Discover health systems最新文献_第9页

Effectiveness of breast cancer campaigns in South Asia 南亚乳腺癌防治运动的有效性

Discover health systems Pub Date : 2023-10-09 DOI: 10.1007/s44250-023-00034-w

Huda Jaffar, Gaurav Kumar, Anfal Atif, Amna Iqbal

引用次数: 0

How deep learning influences workflows and roles in virtual surgical planning 深度学习如何影响虚拟手术计划中的工作流程和角色

Discover health systems Pub Date : 2023-09-26 DOI: 10.1007/s44250-023-00041-x

Beat Hofer, Markus Kittler, Kris Laukens

{"title":"How deep learning influences workflows and roles in virtual surgical planning","authors":"Beat Hofer, Markus Kittler, Kris Laukens","doi":"10.1007/s44250-023-00041-x","DOIUrl":"https://doi.org/10.1007/s44250-023-00041-x","url":null,"abstract":"Abstract Background Deep learning (DL) has the potential to transform surgical practice, altering workflows and changing the roles of practitioners involved. However, studies have shown that introducing such change requires user acceptance. Following the development and presentation of a visual prototype for planning facial surgery interventions, the project aimed to understand the utility of DL, the implied workflow and role changes it would entail, and the potential barriers to its adoption in practice. Method This paper presents a multi-year case study providing insights from developing and introducing a visual prototype. The prototype was co-developed by facial surgeons, DL experts, and business process engineers. The study uses project data involving semi-structured interviews, workgroup results, and feedback from an external practitioner audience exposed to the prototype regarding their views on adopting DL tools in practice. Findings The surgeons attested a high utility to the application. However, the data also highlights a perceived need to remain in control, be able to intervene, and override surgical workflows in short intervals. Longer intervals without opportunities to intervene were seen with skepticism, suggesting that the practitioners’ acceptance of DL requires a carefully designed workflow in which humans can still take control of events. Conclusion Deep learning can improve and accelerate facial surgery intervention planning. Models from the business and management literature partially explain the acceptance of new technologies. Perceived ease of use seems less relevant than the perceived usefulness of new technology. Involving algorithms in clinical decision-making will change workflows and professional identities.","PeriodicalId":72826,"journal":{"name":"Discover health systems","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134958465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Validation of gestational weight gain records on South Carolina birth certificate data 南卡罗来纳州出生证明数据中妊娠体重增加记录的验证

Discover health systems Pub Date : 2023-09-19 DOI: 10.1007/s44250-023-00040-y

Oluwatosin A. Momodu, Ronnie D. Horner, Jihong Liu, Elizabeth Crouch, Brian Chen

{"title":"Validation of gestational weight gain records on South Carolina birth certificate data","authors":"Oluwatosin A. Momodu, Ronnie D. Horner, Jihong Liu, Elizabeth Crouch, Brian Chen","doi":"10.1007/s44250-023-00040-y","DOIUrl":"https://doi.org/10.1007/s44250-023-00040-y","url":null,"abstract":"Abstract Objective To examine the accuracy of gestational weight gain (GWG) reported on birth certificates (BCs) in comparison with electronic medical records (EMRs), the gold standard. Methods BC data and EMR data were from a random sample of pregnant women who enrolled in CenteringPregnancy program, a type of group-based prenatal care, at three obstetric clinics in South Carolina between 2015 and 2019 (n = 206). Retrospective review of EMR data on patients’ prepregnancy BMI and GWG according to 2009 Institute of Medicine guidelines was conducted. Analyses involved summarizing the variables by their mean and mean differences per data source, and then calculating percent-weighted agreement and kappa statistics. Results The mean values of BMI, delivery weight and total weight gain were similar between BC and EMRs. Data correlation for variables was high for both data sources (height: r = 0.94, prepregnancy weight: r = 0.93, prepregnancy BMI: r = 0.92, delivery weight: r = 0.96, total weight gain: r = 0.60). The BCs slightly underestimated the proportion of women in the normal-weight BMI category but overestimated the proportion in the overweight BMI category. Additionally, BCs slightly overestimated women with inadequate GWG and underestimated those with excessive GWG. Overall, the BC and EMR data were in agreement regarding prepregnancy BMI (weighted-agreement = 90%, Kappa = 0.78) and GWG categories (weighted-agreement = 84%, Kappa = 0.63). Conclusion BC estimates of prepregnancy BMI and GWG categories were similar to those recorded in the EMRs. The South Carolina BC database is a valid database for gestational weight and can provide reasonable estimates for the state in the evaluation of the CenteringPregnancy program.","PeriodicalId":72826,"journal":{"name":"Discover health systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135061069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Customising hospitals for the future: a perspective focusing on infection prevention and control 为未来定制医院：关注感染预防和控制的视角

Discover health systems Pub Date : 2023-09-05 DOI: 10.1007/s44250-023-00039-5

Elisabeth Presterl, M. Diab-Elschahawi, Carolina Lohfert-Praetorius, Siegfried Gierlinger, Herwig Wetzlinger, Walter Zingg

引用次数: 0

Impact of natural disasters on health disparities in low- to middle-income countries 自然灾害对中低收入国家健康差距的影响

Discover health systems Pub Date : 2023-09-04 DOI: 10.1007/s44250-023-00038-6

A. Nashwan, S. H. Ahmed, T. Shaikh, Summaiyya Waseem

引用次数: 0

Smart phone based medical record software for short term surgical missions 基于智能手机的医疗记录软件，用于短期手术任务

Discover health systems Pub Date : 2023-08-08 DOI: 10.1007/s44250-023-00035-9

P. Alfille, C. Bean, Gennadiy Fuzaylov