Pub Date : 2024-07-11DOI: 10.1038/s41746-024-01184-8
Elizabeth Goldberg, Dave Kao, Bethany Kwan, Hemali Patel, Amy Hassell, Richard Zane
In the face of formidable healthcare challenges, such as staffing shortages and rising costs, technology has emerged as a crucial ally in enhancing patient care. UCHealth, Colorado’s largest health system, has pioneered the integration of technology into patient care through its Virtual Health Center (VHC). In this Comment, we explore UCHealth’s journey in creating a centralized hub that harnesses innovative digital health solutions to address patient care needs across its 12 hospitals, spanning over 600,000 emergency department visits and nearly 150,000 inpatient and observation encounters annually. The VHC has proven to be a transformative force, providing high-quality care at scale, reducing staff burden, and establishing new career pathways in virtual health. The transformation process involved multiple steps: (a) identifying a need, (b) vetting within health system solutions, (c) searching for industry solutions, and scrutinizing these through meetings with our innovations center, (d) piloting the solution, and (e) sustaining the solution by integrating them within the electronic health record (EHR).
{"title":"UCHealth’s virtual health center: How Colorado’s largest health system creates and integrates technology into patient care","authors":"Elizabeth Goldberg, Dave Kao, Bethany Kwan, Hemali Patel, Amy Hassell, Richard Zane","doi":"10.1038/s41746-024-01184-8","DOIUrl":"10.1038/s41746-024-01184-8","url":null,"abstract":"In the face of formidable healthcare challenges, such as staffing shortages and rising costs, technology has emerged as a crucial ally in enhancing patient care. UCHealth, Colorado’s largest health system, has pioneered the integration of technology into patient care through its Virtual Health Center (VHC). In this Comment, we explore UCHealth’s journey in creating a centralized hub that harnesses innovative digital health solutions to address patient care needs across its 12 hospitals, spanning over 600,000 emergency department visits and nearly 150,000 inpatient and observation encounters annually. The VHC has proven to be a transformative force, providing high-quality care at scale, reducing staff burden, and establishing new career pathways in virtual health. The transformation process involved multiple steps: (a) identifying a need, (b) vetting within health system solutions, (c) searching for industry solutions, and scrutinizing these through meetings with our innovations center, (d) piloting the solution, and (e) sustaining the solution by integrating them within the electronic health record (EHR).","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11239912/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141590869","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 : 2024-07-09DOI: 10.1038/s41746-024-01179-5
Devin M. Mann, Elizabeth R. Stevens, Paul Testa, Nader Mherabi
We have entered a new age of health informatics—applied health informatics—where digital health innovation cannot be pursued without considering operational needs. In this new digital health era, creating an integrated applied health informatics system will be essential for health systems to achieve informatics healthcare goals. Integration of information technology (IT) and health informatics does not naturally occur without a deliberate and intentional shift towards unification. Recognizing this, NYU Langone Health’s (NYULH) Medical Center IT (MCIT) has taken proactive measures to vertically integrate academic informatics and operational IT through the establishment of the MCIT Department of Health Informatics (DHI). The creation of the NYULH DHI showcases the drivers, challenges, and ultimate successes of our enterprise effort to align academic health informatics with IT; providing a model for the creation of the applied health informatics programs required for academic health systems to thrive in the increasingly digitized healthcare landscape.
