基于云的区域电子健康档案存储和交换架构

云概念是近年来IT业在体系架构方面取得的最重大的研究进展,整个产业无处不在的进行着基于云的各种体系建立和重构。我国区域电子健康档案的发展经历了从信息孤岛到目前部分地区关于区域共享互通探索实践的过程,取得了一定的成绩,也存在着很大的问题。通过对区域电子健康档案的基本内容、数据形式分类、存储分布、存储和交换需求的详细分析。利用云架构的特点,对区域电子健康档案的存储和交换架构进行了重构,确立了重构目标,设计出了区域电子健康档案的存储云分布、区域电子健康档案云存储和交换的结构模型。在架构层面上。为区域电子健康档案共享提供指导性方案,最终目标是为了实现区域电子健康档案的可持续性共享。     

基于云的区域电子健康档案存储和交换架构

通过对区域电子健康档案基本内容、数据形式分类、存储分布、存储和交换需求的详细分析,利用云架构的特点,对区域电子健康档案的存储和交换架构进行重构,设计出区域电子健康档案的存储云分布、区域电子健康档案云存储和交换的结构模型,为区域电子健康档案共享提供指导性方案.     

基于Hadoop的电子健康档案云平台设计和实现

阐述基于Hadoop的电子健康档案云平台架构设计,包括服务对象及需求、逻辑架构、软件架构等方面,介绍基于HBase的电子健康档案云平台数据预处理模型,进行实验环境的搭建和配置,通过实验完成Hadoop集群的启动。     

云计算在电子健康中的新应用

在医疗健康信息爆炸式增长的电子健康时代,如何获取存储分析与管理医疗健康数据,并以更低的}T成本实现更加智能、更为个性化的医疗健康服务成为未来电子健康发展的重要课题。云计算作为新兴的信息技术为应对上述挑战提供了新的技术途径。对电子健康领域中基于云计算的新型电子健康应用服务进行了介绍,讨论了云计算对电子健康未来发展的趋势性影响。     

云存储在医院信息管理中的应用研究

随着云计算的思想在存储行业中落地,在非对等网络结构的基础上构建云存储平台,成为新型存储的重要前沿方向之一。在医院的海量数据集群存储中,基于云计算的三层结构——服务、中间件、网格思想成为近年来的发展趋势。     

基于IHEXDS—I技术框架的电子健康档案系统中影像数据的采集与存储

目的：应用云存储理念,设计符合lHEXDS—l技术框架和协议具有影像信息共享交换功能的区域电子健康记录系统（EHR）系统,探讨该系统中影像的采集和存储的实现方式和过程。方法：分析EHR系统中各个应用模块,初步提出可行的EHR影像信息的采集方法,并试图通过WADO协议与区域PACS系统互联,在IHEXDS—I技术框架和协议架构基础上的区域影像信息中心实现EHR影像信息分布式网格存储的概念模型。结果：从目前的以社区卫生服务中心为单位的EHR系统可行性出发,提出了社区服务中心和PACS系统进行影像数据采集的方案,并运用医学影像网格存储技术和云存储理念,结合分布式存储架构来进行EHR影像数据的存储方案设计。结论：基于IHEXDS—I的分布式医学影像信息交换的EHR系统中影像数据的采集与存储对于区域医疗中影像信息传输和利用具有重要意义。     

云电子胶片在医院的应用实践及思考

随着"互联网+医疗"的深入应用,越来越多的患者接受电子胶片这一服务模式."牛皮袋+医用胶片"被"移动端+云电子胶片"所取代,患者的检查报告可以随时随地在手机、PAD、个人电脑上浏览查看与下载.云电子胶片创新了服务理念,方便了患者就医,减轻了患者的费用,促进区域影像诊断中心发展.     

社区护理在电子健康档案中的应用模式研究

社区护理是社区保健工作的重要组成部分,承担着医院外的医疗预防、保健、康复护理工作。我国社区护理从业人员比率低,护理服务范围广,工作量大,这是造成目前社区护理工作无法有效开展的主要矛盾,电子健康档案的实施在一定程度上可以缓解这种矛盾。电子健康档案的应用是社区卫生服务发展的大势所趋,应尽快探索和发展出一条适合我国国情的社区护理模式。     

聚焦电子健康档案

电子健康档案与区域卫生信息网络在全世界范围内正在成为医疗卫生信息化的前沿阵地.从EMR与EHR的区别及其相互关系对电子健康档案的基本内涵加以辨析,并就目前国内外对电子健康档案研究和实践的现状进行介绍,阐明实现电子健康档案的意义,最后指出需要解决的关键问题.     

