group. Our findings indicate that the intervention significantly reduced condomless anal sex behaviors by enhancing self-efficacy and attitudes toward condom use among MSM dating app users, with the effects sustained at both three and six months.
{"title":"Ehealth interactive intervention in promoting safer sex among men who have sex with men","authors":"Edmond Pui Hang Choi, Chanchan Wu, Kitty Wai Ying Choi, Pui Hing Chau, Eric Yuk Fai Wan, William Chi Wai Wong, Janet Yuen Ha Wong, Daniel Yee Tak Fong, Eric Pui Fung Chow","doi":"10.1038/s41746-024-01313-3","DOIUrl":"10.1038/s41746-024-01313-3","url":null,"abstract":"Men who have sex with men (MSM) who use dating applications (apps) have higher rates of engaging in condomless anal sex than those who do not. Therefore, we conducted a two-arm randomized controlled trial to evaluate the effectiveness of an interactive web-based intervention in promoting safer sex among this population. The intervention was guided by the Theory of Planned Behavior and co-designed by researchers, healthcare providers, and MSM participants. The primary outcome was the frequency of condomless anal sex in past three months. Secondary outcomes included five other behavioral outcomes and two psychological outcomes. This trial was registered on ISRCTN (ISRCTN16681863) on 2020/04/28. A total of 480 MSM were enrolled and randomly assigned to the intervention or control group. Our findings indicate that the intervention significantly reduced condomless anal sex behaviors by enhancing self-efficacy and attitudes toward condom use among MSM dating app users, with the effects sustained at both three and six months.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-12"},"PeriodicalIF":12.4,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01313-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142597066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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