我们已经进入了一个健康信息学的新时代--应用健康信息学--在这个时代，追求数字健康创新不能不考虑业务需求。在这个新的数字医疗时代，创建一个整合的应用医疗信息系统对于医疗系统实现信息医疗目标至关重要。如果不有意识地向统一化转变，信息技术（IT）和健康信息学的整合就不会自然而然地发生。纽约大学朗格尼医院（NYULH）医学中心信息技术部（MCIT）认识到了这一点，并采取了积极措施，通过建立医学中心信息技术部健康信息学系（DHI），将学术信息学和运营信息技术进行纵向整合。NYULH DHI 的创建展示了我们将学术健康信息学与 IT 相结合的企业努力的驱动力、挑战和最终成功；为创建学术健康系统所需的应用健康信息学项目提供了一个模式，以便在日益数字化的医疗保健环境中茁壮成长。
{"title":"From silos to synergy: integrating academic health informatics with operational IT for healthcare transformation","authors":"Devin M. Mann, Elizabeth R. Stevens, Paul Testa, Nader Mherabi","doi":"10.1038/s41746-024-01179-5","DOIUrl":"10.1038/s41746-024-01179-5","url":null,"abstract":"We have entered a new age of health informatics—applied health informatics—where digital health innovation cannot be pursued without considering operational needs. In this new digital health era, creating an integrated applied health informatics system will be essential for health systems to achieve informatics healthcare goals. Integration of information technology (IT) and health informatics does not naturally occur without a deliberate and intentional shift towards unification. Recognizing this, NYU Langone Health’s (NYULH) Medical Center IT (MCIT) has taken proactive measures to vertically integrate academic informatics and operational IT through the establishment of the MCIT Department of Health Informatics (DHI). The creation of the NYULH DHI showcases the drivers, challenges, and ultimate successes of our enterprise effort to align academic health informatics with IT; providing a model for the creation of the applied health informatics programs required for academic health systems to thrive in the increasingly digitized healthcare landscape.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01179-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141563948","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 : 2024-07-09DOI: 10.1038/s41746-024-01175-9
Chang Su, Yu Hou, Jielin Xu, Zhenxing Xu, Manqi Zhou, Alison Ke, Haoyang Li, Jie Xu, Matthew Brendel, Jacqueline R. M. A. Maasch, Zilong Bai, Haotan Zhang, Yingying Zhu, Molly C. Cincotta, Xinghua Shi, Claire Henchcliffe, James B. Leverenz, Jeffrey Cummings, Michael S. Okun, Jiang Bian, Feixiong Cheng, Fei Wang
Parkinson’s disease (PD) is a serious neurodegenerative disorder marked by significant clinical and progression heterogeneity. This study aimed at addressing heterogeneity of PD through integrative analysis of various data modalities. We analyzed clinical progression data (≥5 years) of individuals with de novo PD using machine learning and deep learning, to characterize individuals’ phenotypic progression trajectories for PD subtyping. We discovered three pace subtypes of PD exhibiting distinct progression patterns: the Inching Pace subtype (PD-I) with mild baseline severity and mild progression speed; the Moderate Pace subtype (PD-M) with mild baseline severity but advancing at a moderate progression rate; and the Rapid Pace subtype (PD-R) with the most rapid symptom progression rate. We found cerebrospinal fluid P-tau/α-synuclein ratio and atrophy in certain brain regions as potential markers of these subtypes. Analyses of genetic and transcriptomic profiles with network-based approaches identified molecular modules associated with each subtype. For instance, the PD-R-specific module suggested STAT3, FYN, BECN1, APOA1, NEDD4, and GATA2 as potential driver genes of PD-R. It also suggested neuroinflammation, oxidative stress, metabolism, PI3K/AKT, and angiogenesis pathways as potential drivers for rapid PD progression (i.e., PD-R). Moreover, we identified repurposable drug candidates by targeting these subtype-specific molecular modules using network-based approach and cell line drug-gene signature data. We further estimated their treatment effects using two large-scale real-world patient databases; the real-world evidence we gained highlighted the potential of metformin in ameliorating PD progression. In conclusion, this work helps better understand clinical and pathophysiological complexity of PD progression and accelerate precision medicine.