.NET分布式技术在电子健康档案系统中的应用

．NET分布式技术应用和研究是现在分布式应用的热点之一,由于其有着众多的优势和强大的工业强度支持,在各领域都有着十分广阔的应用空间。将．NET分布式技术应用到区域电子健康档案中,对体系结构进行仔细分析,采用多层结构进行开发,具有低耦合性、高内聚性,及安全、稳定、可靠的特点。     

PheMap: a multi-resource knowledge base for high-throughput phenotyping within electronic health records

ObjectiveDeveloping algorithms to extract phenotypes from electronic health records (EHRs) can be challenging and time-consuming. We developed PheMap, a high-throughput phenotyping approach that leverages multiple independent, online resources to streamline the phenotyping process within EHRs.Materials and MethodsPheMap is a knowledge base of medical concepts with quantified relationships to phenotypes that have been extracted by natural language processing from publicly available resources. PheMap searches EHRs for each phenotype’s quantified concepts and uses them to calculate an individual’s probability of having this phenotype. We compared PheMap to clinician-validated phenotyping algorithms from the Electronic Medical Records and Genomics (eMERGE) network for type 2 diabetes mellitus (T2DM), dementia, and hypothyroidism using 84 821 individuals from Vanderbilt Univeresity Medical Center''s BioVU DNA Biobank. We implemented PheMap-based phenotypes for genome-wide association studies (GWAS) for T2DM, dementia, and hypothyroidism, and phenome-wide association studies (PheWAS) for variants in FTO, HLA-DRB1, and TCF7L2. ResultsIn this initial iteration, the PheMap knowledge base contains quantified concepts for 841 disease phenotypes. For T2DM, dementia, and hypothyroidism, the accuracy of the PheMap phenotypes were >97% using a 50% threshold and eMERGE case-control status as a reference standard. In the GWAS analyses, PheMap-derived phenotype probabilities replicated 43 of 51 previously reported disease-associated variants for the 3 phenotypes. For 9 of the 11 top associations, PheMap provided an equivalent or more significant P value than eMERGE-based phenotypes. The PheMap-based PheWAS showed comparable or better performance to a traditional phecode-based PheWAS. PheMap is publicly available online.ConclusionsPheMap significantly streamlines the process of extracting research-quality phenotype information from EHRs, with comparable or better performance to current phenotyping approaches.     

The challenges in making electronic health records accessible to patients

It is becoming increasingly apparent that there is a tension between growing consumer demands for access to information and a healthcare system that may not be prepared to meet these demands. Designing an effective solution for this problem will require a thorough understanding of the barriers that now stand in the way of giving patients electronic access to their health data. This paper reviews the following challenges related to the sharing of electronic health records: cost and security concerns, problems in assigning responsibilities and rights among the various players, liability issues and tensions between flexible access to data and flexible access to physicians.     

A framework for employing longitudinally collected multicenter electronic health records to stratify heterogeneous patient populations on disease history

ObjectiveTo facilitate patient disease subset and risk factor identification by constructing a pipeline which is generalizable, provides easily interpretable results, and allows replication by overcoming electronic health records (EHRs) batch effects.Material and MethodsWe used 1872 billing codes in EHRs of 102 880 patients from 12 healthcare systems. Using tools borrowed from single-cell omics, we mitigated center-specific batch effects and performed clustering to identify patients with highly similar medical history patterns across the various centers. Our visualization method (PheSpec) depicts the phenotypic profile of clusters, applies a novel filtering of noninformative codes (Ranked Scope Pervasion), and indicates the most distinguishing features.ResultsWe observed 114 clinically meaningful profiles, for example, linking prostate hyperplasia with cancer and diabetes with cardiovascular problems and grouping pediatric developmental disorders. Our framework identified disease subsets, exemplified by 6 “other headache” clusters, where phenotypic profiles suggested different underlying mechanisms: migraine, convulsion, injury, eye problems, joint pain, and pituitary gland disorders. Phenotypic patterns replicated well, with high correlations of ≥0.75 to an average of 6 (2–8) of the 12 different cohorts, demonstrating the consistency with which our method discovers disease history profiles.DiscussionCostly clinical research ventures should be based on solid hypotheses. We repurpose methods from single-cell omics to build these hypotheses from observational EHR data, distilling useful information from complex data.ConclusionWe establish a generalizable pipeline for the identification and replication of clinically meaningful (sub)phenotypes from widely available high-dimensional billing codes. This approach overcomes datatype problems and produces comprehensive visualizations of validation-ready phenotypes.     