{"title":"Identification of Parkinson’s disease PACE subtypes and repurposing treatments through integrative analyses of multimodal data","authors":"Chang Su, Yu Hou, Jielin Xu, Zhenxing Xu, Manqi Zhou, Alison Ke, Haoyang Li, Jie Xu, Matthew Brendel, Jacqueline R. M. A. Maasch, Zilong Bai, Haotan Zhang, Yingying Zhu, Molly C. Cincotta, Xinghua Shi, Claire Henchcliffe, James B. Leverenz, Jeffrey Cummings, Michael S. Okun, Jiang Bian, Feixiong Cheng, Fei Wang","doi":"10.1038/s41746-024-01175-9","DOIUrl":"10.1038/s41746-024-01175-9","url":null,"abstract":"Parkinson’s disease (PD) is a serious neurodegenerative disorder marked by significant clinical and progression heterogeneity. This study aimed at addressing heterogeneity of PD through integrative analysis of various data modalities. We analyzed clinical progression data (≥5 years) of individuals with de novo PD using machine learning and deep learning, to characterize individuals’ phenotypic progression trajectories for PD subtyping. We discovered three pace subtypes of PD exhibiting distinct progression patterns: the Inching Pace subtype (PD-I) with mild baseline severity and mild progression speed; the Moderate Pace subtype (PD-M) with mild baseline severity but advancing at a moderate progression rate; and the Rapid Pace subtype (PD-R) with the most rapid symptom progression rate. We found cerebrospinal fluid P-tau/α-synuclein ratio and atrophy in certain brain regions as potential markers of these subtypes. Analyses of genetic and transcriptomic profiles with network-based approaches identified molecular modules associated with each subtype. For instance, the PD-R-specific module suggested STAT3, FYN, BECN1, APOA1, NEDD4, and GATA2 as potential driver genes of PD-R. It also suggested neuroinflammation, oxidative stress, metabolism, PI3K/AKT, and angiogenesis pathways as potential drivers for rapid PD progression (i.e., PD-R). Moreover, we identified repurposable drug candidates by targeting these subtype-specific molecular modules using network-based approach and cell line drug-gene signature data. We further estimated their treatment effects using two large-scale real-world patient databases; the real-world evidence we gained highlighted the potential of metformin in ameliorating PD progression. In conclusion, this work helps better understand clinical and pathophysiological complexity of PD progression and accelerate precision medicine.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01175-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141563949","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 : 2024-07-08DOI: 10.1038/s41746-024-01157-x
Joschka Haltaufderheide, Robert Ranisch
With the introduction of ChatGPT, Large Language Models (LLMs) have received enormous attention in healthcare. Despite potential benefits, researchers have underscored various ethical implications. While individual instances have garnered attention, a systematic and comprehensive overview of practical applications currently researched and ethical issues connected to them is lacking. Against this background, this work maps the ethical landscape surrounding the current deployment of LLMs in medicine and healthcare through a systematic review. Electronic databases and preprint servers were queried using a comprehensive search strategy which generated 796 records. Studies were screened and extracted following a modified rapid review approach. Methodological quality was assessed using a hybrid approach. For 53 records, a meta-aggregative synthesis was performed. Four general fields of applications emerged showcasing a dynamic exploration phase. Advantages of using LLMs are attributed to their capacity in data analysis, information provisioning, support in decision-making or mitigating information loss and enhancing information accessibility. However, our study also identifies recurrent ethical concerns connected to fairness, bias, non-maleficence, transparency, and privacy. A distinctive concern is the tendency to produce harmful or convincing but inaccurate content. Calls for ethical guidance and human oversight are recurrent. We suggest that the ethical guidance debate should be reframed to focus on defining what constitutes acceptable human oversight across the spectrum of applications. This involves considering the diversity of settings, varying potentials for harm, and different acceptable thresholds for performance and certainty in healthcare. Additionally, critical inquiry is needed to evaluate the necessity and justification of LLMs’ current experimental use.
{"title":"The ethics of ChatGPT in medicine and healthcare: a systematic review on Large Language Models (LLMs)","authors":"Joschka Haltaufderheide, Robert Ranisch","doi":"10.1038/s41746-024-01157-x","DOIUrl":"10.1038/s41746-024-01157-x","url":null,"abstract":"With the introduction of ChatGPT, Large Language Models (LLMs) have received enormous attention in healthcare. Despite potential benefits, researchers have underscored various ethical implications. While individual instances have garnered attention, a systematic and comprehensive overview of practical applications currently researched and ethical issues connected to them is lacking. Against this background, this work maps the ethical landscape surrounding the current deployment of LLMs in medicine and healthcare through a systematic review. Electronic databases and preprint servers were queried using a comprehensive search strategy which generated 796 records. Studies were screened and extracted following a modified rapid review approach. Methodological quality was assessed using a hybrid approach. For 53 records, a meta-aggregative synthesis was performed. Four general fields of applications emerged showcasing a dynamic exploration phase. Advantages of using LLMs are attributed to their capacity in data analysis, information provisioning, support in decision-making or mitigating information loss and enhancing information accessibility. However, our study also identifies recurrent ethical concerns connected to fairness, bias, non-maleficence, transparency, and privacy. A distinctive concern is the tendency to produce harmful or convincing but inaccurate content. Calls for ethical guidance and human oversight are recurrent. We suggest that the ethical guidance debate should be reframed to focus on defining what constitutes acceptable human oversight across the spectrum of applications. This involves considering the diversity of settings, varying potentials for harm, and different acceptable thresholds for performance and certainty in healthcare. Additionally, critical inquiry is needed to evaluate the necessity and justification of LLMs’ current experimental use.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01157-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141556776","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}
Automatic assessment of impairment and disease severity is a key challenge in data-driven medicine. We propose a framework to address this challenge, which leverages AI models trained exclusively on healthy individuals. The COnfidence-Based chaRacterization of Anomalies (COBRA) score exploits the decrease in confidence of these models when presented with impaired or diseased patients to quantify their deviation from the healthy population. We applied the COBRA score to address a key limitation of current clinical evaluation of upper-body impairment in stroke patients. The gold-standard Fugl-Meyer Assessment (FMA) requires in-person administration by a trained assessor for 30-45 minutes, which restricts monitoring frequency and precludes physicians from adapting rehabilitation protocols to the progress of each patient. The COBRA score, computed automatically in under one minute, is shown to be strongly correlated with the FMA on an independent test cohort for two different data modalities: wearable sensors (ρ = 0.814, 95% CI [0.700,0.888]) and video (ρ = 0.736, 95% C.I [0.584, 0.838]). To demonstrate the generalizability of the approach to other conditions, the COBRA score was also applied to quantify severity of knee osteoarthritis from magnetic-resonance imaging scans, again achieving significant correlation with an independent clinical assessment (ρ = 0.644, 95% C.I [0.585,0.696]).
{"title":"Quantifying impairment and disease severity using AI models trained on healthy subjects","authors":"Boyang Yu, Aakash Kaku, Kangning Liu, Avinash Parnandi, Emily Fokas, Anita Venkatesan, Natasha Pandit, Rajesh Ranganath, Heidi Schambra, Carlos Fernandez-Granda","doi":"10.1038/s41746-024-01173-x","DOIUrl":"10.1038/s41746-024-01173-x","url":null,"abstract":"Automatic assessment of impairment and disease severity is a key challenge in data-driven medicine. We propose a framework to address this challenge, which leverages AI models trained exclusively on healthy individuals. The COnfidence-Based chaRacterization of Anomalies (COBRA) score exploits the decrease in confidence of these models when presented with impaired or diseased patients to quantify their deviation from the healthy population. We applied the COBRA score to address a key limitation of current clinical evaluation of upper-body impairment in stroke patients. The gold-standard Fugl-Meyer Assessment (FMA) requires in-person administration by a trained assessor for 30-45 minutes, which restricts monitoring frequency and precludes physicians from adapting rehabilitation protocols to the progress of each patient. The COBRA score, computed automatically in under one minute, is shown to be strongly correlated with the FMA on an independent test cohort for two different data modalities: wearable sensors (ρ = 0.814, 95% CI [0.700,0.888]) and video (ρ = 0.736, 95% C.I [0.584, 0.838]). To demonstrate the generalizability of the approach to other conditions, the COBRA score was also applied to quantify severity of knee osteoarthritis from magnetic-resonance imaging scans, again achieving significant correlation with an independent clinical assessment (ρ = 0.644, 95% C.I [0.585,0.696]).","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11226623/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141538266","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 : 2024-07-06DOI: 10.1038/s41746-024-01174-w
Zhongwen Li, He Xie, Zhouqian Wang, Daoyuan Li, Kuan Chen, Xihang Zong, Wei Qiang, Feng Wen, Zhihong Deng, Limin Chen, Huiping Li, He Dong, Pengcheng Wu, Tao Sun, Yan Cheng, Yanning Yang, Jinsong Xue, Qinxiang Zheng, Jiewei Jiang, Wei Chen
The main cause of corneal blindness worldwide is keratitis, especially the infectious form caused by bacteria, fungi, viruses, and Acanthamoeba. The key to effective management of infectious keratitis hinges on prompt and precise diagnosis. Nevertheless, the current gold standard, such as cultures of corneal scrapings, remains time-consuming and frequently yields false-negative results. Here, using 23,055 slit-lamp images collected from 12 clinical centers nationwide, this study constructed a clinically feasible deep learning system, DeepIK, that could emulate the diagnostic process of a human expert to identify and differentiate bacterial, fungal, viral, amebic, and noninfectious keratitis. DeepIK exhibited remarkable performance in internal, external, and prospective datasets (all areas under the receiver operating characteristic curves > 0.96) and outperformed three other state-of-the-art algorithms (DenseNet121, InceptionResNetV2, and Swin-Transformer). Our study indicates that DeepIK possesses the capability to assist ophthalmologists in accurately and swiftly identifying various infectious keratitis types from slit-lamp images, thereby facilitating timely and targeted treatment.