Exploring the relationship between electronic health records and provider burnout: A systematic review

ObjectiveStress and burnout due to electronic health record (EHR) technology has become a focus for burnout intervention. The aim of this study is to systematically review the relationship between EHR use and provider burnout.Materials and MethodsA systematic literature search was performed on PubMed, EMBASE, PsychInfo, ACM Digital Library in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. Inclusion criterion was original research investigating the association between EHR and provider burnout. Studies that did not measure the association objectively were excluded. Study quality was assessed using the Medical Education Research Study Quality Instrument. Qualitative synthesis was also performed.ResultsTwenty-six studies met inclusion criteria. The median sample size of providers was 810 (total 20 885; 44% male; mean age 53 [range, 34-56] years). Twenty-three (88%) studies were cross-sectional studies and 3 were single-arm cohort studies measuring pre- and postintervention burnout prevalence. Burnout was assessed objectively with various validated instruments. Insufficient time for documentation (odds ratio [OR], 1.40-5.83), high inbox or patient call message volumes (OR, 2.06-6.17), and negative perceptions of EHR by providers (OR, 2.17-2.44) were the 3 most cited EHR-related factors associated with higher rates of provider burnout that was assessed objectively.ConclusionsThe included studies were mostly observational studies; thus, we were not able to determine a causal relationship. Currently, there are few studies that objectively assessed the relationship between EHR use and provider burnout. The 3 most cited EHR factors associated with burnout were confirmed and should be the focus of efforts to improve EHR-related provider burnout.     

对电子健康档案APP中虚拟社区建设的文献分析

梳理归纳了我国虚拟健康社区研究的相关文献，以SWOT矩阵分析电子健康档案APP中虚拟社区的发展战略。针对“健康湖州”手机客户端母子健康手册中的虚拟社区现状，结合矩阵分析要点，比对关键文献研究成果，提出相关发展建议。     

Extracting social determinants of health from electronic health records using natural language processing: a systematic review

ObjectiveSocial determinants of health (SDoH) are nonclinical dispositions that impact patient health risks and clinical outcomes. Leveraging SDoH in clinical decision-making can potentially improve diagnosis, treatment planning, and patient outcomes. Despite increased interest in capturing SDoH in electronic health records (EHRs), such information is typically locked in unstructured clinical notes. Natural language processing (NLP) is the key technology to extract SDoH information from clinical text and expand its utility in patient care and research. This article presents a systematic review of the state-of-the-art NLP approaches and tools that focus on identifying and extracting SDoH data from unstructured clinical text in EHRs.Materials and MethodsA broad literature search was conducted in February 2021 using 3 scholarly databases (ACL Anthology, PubMed, and Scopus) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 6402 publications were initially identified, and after applying the study inclusion criteria, 82 publications were selected for the final review.ResultsSmoking status (n = 27), substance use (n = 21), homelessness (n = 20), and alcohol use (n = 15) are the most frequently studied SDoH categories. Homelessness (n = 7) and other less-studied SDoH (eg, education, financial problems, social isolation and support, family problems) are mostly identified using rule-based approaches. In contrast, machine learning approaches are popular for identifying smoking status (n = 13), substance use (n = 9), and alcohol use (n = 9).ConclusionNLP offers significant potential to extract SDoH data from narrative clinical notes, which in turn can aid in the development of screening tools, risk prediction models, and clinical decision support systems.     

高科技医疗——电子病历能够变革医疗卫生吗？

介绍了美国实施国家电子病历计划的前景，实施过程中面临的隐私保护、成本分担、区域利益保护、系统互操作能力等困难，以及联邦政府在协调州际工作中所付出的努力；并分别介绍了明尼苏达州与德克萨斯州在计划推进上所采用的不同策略。     

Measurement of clinical documentation burden among physicians and nurses using electronic health records: a scoping review

Background ObjectiveElectronic health records (EHRs) are linked with documentation burden resulting in clinician burnout. While clear classifications and validated measures of burnout exist, documentation burden remains ill-defined and inconsistently measured. We aim to conduct a scoping review focused on identifying approaches to documentation burden measurement and their characteristics.Materials and MethodsBased on Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Extension for Scoping Reviews (ScR) guidelines, we conducted a scoping review assessing MEDLINE, Embase, Web of Science, and CINAHL from inception to April 2020 for studies investigating documentation burden among physicians and nurses in ambulatory or inpatient settings. Two reviewers evaluated each potentially relevant study for inclusion/exclusion criteria.ResultsOf the 3482 articles retrieved, 35 studies met inclusion criteria. We identified 15 measurement characteristics, including 7 effort constructs: EHR usage and workload, clinical documentation/review, EHR work after hours and remotely, administrative tasks, cognitively cumbersome work, fragmentation of workflow, and patient interaction. We uncovered 4 time constructs: average time, proportion of time, timeliness of completion, activity rate, and 11 units of analysis. Only 45.0% of studies assessed the impact of EHRs on clinicians and/or patients and 40.0% mentioned clinician burnout.DiscussionStandard and validated measures of documentation burden are lacking. While time and effort were the core concepts measured, there appears to be no consensus on the best approach nor degree of rigor to study documentation burden.ConclusionFurther research is needed to reliably operationalize the concept of documentation burden, explore best practices for measurement, and standardize its use.