{"title":"Deep learning for multi-type infectious keratitis diagnosis: A nationwide, cross-sectional, multicenter study","authors":"Zhongwen Li, He Xie, Zhouqian Wang, Daoyuan Li, Kuan Chen, Xihang Zong, Wei Qiang, Feng Wen, Zhihong Deng, Limin Chen, Huiping Li, He Dong, Pengcheng Wu, Tao Sun, Yan Cheng, Yanning Yang, Jinsong Xue, Qinxiang Zheng, Jiewei Jiang, Wei Chen","doi":"10.1038/s41746-024-01174-w","DOIUrl":"10.1038/s41746-024-01174-w","url":null,"abstract":"The main cause of corneal blindness worldwide is keratitis, especially the infectious form caused by bacteria, fungi, viruses, and Acanthamoeba. The key to effective management of infectious keratitis hinges on prompt and precise diagnosis. Nevertheless, the current gold standard, such as cultures of corneal scrapings, remains time-consuming and frequently yields false-negative results. Here, using 23,055 slit-lamp images collected from 12 clinical centers nationwide, this study constructed a clinically feasible deep learning system, DeepIK, that could emulate the diagnostic process of a human expert to identify and differentiate bacterial, fungal, viral, amebic, and noninfectious keratitis. DeepIK exhibited remarkable performance in internal, external, and prospective datasets (all areas under the receiver operating characteristic curves > 0.96) and outperformed three other state-of-the-art algorithms (DenseNet121, InceptionResNetV2, and Swin-Transformer). Our study indicates that DeepIK possesses the capability to assist ophthalmologists in accurately and swiftly identifying various infectious keratitis types from slit-lamp images, thereby facilitating timely and targeted treatment.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11227533/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141545115","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 : 2024-07-06DOI: 10.1038/s41746-024-01178-6
Zonghe Xu, Lin Zhou, Bin Han, Shuang Wu, Yanjun Xiao, Sihui Zhang, Jiang Chen, Jianbin Guo, Dong Wu
Computer-aided implant surgery has undergone continuous development in recent years. In this study, active and passive systems of dynamic navigation were divided into active dynamic navigation system group and passive dynamic navigation system group (ADG and PDG), respectively. Active, passive and semi-active implant robots were divided into active robot group, passive robot group and semi-active robot group (ARG, PRG and SRG), respectively. Each group placed two implants (FDI tooth positions 31 and 36) in a model 12 times. The accuracy of 216 implants in 108 models were analysed. The coronal deviations of ADG, PDG, ARG, PRG and SRG were 0.85 ± 0.17 mm, 1.05 ± 0.42 mm, 0.29 ± 0.15 mm, 0.40 ± 0.16 mm and 0.33 ± 0.14 mm, respectively. The apical deviations of the five groups were 1.11 ± 0.23 mm, 1.07 ± 0.38 mm, 0.29 ± 0.15 mm, 0.50 ± 0.19 mm and 0.36 ± 0.16 mm, respectively. The axial deviations of the five groups were 1.78 ± 0.73°, 1.99 ± 1.20°, 0.61 ± 0.25°, 1.04 ± 0.37° and 0.42 ± 0.18°, respectively. The coronal, apical and axial deviations of ADG were higher than those of ARG, PRG and SRG (all P < 0.001). Similarly, the coronal, apical and axial deviations of PDG were higher than those of ARG, PRG, and SRG (all P < 0.001). Dynamic and robotic computer-aided implant surgery may show good implant accuracy in vitro. However, the accuracy and stability of implant robots are higher than those of dynamic navigation systems.
{"title":"Accuracy of dental implant placement using different dynamic navigation and robotic systems: an in vitro study","authors":"Zonghe Xu, Lin Zhou, Bin Han, Shuang Wu, Yanjun Xiao, Sihui Zhang, Jiang Chen, Jianbin Guo, Dong Wu","doi":"10.1038/s41746-024-01178-6","DOIUrl":"10.1038/s41746-024-01178-6","url":null,"abstract":"Computer-aided implant surgery has undergone continuous development in recent years. In this study, active and passive systems of dynamic navigation were divided into active dynamic navigation system group and passive dynamic navigation system group (ADG and PDG), respectively. Active, passive and semi-active implant robots were divided into active robot group, passive robot group and semi-active robot group (ARG, PRG and SRG), respectively. Each group placed two implants (FDI tooth positions 31 and 36) in a model 12 times. The accuracy of 216 implants in 108 models were analysed. The coronal deviations of ADG, PDG, ARG, PRG and SRG were 0.85 ± 0.17 mm, 1.05 ± 0.42 mm, 0.29 ± 0.15 mm, 0.40 ± 0.16 mm and 0.33 ± 0.14 mm, respectively. The apical deviations of the five groups were 1.11 ± 0.23 mm, 1.07 ± 0.38 mm, 0.29 ± 0.15 mm, 0.50 ± 0.19 mm and 0.36 ± 0.16 mm, respectively. The axial deviations of the five groups were 1.78 ± 0.73°, 1.99 ± 1.20°, 0.61 ± 0.25°, 1.04 ± 0.37° and 0.42 ± 0.18°, respectively. The coronal, apical and axial deviations of ADG were higher than those of ARG, PRG and SRG (all P < 0.001). Similarly, the coronal, apical and axial deviations of PDG were higher than those of ARG, PRG, and SRG (all P < 0.001). Dynamic and robotic computer-aided implant surgery may show good implant accuracy in vitro. However, the accuracy and stability of implant robots are higher than those of dynamic navigation systems.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11227595/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141545114","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 : 2024-07-05DOI: 10.1038/s41746-024-01172-y
Ben Singh, Mavra Ahmed, Amanda E. Staiano, Claire Gough, Jasmine Petersen, Corneel Vandelanotte, Chelsea Kracht, Christopher Huong, Zenong Yin, Maria F. Vasiloglou, Chen-Chia Pan, Camille E. Short, Matthew Mclaughlin, Lauren von Klinggraeff, Christopher D. Pfledderer, Lisa J. Moran, Alyssa M. Button, Carol A. Maher
The aim of this meta-meta-analysis was to systematically review randomised controlled trial (RCT) evidence examining the effectiveness of e- and m-Health interventions designed to improve physical activity, sedentary behaviour, healthy eating and sleep. Nine electronic databases were searched for eligible studies published from inception to 1 June 2023. Systematic reviews with meta-analyses of RCTs that evaluate e- and m-Health interventions designed to improve physical activity, sedentary behaviour, sleep and healthy eating in any adult population were included. Forty-seven meta-analyses were included, comprising of 507 RCTs and 206,873 participants. Interventions involved mobile apps, web-based and SMS interventions, with 14 focused on physical activity, 3 for diet, 4 for sleep and 26 evaluating multiple behaviours. Meta-meta-analyses showed that e- and m-Health interventions resulted in improvements in steps/day (mean difference, MD = 1329 [95% CI = 593.9, 2065.7] steps/day), moderate-to-vigorous physical activity (MD = 55.1 [95% CI = 13.8, 96.4] min/week), total physical activity (MD = 44.8 [95% CI = 21.6, 67.9] min/week), sedentary behaviour (MD = −426.3 [95% CI = −850.2, −2.3] min/week), fruit and vegetable consumption (MD = 0.57 [95% CI = 0.11, 1.02] servings/day), energy intake (MD = −102.9 kcals/day), saturated fat consumption (MD = −5.5 grams/day), and bodyweight (MD = −1.89 [95% CI = −2.42, −1.36] kg). Analyses based on standardised mean differences (SMD) showed improvements in sleep quality (SMD = 0.56, 95% CI = 0.40, 0.72) and insomnia severity (SMD = −0.90, 95% CI = −1.14, −0.65). Most subgroup analyses were not significant, suggesting that a variety of e- and m-Health interventions are effective across diverse age and health populations. These interventions offer scalable and accessible approaches to help individuals adopt and sustain healthier behaviours, with implications for broader public health and healthcare challenges.
本项荟萃分析旨在系统回顾随机对照试验（RCT）证据，研究旨在改善体育锻炼、久坐行为、健康饮食和睡眠的电子和移动健康干预措施的有效性。我们在九个电子数据库中检索了从开始到 2023 年 6 月 1 日发表的符合条件的研究。其中包括系统性综述和荟萃分析，这些系统性综述和荟萃分析对电子健康和移动健康干预措施进行了评估，这些干预措施旨在改善任何成年人群的体育锻炼、久坐行为、睡眠和健康饮食。共纳入 47 项荟萃分析，包括 507 项研究性试验和 206873 名参与者。干预措施包括移动应用程序、网络干预和短信干预，其中 14 项侧重于体育锻炼，3 项侧重于饮食，4 项侧重于睡眠，26 项评估了多种行为。元分析表明，电子健康干预和移动健康干预改善了步数/天（平均差异，MD = 1329 [95% CI = 593.9, 2065.7] 步/天）、中到强度体力活动（MD = 55.1 [95% CI = 13.8, 96.4] 分钟/周）、总体力活动（MD = 44.8 [95% CI = 21.6, 67.9] 分钟/周）。9]分钟/周）、久坐行为（MD = -426.3 [95% CI = -850.2, -2.3]分钟/周）、水果和蔬菜摄入量（MD = 0.57 [95% CI = 0.11, 1.02]份/天）、能量摄入量（MD = -102.9 千卡/天）、饱和脂肪摄入量（MD = -5.5 克/天）和体重（MD = -1.89 [95% CI = -2.42, -1.36] 千克）。基于标准化均值差异（SMD）的分析表明，睡眠质量（SMD = 0.56，95% CI = 0.40，0.72）和失眠严重程度（SMD = -0.90，95% CI = -1.14，-0.65）均有所改善。大多数亚组分析结果并不显著，这表明各种电子和移动保健干预措施在不同年龄和健康人群中均有效。这些干预措施提供了可扩展和可获得的方法，可帮助个人采取并保持更健康的行为，从而应对更广泛的公共卫生和医疗保健挑战。
{"title":"A systematic umbrella review and meta-meta-analysis of eHealth and mHealth interventions for improving lifestyle behaviours","authors":"Ben Singh, Mavra Ahmed, Amanda E. Staiano, Claire Gough, Jasmine Petersen, Corneel Vandelanotte, Chelsea Kracht, Christopher Huong, Zenong Yin, Maria F. Vasiloglou, Chen-Chia Pan, Camille E. Short, Matthew Mclaughlin, Lauren von Klinggraeff, Christopher D. Pfledderer, Lisa J. Moran, Alyssa M. Button, Carol A. Maher","doi":"10.1038/s41746-024-01172-y","DOIUrl":"10.1038/s41746-024-01172-y","url":null,"abstract":"The aim of this meta-meta-analysis was to systematically review randomised controlled trial (RCT) evidence examining the effectiveness of e- and m-Health interventions designed to improve physical activity, sedentary behaviour, healthy eating and sleep. Nine electronic databases were searched for eligible studies published from inception to 1 June 2023. Systematic reviews with meta-analyses of RCTs that evaluate e- and m-Health interventions designed to improve physical activity, sedentary behaviour, sleep and healthy eating in any adult population were included. Forty-seven meta-analyses were included, comprising of 507 RCTs and 206,873 participants. Interventions involved mobile apps, web-based and SMS interventions, with 14 focused on physical activity, 3 for diet, 4 for sleep and 26 evaluating multiple behaviours. Meta-meta-analyses showed that e- and m-Health interventions resulted in improvements in steps/day (mean difference, MD = 1329 [95% CI = 593.9, 2065.7] steps/day), moderate-to-vigorous physical activity (MD = 55.1 [95% CI = 13.8, 96.4] min/week), total physical activity (MD = 44.8 [95% CI = 21.6, 67.9] min/week), sedentary behaviour (MD = −426.3 [95% CI = −850.2, −2.3] min/week), fruit and vegetable consumption (MD = 0.57 [95% CI = 0.11, 1.02] servings/day), energy intake (MD = −102.9 kcals/day), saturated fat consumption (MD = −5.5 grams/day), and bodyweight (MD = −1.89 [95% CI = −2.42, −1.36] kg). Analyses based on standardised mean differences (SMD) showed improvements in sleep quality (SMD = 0.56, 95% CI = 0.40, 0.72) and insomnia severity (SMD = −0.90, 95% CI = −1.14, −0.65). Most subgroup analyses were not significant, suggesting that a variety of e- and m-Health interventions are effective across diverse age and health populations. These interventions offer scalable and accessible approaches to help individuals adopt and sustain healthier behaviours, with implications for broader public health and healthcare challenges.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11226451/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141538265","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 : 2024-07-04DOI: 10.1038/s41746-024-01180-y
Jethro C. C. Kwong, Serena C. Y. Wang, Grace C. Nickel, Giovanni E. Cacciamani, Joseph C. Kvedar
Large language models (LLMs) have shown promise in reducing time, costs, and errors associated with manual data extraction. A recent study demonstrated that LLMs outperformed natural language processing approaches in abstracting pathology report information. However, challenges include the risks of weakening critical thinking, propagating biases, and hallucinations, which may undermine the scientific method and disseminate inaccurate information. Incorporating suitable guidelines (e.g., CANGARU), should be encouraged to ensure responsible LLM use.
{"title":"The long but necessary road to responsible use of large language models in healthcare research","authors":"Jethro C. C. Kwong, Serena C. Y. Wang, Grace C. Nickel, Giovanni E. Cacciamani, Joseph C. Kvedar","doi":"10.1038/s41746-024-01180-y","DOIUrl":"10.1038/s41746-024-01180-y","url":null,"abstract":"Large language models (LLMs) have shown promise in reducing time, costs, and errors associated with manual data extraction. A recent study demonstrated that LLMs outperformed natural language processing approaches in abstracting pathology report information. However, challenges include the risks of weakening critical thinking, propagating biases, and hallucinations, which may undermine the scientific method and disseminate inaccurate information. Incorporating suitable guidelines (e.g., CANGARU), should be encouraged to ensure responsible LLM use.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01180-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141534952","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 : 2024-07-04DOI: 10.1038/s41746-024-01171-z
Ashley Cordes, Marieke Bak, Mataroria Lyndon, Maui Hudson, Amelia Fiske, Leo Anthony Celi, Stuart McLennan
Digital health is increasingly promoting open health data. Although this open approach promises a number of benefits, it also leads to tensions with Indigenous data sovereignty movements led by Indigenous peoples around the world who are asserting control over the use of health data as a part of self-determination. Digital health has a role in improving access to services and delivering improved health outcomes for Indigenous communities. However, we argue that in order to be effective and ethical, it is essential that the field engages more with Indigenous peoples´ rights and interests. We discuss challenges and possible improvements for data acquisition, management, analysis, and integration as they pertain to the health of Indigenous communities around the world.
{"title":"Competing interests: digital health and indigenous data sovereignty","authors":"Ashley Cordes, Marieke Bak, Mataroria Lyndon, Maui Hudson, Amelia Fiske, Leo Anthony Celi, Stuart McLennan","doi":"10.1038/s41746-024-01171-z","DOIUrl":"10.1038/s41746-024-01171-z","url":null,"abstract":"Digital health is increasingly promoting open health data. Although this open approach promises a number of benefits, it also leads to tensions with Indigenous data sovereignty movements led by Indigenous peoples around the world who are asserting control over the use of health data as a part of self-determination. Digital health has a role in improving access to services and delivering improved health outcomes for Indigenous communities. However, we argue that in order to be effective and ethical, it is essential that the field engages more with Indigenous peoples´ rights and interests. We discuss challenges and possible improvements for data acquisition, management, analysis, and integration as they pertain to the health of Indigenous communities around the world.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01171-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141534951","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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